Chain of Thought | AI Agents, Infrastructure & Engineering Transistor (https://transistor.fm) https://feeds.transistor.fm/chain-of-thought AI is reshaping infrastructure, strategy, and entire industries. Host Conor Bronsdon talks to the engineers, founders, and researchers building breakthrough AI systems about what it actually takes to ship AI in production, where the opportunities lie, and how leaders should think about the strategic bets ahead. Chain of Thought translates technical depth into actionable insights for builders and decision-makers. New episodes weekly. Conor Bronsdon is an angel investor in AI and dev tools, Technical Ecosystem Lead at Modular, and previously led growth at AI startups Galileo and LinearB. Disclaimer: All views, opinions and statements expressed on this account are solely my own and are made in my personal capacity. They do not reflect, and should not be construed as reflecting, the views, positions, or policies of Modular. This account is not affiliated with, authorized by, or endorsed by Modular in any way. Conor Bronsdon 90565bd2-4a9b-55b2-a8fd-8811aafe2687 yes Welcome to Chain of Thought en Thu, 25 Jun 2026 03:00:10 -0700 Thu, 25 Jun 2026 03:03:45 -0700 https://newsletter.chainofthought.show/ https://img.transistorcdn.com/Yf0B0akhfAtC-Ahrn6UylfgdusLiIKiLKXlMy29dfwI/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81YjBh/ODMzMTY3ZjQ0MjBj/YTE1ODMwYTZlNDgx/Mjc2Mi5qcGc.jpg Chain of Thought | AI Agents, Infrastructure & Engineering https://newsletter.chainofthought.show/ episodic Conor Bronsdon AI is reshaping infrastructure, strategy, and entire industries. Host Conor Bronsdon talks to the engineers, founders, and researchers building breakthrough AI systems about what it actually takes to ship AI in production, where the opportunities lie, and how leaders should think about the strategic bets ahead. Chain of Thought translates technical depth into actionable insights for builders and decision-makers. New episodes weekly. Conor Bronsdon is an angel investor in AI and dev tools, Technical Ecosystem Lead at Modular, and previously led growth at AI startups Galileo and LinearB. Disclaimer: All views, opinions and statements expressed on this account are solely my own and are made in my personal capacity. They do not reflect, and should not be construed as reflecting, the views, positions, or policies of Modular. This account is not affiliated with, authorized by, or endorsed by Modular in any way. AI is reshaping infrastructure, strategy, and entire industries. technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure Conor Bronsdon podcast@conorbronsdon.com No No Stop Token Maxxing: Find Where AI Actually Pays Off | Jiaona Zhang 3 3 64 64 Stop Token Maxxing: Find Where AI Actually Pays Off | Jiaona Zhang full 41178864-f6d5-4307-8899-d9aaafc98bc7 https://share.transistor.fm/s/81534de0 Jiaona Zhang(JZ) is the Chief Product Officer at Laurel, where the team runs its own product on itself to see exactly where AI helps and where it doesn't. Before Laurel, JZ built products at Airbnb, Dropbox, Webflow, and Linktree, and she has taught product management at Stanford for nearly a decade.

Companies are spending billions on AI tooling, but most still can't say where it returns time or revenue. Jiaona breaks down how to get that visibility, why blanket AI mandates backfire, and what it takes to re-architect a team so anyone can ship.

Her argument is simple: stop token maxing and start measuring time back.

We cover:

  • Why most organizations can't see where AI is actually working, and how Laurel uses time data to fix it
  • The token max trap that "use AI everywhere" mandates create, and how to drive efficient use instead
  • Why former managers make the best operators of agent fleets
  • How Laurel lets PMs, designers, and customer success ship features end to end
  • The bottom-up plus top-down playbook for re-architecting a team around AI
  • Why technology moats are falling away while brand and data moats endure
  • Laurel's bet on returning time to people instead of replacing them

(0:00) The token max trap
(1:47) Why companies can't see where AI is working
(5:03) What Laurel does: turning time into data
(8:53) Agents as an extension of the workforce
(13:43) Why former managers make the best AI users
(18:23) Lean teams and shipping end to end
(22:29) Enabling non-engineers to ship features
(28:30) Re-architecting teams: bottom-up and top-down
(32:09) Keeping your professional identity as AI shifts work
(38:53) The context layer is the new race
(42:06) Fundamentals plus tinkering: how to learn
(48:45) Brand and data moats when tech moats fall away
(54:31) Laurel's movement: returning time to people

Connect with Jiaona Zhang(JZ):

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Jiaona Zhang(JZ) is the Chief Product Officer at Laurel, where the team runs its own product on itself to see exactly where AI helps and where it doesn't. Before Laurel, JZ built products at Airbnb, Dropbox, Webflow, and Linktree, and she has taught product management at Stanford for nearly a decade.

Companies are spending billions on AI tooling, but most still can't say where it returns time or revenue. Jiaona breaks down how to get that visibility, why blanket AI mandates backfire, and what it takes to re-architect a team so anyone can ship.

Her argument is simple: stop token maxing and start measuring time back.

We cover:

  • Why most organizations can't see where AI is actually working, and how Laurel uses time data to fix it
  • The token max trap that "use AI everywhere" mandates create, and how to drive efficient use instead
  • Why former managers make the best operators of agent fleets
  • How Laurel lets PMs, designers, and customer success ship features end to end
  • The bottom-up plus top-down playbook for re-architecting a team around AI
  • Why technology moats are falling away while brand and data moats endure
  • Laurel's bet on returning time to people instead of replacing them

(0:00) The token max trap
(1:47) Why companies can't see where AI is working
(5:03) What Laurel does: turning time into data
(8:53) Agents as an extension of the workforce
(13:43) Why former managers make the best AI users
(18:23) Lean teams and shipping end to end
(22:29) Enabling non-engineers to ship features
(28:30) Re-architecting teams: bottom-up and top-down
(32:09) Keeping your professional identity as AI shifts work
(38:53) The context layer is the new race
(42:06) Fundamentals plus tinkering: how to learn
(48:45) Brand and data moats when tech moats fall away
(54:31) Laurel's movement: returning time to people

Connect with Jiaona Zhang(JZ):

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Thu, 25 Jun 2026 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 3455 Jiaona Zhang is CPO at Laurel and has built products at Airbnb, Dropbox, Webflow, and Linktree. She explains how to measure where AI actually returns time, why most 'use AI everywhere' mandates push teams to token max, and how Laurel lets anyone from customer success to design ship features end to end. Chain of Thought is hosted by Conor Bronsdon. Jiaona Zhang is CPO at Laurel and has built products at Airbnb, Dropbox, Webflow, and Linktree. She explains how to measure where AI actually returns time, why most 'use AI everywhere' mandates push teams to token max, and how Laurel lets anyone from cust Jiaona Zhang, Laurel, AI ROI, AI agents, token spend, AI adoption, product management, AI transformation, agent fleets, time tracking, workforce intelligence, change management, brand moat, data moat, Conor Bronsdon, Chain of Thought, AI strategy, enterprise AI No Most of the Web Will Never Get APIs for AI Agents | Dhruv Batra 3 3 63 63 Most of the Web Will Never Get APIs for AI Agents | Dhruv Batra full 4ee357e6-d039-45a3-9631-2a8fd8670d34 https://share.transistor.fm/s/23e7157b Most of the web will never get APIs for AI agents. School district sites, small business pages, government offices, and the long tail of e-commerce were built for humans, and they will keep working that way for years. So how do agents actually get things done across the web?

Dhruv Batra is co-founder and chief scientist of Yutori, the company building specialized browser and computer-use agents. He previously led embodied AI at Meta's FAIR lab, training robots in simulation and shipping the image question-answering model on Ray-Ban Meta glasses. His bet: the web is a shared roadway, much like roads split between human drivers and self-driving cars, and agents will be built to use it the way people already do.


Pixels in, clicks out. That is the API.

In this conversation:

  • Why the long tail of the web won't re-architect itself for agents
  • How Yutori's Navigator perceives pixels and writes JavaScript on the fly to shorten task trajectories
  • Why Navigator runs 2-3x faster and 4-5x cheaper than Opus 4.7 and GPT-5.5 on browser tasks
  • Learning from live websites, and using URL query parameters as privileged verifiers instead of cloning sites
  • What the shift from American to Chinese open-weight models means for startups
  • How smart glasses and robots share the same perception-action loop
  • Why demand for inference compute is pushing models smaller and onto devices

Chapters:

(00:00) Pixels in, clicks out
 (01:37) Why most of the web will never get APIs
 (08:47) Aggregation, specialization, and human friction
 (11:39) Digital niches and specialized models
 (16:41) The web's heavy tail and where browser agents win
 (20:40) Inside Yutori's Navigator and Scouts
 (24:08) N1.5: writing JavaScript to cut trajectory length
 (27:45) Training on live websites
 (33:29) Open source: FAIR's legacy and the Chinese frontier
 (37:22) Agent frameworks: OpenClaw, Hermes, heartbeats
 (40:57) How non-technical users adopt agents
 (44:25) Smart glasses, robotics, and embodied AI
 (50:57) Compute demand and smaller on-device models
 (53:12) Why the company is called Yutori

Connect with Dhruv Batra:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Most of the web will never get APIs for AI agents. School district sites, small business pages, government offices, and the long tail of e-commerce were built for humans, and they will keep working that way for years. So how do agents actually get things done across the web?

Dhruv Batra is co-founder and chief scientist of Yutori, the company building specialized browser and computer-use agents. He previously led embodied AI at Meta's FAIR lab, training robots in simulation and shipping the image question-answering model on Ray-Ban Meta glasses. His bet: the web is a shared roadway, much like roads split between human drivers and self-driving cars, and agents will be built to use it the way people already do.


Pixels in, clicks out. That is the API.

In this conversation:

  • Why the long tail of the web won't re-architect itself for agents
  • How Yutori's Navigator perceives pixels and writes JavaScript on the fly to shorten task trajectories
  • Why Navigator runs 2-3x faster and 4-5x cheaper than Opus 4.7 and GPT-5.5 on browser tasks
  • Learning from live websites, and using URL query parameters as privileged verifiers instead of cloning sites
  • What the shift from American to Chinese open-weight models means for startups
  • How smart glasses and robots share the same perception-action loop
  • Why demand for inference compute is pushing models smaller and onto devices

Chapters:

(00:00) Pixels in, clicks out
 (01:37) Why most of the web will never get APIs
 (08:47) Aggregation, specialization, and human friction
 (11:39) Digital niches and specialized models
 (16:41) The web's heavy tail and where browser agents win
 (20:40) Inside Yutori's Navigator and Scouts
 (24:08) N1.5: writing JavaScript to cut trajectory length
 (27:45) Training on live websites
 (33:29) Open source: FAIR's legacy and the Chinese frontier
 (37:22) Agent frameworks: OpenClaw, Hermes, heartbeats
 (40:57) How non-technical users adopt agents
 (44:25) Smart glasses, robotics, and embodied AI
 (50:57) Compute demand and smaller on-device models
 (53:12) Why the company is called Yutori

Connect with Dhruv Batra:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Thu, 18 Jun 2026 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 3284 Dhruv Batra, co-founder and chief scientist of Yutori and former head of embodied AI at Meta's FAIR lab, argues that most of the web will never expose APIs for AI agents. He explains why Yutori trains specialized browser agents to perceive pixels and click buttons the way people do, and why they run faster and cheaper than frontier models. Chain of Thought is hosted by Conor Bronsdon. Dhruv Batra, co-founder and chief scientist of Yutori and former head of embodied AI at Meta's FAIR lab, argues that most of the web will never expose APIs for AI agents. He explains why Yutori trains specialized browser agents to perceive pixels and clic Dhruv Batra, Yutori, AI agents, web agents, browser agents, computer use, Yutori Navigator, agentic web, AI infrastructure, web automation, embodied AI, FAIR, Meta AI, Qwen, open weight models, post-training, reinforcement learning, multimodal AI, smart glasses, AI compute, Conor Bronsdon, Chain of Thought No The First Fully Autonomous AI Attack Is 18 Months Away | Kristin Lovejoy 3 3 62 62 The First Fully Autonomous AI Attack Is 18 Months Away | Kristin Lovejoy full 6c940380-1567-4e48-b54e-3b9e05552fa3 https://share.transistor.fm/s/af894bf7 Kristin "Kris" Lovejoy has spent her career inside the systems the global economy runs on: banks, hospitals, energy grids, governments. Today she is Global Head of Strategy at Kyndryl, the world's largest IT infrastructure services provider, working with mission-critical enterprises across more than 60 countries. Before that she ran security businesses at EY and IBM, founded the AI security company BluVector (acquired by Comcast), and now sits on the board of Dominion Energy.

Her prediction: the first fully autonomous AI attack, where an AI takes down an enterprise network with no human driving it, lands within 18 months.

Conor and Kris dig into why 62% of enterprise AI initiatives are still stuck in pilots even as spend climbs 33% year over year, why attackers chaining low-risk vulnerabilities changes the patching math, and why she has a fraught relationship with policy as code.

We cover:

  • The electricity analogy: we can build the models, but the transmission lines for industrial AI don't exist yet
  • Productivity AI vs mission-critical AI, and why banks and healthcare systems aren't running agentic AI at production scale
  • Why deterministic policy as code clashes with autonomous systems, and "human on top" vs human in the loop
  • The 18-month prediction: chaining low-risk vulnerabilities, outcome-oriented agents that take systems down by accident, and insiders armed with AI attack tools
  • The data center build-out from a Dominion Energy board member: PJM load forecasts that miss by double digits every year, water use, density, and rack optimization
  • Privacy as a double-edged sword: data combinations that suddenly become PII and the shift to continuous compliance
  • What's next: open source everywhere, sovereignty as control, autonomous robotics, and quantum

Chapters:

(00:00) Meet Kris Lovejoy: Kyndryl, EY, IBM, and Dominion Energy
 (02:09) Why 62% of AI initiatives are stuck in pilots
 (03:07) The electricity analogy: models without transmission lines
 (04:23) Productivity AI vs mission-critical AI
 (06:53) Vintage systems, hybrid data, and the risk gap
 (11:03) Policy as code and "human on top"
 (16:25) Data centers, energy, and the grid build-out
 (24:44) Data center design: density, cooling, rack optimization
 (26:54) Privacy, continuous compliance, and sovereignty as control
 (32:06) The first fully autonomous AI attack: 18 months away
 (38:06) Predictions: open source, robotics, and quantum
 (42:32) Control planes for agentic AI: closing thoughts

Connect with Kris Lovejoy:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Kristin "Kris" Lovejoy has spent her career inside the systems the global economy runs on: banks, hospitals, energy grids, governments. Today she is Global Head of Strategy at Kyndryl, the world's largest IT infrastructure services provider, working with mission-critical enterprises across more than 60 countries. Before that she ran security businesses at EY and IBM, founded the AI security company BluVector (acquired by Comcast), and now sits on the board of Dominion Energy.

Her prediction: the first fully autonomous AI attack, where an AI takes down an enterprise network with no human driving it, lands within 18 months.

Conor and Kris dig into why 62% of enterprise AI initiatives are still stuck in pilots even as spend climbs 33% year over year, why attackers chaining low-risk vulnerabilities changes the patching math, and why she has a fraught relationship with policy as code.

We cover:

  • The electricity analogy: we can build the models, but the transmission lines for industrial AI don't exist yet
  • Productivity AI vs mission-critical AI, and why banks and healthcare systems aren't running agentic AI at production scale
  • Why deterministic policy as code clashes with autonomous systems, and "human on top" vs human in the loop
  • The 18-month prediction: chaining low-risk vulnerabilities, outcome-oriented agents that take systems down by accident, and insiders armed with AI attack tools
  • The data center build-out from a Dominion Energy board member: PJM load forecasts that miss by double digits every year, water use, density, and rack optimization
  • Privacy as a double-edged sword: data combinations that suddenly become PII and the shift to continuous compliance
  • What's next: open source everywhere, sovereignty as control, autonomous robotics, and quantum

Chapters:

(00:00) Meet Kris Lovejoy: Kyndryl, EY, IBM, and Dominion Energy
 (02:09) Why 62% of AI initiatives are stuck in pilots
 (03:07) The electricity analogy: models without transmission lines
 (04:23) Productivity AI vs mission-critical AI
 (06:53) Vintage systems, hybrid data, and the risk gap
 (11:03) Policy as code and "human on top"
 (16:25) Data centers, energy, and the grid build-out
 (24:44) Data center design: density, cooling, rack optimization
 (26:54) Privacy, continuous compliance, and sovereignty as control
 (32:06) The first fully autonomous AI attack: 18 months away
 (38:06) Predictions: open source, robotics, and quantum
 (42:32) Control planes for agentic AI: closing thoughts

Connect with Kris Lovejoy:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Thu, 11 Jun 2026 04:30:00 -0700 Conor Bronsdon Conor Bronsdon 2736 Kris Lovejoy, Global Head of Strategy at Kyndryl, the world's largest IT infrastructure services provider, predicts the first fully autonomous AI attack will land within 18 months. She breaks down why 62% of enterprise AI initiatives are stuck in pilots and what a real control plane for agentic AI requires. Chain of Thought is hosted by Conor Bronsdon. Kris Lovejoy, Global Head of Strategy at Kyndryl, the world's largest IT infrastructure services provider, predicts the first fully autonomous AI attack will land within 18 months. She breaks down why 62% of enterprise AI initiatives are stuck in pilots a Kris Lovejoy,Kristin Lovejoy,Kyndryl,AI security,agentic AI,autonomous AI attack,AI agents,AI governance,policy as code,CISO,enterprise AI,AI infrastructure,data centers,data sovereignty,continuous compliance,AI privacy,insider threat,vulnerability chaining,critical infrastructure,AI risk,quantum computing,autonomous robotics,cybersecurity,Conor Bronsdon,Chain of Thought No The AI Framework Era Is Over: Why Context Is the Moat | Jerry Liu 3 3 61 61 The AI Framework Era Is Over: Why Context Is the Moat | Jerry Liu full 2a79d8b8-aed3-404e-8316-36b0da7aca13 https://share.transistor.fm/s/ee1f3c09 Jerry Liu built one of the most installed pieces of AI plumbing of the last three years. LlamaIndex became the indexing and retrieval layer a whole generation of RAG apps were stitched together with. Then he started arguing that the framework era he helped create is over.

Jerry is co-founder and CEO of LlamaIndex. In this conversation he walks through the company's pivot from open-source framework to managed document infrastructure with LlamaCloud and LlamaParse, and why he is betting that context quality is the one moat that compounds as agent loops get good enough to absorb the scaffolding.

If you are a founder worried a frontier lab or a coding agent is about to eat your product, this is the playbook for reinventing your ICP without losing the thread.

In this conversation:

  • Why Jerry says the AI framework era is over, and what actually survives
  • How agent harnesses like Claude Code collapsed the old framework patterns into the model
  • Why context quality is the durable moat, not the agent loop
  • How LlamaParse beats legacy OCR and frontier models on document accuracy and cost
  • Why 95%+ accuracy is the real bar for legal, insurance, and financial document work
  • How LlamaIndex disrupted its own product and reinvented its ICP to stay alive
  • Jerry's take on agent memory, model personalities, and why LLMs are still bad writers

(0:00) Is the AI framework era over?
 (1:56) What died and what survived
 (6:31) Why context quality is the moat
 (8:12) Defining the context layer
 (13:18) Coding and vision as the abstraction layer
 (18:13) The bet that context compounds
 (23:59) Which verticals are adopting
 (25:14) Why 95%+ accuracy is the real bar
 (29:49) The file system as an agent primitive
 (34:33) Surviving your own pivot
 (37:15) Reinventing strategy and hiring
 (42:00) Agent memory as persistent context
 (44:41) Model personalities and cultural memory
 (47:51) Writing with AI
 (50:19) Closing thoughts

Connect with Jerry Liu:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Jerry Liu built one of the most installed pieces of AI plumbing of the last three years. LlamaIndex became the indexing and retrieval layer a whole generation of RAG apps were stitched together with. Then he started arguing that the framework era he helped create is over.

Jerry is co-founder and CEO of LlamaIndex. In this conversation he walks through the company's pivot from open-source framework to managed document infrastructure with LlamaCloud and LlamaParse, and why he is betting that context quality is the one moat that compounds as agent loops get good enough to absorb the scaffolding.

If you are a founder worried a frontier lab or a coding agent is about to eat your product, this is the playbook for reinventing your ICP without losing the thread.

In this conversation:

  • Why Jerry says the AI framework era is over, and what actually survives
  • How agent harnesses like Claude Code collapsed the old framework patterns into the model
  • Why context quality is the durable moat, not the agent loop
  • How LlamaParse beats legacy OCR and frontier models on document accuracy and cost
  • Why 95%+ accuracy is the real bar for legal, insurance, and financial document work
  • How LlamaIndex disrupted its own product and reinvented its ICP to stay alive
  • Jerry's take on agent memory, model personalities, and why LLMs are still bad writers

(0:00) Is the AI framework era over?
 (1:56) What died and what survived
 (6:31) Why context quality is the moat
 (8:12) Defining the context layer
 (13:18) Coding and vision as the abstraction layer
 (18:13) The bet that context compounds
 (23:59) Which verticals are adopting
 (25:14) Why 95%+ accuracy is the real bar
 (29:49) The file system as an agent primitive
 (34:33) Surviving your own pivot
 (37:15) Reinventing strategy and hiring
 (42:00) Agent memory as persistent context
 (44:41) Model personalities and cultural memory
 (47:51) Writing with AI
 (50:19) Closing thoughts

Connect with Jerry Liu:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

]]> Wed, 03 Jun 2026 04:30:00 -0700 Conor Bronsdon Conor Bronsdon 3162 Jerry Liu built LlamaIndex into one of the most installed AI frameworks of the last three years, then bet the company that the framework era is over. He explains why context quality is the moat that survives as agent loops get good enough. Chain of Thought is hosted by Conor Bronsdon. Jerry Liu built LlamaIndex into one of the most installed AI frameworks of the last three years, then bet the company that the framework era is over. He explains why context quality is the moat that survives as agent loops get good enough. Chain of Though Jerry Liu, LlamaIndex, LlamaParse, LlamaCloud, AI frameworks, context engineering, RAG, retrieval augmented generation, document AI, OCR, AI agents, agent infrastructure, vector database, enterprise AI, unstructured data, MCP, Claude Code, frontier models, context quality, AI moat, Conor Bronsdon, Chain of Thought, AI infrastructure No We Built Agents, Nobody Built HR | Tyler Akidau, Redpanda 3 3 60 60 We Built Agents, Nobody Built HR | Tyler Akidau, Redpanda full d0a48663-355e-45cf-93d6-a018a1066626 https://share.transistor.fm/s/594a8a88 Tyler Akidau spent 12 years on streaming systems at Google and five years at Snowflake before joining Redpanda as CTO. He wrote the O'Reilly Streaming Systems book most of the field has on its shelf. His new piece on O'Reilly Radar (Post-Human: We All Built Agents, Nobody Built HR) argues that enterprises are stuck in the prototype-to-production gap because they're applying human-era identity, auth, and observability tools to a workforce that's unpredictable in structurally novel ways, runs at machine speed, and follows bad instructions to a fault. Inline guardrails like CLAUDE.md work until they don't. Governance has to be enforced through channels the agent can't see, modify, or override.

We cover:

  • Why AI agents are a new kind of co-worker (unpredictable, machine-speed, directable to a fault) and what that means for enterprise infrastructure
  • The four pillars of agent governance: identity, authorization, observability and explainability, accountability and control
  • Why task-scoped, short-lived identity is the foundation everything else builds on
  • Authorization that's deny-capable and intersection-aware (Tyler's "guest badge" model)
  • Why OpenTelemetry is the right starting point for recording every prompt, tool call, and response
  • How Redpanda's Agentic Data Plane combines streaming topics, Oxla SQL, and Postgres under the hood
  • Tyler's academic paper with a psychologist on the neurobiological systems humans have that AI agents are missing

Chapters:

(00:00) Why nobody built HR for AI agents
(02:12) Three ways agents differ from human employees
(07:53) The four pillars of out-of-band governance
(10:29) Identity: task-scoped, short-lived, chained to humans
(14:40) Authorization: deny-capable and intersection-aware
(18:57) Observability: record everything via OpenTelemetry
(24:24) Redpanda's agents and the $1,000 trade limit example
(30:10) Accountability and the kill switch
(34:02) The Agentic Data Plane: streaming, Oxla SQL, Postgres
(41:20) Should we stop chasing model alignment?
(44:04) Building human-like value systems into agents
(47:25) Tyler's 12-24 month outlook for agent governance

Connect with Tyler:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show


]]> Tyler Akidau spent 12 years on streaming systems at Google and five years at Snowflake before joining Redpanda as CTO. He wrote the O'Reilly Streaming Systems book most of the field has on its shelf. His new piece on O'Reilly Radar (Post-Human: We All Built Agents, Nobody Built HR) argues that enterprises are stuck in the prototype-to-production gap because they're applying human-era identity, auth, and observability tools to a workforce that's unpredictable in structurally novel ways, runs at machine speed, and follows bad instructions to a fault. Inline guardrails like CLAUDE.md work until they don't. Governance has to be enforced through channels the agent can't see, modify, or override.

We cover:

  • Why AI agents are a new kind of co-worker (unpredictable, machine-speed, directable to a fault) and what that means for enterprise infrastructure
  • The four pillars of agent governance: identity, authorization, observability and explainability, accountability and control
  • Why task-scoped, short-lived identity is the foundation everything else builds on
  • Authorization that's deny-capable and intersection-aware (Tyler's "guest badge" model)
  • Why OpenTelemetry is the right starting point for recording every prompt, tool call, and response
  • How Redpanda's Agentic Data Plane combines streaming topics, Oxla SQL, and Postgres under the hood
  • Tyler's academic paper with a psychologist on the neurobiological systems humans have that AI agents are missing

Chapters:

(00:00) Why nobody built HR for AI agents
(02:12) Three ways agents differ from human employees
(07:53) The four pillars of out-of-band governance
(10:29) Identity: task-scoped, short-lived, chained to humans
(14:40) Authorization: deny-capable and intersection-aware
(18:57) Observability: record everything via OpenTelemetry
(24:24) Redpanda's agents and the $1,000 trade limit example
(30:10) Accountability and the kill switch
(34:02) The Agentic Data Plane: streaming, Oxla SQL, Postgres
(41:20) Should we stop chasing model alignment?
(44:04) Building human-like value systems into agents
(47:25) Tyler's 12-24 month outlook for agent governance

Connect with Tyler:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show


]]> Wed, 27 May 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 3050 Tyler Akidau, CTO of Redpanda and author of the O'Reilly Streaming Systems book, makes the case that enterprises are shipping AI agents into production without the governance layer they need. He lays out the four pillars of agent HR (identity, authorization, observability, accountability) and why inline enforcement via CLAUDE.md fails the moment the stakes get real. Chain of Thought is hosted by Conor Bronsdon. Tyler Akidau, CTO of Redpanda and author of the O'Reilly Streaming Systems book, makes the case that enterprises are shipping AI agents into production without the governance layer they need. He lays out the four pillars of agent HR (identity, authorizati Tyler Akidau, Red Panda, AI agents, agentic AI, agent governance, agent infrastructure, AI agent identity, agent authorization, agent observability, agent accountability, OpenTelemetry, agentic data plane, prompt injection, hallucinations, enterprise AI, MCP gateways, LLM governance, agent deployment, AI workforce, Chain of Thought, Conor Bronsdon No How Superhuman Built AI Into a 100ms Product | Loïc Houssier 3 3 59 59 How Superhuman Built AI Into a 100ms Product | Loïc Houssier full 91aff45b-606e-48d3-8fff-f0cd443faf64 https://share.transistor.fm/s/65e3c420 Loïc Houssier leads engineering at Superhuman, the email client Grammarly acquired for ~$825 million in July 2025. Before Superhuman he was CTO of OpenTrust (acquired by DocuSign), ran engineering at ProductBoard, and started his career in applied cryptography for France's defense industry, including work on nuclear submarine systems. Loïc joined Superhuman in early 2024 and within 30 days was leading a six-week sprint to ship AI Inbox.

Superhuman's brand is built on speed: every interaction under 100 milliseconds. LLMs do not run in 100 milliseconds. So Loïc walks Conor through how his team retrofitted AI into a product that was already winning without it: pre-caching context for the mobile voice feature, starting every feature on the smartest available model and only then fine-tuning down to cheap dedicated infrastructure, treating "look foolish" as a P0 bug class, and refusing to auto-send any email even when their agents could.

This is a practitioner's tour of what it actually takes to put AI on top of a product that has to stay fast, stay quiet, and never embarrass the user.

We cover:

  • The model-routing strategy: Opus and frontier models to prove a feature, then fine-tuned BERT classifiers on dedicated inference
  • Pre-caching voice and tone context separately from dictation to keep the mobile voice feature feeling fast
  • Why eval engineering at Superhuman is owned by PMs, and how a single "how much time did I spend in Waymo last month" query exposes the eigenvectors a feature has to cover
  • Why "look foolish" is a P0 bug class, and where the boundary between agent agency and agent laziness actually sits
  • How Superhuman's pod structure (PM, tech lead, designer) and a central AI platform team support aligned autonomy
  • Hiring for AI fluency: how interview questions are changing and what self-augmenting engineers look like
  • Pattern detection as the leadership skill that transfers from nuclear submarines to AI email

Chapters:

(00:00) Cold open: pattern detection beats new tools
 (00:18) Loïc's path: cryptography, OpenTrust, ProductBoard, Superhuman
 (02:13) Retrofitting AI into a 100ms product
 (04:08) Voice on mobile: pre-caching LLM context to keep the feel fast
 (07:46) Frontier first, then fine-tune: model strategy across features
 (11:04) The "double-dipping" trick that worked on GPT-4 and stopped working
 (12:25) Cognitive load and staying current as a leader
 (16:59) Balancing YC founder urgency with peer CTO grounding
 (19:28) Pods, AI Guild, and aligned autonomy
 (23:15) Managing models vs. managing people: delegation in reverse
 (28:27) The Waymo example: eigenvectors of evaluation
 (32:15) Day 30 onboarding: leading the AI Inbox sprint
 (35:04) Why email is the killer agent use case
 (38:51) Auto-draft, never auto-send
 (39:57) Agent agency vs. agent laziness
 (43:07) Hiring for AI fluency
 (45:55) Pattern detection is the leadership skill
 (47:21) Nuclear submarines as engineering reference points
 (48:37) Closing thoughts
 (49:38) Superhuman is hiring

Connect with Loïc:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Loïc Houssier leads engineering at Superhuman, the email client Grammarly acquired for ~$825 million in July 2025. Before Superhuman he was CTO of OpenTrust (acquired by DocuSign), ran engineering at ProductBoard, and started his career in applied cryptography for France's defense industry, including work on nuclear submarine systems. Loïc joined Superhuman in early 2024 and within 30 days was leading a six-week sprint to ship AI Inbox.

Superhuman's brand is built on speed: every interaction under 100 milliseconds. LLMs do not run in 100 milliseconds. So Loïc walks Conor through how his team retrofitted AI into a product that was already winning without it: pre-caching context for the mobile voice feature, starting every feature on the smartest available model and only then fine-tuning down to cheap dedicated infrastructure, treating "look foolish" as a P0 bug class, and refusing to auto-send any email even when their agents could.

This is a practitioner's tour of what it actually takes to put AI on top of a product that has to stay fast, stay quiet, and never embarrass the user.

We cover:

  • The model-routing strategy: Opus and frontier models to prove a feature, then fine-tuned BERT classifiers on dedicated inference
  • Pre-caching voice and tone context separately from dictation to keep the mobile voice feature feeling fast
  • Why eval engineering at Superhuman is owned by PMs, and how a single "how much time did I spend in Waymo last month" query exposes the eigenvectors a feature has to cover
  • Why "look foolish" is a P0 bug class, and where the boundary between agent agency and agent laziness actually sits
  • How Superhuman's pod structure (PM, tech lead, designer) and a central AI platform team support aligned autonomy
  • Hiring for AI fluency: how interview questions are changing and what self-augmenting engineers look like
  • Pattern detection as the leadership skill that transfers from nuclear submarines to AI email

Chapters:

(00:00) Cold open: pattern detection beats new tools
 (00:18) Loïc's path: cryptography, OpenTrust, ProductBoard, Superhuman
 (02:13) Retrofitting AI into a 100ms product
 (04:08) Voice on mobile: pre-caching LLM context to keep the feel fast
 (07:46) Frontier first, then fine-tune: model strategy across features
 (11:04) The "double-dipping" trick that worked on GPT-4 and stopped working
 (12:25) Cognitive load and staying current as a leader
 (16:59) Balancing YC founder urgency with peer CTO grounding
 (19:28) Pods, AI Guild, and aligned autonomy
 (23:15) Managing models vs. managing people: delegation in reverse
 (28:27) The Waymo example: eigenvectors of evaluation
 (32:15) Day 30 onboarding: leading the AI Inbox sprint
 (35:04) Why email is the killer agent use case
 (38:51) Auto-draft, never auto-send
 (39:57) Agent agency vs. agent laziness
 (43:07) Hiring for AI fluency
 (45:55) Pattern detection is the leadership skill
 (47:21) Nuclear submarines as engineering reference points
 (48:37) Closing thoughts
 (49:38) Superhuman is hiring

Connect with Loïc:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Fri, 22 May 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 3011 Loïc Houssier, VP of Engineering at Superhuman (the email client Grammarly acquired for $825M in July 2025), explains how his team retrofitted AI features into a product whose entire brand promise is sub-100ms speed. He breaks down the model-routing strategy, the eval framework his PMs own, and why his team auto-drafts every reply but refuses to auto-send any of them. Chain of Thought is hosted by Conor Bronsdon. Loïc Houssier, VP of Engineering at Superhuman (the email client Grammarly acquired for $825M in July 2025), explains how his team retrofitted AI features into a product whose entire brand promise is sub-100ms speed. He breaks down the model-routing strat Loïc Houssier, Loic Houssier, Superhuman, Grammarly, Superhuman Mail, AI email, email agents, AI agents, voice AI, LLM caching, latency optimization, eval engineering, evaluation framework, BERT classifier, fine-tuning, model routing, engineering leadership, AI Inbox, OpenTrust, DocuSign, ProductBoard, Chain of Thought, Conor Bronsdon, AI infrastructure, agentic systems, product engineering No The AI Hiring Doom Loop: Applications Up 239%, Hires Down 75% 3 3 58 58 The AI Hiring Doom Loop: Applications Up 239%, Hires Down 75% full abc09d9f-a018-4c3d-b76a-34ff5c75c0bf https://share.transistor.fm/s/0ddeba66 Job applications are up 239% since ChatGPT launched, tech layoffs show no signs of slowing down, and the market for technical talent is a topsy turvy mess. 

Greenhouse has a unique vantage point to understand all of this: they process 22 million job applications a month across 7,500+ companies including HubSpot, Anthropic, Coinbase, and the NFL. CEO Daniel Chait has had a front-row seat to the strangest hiring market in decades, and he's here to advise us all on how to navigate it.

Daniel coined the term "AI doom loop" for what's happening: applications up 239% since ChatGPT launched, resume hacks like white-fonting and prompt injection up 500%, and 75% fewer applications reaching the hire stage. 91% of recruiters have spotted candidate deception. 38% of job seekers walk away from processes that include an AI interview.

It's the worst job market for candidates and the hardest hiring market for recruiters.

Daniel explains how technical talent can break the loop.

We cover:

  • Why software engineers, according to Greenhouse data, are the worst auto-appliers and what to do instead
  • The North Korean infiltration problem: deepfakes, laptop farms, and why companies are flying candidates in for in-person interviews again
  • How AI screener interviews open up the funnel when companies are transparent about using them, and break it when they aren't
  • Greenhouse Dream Jobs: how a single high-signal application a month converts at 5x the rate
  • Why take-home assignments don't survive contact with AI and what Greenhouse uses instead
  • What a coding interview looks like when leetcode is dead and engineers run 10+ Claude Code sessions in parallel
  • The case for killing the resume entirely and rebuilding hiring around AI conversations

Chapters:
(00:00) Cold open: 239% more applications, 75% fewer hires
(02:14) Galileo
(03:05) The AI doom loop, defined
(04:01) How we got here: remote work, ZIRP, and ChatGPT
(07:51) Are software engineering jobs really in trouble?
(12:46) The trust crisis: 91% of recruiters spot deception
(15:52) North Korean spies, deepfakes, and laptop farms
(19:34) Can AI fix the problem it created?
(20:52) AI screener interviews and the uncanny valley
(26:33) Greenhouse Dream Jobs: one signal, 5x conversion
(28:31) Why auto-apply doesn't work (and what does)
(30:18) Communities, building in public, and the early-mover advantage
(37:08) Gen Z lost trust, and the bias problem
(39:04) Kill the resume: rethinking hiring from scratch
(43:34) How Greenhouse changed its own interview process
(48:47) Coding interviews in the agent era: leetcode is dead
(51:33) Predictions: more proof, more conversations, less noise
(54:34) Where job seekers and hiring teams should start

Connect with Daniel:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Job applications are up 239% since ChatGPT launched, tech layoffs show no signs of slowing down, and the market for technical talent is a topsy turvy mess. 

Greenhouse has a unique vantage point to understand all of this: they process 22 million job applications a month across 7,500+ companies including HubSpot, Anthropic, Coinbase, and the NFL. CEO Daniel Chait has had a front-row seat to the strangest hiring market in decades, and he's here to advise us all on how to navigate it.

Daniel coined the term "AI doom loop" for what's happening: applications up 239% since ChatGPT launched, resume hacks like white-fonting and prompt injection up 500%, and 75% fewer applications reaching the hire stage. 91% of recruiters have spotted candidate deception. 38% of job seekers walk away from processes that include an AI interview.

It's the worst job market for candidates and the hardest hiring market for recruiters.

Daniel explains how technical talent can break the loop.

We cover:

  • Why software engineers, according to Greenhouse data, are the worst auto-appliers and what to do instead
  • The North Korean infiltration problem: deepfakes, laptop farms, and why companies are flying candidates in for in-person interviews again
  • How AI screener interviews open up the funnel when companies are transparent about using them, and break it when they aren't
  • Greenhouse Dream Jobs: how a single high-signal application a month converts at 5x the rate
  • Why take-home assignments don't survive contact with AI and what Greenhouse uses instead
  • What a coding interview looks like when leetcode is dead and engineers run 10+ Claude Code sessions in parallel
  • The case for killing the resume entirely and rebuilding hiring around AI conversations

Chapters:
(00:00) Cold open: 239% more applications, 75% fewer hires
(02:14) Galileo
(03:05) The AI doom loop, defined
(04:01) How we got here: remote work, ZIRP, and ChatGPT
(07:51) Are software engineering jobs really in trouble?
(12:46) The trust crisis: 91% of recruiters spot deception
(15:52) North Korean spies, deepfakes, and laptop farms
(19:34) Can AI fix the problem it created?
(20:52) AI screener interviews and the uncanny valley
(26:33) Greenhouse Dream Jobs: one signal, 5x conversion
(28:31) Why auto-apply doesn't work (and what does)
(30:18) Communities, building in public, and the early-mover advantage
(37:08) Gen Z lost trust, and the bias problem
(39:04) Kill the resume: rethinking hiring from scratch
(43:34) How Greenhouse changed its own interview process
(48:47) Coding interviews in the agent era: leetcode is dead
(51:33) Predictions: more proof, more conversations, less noise
(54:34) Where job seekers and hiring teams should start

Connect with Daniel:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Wed, 06 May 2026 04:30:00 -0700 Conor Bronsdon Conor Bronsdon 3427 Daniel Chait, CEO of Greenhouse (the hiring platform behind 22 million applications a month), coined the term "AI doom loop": applications up 239% since ChatGPT, but 75% fewer reach the hire stage. Inside: why software engineers are the worst auto-appliers and how Greenhouse is rebuilding hiring. Chain of Thought is hosted by Conor Bronsdon. Daniel Chait, CEO of Greenhouse (the hiring platform behind 22 million applications a month), coined the term "AI doom loop": applications up 239% since ChatGPT, but 75% fewer reach the hire stage. Inside: why software engineers are the worst auto-applier Daniel Chait, Greenhouse, AI hiring, AI doom loop, hiring platform, applicant tracking, job market, AI interviews, AI recruiting, software engineer hiring, coding interview, leetcode, resume screening, prompt injection, candidate deception, North Korean infiltration, deepfake, AI agents, ChatGPT applications, Bureau of Labor Statistics, recruiting, hiring trends, job seekers, AI bias, Conor Bronsdon, Chain of Thought No Every AI Agent Has an Evaluation Gap | Alex Ratner, Snorkel AI 3 3 57 57 Every AI Agent Has an Evaluation Gap | Alex Ratner, Snorkel AI full c2cfe57e-f3f6-41ac-9f8e-b25e683a1d05 https://share.transistor.fm/s/18593a4c Alex Ratner co-founded Snorkel AI out of Chris Ré's Stanford lab and helped establish data-centric AI as a field. Today, Snorkel is a $1.3B company shipping thousands of data sets and environments a week to frontier labs and vertical AI teams like Harvey.

In this conversation, he argues our ability to build AI agents has outpaced our ability to measure them. That gap is what's keeping most enterprise agents stuck in demo purgatory.

If you can't measure it, you can't improve it. And you can't deploy it.

In this conversation:

  • The three axes of the evaluation gap: input complexity, autonomy horizon, and output complexity
  • Big Law Bench: how Snorkel and Harvey benchmarked legal agents on deep-research tasks that take lawyers 10-15 hours
  • What Snorkel's $3M Open Benchmarks Grant is funding, and why "benchmaxxing" critiques don't kill the case for public benchmarks
  • Why 40-50% of Snorkel's data work is still review and labeling, even with the best models in the loop
  • The "expert-agentic" era, where domain expertise (law, finance, coding, even woodworking) is the new bottleneck
  • Why self-supervision is a dead end outside narrow cases like distillation
  • The false dichotomy between data and environments, and why pure-environment vendors miss how AI actually works

Chapters

(00:00) Intro: Alex Ratner and Snorkel AI
 (02:50) What the evaluation gap actually is
 (06:05) Moravec's paradox and the jagged frontier
 (08:46) Where AI agents fall down in enterprise work
 (10:40) Big Law Bench: benchmarking Harvey's legal agents
 (12:00) The three axes: input, autonomy horizon, output
 (18:31) Snorkel's $3M Open Benchmarks Grant
 (22:33) From "janitorial" to epicenter: 15 years of data-centric AI
 (29:26) The expert-agentic data era
 (34:54) The false dichotomy between data and environments
 (40:05) DoorDash Tasks and expert data at scale

Connect with Alex Ratner:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Alex Ratner co-founded Snorkel AI out of Chris Ré's Stanford lab and helped establish data-centric AI as a field. Today, Snorkel is a $1.3B company shipping thousands of data sets and environments a week to frontier labs and vertical AI teams like Harvey.

In this conversation, he argues our ability to build AI agents has outpaced our ability to measure them. That gap is what's keeping most enterprise agents stuck in demo purgatory.

If you can't measure it, you can't improve it. And you can't deploy it.

In this conversation:

  • The three axes of the evaluation gap: input complexity, autonomy horizon, and output complexity
  • Big Law Bench: how Snorkel and Harvey benchmarked legal agents on deep-research tasks that take lawyers 10-15 hours
  • What Snorkel's $3M Open Benchmarks Grant is funding, and why "benchmaxxing" critiques don't kill the case for public benchmarks
  • Why 40-50% of Snorkel's data work is still review and labeling, even with the best models in the loop
  • The "expert-agentic" era, where domain expertise (law, finance, coding, even woodworking) is the new bottleneck
  • Why self-supervision is a dead end outside narrow cases like distillation
  • The false dichotomy between data and environments, and why pure-environment vendors miss how AI actually works

Chapters

(00:00) Intro: Alex Ratner and Snorkel AI
 (02:50) What the evaluation gap actually is
 (06:05) Moravec's paradox and the jagged frontier
 (08:46) Where AI agents fall down in enterprise work
 (10:40) Big Law Bench: benchmarking Harvey's legal agents
 (12:00) The three axes: input, autonomy horizon, output
 (18:31) Snorkel's $3M Open Benchmarks Grant
 (22:33) From "janitorial" to epicenter: 15 years of data-centric AI
 (29:26) The expert-agentic data era
 (34:54) The false dichotomy between data and environments
 (40:05) DoorDash Tasks and expert data at scale

Connect with Alex Ratner:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Wed, 29 Apr 2026 04:58:48 -0700 Conor Bronsdon Conor Bronsdon 2560 Snorkel CEO Alex Ratner maps the evaluation gap blocking AI agents from real enterprise work, walks through the company's $3M Open Benchmarks Grant, and explains why pure 'environment' vendors don't actually understand how AI works. Chain of Thought is hosted by Conor Bronsdon. Snorkel CEO Alex Ratner maps the evaluation gap blocking AI agents from real enterprise work, walks through the company's $3M Open Benchmarks Grant, and explains why pure 'environment' vendors don't actually understand how AI works. Chain of Thought is ho Alex Ratner, Snorkel AI, data-centric AI, AI evaluation, AI benchmarks, AI agents, agentic AI, evaluation gap, Big Law Bench, Open Benchmarks Grant, data labeling, reinforcement learning, RL environments, synthetic data, Harvey AI, Terminal Bench, coding agents, enterprise AI, AI evals, frontier labs, Conor Bronsdon, Chain of Thought No 250,000 Lines of Code/Week: Inside an AMD VP's Agent-First Workflow | Anush Elangovan 3 3 56 56 250,000 Lines of Code/Week: Inside an AMD VP's Agent-First Workflow | Anush Elangovan full 25760281-0c63-40e0-8f25-d7d0dd220e1c https://share.transistor.fm/s/81b87526 What happens when a VP of AI Software at a major chip company goes all-in on AI coding agents for his own team's work?

Anush Elangovan runs 10–12 Claude Code agents across three machines, burns 6.5 billion tokens a week, and rewrote a 25-year-old project (Slurm → Spur in Rust) in a single night.

He does it all on dangerously-skip-permissions.

About Anush
Anush Elangovan is Corporate VP of AI Software at AMD. He founded Nod.ai, where his team built SHARK and was a primary contributor to Torch-MLIR and IREE. AMD acquired Nod.ai in 2023, and Anush now leads AI software strategy across AMD's full silicon portfolio. Before Nod.ai, he shipped the graphics stack on the first ARM Chromebook and led Chrome OS's migration to Gentoo.

We cover:

  • How Anush runs 10–12 parallel agents with a geo-distributed AMD hardware rig
  • Why the test harness is the new code review (and why agents are "sneaky and dumb")
  • Rewriting a 25-year-old project in Rust overnight, without opening the editor
  • Why every new project is in Rust specifically because he refuses to learn it
  • The "HR partner fixing engineering bugs" moment and what it says about upskilling
  • Why normal SDLC is dead and speed is the only durable moat
  • AMD's fully open-source software stack and how community contributions are accelerating ROCm
  • "Software is just tokens" and what that means for AMD's bet against CUDA lock-in

Connect with Anush
LinkedIn: linkedin.com/in/anushelangovan
Twitter/X: @AnushElangovan
AMD AI blog: amd.com
AMD AI Developer Program: amd.com/developer

Connect with Chain of Thought host Conor Bronsdon:
Newsletter: newsletter.chainofthought.show
Twitter/X: @ConorBronsdon
LinkedIn: linkedin.com/in/conorbronsdon
YouTube: @ConorBronsdon

More episodes: chainofthought.show

Chapters
0:00 Cold open
0:21 Welcome + guest intro
3:43 250K lines a week, 10–12 parallel agents
7:34 Agent architecture + geo-distributed test rig
9:57 When does AI-generated code become a liability?
14:12 80% tests first: the test harness philosophy
18:24 Dangerously-skip-permissions + testing as code review
19:52 "Normal SDLC is dead in the agentic world"
20:44 Advice for engineers and leaders who feel behind
24:51 Tokens, throughput, and what happens next
26:29 Block layoffs, uneven AI gains, the 25-year Slurm rewrite
32:55 Galileo sponsor break
34:24 When agents go off the rails: sneaky and dumb
37:52 Orchestrator agents vs. focused multi-threading
40:45 Open source, ROCm, AMD's software bet
44:19 "Software is just tokens"
45:24 AMD Developer Program + community contributions
47:09 Where to start with AMD
48:39 Heterogeneous compute
50:13 Outro

Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

Full show notes: newsletter.chainofthought.show

Disclaimer from our host: All views, opinions and statements expressed on this account are solely my own and are made in my personal capacity. They do not reflect, and should not be construed as reflecting, the views, positions, or policies of my employer. This account is not affiliated with, authorized by, or endorsed by my employer in any way.

]]> What happens when a VP of AI Software at a major chip company goes all-in on AI coding agents for his own team's work?

Anush Elangovan runs 10–12 Claude Code agents across three machines, burns 6.5 billion tokens a week, and rewrote a 25-year-old project (Slurm → Spur in Rust) in a single night.

He does it all on dangerously-skip-permissions.

About Anush
Anush Elangovan is Corporate VP of AI Software at AMD. He founded Nod.ai, where his team built SHARK and was a primary contributor to Torch-MLIR and IREE. AMD acquired Nod.ai in 2023, and Anush now leads AI software strategy across AMD's full silicon portfolio. Before Nod.ai, he shipped the graphics stack on the first ARM Chromebook and led Chrome OS's migration to Gentoo.

We cover:

  • How Anush runs 10–12 parallel agents with a geo-distributed AMD hardware rig
  • Why the test harness is the new code review (and why agents are "sneaky and dumb")
  • Rewriting a 25-year-old project in Rust overnight, without opening the editor
  • Why every new project is in Rust specifically because he refuses to learn it
  • The "HR partner fixing engineering bugs" moment and what it says about upskilling
  • Why normal SDLC is dead and speed is the only durable moat
  • AMD's fully open-source software stack and how community contributions are accelerating ROCm
  • "Software is just tokens" and what that means for AMD's bet against CUDA lock-in

Connect with Anush
LinkedIn: linkedin.com/in/anushelangovan
Twitter/X: @AnushElangovan
AMD AI blog: amd.com
AMD AI Developer Program: amd.com/developer

Connect with Chain of Thought host Conor Bronsdon:
Newsletter: newsletter.chainofthought.show
Twitter/X: @ConorBronsdon
LinkedIn: linkedin.com/in/conorbronsdon
YouTube: @ConorBronsdon

More episodes: chainofthought.show

Chapters
0:00 Cold open
0:21 Welcome + guest intro
3:43 250K lines a week, 10–12 parallel agents
7:34 Agent architecture + geo-distributed test rig
9:57 When does AI-generated code become a liability?
14:12 80% tests first: the test harness philosophy
18:24 Dangerously-skip-permissions + testing as code review
19:52 "Normal SDLC is dead in the agentic world"
20:44 Advice for engineers and leaders who feel behind
24:51 Tokens, throughput, and what happens next
26:29 Block layoffs, uneven AI gains, the 25-year Slurm rewrite
32:55 Galileo sponsor break
34:24 When agents go off the rails: sneaky and dumb
37:52 Orchestrator agents vs. focused multi-threading
40:45 Open source, ROCm, AMD's software bet
44:19 "Software is just tokens"
45:24 AMD Developer Program + community contributions
47:09 Where to start with AMD
48:39 Heterogeneous compute
50:13 Outro

Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

Full show notes: newsletter.chainofthought.show

Disclaimer from our host: All views, opinions and statements expressed on this account are solely my own and are made in my personal capacity. They do not reflect, and should not be construed as reflecting, the views, positions, or policies of my employer. This account is not affiliated with, authorized by, or endorsed by my employer in any way.

]]> Wed, 22 Apr 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 3053 AMD's VP of AI Software runs 10-12 Claude Code agents in parallel, burns 6.5 billion tokens a week, and rewrote a 25-year-old Slurm replacement in Rust overnight. Anush Elangovan on why normal SDLC is dead, testing is the new code review, and software is just tokens. Chain of Thought is hosted by Conor Bronsdon. AMD's VP of AI Software runs 10-12 Claude Code agents in parallel, burns 6.5 billion tokens a week, and rewrote a 25-year-old Slurm replacement in Rust overnight. Anush Elangovan on why normal SDLC is dead, testing is the new code review, and software is AI coding agents, AMD, ROCm, Claude Code, AI agents, agentic coding, Rust, Slurm, test-driven development, Anush Elangovan, Nod.ai, SHARK, open source AI, CUDA alternative, speed is the moat, Chain of Thought, Conor Bronsdon, MLIR, Torch-MLIR, software engineering No Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j 3 3 55 55 Hallucinations Are a Data Architecture Problem | Sudhir Hasbe, Neo4j full 2b560138-9ae3-4e51-9eed-7224bda49ba7 https://share.transistor.fm/s/ec268865 Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics services: BigQuery, Looker, Dataflow, and led the Looker acquisition.

His thesis: the hallucinations we blame on AI models are really a data architecture problem. LLMs weren't trained on your enterprise knowledge, so handing them a data lake with 10,000 disconnected tables and asking them to reason is the wrong design. The fix is knowledge graphs: feeding the model a structured map of relationships, entities, and context so it can reason over meaning, not just vector similarity.

Sudhir breaks down the five capabilities knowledge graphs unlock for enterprise AI: GraphRAG (moving accuracy from 60% to 97%), semantic mapping across siloed systems, context graphs, agent memory, and multi-hop reasoning. He explains three architecture patterns customers are actually shipping, why giving an LLM hundreds of tools makes it worse, and what Uber, EA Sports, Klarna, and Novo Nordisk are doing differently.

This is the case for treating knowledge as infrastructure.

We cover:

  • Why enterprise AI needs a different playbook than consumer AI
  • The five data asset types every agentic system needs: system of record, historical, memory, context, and reference
  • How GraphRAG combines vector search and graph traversal to move from 60% accuracy to 95%+
  • Three architecture patterns: semantic layer only, semantic map plus domain data, full consolidation (the Klarna/Kiki model)
  • What context graphs capture that Salesforce doesn't: the Slack and email negotiation behind every deal
  • Why giving an LLM hundreds of tools drops accuracy, and how Uber uses knowledge graphs as a business validation layer
  • What Neo4j's Aura Agent, MCP server, and A2A support mean for developers starting today

Chapters:

(0:00) Why building a self-driving car is hard
(0:22) Intro
(2:03) Hallucinations as a data architecture problem
(4:31) From models-as-core to systems-of-knowledge
(6:13) Why data lakes fail AI agents
(9:15) The five data asset types enterprise agents need
(11:46) Where basic RAG breaks down: the Spotify metadata lesson
(16:00) GraphRAG: 3x accuracy, easier development, explainability
(18:47) Semantic mapping across the enterprise estate
(19:23) Three knowledge-graph architecture patterns
(22:42) Context graphs: capturing the "why" behind decisions
(25:33) Individual vs. organizational agent memory
(28:40) Multi-hop reasoning for fraud rings and AML
(31:52) Why there are no shortcuts in enterprise AI
(36:38) What happens when you give an LLM 100 tools
(39:19) The Uber example: knowledge graph as business validation
(44:42) First mile of a 26-mile marathon
(48:32) Aura Agent, MCP server, and the A2A protocol
(50:43) Where developers should start

Connect with Sudhir Hasbe:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at: 
galileo.ai/mastering-multi-agent-systems

]]> Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics services: BigQuery, Looker, Dataflow, and led the Looker acquisition.

His thesis: the hallucinations we blame on AI models are really a data architecture problem. LLMs weren't trained on your enterprise knowledge, so handing them a data lake with 10,000 disconnected tables and asking them to reason is the wrong design. The fix is knowledge graphs: feeding the model a structured map of relationships, entities, and context so it can reason over meaning, not just vector similarity.

Sudhir breaks down the five capabilities knowledge graphs unlock for enterprise AI: GraphRAG (moving accuracy from 60% to 97%), semantic mapping across siloed systems, context graphs, agent memory, and multi-hop reasoning. He explains three architecture patterns customers are actually shipping, why giving an LLM hundreds of tools makes it worse, and what Uber, EA Sports, Klarna, and Novo Nordisk are doing differently.

This is the case for treating knowledge as infrastructure.

We cover:

  • Why enterprise AI needs a different playbook than consumer AI
  • The five data asset types every agentic system needs: system of record, historical, memory, context, and reference
  • How GraphRAG combines vector search and graph traversal to move from 60% accuracy to 95%+
  • Three architecture patterns: semantic layer only, semantic map plus domain data, full consolidation (the Klarna/Kiki model)
  • What context graphs capture that Salesforce doesn't: the Slack and email negotiation behind every deal
  • Why giving an LLM hundreds of tools drops accuracy, and how Uber uses knowledge graphs as a business validation layer
  • What Neo4j's Aura Agent, MCP server, and A2A support mean for developers starting today

Chapters:

(0:00) Why building a self-driving car is hard
(0:22) Intro
(2:03) Hallucinations as a data architecture problem
(4:31) From models-as-core to systems-of-knowledge
(6:13) Why data lakes fail AI agents
(9:15) The five data asset types enterprise agents need
(11:46) Where basic RAG breaks down: the Spotify metadata lesson
(16:00) GraphRAG: 3x accuracy, easier development, explainability
(18:47) Semantic mapping across the enterprise estate
(19:23) Three knowledge-graph architecture patterns
(22:42) Context graphs: capturing the "why" behind decisions
(25:33) Individual vs. organizational agent memory
(28:40) Multi-hop reasoning for fraud rings and AML
(31:52) Why there are no shortcuts in enterprise AI
(36:38) What happens when you give an LLM 100 tools
(39:19) The Uber example: knowledge graph as business validation
(44:42) First mile of a 26-mile marathon
(48:32) Aura Agent, MCP server, and the A2A protocol
(50:43) Where developers should start

Connect with Sudhir Hasbe:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at: 
galileo.ai/mastering-multi-agent-systems

]]> Thu, 16 Apr 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 3144 Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics services: BigQuery, Looker, Dataflow, and led the Looker acquisition. His thesis: the hallucinations we blame on AI models are really a data architecture problem. Chain of Thought is hosted by Conor Bronsdon. Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics servi Sudhir Hasbe, Neo4j, knowledge graphs, GraphRAG, enterprise AI, AI agents, agentic systems, LLM hallucinations, vector search, RAG, Aura Agent, MCP server, A2A protocol, agent memory, context graphs, multi-hop reasoning, BigQuery, Looker, Google Cloud, Conor Bronsdon, Chain of Thought, AI infrastructure, data architecture, Klarna, EA Sports No Why LLMs Are Plausibility Engines, Not Truth Engines | Dan Klein 3 3 54 54 Why LLMs Are Plausibility Engines, Not Truth Engines | Dan Klein full c07947c1-7d33-4ff4-94dc-fb7f416a4baf https://share.transistor.fm/s/9ab75e81 Every few weeks at Microsoft, someone would build an AI prototype that blew everyone's minds. Three months later? Dead. "We can never ship that." Dan Klein watched this happen for five years before he decided to do something about it.

Dan is co-founder and CTO of Scaled Cognition, a professor of computer science at UC Berkeley, and winner of the ACM Grace Murray Hopper Award. His previous startups include adap.tv (acquired by AOL for $405M) and Semantic Machines (acquired by Microsoft in 2018), where he spent five years integrating conversational AI. His PhD students now run AI teams at Google, Stanford, MIT, and OpenAI.

At Scaled Cognition, Dan's team built APT1 (the Agentic Pre-trained Transformer) for under $11 million. It's a model designed for actions, not tokens, with structural guarantees that go beyond prompt-and-pray.

Dan makes the case that current LLMs are plausibility engines, not truth engines, and that the gap between demo and production is where most AI projects die.

  • Why prompting is a fundamentally unreliable control surface for production AI
  • How APT1's architecture gives actions and information first-class status instead of treating everything as tokens
  • The specific failure modes that kill enterprise AI prototypes within three months
  • Why stacking multiple models to check each other produces correlated errors, not reliability
  • How Scaled Cognition applied RL to conversational AI when there's no zero-sum winner
  • Why every S-curve in AI gets mistaken for an exponential — and what comes after the current plateau
  • The societal risk of systems that produce output indistinguishable from truth

Chapters
(0:00) Cold open: RL is about doubling down on what works
(0:28) Introducing Dan Klein and Scaled Cognition
(2:53) The demo-to-production gap: why AI prototypes die
(5:40) Why prompting is not a real control surface
(8:06) Modular decomposition vs. end-to-end optimization
(10:55) Are LLMs fundamentally mismatched with how we use them?
(14:26) What's wrong with benchmarks today
(20:27) APT1: building a model for actions, not tokens
(24:14) What makes data truly agentic
(28:02) Hallucinations as an iceberg — visible vs. undetectable
(34:16) Building a prototype model for under $11 million
(39:57) Applying RL to conversations without a zero-sum winner
(43:31) LLMs as a condensation of the web — and what happens when it runs out
(50:07) Reasoning models: where they work and where they don't
(53:04) Early deployments in regulated industries
(57:14) Why multi-model checking fails
(1:00:34) The minimum bar for trustworthy agentic systems
(1:04:07) Societal risk: when AI output is indistinguishable from truth
(1:13:33) Where Dan is inspired in AI research today

Connect with Dan Klein:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Every few weeks at Microsoft, someone would build an AI prototype that blew everyone's minds. Three months later? Dead. "We can never ship that." Dan Klein watched this happen for five years before he decided to do something about it.

Dan is co-founder and CTO of Scaled Cognition, a professor of computer science at UC Berkeley, and winner of the ACM Grace Murray Hopper Award. His previous startups include adap.tv (acquired by AOL for $405M) and Semantic Machines (acquired by Microsoft in 2018), where he spent five years integrating conversational AI. His PhD students now run AI teams at Google, Stanford, MIT, and OpenAI.

At Scaled Cognition, Dan's team built APT1 (the Agentic Pre-trained Transformer) for under $11 million. It's a model designed for actions, not tokens, with structural guarantees that go beyond prompt-and-pray.

Dan makes the case that current LLMs are plausibility engines, not truth engines, and that the gap between demo and production is where most AI projects die.

  • Why prompting is a fundamentally unreliable control surface for production AI
  • How APT1's architecture gives actions and information first-class status instead of treating everything as tokens
  • The specific failure modes that kill enterprise AI prototypes within three months
  • Why stacking multiple models to check each other produces correlated errors, not reliability
  • How Scaled Cognition applied RL to conversational AI when there's no zero-sum winner
  • Why every S-curve in AI gets mistaken for an exponential — and what comes after the current plateau
  • The societal risk of systems that produce output indistinguishable from truth

Chapters
(0:00) Cold open: RL is about doubling down on what works
(0:28) Introducing Dan Klein and Scaled Cognition
(2:53) The demo-to-production gap: why AI prototypes die
(5:40) Why prompting is not a real control surface
(8:06) Modular decomposition vs. end-to-end optimization
(10:55) Are LLMs fundamentally mismatched with how we use them?
(14:26) What's wrong with benchmarks today
(20:27) APT1: building a model for actions, not tokens
(24:14) What makes data truly agentic
(28:02) Hallucinations as an iceberg — visible vs. undetectable
(34:16) Building a prototype model for under $11 million
(39:57) Applying RL to conversations without a zero-sum winner
(43:31) LLMs as a condensation of the web — and what happens when it runs out
(50:07) Reasoning models: where they work and where they don't
(53:04) Early deployments in regulated industries
(57:14) Why multi-model checking fails
(1:00:34) The minimum bar for trustworthy agentic systems
(1:04:07) Societal risk: when AI output is indistinguishable from truth
(1:13:33) Where Dan is inspired in AI research today

Connect with Dan Klein:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Wed, 08 Apr 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 4693 Dan Klein, co-founder & CTO of Scaled Cognition and ACM Grace Murray Hopper Award winner, breaks down why LLMs are fundamentally plausibility engines and how his team built APT1 for under 11 million dollars. He explains why multi-model checking fails, why benchmarks measure the wrong thing, and what it takes to ship AI that enterprises can actually trust. Chain of Thought is hosted by Conor Bronsdon. Dan Klein, co-founder & CTO of Scaled Cognition and ACM Grace Murray Hopper Award winner, breaks down why LLMs are fundamentally plausibility engines and how his team built APT1 for under 11 million dollars. He explains why multi-model checking fails, Dan Klein, Scaled Cognition, APT1, Agentic Pre-trained Transformer, UC Berkeley, Berkeley NLP, hallucinations, plausibility engines, enterprise AI, agentic AI, AI agents, reinforcement learning, demo to production, AI benchmarks, reasoning models, AI trust, AI reliability, controllable AI, Conor Bronsdon, Chain of Thought No Agent Memory: The Last Battleground in the AI Stack | Richmond Alake, Oracle 3 3 53 53 Agent Memory: The Last Battleground in the AI Stack | Richmond Alake, Oracle full 25d6a362-c5ef-47c9-9b85-75a3322090c9 https://share.transistor.fm/s/964292e1 Richmond Alake is Director of AI Developer Experience at Oracle and one of the most concrete voices on agent memory right now. His AI Engineer World's Fair talk on architecting agent memory crossed 100,000 views, he built the open-source MemoRIS library, and he co-created a course with Andrew Ng.

In this conversation, Richmond walks through memory engineering as a distinct discipline from prompt engineering and context engineering, demos a memory-aware financial services agent that runs vector, graph, spatial, and relational search in a single query, and explains the principle that separates production-grade memory systems from prototypes: don't delete, forget. If you're building agents that need to remember anything across sessions, this is the episode.

We cover:
- Why memory engineering deserves its own name, separate from prompt and context engineering
- The two failure modes Richmond sees most: wrong mental model and deleting instead of forgetting
- Four human memory types mapped to agent architecture: working, episodic, semantic, and procedural
- Demo: AFSA, a memory-aware financial services agent with converged search across data types
- How the Generative Agents paper's decay formula (relevance + recency + importance) enables controlled forgetting
- Where context engineering ends and memory engineering begins 
- Why files work for prototypes but databases win in production

Chapters:
(0:00) Memory is the last battleground in AI
(0:28) Meet Richmond Alake, Oracle's AI DevEx lead
(2:23) Why memory engineering is its own discipline
(7:57) The failure modes nobody talks about
(12:49) Demo: a memory-aware financial services agent
(18:30) Segmenting context windows by memory type
(19:22) Four human memory types mapped to agent architecture
(23:51) Procedural memory in production systems
(27:11) Don't delete, forget: implementing controlled decay (33:32) Sponsor: Galileo
(35:46) Where context engineering ends and memory engineering begins
(38:50) Is agent memory fundamentally a database problem?
(44:13) Files vs. databases: what production actually needs
(51:09) Picking your lane in the AI noise
(55:44) Richmond's courses with Andrew Ng, O'Reilly classes, and where to follow

Connect with Richmond Alake: LinkedIn: https://www.linkedin.com/in/richmondalake/
Check out his Youtube: https://www.youtube.com/@richmond_a
O'Reilly courses: https://www.oreilly.com/live-events/ai-memory-management-in-agentic-systems/0642572179274/
Diagrams from the episode: https://imgur.com/a/mMtcAtk

Connect with Chain of Thought host Conor Bronsdon:
Newsletter: https://newsletter.chainofthought.show/
Twitter/X: https://x.com/ConorBronsdon
LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at http://www.galileo.ai/mastering-multi-agent-systems

]]> Richmond Alake is Director of AI Developer Experience at Oracle and one of the most concrete voices on agent memory right now. His AI Engineer World's Fair talk on architecting agent memory crossed 100,000 views, he built the open-source MemoRIS library, and he co-created a course with Andrew Ng.

In this conversation, Richmond walks through memory engineering as a distinct discipline from prompt engineering and context engineering, demos a memory-aware financial services agent that runs vector, graph, spatial, and relational search in a single query, and explains the principle that separates production-grade memory systems from prototypes: don't delete, forget. If you're building agents that need to remember anything across sessions, this is the episode.

We cover:
- Why memory engineering deserves its own name, separate from prompt and context engineering
- The two failure modes Richmond sees most: wrong mental model and deleting instead of forgetting
- Four human memory types mapped to agent architecture: working, episodic, semantic, and procedural
- Demo: AFSA, a memory-aware financial services agent with converged search across data types
- How the Generative Agents paper's decay formula (relevance + recency + importance) enables controlled forgetting
- Where context engineering ends and memory engineering begins 
- Why files work for prototypes but databases win in production

Chapters:
(0:00) Memory is the last battleground in AI
(0:28) Meet Richmond Alake, Oracle's AI DevEx lead
(2:23) Why memory engineering is its own discipline
(7:57) The failure modes nobody talks about
(12:49) Demo: a memory-aware financial services agent
(18:30) Segmenting context windows by memory type
(19:22) Four human memory types mapped to agent architecture
(23:51) Procedural memory in production systems
(27:11) Don't delete, forget: implementing controlled decay (33:32) Sponsor: Galileo
(35:46) Where context engineering ends and memory engineering begins
(38:50) Is agent memory fundamentally a database problem?
(44:13) Files vs. databases: what production actually needs
(51:09) Picking your lane in the AI noise
(55:44) Richmond's courses with Andrew Ng, O'Reilly classes, and where to follow

Connect with Richmond Alake: LinkedIn: https://www.linkedin.com/in/richmondalake/
Check out his Youtube: https://www.youtube.com/@richmond_a
O'Reilly courses: https://www.oreilly.com/live-events/ai-memory-management-in-agentic-systems/0642572179274/
Diagrams from the episode: https://imgur.com/a/mMtcAtk

Connect with Chain of Thought host Conor Bronsdon:
Newsletter: https://newsletter.chainofthought.show/
Twitter/X: https://x.com/ConorBronsdon
LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon

More episodes: https://chainofthought.show

Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at http://www.galileo.ai/mastering-multi-agent-systems

]]> Thu, 02 Apr 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 3564 Richmond Alake is Director of AI Developer Experience at Oracle and one of the most concrete voices on agent memory right now. His AI Engineer World's Fair talk on architecting agent memory crossed 100,000 views, he built the open-source MemoRIS library, and he co-created a course with Andrew Ng. Chain of Thought is hosted by Conor Bronsdon. Richmond Alake is Director of AI Developer Experience at Oracle and one of the most concrete voices on agent memory right now. His AI Engineer World's Fair talk on architecting agent memory crossed 100,000 views, he built the open-source MemoRIS library, Richmond Alake, Oracle, Oracle AI Database, agent memory, memory engineering, context engineering, prompt engineering, AI agents, agentic systems, RAG, retrieval pipelines, vector search, converged database, converged search, procedural memory, episodic memory, semantic memory, working memory, MemoRIS, AFSA, Andrew Ng, memory aware agents, context window, forgetting, Generative Agents, AI infrastructure, Conor Bronsdon, Chain of Thought No Context Poisoning Is Killing Your AI Agents: How to Stop It 3 3 52 52 Context Poisoning Is Killing Your AI Agents: How to Stop It full ae5aee92-b6fa-4ed0-b0d8-c945f377cb6b https://share.transistor.fm/s/e9df3b6f Michel Tricot co-founded Airbyte, the open source data integration platform with 600+ free connectors that hit a $1.5 billion valuation. Now he's building the company's next product: an agent engine, currently in public beta. His thesis is that agents don't fail because models are bad. They fail because the data feeding them is wrong: context poisoning is killing them.

Michel demos this live. A simple Gong query through raw API calls burned 30,000 extra tokens and took three minutes. The same query through Airbyte's context store ran in one minute and used a fraction of the context window. Conor and Michel dig into why RAG alone won't cut it, what a "context engineer" actually does, how Airbyte tracks entities across Salesforce, Zendesk, and Gong without embeddings, and whether the SaaS apocalypse playing out in public markets is overblown.

Chapters:

0:00 Intro
0:20 Meet Michel Tricot, CEO of Airbyte
2:27 Data Got Us to the Information Age. Context Gets Us to Intelligence.
4:48 How Context Poisoning Breaks Agents
7:49 Why Airbyte Customers Stopped Loading Into Warehouses
10:12 Live Demo: Context Store vs Raw API Calls
10:38 What Does a Context Engineer Actually Do?
14:14 RAG Isn't Dead, But How We Build It Will Die
16:41 30K Wasted Tokens Without Proper Context
22:22 Cross-System Joins: Zendesk, Gong, and Salesforce
26:12 The Open Source Agent Connector SDK
29:45 The SaaS Apocalypse Is Overblown
36:09 From Data Pipes to Agent Infrastructure
38:51 What Agents Need to Get Right by Summer
40:48 Memory Is Just Another Form of Context
43:07 Outro

About the Guest:

Michel Tricot is the CEO and co-founder of Airbyte, the open source data integration platform used by thousands of companies to move data between systems. Before Airbyte, he led data ingestion and distribution engineering at LiveRamp. Airbyte raised at a $1.5 billion valuation and offers 600+ free connectors. The company recently launched the public beta of its agent engine, which includes a context store, agent connector SDK, and MCP integration.

Guest Links:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to our presenting sponsor Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai.

]]> Michel Tricot co-founded Airbyte, the open source data integration platform with 600+ free connectors that hit a $1.5 billion valuation. Now he's building the company's next product: an agent engine, currently in public beta. His thesis is that agents don't fail because models are bad. They fail because the data feeding them is wrong: context poisoning is killing them.

Michel demos this live. A simple Gong query through raw API calls burned 30,000 extra tokens and took three minutes. The same query through Airbyte's context store ran in one minute and used a fraction of the context window. Conor and Michel dig into why RAG alone won't cut it, what a "context engineer" actually does, how Airbyte tracks entities across Salesforce, Zendesk, and Gong without embeddings, and whether the SaaS apocalypse playing out in public markets is overblown.

Chapters:

0:00 Intro
0:20 Meet Michel Tricot, CEO of Airbyte
2:27 Data Got Us to the Information Age. Context Gets Us to Intelligence.
4:48 How Context Poisoning Breaks Agents
7:49 Why Airbyte Customers Stopped Loading Into Warehouses
10:12 Live Demo: Context Store vs Raw API Calls
10:38 What Does a Context Engineer Actually Do?
14:14 RAG Isn't Dead, But How We Build It Will Die
16:41 30K Wasted Tokens Without Proper Context
22:22 Cross-System Joins: Zendesk, Gong, and Salesforce
26:12 The Open Source Agent Connector SDK
29:45 The SaaS Apocalypse Is Overblown
36:09 From Data Pipes to Agent Infrastructure
38:51 What Agents Need to Get Right by Summer
40:48 Memory Is Just Another Form of Context
43:07 Outro

About the Guest:

Michel Tricot is the CEO and co-founder of Airbyte, the open source data integration platform used by thousands of companies to move data between systems. Before Airbyte, he led data ingestion and distribution engineering at LiveRamp. Airbyte raised at a $1.5 billion valuation and offers 600+ free connectors. The company recently launched the public beta of its agent engine, which includes a context store, agent connector SDK, and MCP integration.

Guest Links:

Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show

Thanks to our presenting sponsor Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai.

]]> Wed, 25 Mar 2026 04:00:00 -0700 Conor Bronsdon Conor Bronsdon 2658 Michel Tricot co-founded Airbyte, the open source data integration platform with 600+ free connectors that hit a $1.5 billion valuation. Now he's building the company's next product: an agent engine, currently in public beta. His thesis is that agents don't fail because models are bad. They fail because the data feeding them is wrong: context poisoning is killing them. Michel demos this live. A simple Gong query through raw API calls burned 30,000 extra tokens and took three minutes. Chain of Thought is hosted by Conor Bronsdon. Michel Tricot co-founded Airbyte, the open source data integration platform with 600+ free connectors that hit a $1.5 billion valuation. Now he's building the company's next product: an agent engine, currently in public beta. His thesis is that agents don AI agents, context engineering, context poisoning, data infrastructure, Airbyte, RAG, agent connectors, MCP, Michel Tricot, enterprise AI, agentic AI, context store No I Started r/AI_Agents and Now I'm Launching a VC Fund 3 3 51 51 I Started r/AI_Agents and Now I'm Launching a VC Fund full a8c1fd5e-bb3d-4b6e-8721-3028e0b27c4b https://share.transistor.fm/s/de6a08cb Yujian Tang started the r/AI_Agents subreddit in April 2023. For the first year, it barely moved. Then it hit 9,000 members, he went on vacation, came back to 36,000, and now it's approaching 300,000. In this episode, Yujian talks about how that community grew alongside his event business (Seattle Startup Summit, 900+ attendees last year), his two failed startups, and why he just filed paperwork to launch his own venture fund.


Conor and Yujian dig into the mechanics of starting a fund from scratch (Delaware PO boxes, EIN numbers, lawyers), why AI startup valuations have doubled in the last two years, whether a one-person unicorn is realistic, and what failed founders learn that successful ones sometimes miss.


Chapters:

(0:00) Cold Open: The Subreddit Growth Explosion

(0:21) Intro and Meet Yujian Tang

(1:06) From AI Research to Community Building

(7:26) Where AI Applications Are Headed

(10:03) The AI Bubble and a Valuation Reset

(10:39) Getting Deal Flow Through Community Events

(14:02) Filing the Fund: The Boring Side of VC

(16:04) How r/AI_Agents Went from Crickets to 300K

(18:39) Building an Accidental Empire

(26:37) What Two Failed Startups Taught Him

(29:52) Why Pre-Seed Valuations Are Out of Control

(37:37) The One-Person Unicorn Debate

(39:50) Seattle Startup Summit 2026

(42:17) What Chain of Thought Should Cover Next

(43:25) Outro



About the Guest:

Yujian Tang is the founder of Seattle Startup Summit, the largest startup event in the Pacific Northwest. He created the r/AI_Agents subreddit (now nearly 300K members), runs hackathons and developer events across Seattle and the Bay Area, and is launching an early-stage AI venture fund.


Guest Links:

Seattle Startup Summit: seattlestartupsummit.com

Reddit: reddit.com/r/AI_Agents


Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show


Sponsor: Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Yujian Tang started the r/AI_Agents subreddit in April 2023. For the first year, it barely moved. Then it hit 9,000 members, he went on vacation, came back to 36,000, and now it's approaching 300,000. In this episode, Yujian talks about how that community grew alongside his event business (Seattle Startup Summit, 900+ attendees last year), his two failed startups, and why he just filed paperwork to launch his own venture fund.


Conor and Yujian dig into the mechanics of starting a fund from scratch (Delaware PO boxes, EIN numbers, lawyers), why AI startup valuations have doubled in the last two years, whether a one-person unicorn is realistic, and what failed founders learn that successful ones sometimes miss.


Chapters:

(0:00) Cold Open: The Subreddit Growth Explosion

(0:21) Intro and Meet Yujian Tang

(1:06) From AI Research to Community Building

(7:26) Where AI Applications Are Headed

(10:03) The AI Bubble and a Valuation Reset

(10:39) Getting Deal Flow Through Community Events

(14:02) Filing the Fund: The Boring Side of VC

(16:04) How r/AI_Agents Went from Crickets to 300K

(18:39) Building an Accidental Empire

(26:37) What Two Failed Startups Taught Him

(29:52) Why Pre-Seed Valuations Are Out of Control

(37:37) The One-Person Unicorn Debate

(39:50) Seattle Startup Summit 2026

(42:17) What Chain of Thought Should Cover Next

(43:25) Outro



About the Guest:

Yujian Tang is the founder of Seattle Startup Summit, the largest startup event in the Pacific Northwest. He created the r/AI_Agents subreddit (now nearly 300K members), runs hackathons and developer events across Seattle and the Bay Area, and is launching an early-stage AI venture fund.


Guest Links:

Seattle Startup Summit: seattlestartupsummit.com

Reddit: reddit.com/r/AI_Agents


Connect with Chain of Thought host Conor Bronsdon:

More episodes: https://chainofthought.show


Sponsor: Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Tue, 10 Mar 2026 06:00:01 -0700 Conor Bronsdon Conor Bronsdon 2643 Yujian Tang started the r/AI_Agents subreddit in April 2023. For the first year, it barely moved. Then it hit 9,000 members, he went on vacation, came back to 36,000, and now it's approaching 300,000. In this episode, Yujian talks about how that community grew alongside his event business (Seattle Startup Summit, 900+ attendees last year), his two failed startups, and why he just filed paperwork to launch his own venture fund. Chain of Thought is hosted by Conor Bronsdon. Yujian Tang started the r/AI_Agents subreddit in April 2023. For the first year, it barely moved. Then it hit 9,000 members, he went on vacation, came back to 36,000, and now it's approaching 300,000. In this episode, Yujian talks about how that community technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No I Built an AI Coworker That Runs 90% of My Day 3 3 50 50 I Built an AI Coworker That Runs 90% of My Day full 3a4111c1-46c0-4947-ab27-8c46321917ca https://share.transistor.fm/s/59f00caa Sterling Chin stopped thinking of AI as a tool and started treating it like a junior employee. Onboarded it with context, corrected its mistakes, and gave it writing rules.

Forty days later, MARVIN was handling 90% of his workday.

In this episode of Chain of Thought, Sterling (Applied AI Engineer and Senior Developer Advocate at Postman) walks through live demos of MARVIN, his personal AI assistant built on Claude Code. From pulling meeting transcripts and updating Jira tickets to drafting blog posts and managing his calendar, MARVIN runs as a full-time AI chief of staff.

We cover:

  • How MARVIN bookends Sterling's workday from first login to the end of the day
  • Personality, sub-agents, and writing rules that make MARVIN an effective co-worker
  • Automating meeting notes to Jira tickets
  • Why DIY assistants outperform big tech alternatives
  • How Sterling onboarded 12+ colleagues at Postman, including non-technical knowledge workers
  • What the compute crunch means for open source AI

Connect with Sterling:


Connect with Chain of Thought host Conor Bronsdon:


🔗 More episodes:⁠⁠ https://chainofthought.show⁠⁠


Timestamps:

(0:00) Intro

(0:28) Meet Sterling Chin and the MARVIN AI Assistant

(9:10) Live Demo: How MARVIN Bookends Your Workday

(16:04) Personality, Sub-Agents, and Writing Rules

(22:00) Automating Meeting Notes to Jira Tickets

(29:30) Why DIY AI Assistants Outperform Big Tech

(40:55) Treat Your AI Like a Junior Employee

(46:41) How to Get Started with MARVIN

(55:36) The Compute Crunch and Open Source Future


Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Sterling Chin stopped thinking of AI as a tool and started treating it like a junior employee. Onboarded it with context, corrected its mistakes, and gave it writing rules.

Forty days later, MARVIN was handling 90% of his workday.

In this episode of Chain of Thought, Sterling (Applied AI Engineer and Senior Developer Advocate at Postman) walks through live demos of MARVIN, his personal AI assistant built on Claude Code. From pulling meeting transcripts and updating Jira tickets to drafting blog posts and managing his calendar, MARVIN runs as a full-time AI chief of staff.

We cover:

  • How MARVIN bookends Sterling's workday from first login to the end of the day
  • Personality, sub-agents, and writing rules that make MARVIN an effective co-worker
  • Automating meeting notes to Jira tickets
  • Why DIY assistants outperform big tech alternatives
  • How Sterling onboarded 12+ colleagues at Postman, including non-technical knowledge workers
  • What the compute crunch means for open source AI

Connect with Sterling:


Connect with Chain of Thought host Conor Bronsdon:


🔗 More episodes:⁠⁠ https://chainofthought.show⁠⁠


Timestamps:

(0:00) Intro

(0:28) Meet Sterling Chin and the MARVIN AI Assistant

(9:10) Live Demo: How MARVIN Bookends Your Workday

(16:04) Personality, Sub-Agents, and Writing Rules

(22:00) Automating Meeting Notes to Jira Tickets

(29:30) Why DIY AI Assistants Outperform Big Tech

(40:55) Treat Your AI Like a Junior Employee

(46:41) How to Get Started with MARVIN

(55:36) The Compute Crunch and Open Source Future


Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

]]> Wed, 04 Mar 2026 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 3722 Sterling Chin stopped thinking of AI as a tool and started treating it like a junior employee. Onboarded it with context, corrected its mistakes, and gave it writing rules. Forty days later, MARVIN was handling 90% of his workday. In this episode of Chain of Thought, Sterling (Applied AI Engineer and Senior Developer Advocate at Postman) walks through live demos of MARVIN, his personal AI assistant built on Claude Code. Chain of Thought is hosted by Conor Bronsdon. Sterling Chin stopped thinking of AI as a tool and started treating it like a junior employee. Onboarded it with context, corrected its mistakes, and gave it writing rules. Forty days later, MARVIN was handling 90% of his workday. In this episode of Chain technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No How Intercom Cut $250K/Month by Ditching GPT for Qwen 3 3 49 49 How Intercom Cut $250K/Month by Ditching GPT for Qwen full c033a9dd-d395-4f35-841f-7566b72ec4b7 https://share.transistor.fm/s/0fd18337 Intercom was spending $250K/month on a single summarization task using GPT. Then they replaced it with a fine-tuned 14B parameter Qwen model and saved almost all of it. In this episode, Intercom's Chief AI Officer, Fergal Reid, walks through exactly how they made that call, where their approach has changed over time, and how all of their efforts built their Fin customer service agent.

Fergal breaks down how Fin went from 30% to nearly 70% resolution rate and why most of those gains came from surrounding systems (custom re-rankers, retrieval models, query canonicalization), not the core frontier LLM. He explains why higher latency counterintuitively increases resolution rates, how they built a custom re-ranker that outperformed Cohere using ModernBERT, and why he believes vertically integrated AI products will win in the long term.

If you're deciding between fine-tuning open-weight models and using frontier APIs in production, you won't find a more detailed decision process walkthrough.

🔗 Connect with Fergal: 

🔗 Connect with Chain of Thought host Conor Bronsdon:

🔗 More episodes: https://chainofthought.showCHAPTERS

0:00 Intro

0:46 Why Intercom Completely Reversed Their Fine-Tuning Position

8:00 The $250K/Month Summarization Task (Query Canonicalization)

11:25 Training Infrastructure: H200s, LoRA to Full SFT, and GRPO

14:09 Why Qwen Models Specifically Work for Production

18:03 Goodhart's Law: When Benchmarks Lie

19:47 A/B Testing AI in Production: Soft vs. Hard Resolutions

25:09 The Latency Paradox: Why Slower Responses Get More Resolutions

26:33 Why Per-Customer Prompt Branching Is Technical Debt

28:51 Sponsor: Galileo

29:36 Hiring Scientists, Not Just Engineers

32:15 Context Engineering: Intercom's Full RAG Pipeline

35:35 Customer Agent, Voice, and What's Next for Fin

39:30 Vertical Integration: Can App Companies Outrun the Labs?

47:45 When Engineers Laughed at Claude Code

52:23 Closing Thoughts

TAGSFergal Reid, Intercom, Fin AI agent, open-weight models, Qwen models, fine-tuning LLMs, post-training, RAG pipeline, customer service AI, GRPO reinforcement learning, A/B testing AI, Claude Code, vertical AI integration, inference cost optimization, context engineering, AI agents, ModernBERT reranker, scaling AI teams, Conor Bronsdon, Chain of Thought

]]> Intercom was spending $250K/month on a single summarization task using GPT. Then they replaced it with a fine-tuned 14B parameter Qwen model and saved almost all of it. In this episode, Intercom's Chief AI Officer, Fergal Reid, walks through exactly how they made that call, where their approach has changed over time, and how all of their efforts built their Fin customer service agent.

Fergal breaks down how Fin went from 30% to nearly 70% resolution rate and why most of those gains came from surrounding systems (custom re-rankers, retrieval models, query canonicalization), not the core frontier LLM. He explains why higher latency counterintuitively increases resolution rates, how they built a custom re-ranker that outperformed Cohere using ModernBERT, and why he believes vertically integrated AI products will win in the long term.

If you're deciding between fine-tuning open-weight models and using frontier APIs in production, you won't find a more detailed decision process walkthrough.

🔗 Connect with Fergal: 

🔗 Connect with Chain of Thought host Conor Bronsdon:

🔗 More episodes: https://chainofthought.showCHAPTERS

0:00 Intro

0:46 Why Intercom Completely Reversed Their Fine-Tuning Position

8:00 The $250K/Month Summarization Task (Query Canonicalization)

11:25 Training Infrastructure: H200s, LoRA to Full SFT, and GRPO

14:09 Why Qwen Models Specifically Work for Production

18:03 Goodhart's Law: When Benchmarks Lie

19:47 A/B Testing AI in Production: Soft vs. Hard Resolutions

25:09 The Latency Paradox: Why Slower Responses Get More Resolutions

26:33 Why Per-Customer Prompt Branching Is Technical Debt

28:51 Sponsor: Galileo

29:36 Hiring Scientists, Not Just Engineers

32:15 Context Engineering: Intercom's Full RAG Pipeline

35:35 Customer Agent, Voice, and What's Next for Fin

39:30 Vertical Integration: Can App Companies Outrun the Labs?

47:45 When Engineers Laughed at Claude Code

52:23 Closing Thoughts

TAGSFergal Reid, Intercom, Fin AI agent, open-weight models, Qwen models, fine-tuning LLMs, post-training, RAG pipeline, customer service AI, GRPO reinforcement learning, A/B testing AI, Claude Code, vertical AI integration, inference cost optimization, context engineering, AI agents, ModernBERT reranker, scaling AI teams, Conor Bronsdon, Chain of Thought

]]> Thu, 26 Feb 2026 02:00:00 -0800 Conor Bronsdon Conor Bronsdon 3211 Intercom was spending $250K/month on a single summarization task using GPT. Then they replaced it with a fine-tuned 14B parameter Qwen model and saved almost all of it. In this episode, Intercom's Chief AI Officer, Fergal Reid, walks through exactly how they made that call, where their approach has changed over time, and how all of their efforts built their Fin customer service agent. Chain of Thought is hosted by Conor Bronsdon. Intercom was spending $250K/month on a single summarization task using GPT. Then they replaced it with a fine-tuned 14B parameter Qwen model and saved almost all of it. In this episode, Intercom's Chief AI Officer, Fergal Reid, walks through exactly how t technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No How Block Deployed AI Agents to 12,000 Employees in 8 Weeks w/ MCP | Angie Jones 3 3 48 48 How Block Deployed AI Agents to 12,000 Employees in 8 Weeks w/ MCP | Angie Jones full 7ed7a4c5-7b26-47fe-afe4-d75f9b5bdaf8 https://share.transistor.fm/s/90efac38 How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off.


Block (the company behind Square and Cash App) became an early adopter of Model Context Protocol (MCP) and built Goose, their open-source AI agent that's now a reference implementation for the Agentic AI Foundation. Angie shares the challenges they faced, the security guardrails they built, and why letting employees choose their own models was critical to adoption.


We also dive into vibe coding (including Angie's experience watching Jack Dorsey vibe code a feature in 2 hours), how non-engineers are building their own tools, and what MCP unlocks when you connect multiple systems together.


Chapters:

00:00 Introduction

02:02 How Block deployed AI agents to 12,000 employees

05:04 Challenges with MCP adoption and security at scale

07:10 Why Block supports multiple AI models (Claude, GPT, Gemini)

08:40 Open source models and local LLM usage

09:58 Measuring velocity gains across the organization

10:49 Vibe coding: Benefits, risks & Jack Dorsey's 2-hour feature build

13:46 Block's contributions to the MCP protocol

14:38 MCP in action: Incident management + GitHub workflow demo

15:52 Addressing MCP criticism and security concerns

18:41 The Agentic AI Foundation announcement (Block, Anthropic, OpenAI, Google, Microsoft)

21:46 AI democratization: Non-engineers building MCP servers

24:11 How to get started with MCP and prompting tips

25:42 Security guardrails for enterprise AI deployment

29:25 Tool annotations and human-in-the-loop controls

30:22 OAuth and authentication in Goose

32:11 Use cases: Engineering, data analysis, fraud detection

35:22 Goose in Slack: Bug detection and PR creation in 5 minutes

38:05 Goose vs Claude Code: Open source, model-agnostic philosophy

38:17 Live Demo: Council of Minds MCP server (9-persona debate)

45:52 What's next for Goose: IDE support, ACP, and the $100K contributor grant

47:57 Where to get started with Goose


Connect with Angie on LinkedIn: https://www.linkedin.com/in/angiejones/

Angie's Website: https://angiejones.tech/

Follow Angie on X: https://x.com/techgirl1908

Goose GitHub: https://github.com/block/goose


Connect with Chain of Thought host Conor Bronsdon:


Presented By: Galileo AI

Download Galileo's Mastering Multi-Agent Systems for free here: https://galileo.ai/mastering-multi-agent-systems


Topics Covered:

- How Block deployed Goose to all 12,000 employees

- Building enterprise security guardrails for AI agents

- Model Context Protocol (MCP) deep dive

- Vibe coding benefits and risks

- The Agentic AI Foundation (Block, Anthropic, OpenAI, Google, Microsoft, AWS)

- MCP sampling and the Council of Minds demo

- OAuth authentication for MCP servers

- Goose vs Claude Code and other AI coding tools

- Non-engineers building AI tools

- Fraud detection with AI agents

- Goose in Slack for real-time bug fixing

]]> How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off.


Block (the company behind Square and Cash App) became an early adopter of Model Context Protocol (MCP) and built Goose, their open-source AI agent that's now a reference implementation for the Agentic AI Foundation. Angie shares the challenges they faced, the security guardrails they built, and why letting employees choose their own models was critical to adoption.


We also dive into vibe coding (including Angie's experience watching Jack Dorsey vibe code a feature in 2 hours), how non-engineers are building their own tools, and what MCP unlocks when you connect multiple systems together.


Chapters:

00:00 Introduction

02:02 How Block deployed AI agents to 12,000 employees

05:04 Challenges with MCP adoption and security at scale

07:10 Why Block supports multiple AI models (Claude, GPT, Gemini)

08:40 Open source models and local LLM usage

09:58 Measuring velocity gains across the organization

10:49 Vibe coding: Benefits, risks & Jack Dorsey's 2-hour feature build

13:46 Block's contributions to the MCP protocol

14:38 MCP in action: Incident management + GitHub workflow demo

15:52 Addressing MCP criticism and security concerns

18:41 The Agentic AI Foundation announcement (Block, Anthropic, OpenAI, Google, Microsoft)

21:46 AI democratization: Non-engineers building MCP servers

24:11 How to get started with MCP and prompting tips

25:42 Security guardrails for enterprise AI deployment

29:25 Tool annotations and human-in-the-loop controls

30:22 OAuth and authentication in Goose

32:11 Use cases: Engineering, data analysis, fraud detection

35:22 Goose in Slack: Bug detection and PR creation in 5 minutes

38:05 Goose vs Claude Code: Open source, model-agnostic philosophy

38:17 Live Demo: Council of Minds MCP server (9-persona debate)

45:52 What's next for Goose: IDE support, ACP, and the $100K contributor grant

47:57 Where to get started with Goose


Connect with Angie on LinkedIn: https://www.linkedin.com/in/angiejones/

Angie's Website: https://angiejones.tech/

Follow Angie on X: https://x.com/techgirl1908

Goose GitHub: https://github.com/block/goose


Connect with Chain of Thought host Conor Bronsdon:


Presented By: Galileo AI

Download Galileo's Mastering Multi-Agent Systems for free here: https://galileo.ai/mastering-multi-agent-systems


Topics Covered:

- How Block deployed Goose to all 12,000 employees

- Building enterprise security guardrails for AI agents

- Model Context Protocol (MCP) deep dive

- Vibe coding benefits and risks

- The Agentic AI Foundation (Block, Anthropic, OpenAI, Google, Microsoft, AWS)

- MCP sampling and the Council of Minds demo

- OAuth authentication for MCP servers

- Goose vs Claude Code and other AI coding tools

- Non-engineers building AI tools

- Fraud detection with AI agents

- Goose in Slack for real-time bug fixing

]]> Wed, 21 Jan 2026 04:00:00 -0800 Conor Bronsdon Conor Bronsdon 3027 How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off. Block (the company behind Square and Cash App) became an early adopter of Model Context Protocol (MCP) and built Goose, their open-source AI agent that's now a reference implementation for the Agentic AI Foundation. Chain of Thought is hosted by Conor Bronsdon. How do you deploy AI agents to 12,000 employees in just 8 weeks? How do you do it safely? Angie Jones, VP of Engineering for AI Tools and Enablement at Block, joins the show to share exactly how her team pulled it off. Block (the company behind Square and technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Gemini 3 & Robot Dogs: Inside Google DeepMind's AI Experiments | Paige Bailey 3 3 47 47 Gemini 3 & Robot Dogs: Inside Google DeepMind's AI Experiments | Paige Bailey full 915e691b-f56d-493b-9856-f839f915dd32 https://share.transistor.fm/s/9751537d Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space.

Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosystem. From her unique journey as a geophysicist-turned-AI-leader who helped ship GitHub Copilot, to now running developer experience for DeepMind's entire platform, Paige offers an insider's view of how Google is thinking about the future of AI.

The conversation covers the practical differences between Gemini 3 Pro and Flash, when to use the open-source Gemma models, and how tools like Anti-Gravity IDE, Jules, and Gemini CLI fit into developer workflows. Paige also demonstrates Space Math Academy—a gamified NASA curriculum she built using AI Studio, Colab, and Anti-Gravity—showing how modern AI tools enable rapid prototyping.


The discussion then ventures into AI's physical frontier: robotics powered by Gemini on Raspberry Pi, Google's robotics trusted tester program, and the ambitious Project Suncatcher exploring data centers in space.

00:00 Introduction

01:30 Paige's Background & Connection to Modular

02:29 Gemini Integration Across Google Products

03:04 Jules, Gemini CLI & Anti-Gravity IDE Overview

03:48 Gemini 3 Flash vs Pro: Live Demo & Pricing

06:10 Choosing the Right Gemini Model

09:42 Google's Hardware Advantage: TPUs & JAX

10:16 TensorFlow History & Evolution to JAX

11:45 NeurIPS 2025 & Google's Research Culture

14:40 Google Brain to DeepMind: The Merger Story

15:24 Palm II to Gemini: Scaling from 40 People

18:42 Gemma Open Source Models

20:46 Anti-Gravity IDE Deep Dive

23:53 MCP Protocol & Chrome DevTools Integration

26:57 Gemini CLI in Google Colab

28:00 Image Generation & AI Studio Traffic Spikes

28:46 Space Math Academy: Gamified NASA Curriculum

31:31 Vibe Coding: Building with AI Studio & Anti-Gravity

36:02 AI From Bits to Atoms: The Robotics Frontier

36:40 Stanford Puppers: Gemini on Raspberry Pi Robots

38:35 Google's Robotics Trusted Tester Program

40:59 AI in Scientific Research & Automation

42:25 Project Suncatcher: Data Centers in Space

45:00 Sustainable AI Infrastructure

47:14 Non-Dystopian Sci-Fi Futures

47:48 Closing Thoughts & Resources


- Connect with Paige on LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/

- Follow Paige on X: https://x.com/DynamicWebPaige

- Paige's Website: https://webpaige.dev/

- Google DeepMind: https://deepmind.google/

- AI Studio: https://ai.google.dev


Connect with Chain of Thought host Conor Bronsdon:

- Substack – https://conorbronsdon.substack.com/

- LinkedIn https://www.linkedin.com/in/conorbronsdon/


Presented By: Galileo.ai

Download Galileo's Mastering Multi-Agent Systems for free here!: https://galileo.ai/mastering-multi-agent-systems


Topics Covered:

- Gemini 3 Pro vs Flash comparison (pricing, speed, capabilities)

- When to use Gemma open-source models

- Anti-Gravity IDE, Jules, and Gemini CLI workflows

- Google's TPU hardware advantage

- History of TensorFlow, JAX, and Google Brain

- Space Math Academy demo (gamified education)

- AI-powered robotics (Stanford Puppers on Raspberry Pi)

- Project Suncatcher (orbital data centers)

]]> Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space.

Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosystem. From her unique journey as a geophysicist-turned-AI-leader who helped ship GitHub Copilot, to now running developer experience for DeepMind's entire platform, Paige offers an insider's view of how Google is thinking about the future of AI.

The conversation covers the practical differences between Gemini 3 Pro and Flash, when to use the open-source Gemma models, and how tools like Anti-Gravity IDE, Jules, and Gemini CLI fit into developer workflows. Paige also demonstrates Space Math Academy—a gamified NASA curriculum she built using AI Studio, Colab, and Anti-Gravity—showing how modern AI tools enable rapid prototyping.


The discussion then ventures into AI's physical frontier: robotics powered by Gemini on Raspberry Pi, Google's robotics trusted tester program, and the ambitious Project Suncatcher exploring data centers in space.

00:00 Introduction

01:30 Paige's Background & Connection to Modular

02:29 Gemini Integration Across Google Products

03:04 Jules, Gemini CLI & Anti-Gravity IDE Overview

03:48 Gemini 3 Flash vs Pro: Live Demo & Pricing

06:10 Choosing the Right Gemini Model

09:42 Google's Hardware Advantage: TPUs & JAX

10:16 TensorFlow History & Evolution to JAX

11:45 NeurIPS 2025 & Google's Research Culture

14:40 Google Brain to DeepMind: The Merger Story

15:24 Palm II to Gemini: Scaling from 40 People

18:42 Gemma Open Source Models

20:46 Anti-Gravity IDE Deep Dive

23:53 MCP Protocol & Chrome DevTools Integration

26:57 Gemini CLI in Google Colab

28:00 Image Generation & AI Studio Traffic Spikes

28:46 Space Math Academy: Gamified NASA Curriculum

31:31 Vibe Coding: Building with AI Studio & Anti-Gravity

36:02 AI From Bits to Atoms: The Robotics Frontier

36:40 Stanford Puppers: Gemini on Raspberry Pi Robots

38:35 Google's Robotics Trusted Tester Program

40:59 AI in Scientific Research & Automation

42:25 Project Suncatcher: Data Centers in Space

45:00 Sustainable AI Infrastructure

47:14 Non-Dystopian Sci-Fi Futures

47:48 Closing Thoughts & Resources


- Connect with Paige on LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/

- Follow Paige on X: https://x.com/DynamicWebPaige

- Paige's Website: https://webpaige.dev/

- Google DeepMind: https://deepmind.google/

- AI Studio: https://ai.google.dev


Connect with Chain of Thought host Conor Bronsdon:

- Substack – https://conorbronsdon.substack.com/

- LinkedIn https://www.linkedin.com/in/conorbronsdon/


Presented By: Galileo.ai

Download Galileo's Mastering Multi-Agent Systems for free here!: https://galileo.ai/mastering-multi-agent-systems


Topics Covered:

- Gemini 3 Pro vs Flash comparison (pricing, speed, capabilities)

- When to use Gemma open-source models

- Anti-Gravity IDE, Jules, and Gemini CLI workflows

- Google's TPU hardware advantage

- History of TensorFlow, JAX, and Google Brain

- Space Math Academy demo (gamified education)

- AI-powered robotics (Stanford Puppers on Raspberry Pi)

- Project Suncatcher (orbital data centers)

]]> Wed, 14 Jan 2026 08:30:00 -0800 Conor Bronsdon Conor Bronsdon 3053 Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space. Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosystem. From her unique journey as a geophysicist-turned-AI-leader who helped ship GitHub Copilot, to now running developer experience for DeepMind's entire platform, Paige offers an insider's view of how Google is thinking about the future of AI.The conversation covers the practical differences between Gemini 3 Pro and Flash, when to use the open-source Gemma models, and how tools like Anti-Gravity IDE, Jules, and Gemini CLI fit into developer workflows. Paige also demonstrates Space Math Academy—a gamified NASA curriculum she built using AI Studio, Colab, and Anti-Gravity—showing how modern AI tools enable rapid prototyping. The discussion then ventures into AI's physical frontier: robotics powered by Gemini on Raspberry Pi, Google's robotics trusted tester program, and the ambitious Project Suncatcher exploring data centers in space.00:00 Introduction01:30 Paige's Background & Connection to Modular02:29 Gemini Integration Across Google Products03:04 Jules, Gemini CLI & Anti-Gravity IDE Overview03:48 Gemini 3 Flash vs Pro: Live Demo & Pricing06:10 Choosing the Right Gemini Model09:42 Google's Hardware Advantage: TPUs & JAX10:16 TensorFlow History & Evolution to JAX11:45 NeurIPS 2025 & Google's Research Culture14:40 Google Brain to DeepMind: The Merger Story15:24 Palm II to Gemini: Scaling from 40 People18:42 Gemma Open Source Models20:46 Anti-Gravity IDE Deep Dive23:53 MCP Protocol & Chrome DevTools Integration26:57 Gemini CLI in Google Colab28:00 Image Generation & AI Studio Traffic Spikes28:46 Space Math Academy: Gamified NASA Curriculum31:31 Vibe Coding: Building with AI Studio & Anti-Gravity36:02 AI From Bits to Atoms: The Robotics Frontier36:40 Stanford Puppers: Gemini on Raspberry Pi Robots38:35 Google's Robotics Trusted Tester Program40:59 AI in Scientific Research & Automation42:25 Project Suncatcher: Data Centers in Space45:00 Sustainable AI Infrastructure47:14 Non-Dystopian Sci-Fi Futures47:48 Closing Thoughts & Resources- Connect with Paige on LinkedIn: https://www.linkedin.com/in/dynamicwebpaige/- Follow Paige on X: https://x.com/DynamicWebPaige- Paige's Website: https://webpaige.dev/- Google DeepMind: https://deepmind.google/- AI Studio: https://ai.google.devConnect with our host Conor Bronsdon:- Substack – https://conorbronsdon.substack.com/ - LinkedIn https://www.linkedin.com/in/conorbronsdon/Presented By: Galileo.aiDownload Galileo's Mastering Multi-Agent Systems for free here!: https://galileo.ai/mastering-multi-agent-systemsTopics Covered:- Gemini 3 Pro vs Flash comparison (pricing, speed, capabilities)- When to use Gemma open-source models- Anti-Gravity IDE, Jules, and Gemini CLI workflows- Google's TPU hardware advantage- History of TensorFlow, JAX, and Google Brain- Space Math Academy demo (gamified education)- AI-powered robotics (Stanford Puppers on Raspberry Pi)- Project Suncatcher (orbital data centers) Google DeepMind is reshaping the AI landscape with an unprecedented wave of releases—from Gemini 3 to robotics and even data centers in space. Paige Bailey, AI Developer Relations Lead at Google DeepMind, joins us to break down the full Google AI ecosyste technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Explaining Eval Engineering | Galileo's Vikram Chatterji 3 3 46 46 Explaining Eval Engineering | Galileo's Vikram Chatterji full 6fc7be20-14f4-4158-853a-437a442e8b9c https://share.transistor.fm/s/28aaae24 You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype.

Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real decisions, touch real customers, and cost real money, those one-time tests don't cut it. The companies actually shipping production AI at scale have figured out something different—they've turned evaluations into infrastructure, into IP, into the layer where domain expertise becomes executable governance.

Vikram Chatterji, CEO and Co-founder of Galileo, returns to Chain of Thought to break down eval engineering: what it is, why it's becoming a dedicated discipline, and what it takes to actually make it work. Vikram shares why generic evals are plateauing, how continuous learning loops drive accuracy, and why he predicts "eval engineer" will become as common a role as "prompt engineer" once was.

In this conversation, Conor and Vikram explore:

  • Why treating evals as infrastructure—not checkboxes—separates production AI from prototypes
  • The plateau problem: why generic LLM-as-a-judge metrics can't break 90% accuracy
  • How continuous human feedback loops improve eval precision over time
  • The emerging "eval engineer" role and what the job actually looks like
  • Why 60-70% of AI engineers' time is already spent on evals
  • What multi-agent systems mean for the future of evaluation
  • Vikram's framework for baking trust AND control into agentic applications

Plus: Conor shares news about his move to Modular and what it means for Chain of Thought going forward.

Chapters:00:00 – Introduction: Why Evals Are Becoming IP01:37 – What Is Eval Engineering?04:24 – The Eval Engineering Course for Developers05:24 – Generic Evals Are Plateauing08:21 – Continuous Learning and Human Feedback11:01 – Human Feedback Loops and Eval Calibration13:37 – The Emerging Eval Engineer Role16:15 – What Production AI Teams Actually Spend Time On18:52 – Customer Impact and Lessons Learned24:28 – Multi-Agent Systems and the Future of Evals30:27 – MCP, A2A Protocols, and Agent Authentication33:23 – The Eval Engineer Role: Product-Minded + Technical34:53 – Final Thoughts: Trust, Control, and What's Next

Connect with Chain of Thought host Conor Bronsdon:Substack – https://conorbronsdon.substack.com/LinkedIn – https://www.linkedin.com/in/conorbronsdon/X (Twitter) – https://x.com/ConorBronsdon

Learn more about Eval Engineering:⁠https://galileo.ai/evalengineering⁠

Connect with Vikram Chatterji:LinkedIn – ⁠https://www.linkedin.com/in/vikram-chatterji/⁠

]]> You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype.

Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real decisions, touch real customers, and cost real money, those one-time tests don't cut it. The companies actually shipping production AI at scale have figured out something different—they've turned evaluations into infrastructure, into IP, into the layer where domain expertise becomes executable governance.

Vikram Chatterji, CEO and Co-founder of Galileo, returns to Chain of Thought to break down eval engineering: what it is, why it's becoming a dedicated discipline, and what it takes to actually make it work. Vikram shares why generic evals are plateauing, how continuous learning loops drive accuracy, and why he predicts "eval engineer" will become as common a role as "prompt engineer" once was.

In this conversation, Conor and Vikram explore:

  • Why treating evals as infrastructure—not checkboxes—separates production AI from prototypes
  • The plateau problem: why generic LLM-as-a-judge metrics can't break 90% accuracy
  • How continuous human feedback loops improve eval precision over time
  • The emerging "eval engineer" role and what the job actually looks like
  • Why 60-70% of AI engineers' time is already spent on evals
  • What multi-agent systems mean for the future of evaluation
  • Vikram's framework for baking trust AND control into agentic applications

Plus: Conor shares news about his move to Modular and what it means for Chain of Thought going forward.

Chapters:00:00 – Introduction: Why Evals Are Becoming IP01:37 – What Is Eval Engineering?04:24 – The Eval Engineering Course for Developers05:24 – Generic Evals Are Plateauing08:21 – Continuous Learning and Human Feedback11:01 – Human Feedback Loops and Eval Calibration13:37 – The Emerging Eval Engineer Role16:15 – What Production AI Teams Actually Spend Time On18:52 – Customer Impact and Lessons Learned24:28 – Multi-Agent Systems and the Future of Evals30:27 – MCP, A2A Protocols, and Agent Authentication33:23 – The Eval Engineer Role: Product-Minded + Technical34:53 – Final Thoughts: Trust, Control, and What's Next

Connect with Chain of Thought host Conor Bronsdon:Substack – https://conorbronsdon.substack.com/LinkedIn – https://www.linkedin.com/in/conorbronsdon/X (Twitter) – https://x.com/ConorBronsdon

Learn more about Eval Engineering:⁠https://galileo.ai/evalengineering⁠

Connect with Vikram Chatterji:LinkedIn – ⁠https://www.linkedin.com/in/vikram-chatterji/⁠

]]> Fri, 19 Dec 2025 02:00:00 -0800 Conor Bronsdon Conor Bronsdon 2235 You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype. Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real decisions, touch real customers, and cost real money, those one-time tests don't cut it. Chain of Thought is hosted by Conor Bronsdon. You've heard of evaluations—but eval engineering is the difference between AI that ships and AI that's stuck in prototype. Most teams still treat evals like unit tests: write them once, check a box, move on. But when you're deploying agents that make real technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Debunking AI's Environmental Panic | Andy Masley 3 3 45 45 Debunking AI's Environmental Panic | Andy Masley full fc0072cd-b6bc-492b-8115-70a62e614675 https://share.transistor.fm/s/ceb63059 AI is destroying the planet—or so we've been told. This week on Chain of Thought, we tackle one of the most persistent and misleading narratives in the AI conversation.

Andy Masley, Director of Effective Altruism DC, joins host Conor Bronsdon to fact-check the absurd AI environmental claims you've heard at parties, in articles, and even in bestselling books. Andy recently went viral for discovering what he calls "the single most egregious math mistake" he's ever seen in a book—a data center water usage calculation in Karen Hao's NYT Bestseller, Empire of AI, that was off by a factor of 4,500.

In this conversation, Andy and Conor break down the myths around AI’s water and energy usage and explore:

  • The viral Empire of AI error and what it reveals about the broader debate

  • Why most AI water usage statistics are misleading or flat-out wrong

  • How one ChatGPT prompt represents just 1/150,000th of your daily emissions

  • Trade-offs around data center cooling + decision making

  • Why "tribal thinking" about AI is distorting environmental activism

  • Where AI might actually help the climate through deep learning optimization

If you've ever felt guilty about using AI tools, been cornered at a party about AI's environmental impact, or simply want to understand what the data actually says, this episode, and Andy’s deep dive articles, arm you with the facts.

Chapters:

00:00 – Introduction: The Party Guilt Problem

01:54 – Andy's Background and What Sparked This Work

03:50 – The 4,500x Error in Empire of AI

06:39 – Breaking Down the Math: Liters vs. Cubic Meters

10:39 – The Unintended Consequence: Air Cooling vs. Water Cooling

12:51 – Karen Hao's Response and What's Still Missing

19:08 – Why Environmentalists Should Focus Elsewhere

21:41 – The Danger of Tribal Thinking About AI

25:49 – What Is Effective Altruism (And Why People Attack It)

29:15 – EA, AI Risk, and P(doom)

34:31 – Why Misinformation Hurts Your Own Side

37:39 – Using ChatGPT Is Not Bad for the Environment

42:14 – The Party Rebuttal: Practical Comparisons

45:23 – Water Use Reality: 1/800,000th of Your Daily Footprint

48:27 – The Personal Carbon Footprint Distraction

53:38 – Data Centers: Efficiency vs. Whether to Build

55:13 – AI's Net Climate Impact: The Positive Case

59:34 – Deep Learning, Smart Grids, and Climate Optimization

1:03:45 – Final Thoughts


Key references

IEA Study: AI and climate change - https://www.iea.org/reports/energy-and-ai/ai-and-climate-change#abstract 

Nature: https://www.nature.com/articles/s44168-025-00252-3 

The Empire of AI Error: https://andymasley.substack.com/p/empire-of-ai-is-wildly-misleading 

Using ChatGPT isn’t bad for the environment: https://andymasley.substack.com/p/a-short-summary-of-my-argument-that

https://andymasley.substack.com/p/a-cheat-sheet-for-conversations-about 


Connect with Andy Masley: 

Substack – https://andymasley.substack.com/

X (Twitter) – https://x.com/AndyMasley

Connect with Chain of Thought host Conor Bronsdon: 

Substack – https://conorbronsdon.substack.com/

LinkedIn – https://www.linkedin.com/in/conorbronsdon/

X (Twitter) – https://x.com/ConorBronsdon

]]> AI is destroying the planet—or so we've been told. This week on Chain of Thought, we tackle one of the most persistent and misleading narratives in the AI conversation.

Andy Masley, Director of Effective Altruism DC, joins host Conor Bronsdon to fact-check the absurd AI environmental claims you've heard at parties, in articles, and even in bestselling books. Andy recently went viral for discovering what he calls "the single most egregious math mistake" he's ever seen in a book—a data center water usage calculation in Karen Hao's NYT Bestseller, Empire of AI, that was off by a factor of 4,500.

In this conversation, Andy and Conor break down the myths around AI’s water and energy usage and explore:

  • The viral Empire of AI error and what it reveals about the broader debate

  • Why most AI water usage statistics are misleading or flat-out wrong

  • How one ChatGPT prompt represents just 1/150,000th of your daily emissions

  • Trade-offs around data center cooling + decision making

  • Why "tribal thinking" about AI is distorting environmental activism

  • Where AI might actually help the climate through deep learning optimization

If you've ever felt guilty about using AI tools, been cornered at a party about AI's environmental impact, or simply want to understand what the data actually says, this episode, and Andy’s deep dive articles, arm you with the facts.

Chapters:

00:00 – Introduction: The Party Guilt Problem

01:54 – Andy's Background and What Sparked This Work

03:50 – The 4,500x Error in Empire of AI

06:39 – Breaking Down the Math: Liters vs. Cubic Meters

10:39 – The Unintended Consequence: Air Cooling vs. Water Cooling

12:51 – Karen Hao's Response and What's Still Missing

19:08 – Why Environmentalists Should Focus Elsewhere

21:41 – The Danger of Tribal Thinking About AI

25:49 – What Is Effective Altruism (And Why People Attack It)

29:15 – EA, AI Risk, and P(doom)

34:31 – Why Misinformation Hurts Your Own Side

37:39 – Using ChatGPT Is Not Bad for the Environment

42:14 – The Party Rebuttal: Practical Comparisons

45:23 – Water Use Reality: 1/800,000th of Your Daily Footprint

48:27 – The Personal Carbon Footprint Distraction

53:38 – Data Centers: Efficiency vs. Whether to Build

55:13 – AI's Net Climate Impact: The Positive Case

59:34 – Deep Learning, Smart Grids, and Climate Optimization

1:03:45 – Final Thoughts


Key references

IEA Study: AI and climate change - https://www.iea.org/reports/energy-and-ai/ai-and-climate-change#abstract 

Nature: https://www.nature.com/articles/s44168-025-00252-3 

The Empire of AI Error: https://andymasley.substack.com/p/empire-of-ai-is-wildly-misleading 

Using ChatGPT isn’t bad for the environment: https://andymasley.substack.com/p/a-short-summary-of-my-argument-that

https://andymasley.substack.com/p/a-cheat-sheet-for-conversations-about 


Connect with Andy Masley: 

Substack – https://andymasley.substack.com/

X (Twitter) – https://x.com/AndyMasley

Connect with Chain of Thought host Conor Bronsdon: 

Substack – https://conorbronsdon.substack.com/

LinkedIn – https://www.linkedin.com/in/conorbronsdon/

X (Twitter) – https://x.com/ConorBronsdon

]]> Wed, 26 Nov 2025 02:32:57 -0800 Conor Bronsdon Conor Bronsdon 3543 AI is destroying the planet—or so we've been told. This week on Chain of Thought, we tackle one of the most persistent and misleading narratives in the AI conversation. Andy Masley, Director of Effective Altruism DC, joins host Conor Bronsdon to fact-check the absurd AI environmental claims you've heard at parties, in articles, and even in bestselling books. AI is destroying the planet—or so we've been told. This week on Chain of Thought, we tackle one of the most persistent and misleading narratives in the AI conversation. Andy Masley, Director of Effective Altruism DC, joins host Conor Bronsdon to fact-chec technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The Critical Infrastructure Behind the AI Boom | Cisco CPO Jeetu Patel 2 2 44 44 The Critical Infrastructure Behind the AI Boom | Cisco CPO Jeetu Patel full a9154146-f1d0-49f4-8835-aa768b3ceb9c https://share.transistor.fm/s/43ad9185 AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and Chief Product Officer Jeetu Patel joins host Conor Bronsdon to reveal what it actually takes to build the critical foundation for the AI era.

Jeetu breaks down the three bottlenecks he sees holding AI back today:

 • Infrastructure limits: not enough power, compute, or data center capacity

 • A trust deficit: non-deterministic models powering systems that must be predictable

 • A widening data gap: human-generated data plateauing while machine data explodes

Jeetu then shares how Cisco is tackling these challenges through secure AI factories, edge inference, open multi-model architectures, and global partnerships with Nvidia, G42, and sovereign cloud providers. Jeetu also explains why he thinks enterprises will soon rely on thousands of specialized models — not just one — and how routing, latency, cost, and security shape this new landscape.

Conor and Jeetu also explore high-performance leadership and team culture, discussing building high-trust teams, embracing constructive tension, staying vigilant in moments of success, and the personal experiences that shaped Jeetu’s approach to innovation and resilience.

If you want a clearer picture of the global AI infrastructure race, how high-level leaders are thinking about the future, and what it all means for enterprises, developers, and the future of work, this conversation is essential.

Chapters:

00:00 – Welcome to Chain of Thought

0:48 - AI and Jobs: Beyond the Hype

6:15 - The Real AI Opportunity: Original Insights

10:00 - Three Critical AI Constraints: Infrastructure, Trust, and Data

16:27 - Cisco's AI Strategy and Platform Approach

19:18 - Edge Computing and Model Innovation

22:06 - Strategic Partnerships: Nvidia, G42, and the Middle East

29:18 - Acquisition Strategy: Platform Over Products

32:03 - Power and Infrastructure Challenges

36:06 - Building Trust Across Global Partnerships

38:03 - US vs. China: The AI Infrastructure Race

40:33 - America's Venture Capital Advantage

42:06 - Acquisition Philosophy: Strategy First

45:45 - Defining Cisco's True North

48:06 - Mission-Driven Innovation Culture

50:15 - Hiring for Hunger, Curiosity, and Clarity

56:27 - The Power of Constructive Conflict

1:00:00 - Career Lessons: Continuous Learning

1:02:24 - The Email Question

1:04:12 - Joe Tucci's Four-Column Exercise

1:08:15 - Building High-Trust Teams

1:10:12 - The Five Dysfunctions Framework

1:12:09 - Leading with Vulnerability

1:16:18 - Closing Thoughts and Where to Connect


Connect with Jeetu Patel:

LinkedIn – https://www.linkedin.com/in/jeetupatel/ 

X(twitter) – https://x.com/jpatel41

Cisco - https://www.cisco.com/


Connect with Chain of Thought host Conor Bronsdon:


]]> AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and Chief Product Officer Jeetu Patel joins host Conor Bronsdon to reveal what it actually takes to build the critical foundation for the AI era.

Jeetu breaks down the three bottlenecks he sees holding AI back today:

 • Infrastructure limits: not enough power, compute, or data center capacity

 • A trust deficit: non-deterministic models powering systems that must be predictable

 • A widening data gap: human-generated data plateauing while machine data explodes

Jeetu then shares how Cisco is tackling these challenges through secure AI factories, edge inference, open multi-model architectures, and global partnerships with Nvidia, G42, and sovereign cloud providers. Jeetu also explains why he thinks enterprises will soon rely on thousands of specialized models — not just one — and how routing, latency, cost, and security shape this new landscape.

Conor and Jeetu also explore high-performance leadership and team culture, discussing building high-trust teams, embracing constructive tension, staying vigilant in moments of success, and the personal experiences that shaped Jeetu’s approach to innovation and resilience.

If you want a clearer picture of the global AI infrastructure race, how high-level leaders are thinking about the future, and what it all means for enterprises, developers, and the future of work, this conversation is essential.

Chapters:

00:00 – Welcome to Chain of Thought

0:48 - AI and Jobs: Beyond the Hype

6:15 - The Real AI Opportunity: Original Insights

10:00 - Three Critical AI Constraints: Infrastructure, Trust, and Data

16:27 - Cisco's AI Strategy and Platform Approach

19:18 - Edge Computing and Model Innovation

22:06 - Strategic Partnerships: Nvidia, G42, and the Middle East

29:18 - Acquisition Strategy: Platform Over Products

32:03 - Power and Infrastructure Challenges

36:06 - Building Trust Across Global Partnerships

38:03 - US vs. China: The AI Infrastructure Race

40:33 - America's Venture Capital Advantage

42:06 - Acquisition Philosophy: Strategy First

45:45 - Defining Cisco's True North

48:06 - Mission-Driven Innovation Culture

50:15 - Hiring for Hunger, Curiosity, and Clarity

56:27 - The Power of Constructive Conflict

1:00:00 - Career Lessons: Continuous Learning

1:02:24 - The Email Question

1:04:12 - Joe Tucci's Four-Column Exercise

1:08:15 - Building High-Trust Teams

1:10:12 - The Five Dysfunctions Framework

1:12:09 - Leading with Vulnerability

1:16:18 - Closing Thoughts and Where to Connect


Connect with Jeetu Patel:

LinkedIn – https://www.linkedin.com/in/jeetupatel/ 

X(twitter) – https://x.com/jpatel41

Cisco - https://www.cisco.com/


Connect with Chain of Thought host Conor Bronsdon:


]]> Wed, 19 Nov 2025 06:00:00 -0800 Conor Bronsdon Conor Bronsdon 4691 AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and Chief Product Officer Jeetu Patel joins host Conor Bronsdon to reveal what it actually takes to build the critical foundation for the AI era. AI is accelerating at a breakneck pace, but model quality isn’t the only constraint we face.. There are major infrastructure requirements, energy needs, security, and data pipelines to run AI at scale. This week on Chain of Thought, Cisco’s President and technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne 2 2 43 43 Beyond Transformers: How Liquid AI Is Rethinking LLM Architecture | Maxime Labonne full f3f1a777-7d13-452f-bfc6-b200d5beab91 https://share.transistor.fm/s/645ba9fe The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devices


Maxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability.

This alternative architectural philosophy creates models that run effectively on phones and laptops without compromise.


But reimagined architecture is only half the story. Maxime unpacks the post-training reality most teams struggle with: challenges and opportunities of synthetic data, how to balance helpfulness against safety, Liquid AI’s approach to evals, RAG architectural approaches, how he sees AI on edge devices evolving, hard won lessons from shipping LFM1 through 2, and much more.

If you're tired of surface-level AI takes and want to understand the architectural and engineering decisions behind production LLMs from someone building them in the trenches, this is your episode.


Connect with ⁨Maxime Labonne⁩ :

LinkedIn – https://www.linkedin.com/in/maxime-labonne/

X (Twitter) – @maximelabonne

About Maxime – https://mlabonne.github.io/blog/about.html

HuggingFace – https://huggingface.co/mlabonne

The LLM Course – https://github.com/mlabonne/llm-course

Liquid AI – https://liquid.ai


Connect with Chain of Thought host Conor Bronsdon:


00:00 Intro — Welcome to Chain of Thought

00:27 Guest Intro — Maxime Labonne of Liquid AI

02:21 The Hybrid LLM Architecture Explained

06:30 Why Bigger Models Aren’t Always Better

11:10 Convolution + Transformers: A New Approach to Efficiency

18:00 Running LLMs on Laptops and Wearables

22:20 Post-Training as the Real Moat

25:45 Synthetic Data and Reliability in Model Refinement

32:30 Evaluating AI in the Real World

38:11 Benchmarks vs Functional Evals

43:05 The Future of Edge-Native Intelligence

48:10 Closing Thoughts & Where to Find Maxime Online


]]> The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devices


Maxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability.

This alternative architectural philosophy creates models that run effectively on phones and laptops without compromise.


But reimagined architecture is only half the story. Maxime unpacks the post-training reality most teams struggle with: challenges and opportunities of synthetic data, how to balance helpfulness against safety, Liquid AI’s approach to evals, RAG architectural approaches, how he sees AI on edge devices evolving, hard won lessons from shipping LFM1 through 2, and much more.

If you're tired of surface-level AI takes and want to understand the architectural and engineering decisions behind production LLMs from someone building them in the trenches, this is your episode.


Connect with ⁨Maxime Labonne⁩ :

LinkedIn – https://www.linkedin.com/in/maxime-labonne/

X (Twitter) – @maximelabonne

About Maxime – https://mlabonne.github.io/blog/about.html

HuggingFace – https://huggingface.co/mlabonne

The LLM Course – https://github.com/mlabonne/llm-course

Liquid AI – https://liquid.ai


Connect with Chain of Thought host Conor Bronsdon:


00:00 Intro — Welcome to Chain of Thought

00:27 Guest Intro — Maxime Labonne of Liquid AI

02:21 The Hybrid LLM Architecture Explained

06:30 Why Bigger Models Aren’t Always Better

11:10 Convolution + Transformers: A New Approach to Efficiency

18:00 Running LLMs on Laptops and Wearables

22:20 Post-Training as the Real Moat

25:45 Synthetic Data and Reliability in Model Refinement

32:30 Evaluating AI in the Real World

38:11 Benchmarks vs Functional Evals

43:05 The Future of Edge-Native Intelligence

48:10 Closing Thoughts & Where to Find Maxime Online


]]> Wed, 12 Nov 2025 02:00:00 -0800 Conor Bronsdon Conor Bronsdon 3151 The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devices Maxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Course, joins host Conor Bronsdon to challenge the AI architecture status quo. Liquid AI’s hybrid architecture, combining transformers with convolutional layers, delivers faster inference, lower latency, and dramatically smaller footprints without sacrificing capability. The transformer architecture has dominated AI since 2017, but it’s not the only approach to building LLMs - and new architectures are bringing LLMs to edge devices Maxime Labonne, Head of Post-Training at Liquid AI and creator of the 67,000+ star LLM Cour technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Architecting AI Agents: The Shift from Models to Systems | Aishwarya Srinivasan 2 2 42 42 Architecting AI Agents: The Shift from Models to Systems | Aishwarya Srinivasan full fd476ae3-ec42-4b28-904a-2f1871ec233c https://share.transistor.fm/s/a22109d3 Most AI agents are built backwards, starting with models instead of system architecture.

Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you. 

Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments.

Connect with Aishwarya Srinivasan:

LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/

Instagram: https://www.instagram.com/the.datascience.gal/

Connect with Chain of Thought host Conor Bronsdon: https://www.linkedin.com/in/conorbronsdon/

00:00 Intro — Welcome to Chain of Thought

00:22 Guest Intro — Ash Srinivasan of Fireworks AI

02:37 The Challenge of Responsible AI

05:44 The Hidden Risks of Reward Hacking

07:22 From Prompt to Context Engineering

10:14 Data Quality and Human Feedback

14:43 Quantifying Trust and Observability

20:27 Evaluation-Driven Development

30:10 Open Source Models vs. Proprietary Systems

34:56 Gaps in the Open-Source AI Stack

38:45 When to Use Different Models

45:36 Governance and Compliance in AI Systems

50:11 The Future of AI Builders

56:00 Closing Thoughts & Follow Ash Online

]]> Most AI agents are built backwards, starting with models instead of system architecture.

Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you. 

Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments.

Connect with Aishwarya Srinivasan:

LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/

Instagram: https://www.instagram.com/the.datascience.gal/

Connect with Chain of Thought host Conor Bronsdon: https://www.linkedin.com/in/conorbronsdon/

00:00 Intro — Welcome to Chain of Thought

00:22 Guest Intro — Ash Srinivasan of Fireworks AI

02:37 The Challenge of Responsible AI

05:44 The Hidden Risks of Reward Hacking

07:22 From Prompt to Context Engineering

10:14 Data Quality and Human Feedback

14:43 Quantifying Trust and Observability

20:27 Evaluation-Driven Development

30:10 Open Source Models vs. Proprietary Systems

34:56 Gaps in the Open-Source AI Stack

38:45 When to Use Different Models

45:36 Governance and Compliance in AI Systems

50:11 The Future of AI Builders

56:00 Closing Thoughts & Follow Ash Online

]]> Wed, 08 Oct 2025 06:00:00 -0700 Conor Bronsdon Conor Bronsdon 3206 Most AI agents are built backwards, starting with models instead of system architecture.Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you. Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments.Connect with Aishwarya Srinivasan:LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/Instagram: https://www.instagram.com/the.datascience.gal/Connect with Chain of Thought host Conor Bronsdon: https://www.linkedin.com/in/conorbronsdon/00:00 Intro — Welcome to Chain of Thought00:22 Guest Intro — Ash Srinivasan of Fireworks AI02:37 The Challenge of Responsible AI05:44 The Hidden Risks of Reward Hacking07:22 From Prompt to Context Engineering10:14 Data Quality and Human Feedback14:43 Quantifying Trust and Observability20:27 Evaluation-Driven Development30:10 Open Source Models vs. Proprietary Systems34:56 Gaps in the Open-Source AI Stack38:45 When to Use Different Models45:36 Governance and Compliance in AI Systems50:11 The Future of AI Builders56:00 Closing Thoughts & Follow Ash Online Most AI agents are built backwards, starting with models instead of system architecture.Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating t technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The Accidental Algorithm | Humans of AI Crossover with Writer's Melisa Russak 2 2 41 41 The Accidental Algorithm | Humans of AI Crossover with Writer's Melisa Russak full dc73c87a-0e09-4695-8318-911ff8c8edcf https://share.transistor.fm/s/cddc3015 This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show.

From The Humans of AI:

Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Her story reveals the power of approaching problems with fresh eyes and the innovative breakthroughs that can occur when constraints become catalysts for creativity.

Melisa explores the intersection of curiosity-driven research, accidental discovery, and systematic innovation, offering valuable insights into how WRITER is pushing the boundaries of enterprise AI. Tune in to learn how her journey from a math teacher in China to a pioneer in AI research illuminates the future of technological advancement.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Check out Writer’s YouTube channel to watch the full interviews.

Learn more about WRITER at writer.com.

Follow Melisa on LinkedIn

Follow May on LinkedIn


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show.

From The Humans of AI:

Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Her story reveals the power of approaching problems with fresh eyes and the innovative breakthroughs that can occur when constraints become catalysts for creativity.

Melisa explores the intersection of curiosity-driven research, accidental discovery, and systematic innovation, offering valuable insights into how WRITER is pushing the boundaries of enterprise AI. Tune in to learn how her journey from a math teacher in China to a pioneer in AI research illuminates the future of technological advancement.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Check out Writer’s YouTube channel to watch the full interviews.

Learn more about WRITER at writer.com.

Follow Melisa on LinkedIn

Follow May on LinkedIn


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 01 Oct 2025 07:00:00 -0700 Conor Bronsdon Conor Bronsdon 1270 This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show. From The Humans of AI : Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Chain of Thought is hosted by Conor Bronsdon. This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own s technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No After Code Gen: What Graphite Is Building for the Post-AI Dev Stack | Greg Foster 2 2 40 40 After Code Gen: What Graphite Is Building for the Post-AI Dev Stack | Greg Foster full 1e243bbc-aa44-4c29-a3bb-72d198506b79 https://share.transistor.fm/s/5fbf1f25 The incredible velocity of AI coding tools has shifted the critical bottleneck in software development from code generation to code reviews. 

Greg Foster, Co-Founder & CTO of Graphite, joins the conversation to explore this new reality, outlining the three waves of AI that are leading to autonomous agents spawning pull requests in the background. He argues that as AI automates the "inner loop" of writing code, the human-centric "outer loop"—reviewing, merging, and deploying—is now under immense pressure, demanding a complete rethinking of our tools and processes.

The conversation then gets tactical, with Greg detailing how a technique called "stacking" can break down large code changes into manageable units for both humans and AI. He also identifies an emerging hiring gap where experienced engineers with strong architectural context are becoming "lethal" with AI tools. This episode is an essential guide to navigating the new bottlenecks in software development and understanding the skills that will define the next generation of high-impact engineers.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Greg on LinkedIn

Follow Greg on X

Graphite Website: graphite.dev


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> The incredible velocity of AI coding tools has shifted the critical bottleneck in software development from code generation to code reviews. 

Greg Foster, Co-Founder & CTO of Graphite, joins the conversation to explore this new reality, outlining the three waves of AI that are leading to autonomous agents spawning pull requests in the background. He argues that as AI automates the "inner loop" of writing code, the human-centric "outer loop"—reviewing, merging, and deploying—is now under immense pressure, demanding a complete rethinking of our tools and processes.

The conversation then gets tactical, with Greg detailing how a technique called "stacking" can break down large code changes into manageable units for both humans and AI. He also identifies an emerging hiring gap where experienced engineers with strong architectural context are becoming "lethal" with AI tools. This episode is an essential guide to navigating the new bottlenecks in software development and understanding the skills that will define the next generation of high-impact engineers.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Greg on LinkedIn

Follow Greg on X

Graphite Website: graphite.dev


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 24 Sep 2025 07:00:00 -0700 Conor Bronsdon Conor Bronsdon 3280 The incredible velocity of AI coding tools has shifted the critical bottleneck in software development from code generation to code reviews. Greg Foster, Co-Founder & CTO of Graphite, joins the conversation to explore this new reality, outlining the three waves of AI that are leading to autonomous agents spawning pull requests in the background. Chain of Thought is hosted by Conor Bronsdon. The incredible velocity of AI coding tools has shifted the critical bottleneck in software development from code generation to code reviews. Greg Foster, Co-Founder & CTO of Graphite, joins the conversation to explore this new reality, outlining the t technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Vercel's Playbook for AI Agents: From Vibe Check to Production | Malte Ubl 2 2 39 39 Vercel's Playbook for AI Agents: From Vibe Check to Production | Malte Ubl full b53d21b3-07f2-44d9-b0ac-53f80289f557 https://share.transistor.fm/s/fdb1e9d0 What’s the first step to building an enterprise-grade AI tool? 

Malte Ubl, CTO of Vercel, joins us this week to share Vercel’s playbook for agents, explaining how agents are a new type of software for solving flexible tasks. He shares how Vercel's developer-first ecosystem, including tools like the AI SDK and AI Gateway, is designed to help teams move from a quick proof-of-concept to a trusted, production-ready application.

Malte explores the practicalities of production AI, from the importance of eval-driven development to debugging chaotic agents with robust tracing. He offers a critical lesson on security, explaining why prompt injection requires a totally different solution - tool constraint - than traditional threats like SQL injection. This episode is a deep dive into the infrastructure and mindset, from sandboxes to specialized SLMs, required to build the next generation of AI tools.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Malte on LinkedIn

Follow Malte on X (formerly Twitter)

Learn more about Vercel


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> What’s the first step to building an enterprise-grade AI tool? 

Malte Ubl, CTO of Vercel, joins us this week to share Vercel’s playbook for agents, explaining how agents are a new type of software for solving flexible tasks. He shares how Vercel's developer-first ecosystem, including tools like the AI SDK and AI Gateway, is designed to help teams move from a quick proof-of-concept to a trusted, production-ready application.

Malte explores the practicalities of production AI, from the importance of eval-driven development to debugging chaotic agents with robust tracing. He offers a critical lesson on security, explaining why prompt injection requires a totally different solution - tool constraint - than traditional threats like SQL injection. This episode is a deep dive into the infrastructure and mindset, from sandboxes to specialized SLMs, required to build the next generation of AI tools.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Malte on LinkedIn

Follow Malte on X (formerly Twitter)

Learn more about Vercel


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 10 Sep 2025 06:00:00 -0700 Conor Bronsdon Conor Bronsdon 3265 What’s the first step to building an enterprise-grade AI tool? Malte Ubl, CTO of Vercel, joins us this week to share Vercel’s playbook for agents, explaining how agents are a new type of software for solving flexible tasks. He shares how Vercel's developer-first ecosystem, including tools like the AI SDK and AI Gateway, is designed to help teams move from a quick proof-of-concept to a trusted, production-ready application. Chain of Thought is hosted by Conor Bronsdon. What’s the first step to building an enterprise-grade AI tool? Malte Ubl, CTO of Vercel, joins us this week to share Vercel’s playbook for agents, explaining how agents are a new type of software for solving flexible tasks. He shares how Vercel's develope technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No From Demo to Defensibility: How to Build an AI Business that Lasts | Aurimas Griciūnas 2 2 38 38 From Demo to Defensibility: How to Build an AI Business that Lasts | Aurimas Griciūnas full 264fa324-c5d3-4a16-9839-3267902030bb https://share.transistor.fm/s/fdb45b54 The technological moat is eroding in the AI era, what new factors separate a successful startup from the rest?

Aurimas Griciūnas, CEO of SwirlAI, joins the show to break down the realities of building in this new landscape. Startup success now hinges on speed, strong financial backing, or immediate distribution. Aurimas warns against the critical mistake of prioritizing shiny tools over fundamental engineering and the market gaps this creates.

Discover the new moats for AI companies, built on a culture of relentless execution, tight feedback loops, and the surprising skills that define today's most valuable engineers.The episode also looks to the future, with bold predictions about a slowdown in LLM leaps and the coming impact of coding agents and self-improving systems.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Aurimas on⁠ ⁠⁠LinkedIn⁠

Aurimas' Course: ⁠End-to-End AI Engineering Bootcamp


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> The technological moat is eroding in the AI era, what new factors separate a successful startup from the rest?

Aurimas Griciūnas, CEO of SwirlAI, joins the show to break down the realities of building in this new landscape. Startup success now hinges on speed, strong financial backing, or immediate distribution. Aurimas warns against the critical mistake of prioritizing shiny tools over fundamental engineering and the market gaps this creates.

Discover the new moats for AI companies, built on a culture of relentless execution, tight feedback loops, and the surprising skills that define today's most valuable engineers.The episode also looks to the future, with bold predictions about a slowdown in LLM leaps and the coming impact of coding agents and self-improving systems.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Aurimas on⁠ ⁠⁠LinkedIn⁠

Aurimas' Course: ⁠End-to-End AI Engineering Bootcamp


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 27 Aug 2025 07:00:00 -0700 Conor Bronsdon Conor Bronsdon 3107 The technological moat is eroding in the AI era, what new factors separate a successful startup from the rest? Aurimas Griciūnas, CEO of SwirlAI, joins the show to break down the realities of building in this new landscape. Startup success now hinges on speed, strong financial backing, or immediate distribution. Aurimas warns against the critical mistake of prioritizing shiny tools over fundamental engineering and the market gaps this creates. Chain of Thought is hosted by Conor Bronsdon. The technological moat is eroding in the AI era, what new factors separate a successful startup from the rest? Aurimas Griciūnas, CEO of SwirlAI, joins the show to break down the realities of building in this new landscape. Startup success now hinges on s technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Mindset Over Metrics: How to Approach AI Engineering | Hamel Husain 2 2 37 37 Mindset Over Metrics: How to Approach AI Engineering | Hamel Husain full ef05864e-9134-4b6f-b1c3-ab9cc910ffb3 https://share.transistor.fm/s/b99e3a16 As we enter the era of the AI engineer, the biggest challenge isn't technical - it's a shift in mindset. Hamel Husain, a leading AI consultant and luminary in the eval space, joins the podcast to explore the skills and processes needed to build reliable AI. 

Hamel explains why many teams relying on vanity dashboards and a "buffet of metrics" experience a false sense of security, which is no substitute for customized evals tailored to domain-specific risks. The solution? A disciplined process of error analysis, grounded in manually looking at data to identify real-world failures 

This discussion is an essential guide to building the continuous learning loops and "experimentation mindset" required to take AI products from prototype to production with confidence. Listen to learn the playbook for building AI reliability, and derive qualitative insights from log data to build customized quantitative guardrails.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Hamel on LinkedIn

Follow Hamel on X/Twitter

Check out his blog: hamel.dev


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> As we enter the era of the AI engineer, the biggest challenge isn't technical - it's a shift in mindset. Hamel Husain, a leading AI consultant and luminary in the eval space, joins the podcast to explore the skills and processes needed to build reliable AI. 

Hamel explains why many teams relying on vanity dashboards and a "buffet of metrics" experience a false sense of security, which is no substitute for customized evals tailored to domain-specific risks. The solution? A disciplined process of error analysis, grounded in manually looking at data to identify real-world failures 

This discussion is an essential guide to building the continuous learning loops and "experimentation mindset" required to take AI products from prototype to production with confidence. Listen to learn the playbook for building AI reliability, and derive qualitative insights from log data to build customized quantitative guardrails.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Hamel on LinkedIn

Follow Hamel on X/Twitter

Check out his blog: hamel.dev


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 20 Aug 2025 06:00:00 -0700 Conor Bronsdon Conor Bronsdon 2530 As we enter the era of the AI engineer, the biggest challenge isn't technical - it's a shift in mindset. Hamel Husain, a leading AI consultant and luminary in the eval space, joins the podcast to explore the skills and processes needed to build reliable AI. Hamel explains why many teams relying on vanity dashboards and a "buffet of metrics" experience a false sense of security, which is no substitute for customized evals tailored to domain-specific risks. The solution? Chain of Thought is hosted by Conor Bronsdon. As we enter the era of the AI engineer, the biggest challenge isn't technical - it's a shift in mindset. Hamel Husain, a leading AI consultant and luminary in the eval space, joins the podcast to explore the skills and processes needed to build reliable A technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No How AI Velocity is Rewriting the Rules for Engineering Leaders | ChatPRD's Claire Vo 2 2 36 36 How AI Velocity is Rewriting the Rules for Engineering Leaders | ChatPRD's Claire Vo full 3af0121d-f433-486a-a2ac-029967a2447d https://share.transistor.fm/s/5178eb0a What if your next competitor is not a startup, but a solo builder on a side project shipping features faster than your entire team?

For Claire Vo, that's not a hypothetical. As the founder of ChatPRD, formerly the Chief Product and Technology Officer at LaunchDarkly, and host of the How I AI podcast, she has a unique vantage point on the driving forces behind a new blueprint for success.

She argues that AI accountability must be driven from the top by an "AI czar" and reveals how a culture of experimentation is the key to overcoming organizational hesitancy. Drawing from her experience as a solo founder, she warns that for incumbents, the cost of moving slowly is the biggest threat and details how AI can finally be used to tackle legacy codebases. The conversation closes with bold predictions on the rise of the "super IC" - who can achieve top-tier impact and salary without managing a team - and the death of product management.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Claire on LinkedIn

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Claire’s podcast How I AI


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> What if your next competitor is not a startup, but a solo builder on a side project shipping features faster than your entire team?

For Claire Vo, that's not a hypothetical. As the founder of ChatPRD, formerly the Chief Product and Technology Officer at LaunchDarkly, and host of the How I AI podcast, she has a unique vantage point on the driving forces behind a new blueprint for success.

She argues that AI accountability must be driven from the top by an "AI czar" and reveals how a culture of experimentation is the key to overcoming organizational hesitancy. Drawing from her experience as a solo founder, she warns that for incumbents, the cost of moving slowly is the biggest threat and details how AI can finally be used to tackle legacy codebases. The conversation closes with bold predictions on the rise of the "super IC" - who can achieve top-tier impact and salary without managing a team - and the death of product management.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Claire on LinkedIn

Follow Claire on X/Twitter

Claire’s podcast How I AI


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 13 Aug 2025 06:00:00 -0700 Conor Bronsdon Conor Bronsdon 2576 What if your next competitor is not a startup, but a solo builder on a side project shipping features faster than your entire team? For Claire Vo, that's not a hypothetical. As the founder of ChatPRD, formerly the Chief Product and Technology Officer at LaunchDarkly, and host of the How I AI podcast, she has a unique vantage point on the driving forces behind a new blueprint for success. Chain of Thought is hosted by Conor Bronsdon. What if your next competitor is not a startup, but a solo builder on a side project shipping features faster than your entire team? For Claire Vo, that's not a hypothetical. As the founder of ChatPRD, formerly the Chief Product and Technology Officer at L technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Building an AI-Native Startup | GrowthX's Marcel Santilli 2 2 35 35 Building an AI-Native Startup | GrowthX's Marcel Santilli full bb261608-b2de-42d3-9cee-6d990723b5a8 https://share.transistor.fm/s/b438d900 How do you build an AI-native company to a $7M run rate in just six months?

According to Marcel Santilli, Founder and CEO of GrowthX, the secret isn't chasing the next frontier model, it's mastering the "messy middle." Drawing on his deep experience at Scale AI and Deepgram, Marcel joins host Conor Bronsdon to share his framework for building durable, customer-obsessed businesses.

Marcel argues that the most critical skills for the AI era aren't technical but philosophical: first-principles thinking and the art of delegation.

Tune in to learn why GrowthX first focused on services to codify expert work, how AI can augment human talent instead of replacing it, and why speed and brand are a startup's greatest competitive advantages. This conversation offers a clear playbook for building a resilient company by prioritizing culture and relentless shipping.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Marcel on LinkedIn

Follow Marcel on X (formerly Twitter)

Learn more about GrowthX


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> How do you build an AI-native company to a $7M run rate in just six months?

According to Marcel Santilli, Founder and CEO of GrowthX, the secret isn't chasing the next frontier model, it's mastering the "messy middle." Drawing on his deep experience at Scale AI and Deepgram, Marcel joins host Conor Bronsdon to share his framework for building durable, customer-obsessed businesses.

Marcel argues that the most critical skills for the AI era aren't technical but philosophical: first-principles thinking and the art of delegation.

Tune in to learn why GrowthX first focused on services to codify expert work, how AI can augment human talent instead of replacing it, and why speed and brand are a startup's greatest competitive advantages. This conversation offers a clear playbook for building a resilient company by prioritizing culture and relentless shipping.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

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Learn more about GrowthX


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> Wed, 06 Aug 2025 07:00:00 -0700 Conor Bronsdon Conor Bronsdon 1434 How do you build an AI-native company to a $7M run rate in just six months? According to Marcel Santilli, Founder and CEO of GrowthX, the secret isn't chasing the next frontier model, it's mastering the "messy middle." Drawing on his deep experience at Scale AI and Deepgram, Marcel joins host Conor Bronsdon to share his framework for building durable, customer-obsessed businesses. Marcel argues that the most critical skills for the AI era aren't technical but philosophical: first-principles thinking and the art of delegation. How do you build an AI-native company to a $7M run rate in just six months? According to Marcel Santilli, Founder and CEO of GrowthX, the secret isn't chasing the next frontier model, it's mastering the "messy middle." Drawing on his deep experience at Sc technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI's Trillion-Dollar Healthcare Bet | Corti's Andreas Cleve 2 2 34 34 AI's Trillion-Dollar Healthcare Bet | Corti's Andreas Cleve full 0daa4e04-d639-4a8e-8d57-fb0c84b1b992 https://share.transistor.fm/s/08a1eb63 AI isn't just changing healthcare; it's providing the essential help needed to unlock a trillion-dollar opportunity for better care.

Andreas Cleve, CEO & Co-founder of Corti, steps in to shed light on AI's immense, yet often misunderstood, transformative potential in this high-stakes environment. Andreas refutes the narrative of healthcare being slow adopters, emphasizing its high bar for trustworthy technology and its constant embrace of new tools. He reveals how purpose-built AI models are already alleviating the "pajama time" burden of documentation for clinicians, enabling faster and more accurate assessments in various specializations. This quiet, impactful adoption is seeing companies grow "like weeds" beyond common expectations.

The conversation addresses how AI can tackle the looming global shortage of 10 million healthcare professionals by 2030, reallocating a trillion dollars worth of administrative work back into care. Andreas details Corti’s approach to building invisible, reliable AI through rigorous, compliance-first evaluation, ensuring accuracy and efficiency in real-time. He emphasizes that AI's true role is not replacement, but augmentation, empowering professionals to deliver more care, attract talent, and drive organizational growth.



Connect with Chain of Thought host Conor Bronsdon:

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LinkedIn: linkedin.com/in/andreascleve

X (formerly Twitter): andreascleve

Corti Website: corti.ai


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> AI isn't just changing healthcare; it's providing the essential help needed to unlock a trillion-dollar opportunity for better care.

Andreas Cleve, CEO & Co-founder of Corti, steps in to shed light on AI's immense, yet often misunderstood, transformative potential in this high-stakes environment. Andreas refutes the narrative of healthcare being slow adopters, emphasizing its high bar for trustworthy technology and its constant embrace of new tools. He reveals how purpose-built AI models are already alleviating the "pajama time" burden of documentation for clinicians, enabling faster and more accurate assessments in various specializations. This quiet, impactful adoption is seeing companies grow "like weeds" beyond common expectations.

The conversation addresses how AI can tackle the looming global shortage of 10 million healthcare professionals by 2030, reallocating a trillion dollars worth of administrative work back into care. Andreas details Corti’s approach to building invisible, reliable AI through rigorous, compliance-first evaluation, ensuring accuracy and efficiency in real-time. He emphasizes that AI's true role is not replacement, but augmentation, empowering professionals to deliver more care, attract talent, and drive organizational growth.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

LinkedIn: linkedin.com/in/andreascleve

X (formerly Twitter): andreascleve

Corti Website: corti.ai


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 30 Jul 2025 07:00:00 -0700 Conor Bronsdon Conor Bronsdon 2832 AI isn't just changing healthcare; it's providing the essential help needed to unlock a trillion-dollar opportunity for better care. Andreas Cleve, CEO & Co-founder of Corti, steps in to shed light on AI's immense, yet often misunderstood, transformative potential in this high-stakes environment. Andreas refutes the narrative of healthcare being slow adopters, emphasizing its high bar for trustworthy technology and its constant embrace of new tools. Chain of Thought is hosted by Conor Bronsdon. AI isn't just changing healthcare; it's providing the essential help needed to unlock a trillion-dollar opportunity for better care. Andreas Cleve, CEO & Co-founder of Corti, steps in to shed light on AI's immense, yet often misunderstood, transformat technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Mastering Multi-Agent Systems | MongoDB’s Mikiko Chandrasekhar 2 2 33 33 Mastering Multi-Agent Systems | MongoDB’s Mikiko Chandrasekhar full 4fa9748a-ccb7-4733-9cc4-ff384979b54c https://share.transistor.fm/s/830c364e AI agents offer unprecedented power, but mastering agent reliability is the ultimate challenge for agentic systems to actually work in production.

Mikiko Chandrashekar, Staff Developer Advocate at MongoDB, whose background spans the entire data-to-AI pipeline, unveils MongoDB's vision as the memory store for agents, supporting complex multi-agent systems from data storage and vector search to debugging chat logs. She highlights how MongoDB, reinforced by the acquisition of Voyage, empowers developers to build production-scale agents across various industries, from solo projects to major enterprises. This robust data layer is foundational to ensure agent performance and improve the end user experience.

Mikiko advocates for treating agents as software products, applying rigorous engineering best practices to ensure reliability, even for non-deterministic systems. She details MongoDB's unique position to balance GPU/CPU loads and manage data for performance and observability, including Galileo's integrations. 

The conversation emphasizes the profound need to rethink observability, evaluations, and guardrails in the era of agents, showcasing Galileo's family of small language models for real-time guardrailing, Luna-2, and Insights Engine for automated failure analysis. Discover how building trustworthiness through systematic evaluation, beyond just "vibe checks," is essential for AI agents to scale and deliver value in high-stakes use cases.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Mikiko on LinkedIn

Follow Mikiko on X/Twitter

Explore Mikiko's YouTube channel

Check out Mikiko's ⁠Substack

Connect with MongoDB on LinkedIn

Connect with MongoDB on YouTube


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> AI agents offer unprecedented power, but mastering agent reliability is the ultimate challenge for agentic systems to actually work in production.

Mikiko Chandrashekar, Staff Developer Advocate at MongoDB, whose background spans the entire data-to-AI pipeline, unveils MongoDB's vision as the memory store for agents, supporting complex multi-agent systems from data storage and vector search to debugging chat logs. She highlights how MongoDB, reinforced by the acquisition of Voyage, empowers developers to build production-scale agents across various industries, from solo projects to major enterprises. This robust data layer is foundational to ensure agent performance and improve the end user experience.

Mikiko advocates for treating agents as software products, applying rigorous engineering best practices to ensure reliability, even for non-deterministic systems. She details MongoDB's unique position to balance GPU/CPU loads and manage data for performance and observability, including Galileo's integrations. 

The conversation emphasizes the profound need to rethink observability, evaluations, and guardrails in the era of agents, showcasing Galileo's family of small language models for real-time guardrailing, Luna-2, and Insights Engine for automated failure analysis. Discover how building trustworthiness through systematic evaluation, beyond just "vibe checks," is essential for AI agents to scale and deliver value in high-stakes use cases.



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Mikiko on LinkedIn

Follow Mikiko on X/Twitter

Explore Mikiko's YouTube channel

Check out Mikiko's ⁠Substack

Connect with MongoDB on LinkedIn

Connect with MongoDB on YouTube


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 23 Jul 2025 06:00:00 -0700 Conor Bronsdon Conor Bronsdon 2424 AI agents offer unprecedented power, but mastering agent reliability is the ultimate challenge for agentic systems to actually work in production. Mikiko Chandrashekar, Staff Developer Advocate at MongoDB, whose background spans the entire data-to-AI pipeline, unveils MongoDB's vision as the memory store for agents, supporting complex multi-agent systems from data storage and vector search to debugging chat logs. Chain of Thought is hosted by Conor Bronsdon. AI agents offer unprecedented power, but mastering agent reliability is the ultimate challenge for agentic systems to actually work in production. Mikiko Chandrashekar, Staff Developer Advocate at MongoDB, whose background spans the entire data-to-AI pipe technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn 2 2 32 32 The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn full fb437799-f6ca-4d02-866b-82dfee4b8b56 https://share.transistor.fm/s/364dd205 The age of ubiquitous AI agents is here, bringing immense potential - and unprecedented risk.

Hosts Conor Bronsdon and Vikram Chatterji open the episode by discussing the urgent need for building trust and reliability into next-generation AI agents. Vikram unveils Galileo's free AI reliability platform for agents, featuring Luna 2 SLMs for real-time guardrails and its Insights Engine for automatic failure mode analysis. This platform enables cost-effective, low-latency production evaluations, significantly transforming debugging. Achieving trustworthy AI agents demands rigorous testing, continuous feedback, and robust guardrailing—complex challenges requiring powerful solutions from partners like Elastic.

Conor welcomes Philipp Krenn, Director of Developer Relations at Elastic, to discuss their collaboration in ensuring AI agent reliability, including how Elastic leverages Galileo's platform for evaluation. Philipp details Elastic's evolution from a search powerhouse to a key AI enabler, transforming data access with Retrieval-Augmented Generation (RAG) and new interaction modes. He discusses Elastic's investment in SLMs for efficient re-ranking and embeddings, emphasizing robust evaluation and observability for production. This collaborative effort aims to equip developers to build reliable, high-performing AI systems for every enterprise.


Chapters:

00:00 Introduction 

01:09 Galileo's AI Reliability Platform

01:43 Challenges in AI Agent Reliability

06:17 Insights Engine and Its Importance

11:00 Luna 2: Small Language Models

14:42 Custom Metrics and Agent Leaderboard

19:16 Galileo's Integrations and Partnerships

21:04 Philipp Krenn from Elastic

24:47 Optimizing LLM Responses 

25:41 Galileo and Elastic: A Powerful Partnership

28:20 Challenges in AI Production and Trust

30:02 Guardrails and Reliability in AI Systems

32:17 The Future of AI in Customer Interaction



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Philipp on LinkedIn

Learn more about Elastic


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> The age of ubiquitous AI agents is here, bringing immense potential - and unprecedented risk.

Hosts Conor Bronsdon and Vikram Chatterji open the episode by discussing the urgent need for building trust and reliability into next-generation AI agents. Vikram unveils Galileo's free AI reliability platform for agents, featuring Luna 2 SLMs for real-time guardrails and its Insights Engine for automatic failure mode analysis. This platform enables cost-effective, low-latency production evaluations, significantly transforming debugging. Achieving trustworthy AI agents demands rigorous testing, continuous feedback, and robust guardrailing—complex challenges requiring powerful solutions from partners like Elastic.

Conor welcomes Philipp Krenn, Director of Developer Relations at Elastic, to discuss their collaboration in ensuring AI agent reliability, including how Elastic leverages Galileo's platform for evaluation. Philipp details Elastic's evolution from a search powerhouse to a key AI enabler, transforming data access with Retrieval-Augmented Generation (RAG) and new interaction modes. He discusses Elastic's investment in SLMs for efficient re-ranking and embeddings, emphasizing robust evaluation and observability for production. This collaborative effort aims to equip developers to build reliable, high-performing AI systems for every enterprise.


Chapters:

00:00 Introduction 

01:09 Galileo's AI Reliability Platform

01:43 Challenges in AI Agent Reliability

06:17 Insights Engine and Its Importance

11:00 Luna 2: Small Language Models

14:42 Custom Metrics and Agent Leaderboard

19:16 Galileo's Integrations and Partnerships

21:04 Philipp Krenn from Elastic

24:47 Optimizing LLM Responses 

25:41 Galileo and Elastic: A Powerful Partnership

28:20 Challenges in AI Production and Trust

30:02 Guardrails and Reliability in AI Systems

32:17 The Future of AI in Customer Interaction



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Philipp on LinkedIn

Learn more about Elastic


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 16 Jul 2025 08:00:00 -0700 Conor Bronsdon Conor Bronsdon 2652 The age of ubiquitous AI agents is here, bringing immense potential - and unprecedented risk. Hosts Conor Bronsdon and Vikram Chatterji open the episode by discussing the urgent need for building trust and reliability into next-generation AI agents. Vikram unveils Galileo's free AI reliability platform for agents, featuring Luna 2 SLMs for real-time guardrails and its Insights Engine for automatic failure mode analysis. This platform enables cost-effective, low-latency production evaluations, significantly transforming debugging. The age of ubiquitous AI agents is here, bringing immense potential - and unprecedented risk. Hosts Conor Bronsdon and Vikram Chatterji open the episode by discussing the urgent need for building trust and reliability into next-generation AI agents. Vikra technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Architecting Reliable Agentic AI | Cisco’s Giovanna Carofiglio on the AGNTCY Collective 2 2 31 31 Architecting Reliable Agentic AI | Cisco’s Giovanna Carofiglio on the AGNTCY Collective full e819e134-3e07-466d-91b2-2c5a6bcb1a2f https://share.transistor.fm/s/c73f551c The Internet of Agents is rapidly taking shape, necessitating innovative foundational standards, protocols, and evaluation methods for its success.

Recorded at Cisco's office in San Jose, we welcome Giovanna Carofiglio, Distinguished Engineer and Senior Director at Outshift by Cisco. As a leader of the AGNTCY Collective (an open-source initiative by Cisco, Galileo, LangChain, and many other participating companies), Giovanna outlines the vision for agents to collaborate seamlessly across the enterprise and the internet. She details the collective's pillars, from agent discovery and deployment using new agentic protocols like Slim, to ensuring a secure, low-latency communication transport layer. This groundbreaking work aims to make distributed agentic communication a reality.

The conversation then explores the critical role of observability and evaluation in building trustworthy agent applications, including defining an interoperable standard schema for communications. Giovanna highlights the complex challenges of scaling agents to thousands or millions, emphasizing the need for robust security (agent identity with OSF schema) and predictable agent behavior through extensive testing and characterization. She distinguishes between protocols like MCP (agent-to-tool) and A2A (agent-to-agent), advocating for open standards and underlying transport layers akin to TCP.


Chapters:

00:00 Introduction

01:00 Overview of Agent Interoperability

02:20 What is AGNTCY

03:45 Agent Discovery and Composition

04:38 Agent Protocols and Communication

05:45 Observability and Evaluation

07:00 Metrics and Standards for Agents

09:45 Challenges in Agent Evaluation

14:15 Low Latency and Active Evaluation

23:34 Synthetic Data and Ground Truth

25:07 Interoperable Agent Schema

26:37 MCP & A2A

30:17 Future of Agent Communication

32:03 Security and Agent Identity

34:37 Collaboration and Community Involvement

38:28 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

AGNTCY Collective: agntcy.org

Connect with Giovanna on LinkedIn

Learn more about Outshift: outshift.cisco.com


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> The Internet of Agents is rapidly taking shape, necessitating innovative foundational standards, protocols, and evaluation methods for its success.

Recorded at Cisco's office in San Jose, we welcome Giovanna Carofiglio, Distinguished Engineer and Senior Director at Outshift by Cisco. As a leader of the AGNTCY Collective (an open-source initiative by Cisco, Galileo, LangChain, and many other participating companies), Giovanna outlines the vision for agents to collaborate seamlessly across the enterprise and the internet. She details the collective's pillars, from agent discovery and deployment using new agentic protocols like Slim, to ensuring a secure, low-latency communication transport layer. This groundbreaking work aims to make distributed agentic communication a reality.

The conversation then explores the critical role of observability and evaluation in building trustworthy agent applications, including defining an interoperable standard schema for communications. Giovanna highlights the complex challenges of scaling agents to thousands or millions, emphasizing the need for robust security (agent identity with OSF schema) and predictable agent behavior through extensive testing and characterization. She distinguishes between protocols like MCP (agent-to-tool) and A2A (agent-to-agent), advocating for open standards and underlying transport layers akin to TCP.


Chapters:

00:00 Introduction

01:00 Overview of Agent Interoperability

02:20 What is AGNTCY

03:45 Agent Discovery and Composition

04:38 Agent Protocols and Communication

05:45 Observability and Evaluation

07:00 Metrics and Standards for Agents

09:45 Challenges in Agent Evaluation

14:15 Low Latency and Active Evaluation

23:34 Synthetic Data and Ground Truth

25:07 Interoperable Agent Schema

26:37 MCP & A2A

30:17 Future of Agent Communication

32:03 Security and Agent Identity

34:37 Collaboration and Community Involvement

38:28 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

AGNTCY Collective: agntcy.org

Connect with Giovanna on LinkedIn

Learn more about Outshift: outshift.cisco.com


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 09 Jul 2025 07:00:00 -0700 Conor Bronsdon Conor Bronsdon 2463 The Internet of Agents is rapidly taking shape, necessitating innovative foundational standards, protocols, and evaluation methods for its success. Recorded at Cisco's office in San Jose, we welcome Giovanna Carofiglio, Distinguished Engineer and Senior Director at Outshift by Cisco. As a leader of the AGNTCY Collective (an open-source initiative by Cisco, Galileo, LangChain, and many other participating companies), Giovanna outlines the vision for agents to collaborate seamlessly across the enterprise and the internet. Chain of Thought is hosted by Conor Bronsdon. The Internet of Agents is rapidly taking shape, necessitating innovative foundational standards, protocols, and evaluation methods for its success. Recorded at Cisco's office in San Jose, we welcome Giovanna Carofiglio, Distinguished Engineer and Senior D technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Taste Is The New Moat | Why Customer Obsession Wins in the AI Era 2 2 30 30 Taste Is The New Moat | Why Customer Obsession Wins in the AI Era full f6eb4e29-bc0f-4330-85e3-76881e4f3a96 https://share.transistor.fm/s/e186f1f4 When AI makes creating content and code nearly free, how do you stand out? Differentiation now hinges on two things: unique taste and effective distribution.

This week, Bharat Vasan, founder & CEO at Intangible and a "recovering VC," explains why the AI landscape compelled him to return to founding. He sees AI sparking a new creative revolution, similar to the early internet, that makes it easier than ever to bring ideas to life. The conversation delivers essential advice for founders, revealing why relentless shipping is the ultimate clarifier for a business and why resilience, not just intelligence, is the key to survival.

Drawing from his experience on both sides of the venture table, Bharat breaks down the brutally competitive VC landscape and shares Intangible's mission: to simplify 3D creative tools with AI, finally bridging the gap between human vision and machine power. Listeners will gain insights on company building, brand strategy, and why customer obsession is the ultimate moat in the AI age.


Chapters:

00:00 Introduction

00:45 From Founder to VC and Back

03:17 Human Creativity in the Age of AI

07:50 The Role of Taste and Distribution

11:49 Building a Brand in the AI Era

16:17 The Venture Capital Landscape for AI Startups

20:11 Advice for Founders in the AI Boom

23:55 Incumbents vs. Startups

27:10 The New Generation of Innovators

29:19 Pirate Mentality in Startups

30:00 Building a Brand

36:28 Shipping and Resilience

41:49 Customer Obsession

46:58 The Vision for Intangible

51:52 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Bharat on LinkedIn.

Follow Bharat on X.

Learn more about Intangible at intangible.ai.


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> When AI makes creating content and code nearly free, how do you stand out? Differentiation now hinges on two things: unique taste and effective distribution.

This week, Bharat Vasan, founder & CEO at Intangible and a "recovering VC," explains why the AI landscape compelled him to return to founding. He sees AI sparking a new creative revolution, similar to the early internet, that makes it easier than ever to bring ideas to life. The conversation delivers essential advice for founders, revealing why relentless shipping is the ultimate clarifier for a business and why resilience, not just intelligence, is the key to survival.

Drawing from his experience on both sides of the venture table, Bharat breaks down the brutally competitive VC landscape and shares Intangible's mission: to simplify 3D creative tools with AI, finally bridging the gap between human vision and machine power. Listeners will gain insights on company building, brand strategy, and why customer obsession is the ultimate moat in the AI age.


Chapters:

00:00 Introduction

00:45 From Founder to VC and Back

03:17 Human Creativity in the Age of AI

07:50 The Role of Taste and Distribution

11:49 Building a Brand in the AI Era

16:17 The Venture Capital Landscape for AI Startups

20:11 Advice for Founders in the AI Boom

23:55 Incumbents vs. Startups

27:10 The New Generation of Innovators

29:19 Pirate Mentality in Startups

30:00 Building a Brand

36:28 Shipping and Resilience

41:49 Customer Obsession

46:58 The Vision for Intangible

51:52 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Bharat on LinkedIn.

Follow Bharat on X.

Learn more about Intangible at intangible.ai.


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 02 Jul 2025 08:00:00 -0700 Conor Bronsdon Conor Bronsdon 3182 When AI makes creating content and code nearly free, how do you stand out? Differentiation now hinges on two things: unique taste and effective distribution. This week, Bharat Vasan, founder & CEO at Intangible and a "recovering VC," explains why the AI landscape compelled him to return to founding. He sees AI sparking a new creative revolution, similar to the early internet, that makes it easier than ever to bring ideas to life. Chain of Thought is hosted by Conor Bronsdon. When AI makes creating content and code nearly free, how do you stand out? Differentiation now hinges on two things: unique taste and effective distribution. This week, Bharat Vasan, founder & CEO at Intangible and a "recovering VC," explains why the technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The Emerging AI Agent Stack | CrewAI’s João Moura 2 2 29 29 The Emerging AI Agent Stack | CrewAI’s João Moura full e4415c0b-2b6f-4241-8d68-2f196a6e98b7 https://share.transistor.fm/s/6d776ed2 Unlocking AI agents for knowledge work automation and scaling intelligent, multi-agent systems within enterprises fundamentally requires measurability, reliability, and trust.

João Moura, founder & CEO of CrewAI, joins Galileo’s Conor Bronsdon and Vikram Chatterji to unpack and define the emerging AI agent stack. They explore how enterprises are moving beyond initial curiosity to tackle critical questions around provisioning, authentication, and measurement for hundreds or thousands of agents in production. The discussion highlights a crucial "gold rush" among middleware providers, all racing to standardize the orchestration and frameworks needed for seamless agent deployment and interoperability. This new era demands a re-evaluation of everything from cloud choices to communication protocols as agents reshape the market.

João and Vikram then dive into the complexities of building for non-deterministic multi-agent systems, emphasizing the challenges of increased failure modes and the need for rigorous testing beyond traditional software. They detail how CrewAI is democratizing agent access with a focus on orchestration, while Galileo provides the essential reliability platform, offering advanced evaluation, observability, and automated feedback loops. From specific use cases in financial services to the re-emergence of core data science principles, discover how companies are building trustworthy, high-quality AI products and prepare for the coming agent marketplace.


Chapters:

00:00 Introduction and Guest Welcome

02:04 Defining the AI Agent Stack

03:49 Challenges in Building AI Agents

05:52 The Future of AI Agent Marketplaces

06:59 Infrastructure and Protocols

09:05 Interoperability and Flexibility

20:18 Governance and Security Concerns

24:12 Industry Adoption and Use Cases

25:57 Unlocking Faster Development with Success Metrics

28:40 Challenges in Managing Complex Systems

30:10 Introducing the Insights Engine

30:33 The Importance of Observability and Control

32:33 Democratizing Access with No-Code Tools

35:39 Ensuring Quality and Reliability in Production

41:08 Future of Agentic Systems and Industry Transformation



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Joao Moura: LinkedIn | X/Twitter

CrewAI: crewai.com | X/Twitter


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Unlocking AI agents for knowledge work automation and scaling intelligent, multi-agent systems within enterprises fundamentally requires measurability, reliability, and trust.

João Moura, founder & CEO of CrewAI, joins Galileo’s Conor Bronsdon and Vikram Chatterji to unpack and define the emerging AI agent stack. They explore how enterprises are moving beyond initial curiosity to tackle critical questions around provisioning, authentication, and measurement for hundreds or thousands of agents in production. The discussion highlights a crucial "gold rush" among middleware providers, all racing to standardize the orchestration and frameworks needed for seamless agent deployment and interoperability. This new era demands a re-evaluation of everything from cloud choices to communication protocols as agents reshape the market.

João and Vikram then dive into the complexities of building for non-deterministic multi-agent systems, emphasizing the challenges of increased failure modes and the need for rigorous testing beyond traditional software. They detail how CrewAI is democratizing agent access with a focus on orchestration, while Galileo provides the essential reliability platform, offering advanced evaluation, observability, and automated feedback loops. From specific use cases in financial services to the re-emergence of core data science principles, discover how companies are building trustworthy, high-quality AI products and prepare for the coming agent marketplace.


Chapters:

00:00 Introduction and Guest Welcome

02:04 Defining the AI Agent Stack

03:49 Challenges in Building AI Agents

05:52 The Future of AI Agent Marketplaces

06:59 Infrastructure and Protocols

09:05 Interoperability and Flexibility

20:18 Governance and Security Concerns

24:12 Industry Adoption and Use Cases

25:57 Unlocking Faster Development with Success Metrics

28:40 Challenges in Managing Complex Systems

30:10 Introducing the Insights Engine

30:33 The Importance of Observability and Control

32:33 Democratizing Access with No-Code Tools

35:39 Ensuring Quality and Reliability in Production

41:08 Future of Agentic Systems and Industry Transformation



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Joao Moura: LinkedIn | X/Twitter

CrewAI: crewai.com | X/Twitter


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 25 Jun 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2994 Unlocking AI agents for knowledge work automation and scaling intelligent, multi-agent systems within enterprises fundamentally requires measurability, reliability, and trust. João Moura, founder & CEO of CrewAI, joins Galileo’s host Conor Bronsdon and Vikram Chatterji to unpack and define the emerging AI agent stack. They explore how enterprises are moving beyond initial curiosity to tackle critical questions around provisioning, authentication, and measurement for hundreds or thousands of agents in production. Unlocking AI agents for knowledge work automation and scaling intelligent, multi-agent systems within enterprises fundamentally requires measurability, reliability, and trust. João Moura, founder & CEO of CrewAI, joins Galileo’s host Conor Bronsdon an technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AMD's Challenge to NVIDIA: The Open Ecosystem Bet | Anush Elangovan & Sharon Zhou 2 2 28 28 AMD's Challenge to NVIDIA: The Open Ecosystem Bet | Anush Elangovan & Sharon Zhou full 4e25225b-b5a2-46a2-868f-825a5a6f74ab https://share.transistor.fm/s/5b5f926d How is an open ecosystem powering the next generation of AI for developers and leaders?

Broadcasting live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. They unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models.

Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications.


Chapters:

00:00 Live from AMD's Advancing AI 2025 Event

00:30 Introduction to Anush Elangovan

01:38 The MI350 GPU Series Unveiled

04:57 CDNA4 Architecture Explained

07:00 The Future of AI Infrastructure

08:32 AMD's Developer Cloud and ROCm 7

11:50 Cultural Shift at AMD

14:48 Open Source and Community Contributions

18:35 Software Longevity and Ecosystem Strategy

22:19 AI Agents and Performance Gains

27:36 AI's Role in Solving Power Challenges

28:11 Thanking Anush

28:42 Introduction to Sharon Zhou

29:45 Sharon's Focus at AMD

30:39 Engaging Developers with AMD's AI Tools

31:24 Listening to the AI Community

33:56 Open Source and AI Development

45:04 Future of AI and Self-Improving Models

48:04 Final Thoughts and Farewell



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Anush Elangovan: LinkedIn

Sharon Zhou: LinkedIn

AMD Official Site: amd.com

AMD Developer Resources: AMD Developer Central


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠

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]]> How is an open ecosystem powering the next generation of AI for developers and leaders?

Broadcasting live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. They unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. Discover how new MI350 GPUs deliver mind-blowing performance with advanced data types and why ROCm 7 and AMD Developer Cloud offer Day Zero support for frontier models.

Then Conor welcomes Sharon Zhou, VP of AI at AMD, to discuss making AMD's powerful software stack truly accessible and how to drive developer curiosity. Sharon explains strategies for creating a "happy path" for community contributions, fostering engagement through teaching, and listening to developers at every stage. She shares her predictions for the future, including the rise of self-improving AI, the critical role of heterogeneous compute, and the potential of "vibes based feedback" to guide models. This vision for democratizing access to high-performance AI, driven by a deep understanding of the developer journey, promises to unlock the next generation of applications.


Chapters:

00:00 Live from AMD's Advancing AI 2025 Event

00:30 Introduction to Anush Elangovan

01:38 The MI350 GPU Series Unveiled

04:57 CDNA4 Architecture Explained

07:00 The Future of AI Infrastructure

08:32 AMD's Developer Cloud and ROCm 7

11:50 Cultural Shift at AMD

14:48 Open Source and Community Contributions

18:35 Software Longevity and Ecosystem Strategy

22:19 AI Agents and Performance Gains

27:36 AI's Role in Solving Power Challenges

28:11 Thanking Anush

28:42 Introduction to Sharon Zhou

29:45 Sharon's Focus at AMD

30:39 Engaging Developers with AMD's AI Tools

31:24 Listening to the AI Community

33:56 Open Source and AI Development

45:04 Future of AI and Self-Improving Models

48:04 Final Thoughts and Farewell



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Anush Elangovan: LinkedIn

Sharon Zhou: LinkedIn

AMD Official Site: amd.com

AMD Developer Resources: AMD Developer Central


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 18 Jun 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2957 How is an open ecosystem powering the next generation of AI for developers and leaders? Broadcasting live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and Sharon Zhou, VP of AI. They unpack AMD's groundbreaking transformation from a hardware giant to a leader in full-stack AI, committed to an open ecosystem. How is an open ecosystem powering the next generation of AI for developers and leaders? Broadcasting live from the heart of the action at AMD's Advancing AI 2025, Chain of Thought host Conor Bronsdon welcomes AMD’s Anush Elangovan, VP of AI Software, and technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Your Key to AI Success is Hiding in Plain Sight | Cohesity's Greg Statton 2 2 27 27 Your Key to AI Success is Hiding in Plain Sight | Cohesity's Greg Statton full aa7e440b-dcd3-43c5-9303-84f4c3d5ca13 https://share.transistor.fm/s/d1c38288 What if the most valuable data in your enterprise—the key to your AI future—is sitting dormant in your backups, treated like an insurance policy you hope to never use?

Join Conor Bronsdon with Greg Statton, VP of AI Solutions at Cohesity, for an inside look at how they are turning this passive data into an active asset to power generative AI applications. Greg details Cohesity’s evolution from an infinitely scalable file system built for backups into a data intelligence powerhouse, managing hundreds of exabytes of enterprise data globally. He recounts how early successes in using this data for security and anomaly detection paved the way for more advanced AI applications. This foundational work was crucial in preparing Cohesity to meet the new demands of generative AI.

Greg offers a candid look at the real-world challenges enterprises face, arguing that establishing data hygiene and a cross-functional governance model is the most critical step before building reliable AI applications. He shares the compelling story of how Cohesity's focus on generative AI was sparked by an internal RAG experiment he built to solve a "semantic divide" in team communication, which quickly grew into a company-wide initiative. He also provides essential advice for data professionals, emphasizing the need to focus on solving core business problems.


Chapters:

00:00 Introduction

00:36 The Role of Gaming in AI Development

05:43 Personal Gaming Experiences

08:26 The Intersection of AI and Gaming

12:53 Importance of Data in Game Development

19:03 User Testing and QA in Gaming

25:49 Postmortems and Telemetry

27:21 Beta Testing and Data Preparedness

29:18 Traditional AI vs Generative AI

31:31 Challenges of Implementing AI in Games

35:57 Leveraging AI for Data Analytics

39:41 Automated QA and Reinforcement Learning

42:01 AI for Localization and Sentiment Analysis

44:21 Future of AI in Gaming



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Company Website: cohesity.com

LinkedIn: Gregory Statton


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠Agent Leaderboard


]]> What if the most valuable data in your enterprise—the key to your AI future—is sitting dormant in your backups, treated like an insurance policy you hope to never use?

Join Conor Bronsdon with Greg Statton, VP of AI Solutions at Cohesity, for an inside look at how they are turning this passive data into an active asset to power generative AI applications. Greg details Cohesity’s evolution from an infinitely scalable file system built for backups into a data intelligence powerhouse, managing hundreds of exabytes of enterprise data globally. He recounts how early successes in using this data for security and anomaly detection paved the way for more advanced AI applications. This foundational work was crucial in preparing Cohesity to meet the new demands of generative AI.

Greg offers a candid look at the real-world challenges enterprises face, arguing that establishing data hygiene and a cross-functional governance model is the most critical step before building reliable AI applications. He shares the compelling story of how Cohesity's focus on generative AI was sparked by an internal RAG experiment he built to solve a "semantic divide" in team communication, which quickly grew into a company-wide initiative. He also provides essential advice for data professionals, emphasizing the need to focus on solving core business problems.


Chapters:

00:00 Introduction

00:36 The Role of Gaming in AI Development

05:43 Personal Gaming Experiences

08:26 The Intersection of AI and Gaming

12:53 Importance of Data in Game Development

19:03 User Testing and QA in Gaming

25:49 Postmortems and Telemetry

27:21 Beta Testing and Data Preparedness

29:18 Traditional AI vs Generative AI

31:31 Challenges of Implementing AI in Games

35:57 Leveraging AI for Data Analytics

39:41 Automated QA and Reinforcement Learning

42:01 AI for Localization and Sentiment Analysis

44:21 Future of AI in Gaming



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Company Website: cohesity.com

LinkedIn: Gregory Statton


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠⁠Agent Leaderboard


]]> Wed, 11 Jun 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2749 What if the most valuable data in your enterprise—the key to your AI future—is sitting dormant in your backups, treated like an insurance policy you hope to never use? Join host Conor Bronsdon with Greg Statton, VP of AI Solutions at Cohesity, for an inside look at how they are turning this passive data into an active asset to power generative AI applications. What if the most valuable data in your enterprise—the key to your AI future—is sitting dormant in your backups, treated like an insurance policy you hope to never use? Join host Conor Bronsdon with Greg Statton, VP of AI Solutions at Cohesity, for an insi technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Why Gamers Paved the Way for AI | Databricks' Carly Taylor 2 2 26 26 Why Gamers Paved the Way for AI | Databricks' Carly Taylor full 5d6ae02f-6602-4820-8daa-1fb82cce9ab5 https://share.transistor.fm/s/40107f31 What if the pixels and polygons of your favorite video games were the secret architects of today's AI revolution?

Carly Taylor, Field CTO for Gaming at Databricks and founder of ggAI, joins host Conor Bronsdon to illuminate the direct line from video game innovation to the current AI landscape. She explains how the gaming industry's relentless pursuit of better graphics and performance not only drove pivotal GPU advancements and cost reductions, but also fundamentally shaped our popular understanding of artificial intelligence by popularizing the very term "AI" through decades of in-game experiences. Carly shares her personal journey, from a childhood passion for games like Rollercoaster Tycoon ignited while playing with her mom, to becoming a data scientist for Call of Duty. 

The discussion then confronts a long-standing tension in game development: how the critical need to ship titles often relegates vital game data to a secondary concern, a dynamic Carly explains is now being reshaped by AI. She details the inherent challenges game studios face in capturing and leveraging telemetry, from disparate development processes to the lengthy pipeline required for updates. Carly illuminates how modern AI, particularly generative AI, presents a massive opportunity for studios to finally unlock their vast data troves for everything from self-service analytics and community insight generation to revolutionizing QA processes. This pivotal intersection of evolving game data practices and new AI capabilities is poised to redefine how games are made, understood, and ultimately experienced.


Chapters

00:00 Introduction

00:28 The Role of Gaming in AI Development

05:35 Personal Gaming Experiences

08:18 The Intersection of AI and Gaming

12:45 Importance of Data in Game Development

18:55 User Testing and QA in Gaming

25:41 Postmortems and Telemetry

27:13 Beta Testing and Data Preparedness

29:10 Traditional AI vs Generative AI

31:23 Challenges of Implementing AI in Games

35:49 Leveraging AI for Data Analytics

39:33 Automated QA and Reinforcement Learning

41:53 AI for Localization and Sentiment Analysis

44:13 Future of AI in Gaming



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Carly on LinkedIn

Subscribe to Carly's Substack: Good At Business


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠Agent Leaderboard

]]> What if the pixels and polygons of your favorite video games were the secret architects of today's AI revolution?

Carly Taylor, Field CTO for Gaming at Databricks and founder of ggAI, joins host Conor Bronsdon to illuminate the direct line from video game innovation to the current AI landscape. She explains how the gaming industry's relentless pursuit of better graphics and performance not only drove pivotal GPU advancements and cost reductions, but also fundamentally shaped our popular understanding of artificial intelligence by popularizing the very term "AI" through decades of in-game experiences. Carly shares her personal journey, from a childhood passion for games like Rollercoaster Tycoon ignited while playing with her mom, to becoming a data scientist for Call of Duty. 

The discussion then confronts a long-standing tension in game development: how the critical need to ship titles often relegates vital game data to a secondary concern, a dynamic Carly explains is now being reshaped by AI. She details the inherent challenges game studios face in capturing and leveraging telemetry, from disparate development processes to the lengthy pipeline required for updates. Carly illuminates how modern AI, particularly generative AI, presents a massive opportunity for studios to finally unlock their vast data troves for everything from self-service analytics and community insight generation to revolutionizing QA processes. This pivotal intersection of evolving game data practices and new AI capabilities is poised to redefine how games are made, understood, and ultimately experienced.


Chapters

00:00 Introduction

00:28 The Role of Gaming in AI Development

05:35 Personal Gaming Experiences

08:18 The Intersection of AI and Gaming

12:45 Importance of Data in Game Development

18:55 User Testing and QA in Gaming

25:41 Postmortems and Telemetry

27:13 Beta Testing and Data Preparedness

29:10 Traditional AI vs Generative AI

31:23 Challenges of Implementing AI in Games

35:49 Leveraging AI for Data Analytics

39:33 Automated QA and Reinforcement Learning

41:53 AI for Localization and Sentiment Analysis

44:13 Future of AI in Gaming



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Connect with Carly on LinkedIn

Subscribe to Carly's Substack: Good At Business


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 04 Jun 2025 03:30:00 -0700 Conor Bronsdon Conor Bronsdon 2951 What if the pixels and polygons of your favorite video games were the secret architects of today's AI revolution? Carly Taylor, Field CTO for Gaming at Databricks and founder of ggAI, joins host Conor Bronsdon to illuminate the direct line from video game innovation to the current AI landscape. What if the pixels and polygons of your favorite video games were the secret architects of today's AI revolution? Carly Taylor, Field CTO for Gaming at Databricks and founder of ggAI, joins host Conor Bronsdon to illuminate the direct line from video game technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The 2025 AI Shift: From Chat to Task Completion & Reliable Action | Galileo Founders 2 2 25 25 The 2025 AI Shift: From Chat to Task Completion & Reliable Action | Galileo Founders full 0b69225f-c80d-4c23-a2f0-5bf1e7495c2d https://share.transistor.fm/s/3577fd8c AI in 2025 promises intelligent action, not just smarter chat. But are enterprises prepared for the agentic shift and the complex reliability hurdles it brings?

Join Conor Bronsdon on Chain of Thought with fellow co-hosts and Galileo co-founders, Vikram Chatterji (CEO) and Atindriyo Sanyal (CTO), as they explore this pivotal transformation. They discuss how generative AI is evolving from a simple tool into a powerful engine for enterprise task automation, a significant advance driving the pursuit of substantial ROI. This shift is also fueling what Vikram observes as a "gold rush" for middleware and frameworks, alongside healthy skepticism about making widespread agentic task completion a practical reality.

As these AI systems grow into highly complex, compound structures—often incorporating multimodal inputs and multi-agent designs—Vikram and Atin address the critical challenges around debugging, achieving reliability, and solving the profound measurement problem. They share Galileo's vision for an AI reliability platform designed to tame these intricate systems through robust guardrailing, advanced metric engines like Luna, and actionable developer insights. Tune in to understand how the industry is moving beyond point-in-time evaluations to continuous AI reliability, crucial for building trustworthy, high-performing AI applications at scale.


Chapters

00:00 Welcome and Introductions

01:05 Generative AI and Task Completion

02:13 Middleware and Orchestration Systems

03:17 Enterprise Adoption and Challenges

05:55 Multimodal AI and Future Plans

08:37 AI Reliability and Evaluation

11:08 Complex AI Systems and Developer Challenges

13:45 Galileo's Vision and Product Roadmap

18:59 Modern AI Evaluation Agents

20:10 Galileo's Powerful SDK and Tools

21:24 The Importance of Observability and Robust Testing

22:27 The Rise of Vibe Coding

24:48 Balancing Creativity and Reliability in AI

31:26 Enterprise Adoption of AI Systems

36:59 Challenges and Opportunities in Regulated Industries

42:10 Future of AI Reliability and Industry Impact



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: galileo.ai

Read: Galileo Optimizes Enterprise–Scale Agentic AI Stack with NVIDIA


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠Agent Leaderboard

]]> AI in 2025 promises intelligent action, not just smarter chat. But are enterprises prepared for the agentic shift and the complex reliability hurdles it brings?

Join Conor Bronsdon on Chain of Thought with fellow co-hosts and Galileo co-founders, Vikram Chatterji (CEO) and Atindriyo Sanyal (CTO), as they explore this pivotal transformation. They discuss how generative AI is evolving from a simple tool into a powerful engine for enterprise task automation, a significant advance driving the pursuit of substantial ROI. This shift is also fueling what Vikram observes as a "gold rush" for middleware and frameworks, alongside healthy skepticism about making widespread agentic task completion a practical reality.

As these AI systems grow into highly complex, compound structures—often incorporating multimodal inputs and multi-agent designs—Vikram and Atin address the critical challenges around debugging, achieving reliability, and solving the profound measurement problem. They share Galileo's vision for an AI reliability platform designed to tame these intricate systems through robust guardrailing, advanced metric engines like Luna, and actionable developer insights. Tune in to understand how the industry is moving beyond point-in-time evaluations to continuous AI reliability, crucial for building trustworthy, high-performing AI applications at scale.


Chapters

00:00 Welcome and Introductions

01:05 Generative AI and Task Completion

02:13 Middleware and Orchestration Systems

03:17 Enterprise Adoption and Challenges

05:55 Multimodal AI and Future Plans

08:37 AI Reliability and Evaluation

11:08 Complex AI Systems and Developer Challenges

13:45 Galileo's Vision and Product Roadmap

18:59 Modern AI Evaluation Agents

20:10 Galileo's Powerful SDK and Tools

21:24 The Importance of Observability and Robust Testing

22:27 The Rise of Vibe Coding

24:48 Balancing Creativity and Reliability in AI

31:26 Enterprise Adoption of AI Systems

36:59 Challenges and Opportunities in Regulated Industries

42:10 Future of AI Reliability and Industry Impact



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: galileo.ai

Read: Galileo Optimizes Enterprise–Scale Agentic AI Stack with NVIDIA


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠⁠

⁠⁠⁠⁠Agent Leaderboard

]]> Wed, 28 May 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2682 AI in 2025 promises intelligent action, not just smarter chat. But are enterprises prepared for the agentic shift and the complex reliability hurdles it brings? Join host Conor Bronsdon on Chain of Thought with fellow co-hosts and Galileo co-founders, Vikram Chatterji (CEO) and Atindriyo Sanyal (CTO), as they explore this pivotal transformation. They discuss how generative AI is evolving from a simple tool into a powerful engine for enterprise task automation, a significant advance driving the pursuit of substantial ROI. AI in 2025 promises intelligent action, not just smarter chat. But are enterprises prepared for the agentic shift and the complex reliability hurdles it brings? Join host Conor Bronsdon on Chain of Thought with fellow co-hosts and Galileo co-founders, Vik technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Amplitude's AI Playbook: How Wade Chambers Builds for the Agentic Future 2 2 24 24 Amplitude's AI Playbook: How Wade Chambers Builds for the Agentic Future full dffa9e09-17a0-460f-a73c-a03711decbfa https://share.transistor.fm/s/aaf94c30 As AI redefines how products are built and customers are understood, what are the core strategies engineering leaders use to drive innovation and create lasting value?

Join Conor Bronsdon as he welcomes Wade Chambers, Chief Engineering Officer at Amplitude, to explore these critical questions. Wade shares how Amplitude is leveraging AI to deepen customer understanding and enhance product experiences, transforming raw data into actionable insights across their platform. He also discusses their approach to navigating constant change while building an adaptable, high-performing engineering culture that thrives in the current AI landscape.

The conversation explores Amplitude's strategy for building a sustainable AI advantage through proprietary data, deep domain expertise, and robust feedback loops, moving beyond superficial AI applications. Wade offers insights on fostering an AI-ready engineering culture through empowerment and clear alignment, alongside exploring the exciting potential of agentic AI to create proactive, intelligent copilots for product teams. He then details Amplitude’s successful approach to integrating specialized AI talent, drawing key lessons from their acquisition of Command AI.


Chapters

00:00 Introduction and Guest Welcome

01:55 Understanding and Acting on Data with AI

06:42 Amplitude's Unique Position in the Market

08:36 Differentiation and Competitive Advantage

09:58 Incorporating Customer Feedback

12:48 Evaluating AI Outcomes

17:21 Agentic AI and Future Prospects

21:38 Acquiring and Integrating AI Talent

28:44 Building a Culture of Innovation

37:21 Advice for Leaders and Individual Contributors

43:26 The Future of AI in the Workplace

45:38 Closing Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

LinkedIn: Wade Chambers

Website: amplitude.com


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠

⁠⁠⁠Agent Leaderboard


]]> As AI redefines how products are built and customers are understood, what are the core strategies engineering leaders use to drive innovation and create lasting value?

Join Conor Bronsdon as he welcomes Wade Chambers, Chief Engineering Officer at Amplitude, to explore these critical questions. Wade shares how Amplitude is leveraging AI to deepen customer understanding and enhance product experiences, transforming raw data into actionable insights across their platform. He also discusses their approach to navigating constant change while building an adaptable, high-performing engineering culture that thrives in the current AI landscape.

The conversation explores Amplitude's strategy for building a sustainable AI advantage through proprietary data, deep domain expertise, and robust feedback loops, moving beyond superficial AI applications. Wade offers insights on fostering an AI-ready engineering culture through empowerment and clear alignment, alongside exploring the exciting potential of agentic AI to create proactive, intelligent copilots for product teams. He then details Amplitude’s successful approach to integrating specialized AI talent, drawing key lessons from their acquisition of Command AI.


Chapters

00:00 Introduction and Guest Welcome

01:55 Understanding and Acting on Data with AI

06:42 Amplitude's Unique Position in the Market

08:36 Differentiation and Competitive Advantage

09:58 Incorporating Customer Feedback

12:48 Evaluating AI Outcomes

17:21 Agentic AI and Future Prospects

21:38 Acquiring and Integrating AI Talent

28:44 Building a Culture of Innovation

37:21 Advice for Leaders and Individual Contributors

43:26 The Future of AI in the Workplace

45:38 Closing Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

LinkedIn: Wade Chambers

Website: amplitude.com


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠⁠

⁠⁠⁠Agent Leaderboard


]]> Wed, 21 May 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2828 As AI redefines how products are built and customers are understood, what are the core strategies engineering leaders use to drive innovation and create lasting value? Join host Conor Bronsdon as he welcomes Wade Chambers, Chief Engineering Officer at Amplitude, to explore these critical questions. Wade shares how Amplitude is leveraging AI to deepen customer understanding and enhance product experiences, transforming raw data into actionable insights across their platform. As AI redefines how products are built and customers are understood, what are the core strategies engineering leaders use to drive innovation and create lasting value? Join host Conor Bronsdon as he welcomes Wade Chambers, Chief Engineering Officer at Amp technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No First Code, Then AGI: Software’s Event Horizon with Poolside Founders Jason Warner & Eiso Kant 2 2 23 23 First Code, Then AGI: Software’s Event Horizon with Poolside Founders Jason Warner & Eiso Kant full c7840db4-d44e-4dc0-b7db-3580ba613d5c https://share.transistor.fm/s/17ac922c Is the prevailing approach to Artificial General Intelligence (AGI) missing a crucial step – deep, focused specialization? 

For the first time since co-founding Poolside, CEO Jason Warner & CTO Eiso Kant reunite on a podcast articulating their distinct vision for AI's future with our host, Conor Bronsdon. Poolside has intentionally diverged from general-purpose models, developing highly specialized AI meticulously designed for the specific, complex task of coding, viewing it as a direct and robust pathway towards achieving AGI, and revolutionizing how software is created.

Jason and Eiso dive deep into the core tenets of their strategy: an unwavering conviction in reinforcement learning through code execution feedback and the burgeoning power of synthetic data, which they believe will help expand the surface area of software by an astounding 1000x. They candidly discuss the "devil's trade" of data privacy, Poolside's commitment to enterprise-grade AI for high-consequence systems, and why true innovation requires moving beyond flashy demos to solve real-world, critical challenges. 

Looking towards the horizon, they also share their insights on the evolving role of software engineers, where human agency, taste, and judgment become paramount in a landscape augmented by AI "coworkers." They also explore the profound societal implications of their work and the AI industry more generally, touching upon the "event horizon" of intelligent systems and the immense responsibility that comes with being at the forefront of this technological wave.


Chapters

00:00 Introduction and Guest Welcome

01:19 Founding of Poolside

02:56 Vision for AGI and Reinforcement Learning

05:36 Defining AGI and Its Implications

10:03 Training Models for Software Development

17:08 Scaling and Synthetic Data

20:12 Focus on High-Consequence Systems

26:17 Privacy and Security in AI Solutions

28:09 Earning Trust with Developers

31:08 Reinforcement Learning and Compute

34:29 The Vision for AI's Future

39:50 Will Developers Still Exist?

47:07 Poolside Cloud's Ambitions

49:37 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: poolside.ai

LinkedIn: Jason Warner

LinkedIn: Eiso Kant


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠⁠

⁠⁠Agent Leaderboard


]]> Is the prevailing approach to Artificial General Intelligence (AGI) missing a crucial step – deep, focused specialization? 

For the first time since co-founding Poolside, CEO Jason Warner & CTO Eiso Kant reunite on a podcast articulating their distinct vision for AI's future with our host, Conor Bronsdon. Poolside has intentionally diverged from general-purpose models, developing highly specialized AI meticulously designed for the specific, complex task of coding, viewing it as a direct and robust pathway towards achieving AGI, and revolutionizing how software is created.

Jason and Eiso dive deep into the core tenets of their strategy: an unwavering conviction in reinforcement learning through code execution feedback and the burgeoning power of synthetic data, which they believe will help expand the surface area of software by an astounding 1000x. They candidly discuss the "devil's trade" of data privacy, Poolside's commitment to enterprise-grade AI for high-consequence systems, and why true innovation requires moving beyond flashy demos to solve real-world, critical challenges. 

Looking towards the horizon, they also share their insights on the evolving role of software engineers, where human agency, taste, and judgment become paramount in a landscape augmented by AI "coworkers." They also explore the profound societal implications of their work and the AI industry more generally, touching upon the "event horizon" of intelligent systems and the immense responsibility that comes with being at the forefront of this technological wave.


Chapters

00:00 Introduction and Guest Welcome

01:19 Founding of Poolside

02:56 Vision for AGI and Reinforcement Learning

05:36 Defining AGI and Its Implications

10:03 Training Models for Software Development

17:08 Scaling and Synthetic Data

20:12 Focus on High-Consequence Systems

26:17 Privacy and Security in AI Solutions

28:09 Earning Trust with Developers

31:08 Reinforcement Learning and Compute

34:29 The Vision for AI's Future

39:50 Will Developers Still Exist?

47:07 Poolside Cloud's Ambitions

49:37 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: poolside.ai

LinkedIn: Jason Warner

LinkedIn: Eiso Kant


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⁠⁠Agent Leaderboard


]]> Wed, 14 May 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 3067 Is the prevailing approach to Artificial General Intelligence (AGI) missing a crucial step – deep, focused specialization? For the first time since co-founding Poolside, CEO Jason Warner & CTO Eiso Kant reunite on a podcast articulating their distinct vision for AI's future with our host, Conor Bronsdon. Is the prevailing approach to Artificial General Intelligence (AGI) missing a crucial step – deep, focused specialization? For the first time since co-founding Poolside, CEO Jason Warner & CTO Eiso Kant reunite on a podcast articulating their distinct technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI's Two Extremes – Foundations & The Frontier | Databricks’ Denny Lee 2 2 22 22 AI's Two Extremes – Foundations & The Frontier | Databricks’ Denny Lee full d57b6a4d-3d92-409f-ad88-f7d9c55c859b https://share.transistor.fm/s/e36ce673 The AI landscape often pulls us between the allure of cutting-edge models and the quiet necessity of foundational work—yet how do these extremes actually connect to deliver value?

Join Conor Bronsdon as he welcomes Denny Lee, a self-proclaimed "data nerd" and Product Management Director, Developer Relations at Dataricks, to unpack this very spectrum, from AI's core infrastructure to its most advanced applications. Denny explains why robust logging, tracing, and data lineage are indispensable for credible AI evaluation and feedback, ultimately making AI systems more affordable, accessible, and impactful.

The discussion ventures into strategies for democratizing AI, exploring the "GenAI ladder" from efficient inference and retrieval-augmented generation to deciding when to fine-tune or pre-train models. Denny also tackles the industry's pressing hardware bottlenecks, the critical role of open standards, and the imperative of navigating data privacy in an increasingly AI-driven world. Listen for grounded advice on moving beyond the hype and making practical, value-driven decisions in your AI journey.


Chapters

00:00 Introduction and Guest Welcome

01:31 Diving into AI Foundations

02:25 Importance of Logging and Tracing

08:40 Challenges in Data Quality and Lineage

14:49 Strategies for Cost-Effective AI

19:52 Partnerships and Collaborative Opportunities

22:10 Hardware Bottlenecks in AI

24:56 China's Power and Networking Advantage

25:26 Nvidia's Super Chip and Network Fabrics

26:39 The Growing Demand for Power in AI

29:26 Practical Advice for Data Governance

35:47 Understanding Privacy in AI

36:25 Differential Privacy and Its Challenges

41:57 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: Databricks.com

Podcast: Data Brew by Databricks (available on major podcast platforms)

YouTube: @Databricks

LinkedIn: Denny Lee


Read

SemiAnalysis Blog: https://semianalysis.com/


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠

⁠Agent Leaderboard

]]> The AI landscape often pulls us between the allure of cutting-edge models and the quiet necessity of foundational work—yet how do these extremes actually connect to deliver value?

Join Conor Bronsdon as he welcomes Denny Lee, a self-proclaimed "data nerd" and Product Management Director, Developer Relations at Dataricks, to unpack this very spectrum, from AI's core infrastructure to its most advanced applications. Denny explains why robust logging, tracing, and data lineage are indispensable for credible AI evaluation and feedback, ultimately making AI systems more affordable, accessible, and impactful.

The discussion ventures into strategies for democratizing AI, exploring the "GenAI ladder" from efficient inference and retrieval-augmented generation to deciding when to fine-tune or pre-train models. Denny also tackles the industry's pressing hardware bottlenecks, the critical role of open standards, and the imperative of navigating data privacy in an increasingly AI-driven world. Listen for grounded advice on moving beyond the hype and making practical, value-driven decisions in your AI journey.


Chapters

00:00 Introduction and Guest Welcome

01:31 Diving into AI Foundations

02:25 Importance of Logging and Tracing

08:40 Challenges in Data Quality and Lineage

14:49 Strategies for Cost-Effective AI

19:52 Partnerships and Collaborative Opportunities

22:10 Hardware Bottlenecks in AI

24:56 China's Power and Networking Advantage

25:26 Nvidia's Super Chip and Network Fabrics

26:39 The Growing Demand for Power in AI

29:26 Practical Advice for Data Governance

35:47 Understanding Privacy in AI

36:25 Differential Privacy and Its Challenges

41:57 Conclusion



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: Databricks.com

Podcast: Data Brew by Databricks (available on major podcast platforms)

YouTube: @Databricks

LinkedIn: Denny Lee


Read

SemiAnalysis Blog: https://semianalysis.com/


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠⁠

⁠Agent Leaderboard

]]> Wed, 07 May 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2611 The AI landscape often pulls us between the allure of cutting-edge models and the quiet necessity of foundational work—yet how do these extremes actually connect to deliver value? Join host Conor Bronsdon as he welcomes Denny Lee, a self-proclaimed "data nerd" and Product Management Director, Developer Relations at Dataricks, to unpack this very spectrum, from AI's core infrastructure to its most advanced applications. The AI landscape often pulls us between the allure of cutting-edge models and the quiet necessity of foundational work—yet how do these extremes actually connect to deliver value? Join host Conor Bronsdon as he welcomes Denny Lee, a self-proclaimed "data technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Why Enterprises Need a Different Approach to AI Agents | Lyzr’s Siva Surendira 2 2 21 21 Why Enterprises Need a Different Approach to AI Agents | Lyzr’s Siva Surendira full 3f75206b-4922-4a4d-8300-7c4115eda969 https://share.transistor.fm/s/8d6b1e42 Agentic AI exploded in 2025, but how do businesses move beyond prototypes to deploy reliable, valuable agents at scale?

Join host Conor Bronsdon and Lyzr AI CEO Siva Surendira as they discuss the complexities of building and managing AI agents for enterprises. Siva shares his journey creating Lyzr, focusing on making powerful agent frameworks accessible and trustworthy for enterprise developers. They discuss the critical hurdles businesses face, including productionization challenges, ensuring responsible AI, and bridging the gap between rapid innovation and the stringent requirements of regulated industries.

Listen as Siva explains Lyzr's approach to embedding safety guardrails natively and learn about the nuances of multi-agent orchestration, including managerial, DAG, and hybrid flows. Siva also offers insights into the limitations of "vibe coding" for enterprise use cases and stresses the crucial role of robust evaluation (evals) and choosing the right models—from local open-source options to frontier LLMs. Explore the bottlenecks hindering adoption, like custom application integration and data readiness, and learn why Siva believes the biggest opportunity for agent companies may not lie in replacing SaaS platforms but rather in automating the mundane work currently performed by humans.


Chapters

00:22 Introduction and Guest Welcome

00:52 Enterprise Agent Framework

02:48 Building Enterprise-Friendly AI Frameworks

04:56 Enterprise Concerns with Vibe Coding

09:23 Safe and Responsible AI Implementation

11:05 Multi-Agent Orchestration

14:13 Challenges in Multi-Agent Systems

14:22 Enterprise Integration Bottlenecks

17:37 The Role of Low-Code and No-Code Solutions

19:55 Inter-Agent Communication Standards

21:49 Future of AI Agents in Enterprises

29:37 Evaluating AI Agents

36:34 Conclusion and Final Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: lyzr.ai

LinkedIn: Siva Surendira


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Agent Leaderboard

]]> Agentic AI exploded in 2025, but how do businesses move beyond prototypes to deploy reliable, valuable agents at scale?

Join host Conor Bronsdon and Lyzr AI CEO Siva Surendira as they discuss the complexities of building and managing AI agents for enterprises. Siva shares his journey creating Lyzr, focusing on making powerful agent frameworks accessible and trustworthy for enterprise developers. They discuss the critical hurdles businesses face, including productionization challenges, ensuring responsible AI, and bridging the gap between rapid innovation and the stringent requirements of regulated industries.

Listen as Siva explains Lyzr's approach to embedding safety guardrails natively and learn about the nuances of multi-agent orchestration, including managerial, DAG, and hybrid flows. Siva also offers insights into the limitations of "vibe coding" for enterprise use cases and stresses the crucial role of robust evaluation (evals) and choosing the right models—from local open-source options to frontier LLMs. Explore the bottlenecks hindering adoption, like custom application integration and data readiness, and learn why Siva believes the biggest opportunity for agent companies may not lie in replacing SaaS platforms but rather in automating the mundane work currently performed by humans.


Chapters

00:22 Introduction and Guest Welcome

00:52 Enterprise Agent Framework

02:48 Building Enterprise-Friendly AI Frameworks

04:56 Enterprise Concerns with Vibe Coding

09:23 Safe and Responsible AI Implementation

11:05 Multi-Agent Orchestration

14:13 Challenges in Multi-Agent Systems

14:22 Enterprise Integration Bottlenecks

17:37 The Role of Low-Code and No-Code Solutions

19:55 Inter-Agent Communication Standards

21:49 Future of AI Agents in Enterprises

29:37 Evaluating AI Agents

36:34 Conclusion and Final Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website: lyzr.ai

LinkedIn: Siva Surendira


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

Agent Leaderboard

]]> Wed, 30 Apr 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2329 Agentic AI exploded in 2025, but how do businesses move beyond prototypes to deploy reliable, valuable agents at scale? Join host Conor Bronsdon and Lyzr AI CEO Siva Surendira as they discuss the complexities of building and managing AI agents for enterprises. Siva shares his journey creating Lyzr, focusing on making powerful agent frameworks accessible and trustworthy for enterprise developers. Agentic AI exploded in 2025, but how do businesses move beyond prototypes to deploy reliable, valuable agents at scale? Join host Conor Bronsdon and Lyzr AI CEO Siva Surendira as they discuss the complexities of building and managing AI agents for enterpr technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Breaking the Language Barrier: Smartling's AI Translation Pipeline | Olga Beregovaya 2 2 20 20 Breaking the Language Barrier: Smartling's AI Translation Pipeline | Olga Beregovaya full 234eeec5-f216-4d01-9a70-f480a10117e0 https://share.transistor.fm/s/a85213c8 Are we on the verge of removing all language barriers with AI?

Olga Beregovaya, VP of AI at Smartling, joins host Conor Bronsdon to tackle this question, discussing the evolution from rule-based NLP to today's powerful LLMs. Together, they confront the persistent challenges that stand in the way, like the English-centric nature of AI, domain-specific inaccuracies, and the unpredictability of model hallucinations. Olga unpacks the difficulties faced when striving for accurate, nuanced translation across all languages, especially under-resourced ones.

Beyond these hurdles, the conversation explores the cutting-edge opportunities and technical innovations driving progress, including RAG, the rise of purpose-built models, agentic AI workflows, and the potential of multilingual multimodality. Olga shares insights into boosting translator productivity, achieving more predictable quality, and the path toward human parity in translation, examining how technology and human expertise will shape the future of global communication.


Chapters

00:00 Introduction and Guest Welcome

01:14 Evolution of NLP: From Rule-Based to Machine Learning

02:40 Challenges in AI Translation

04:21 Biases in Language Models

05:28 Inference Time and Latency

05:44 English-Centric AI Models

08:53 Opportunities in AI Translation

09:14 Industries Benefiting from Language AI

10:36 Human-in-the-Loop Translation

12:06 Architectural Innovations in Language AI

16:20 Success with RAG Architectures

17:58 Multilingual Vectorization

19:54 Agentic AI in Translation

24:35 Data Sets and Data Privacy

28:30 Using Smaller, Purpose-Built Models

32:10 Future of AI in Translation

36:37 Conclusion and Farewell



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

LinkedIn Olga Beregovaya

LinkedIn ⁠Smartling


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Are we on the verge of removing all language barriers with AI?

Olga Beregovaya, VP of AI at Smartling, joins host Conor Bronsdon to tackle this question, discussing the evolution from rule-based NLP to today's powerful LLMs. Together, they confront the persistent challenges that stand in the way, like the English-centric nature of AI, domain-specific inaccuracies, and the unpredictability of model hallucinations. Olga unpacks the difficulties faced when striving for accurate, nuanced translation across all languages, especially under-resourced ones.

Beyond these hurdles, the conversation explores the cutting-edge opportunities and technical innovations driving progress, including RAG, the rise of purpose-built models, agentic AI workflows, and the potential of multilingual multimodality. Olga shares insights into boosting translator productivity, achieving more predictable quality, and the path toward human parity in translation, examining how technology and human expertise will shape the future of global communication.


Chapters

00:00 Introduction and Guest Welcome

01:14 Evolution of NLP: From Rule-Based to Machine Learning

02:40 Challenges in AI Translation

04:21 Biases in Language Models

05:28 Inference Time and Latency

05:44 English-Centric AI Models

08:53 Opportunities in AI Translation

09:14 Industries Benefiting from Language AI

10:36 Human-in-the-Loop Translation

12:06 Architectural Innovations in Language AI

16:20 Success with RAG Architectures

17:58 Multilingual Vectorization

19:54 Agentic AI in Translation

24:35 Data Sets and Data Privacy

28:30 Using Smaller, Purpose-Built Models

32:10 Future of AI in Translation

36:37 Conclusion and Farewell



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

LinkedIn Olga Beregovaya

LinkedIn ⁠Smartling


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Wed, 23 Apr 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2431 Are we on the verge of removing all language barriers with AI? Olga Beregovaya, VP of AI at Smartling, joins host Conor Bronsdon to tackle this question, discussing the evolution from rule-based NLP to today's powerful LLMs. Together, they confront the persistent challenges that stand in the way, like the English-centric nature of AI, domain-specific inaccuracies, and the unpredictability of model hallucinations. Are we on the verge of removing all language barriers with AI? Olga Beregovaya, VP of AI at Smartling, joins host Conor Bronsdon to tackle this question, discussing the evolution from rule-based NLP to today's powerful LLMs. Together, they confront the pe technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Low-Code AI: From Requirements to Apps in Minutes | OutSystems' Rodrigo Coutinho 2 2 19 19 Low-Code AI: From Requirements to Apps in Minutes | OutSystems' Rodrigo Coutinho full e8971e87-1166-4803-a933-c057d0fc1dd4 https://share.transistor.fm/s/4dd2a8ce What if you could turn a requirement document into a full enterprise application in just minutes?

Rodrigo Coutinho, co-founder and AI Product Manager at OutSystems, joins hosts Conor Bronsdon and Atin Sanyal to explore this new reality of AI-driven development. Rodrigo shares insights from OutSystems' nearly 25-year journey, detailing their early adoption of AI and the development of their AI platform, Mentor. Discover how the pairing of AI and low-code empowers developers, accelerates the creation of enterprise applications, and shortens the cycle from idea to deployment.

But this newfound speed brings its own set of challenges. The discussion addresses the hurdles of managing AI-generated code, contrasting experiences with traditional versus low-code approaches. Learn why a dev's focus pivots from syntax to strategy, pinpointing human creativity and ideation as the crucial limiter in today's development lifecycle.


Chapters

00:00 Welcoming Rodrigo Coutinho of OutSystems

01:30 OutSystems' Early AI Journey (Pre-LLM)

03:30 The LLM Revolution & OutSystems Mentor Emerges

07:30 The Critical Need for Validating AI-Generated Apps

12:00 The Shifting Role of the Modern Developer

13:30 Quality Control & Accountability in the AI Era

16:00 Low-Code's Edge in AI Validation

18:30 OutSystems Mentor: A Deeper Look

23:30 Choosing the Right AI Models (In-House vs Public)

27:30 Future Opportunities: Speed, Experimentation & Multimodal AI

37:00 The Use Case Hurdle & Final Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website www.outsystems.com

OutSystems Mentor

LinkedIn Rodrigo Sousa Coutinho


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> What if you could turn a requirement document into a full enterprise application in just minutes?

Rodrigo Coutinho, co-founder and AI Product Manager at OutSystems, joins hosts Conor Bronsdon and Atin Sanyal to explore this new reality of AI-driven development. Rodrigo shares insights from OutSystems' nearly 25-year journey, detailing their early adoption of AI and the development of their AI platform, Mentor. Discover how the pairing of AI and low-code empowers developers, accelerates the creation of enterprise applications, and shortens the cycle from idea to deployment.

But this newfound speed brings its own set of challenges. The discussion addresses the hurdles of managing AI-generated code, contrasting experiences with traditional versus low-code approaches. Learn why a dev's focus pivots from syntax to strategy, pinpointing human creativity and ideation as the crucial limiter in today's development lifecycle.


Chapters

00:00 Welcoming Rodrigo Coutinho of OutSystems

01:30 OutSystems' Early AI Journey (Pre-LLM)

03:30 The LLM Revolution & OutSystems Mentor Emerges

07:30 The Critical Need for Validating AI-Generated Apps

12:00 The Shifting Role of the Modern Developer

13:30 Quality Control & Accountability in the AI Era

16:00 Low-Code's Edge in AI Validation

18:30 OutSystems Mentor: A Deeper Look

23:30 Choosing the Right AI Models (In-House vs Public)

27:30 Future Opportunities: Speed, Experimentation & Multimodal AI

37:00 The Use Case Hurdle & Final Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Website www.outsystems.com

OutSystems Mentor

LinkedIn Rodrigo Sousa Coutinho


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Wed, 16 Apr 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2417 What if you could turn a requirement document into a full enterprise application in just minutes? Rodrigo Coutinho, co-founder and AI Product Manager at OutSystems, joins hosts Conor Bronsdon and Atin Sanyal to explore this new reality of AI-driven development. Rodrigo shares insights from OutSystems' nearly 25-year journey, detailing their early adoption of AI and the development of their AI platform, Mentor. What if you could turn a requirement document into a full enterprise application in just minutes? Rodrigo Coutinho, co-founder and AI Product Manager at OutSystems, joins hosts Conor Bronsdon and Atin Sanyal to explore this new reality of AI-driven develo technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI Won't Solve Your Toughest Engineering Problems | Honeycomb’s Charity Majors 2 2 18 18 AI Won't Solve Your Toughest Engineering Problems | Honeycomb’s Charity Majors full 0b276eb0-c920-4a4d-99f9-f25ecf239a7c https://share.transistor.fm/s/901838ab Generative AI dominates the conversation, but does it actually make it easier to build, lead, and sustain high-performing engineering teams?

Host Conor Bronsdon sits down with Charity Majors, co-founder and CTO of Honeycomb (.io), and the mind behind charity.wtf. Known for her sharp insights and unfiltered opinions, Charity kicks off the discussion by expanding on her popular article: 'Generative AI is not going to build your engineering team for you.' Together, they explore how AI has altered the dynamics for engineering teams and leaders. 

The discussion navigates the complex dynamics of hiring in an AI-enabled era, challenging the "senior-only" trend and championing the vital role of junior engineers in creating learning organizations. Charity also explains why writing code is often the "easy part" compared to the full lifecycle of owning and operating systems, a challenge amplified by AI-generated code. 

Finally, Conor and Charity discuss the risk of "cognitive decay" from over-reliance on AI tools and why fostering deep system understanding remains paramount for engineers and leaders.


Chapters

00:00 Introduction and Guest Welcome

01:51 Generative AI and Engineering Teams

02:26 The Writing Process and Inspiration

03:49 AI's Impact on Hiring and Team Building

05:30 Embracing AI and Automation

07:43 The Role of Junior Engineers

09:33 Building Effective Engineering Teams

17:01 Future of AI in Code Generation

20:07 High Performing Engineering Teams

21:48 Evolving Expectations for Engineering Managers

22:41 Cognitive Decay

25:00 Feedback Loops in Software Systems

26:56 Hiring for Potential vs. Experience

29:17 The Future of Observability

39:50 Closing Thoughts and Advice for Engineers



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Follow Charity: charity.wtf

Learn more about Honeycomb: www.honeycomb.io

Read: Generative AI is not going to build your engineering team for you


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Generative AI dominates the conversation, but does it actually make it easier to build, lead, and sustain high-performing engineering teams?

Host Conor Bronsdon sits down with Charity Majors, co-founder and CTO of Honeycomb (.io), and the mind behind charity.wtf. Known for her sharp insights and unfiltered opinions, Charity kicks off the discussion by expanding on her popular article: 'Generative AI is not going to build your engineering team for you.' Together, they explore how AI has altered the dynamics for engineering teams and leaders. 

The discussion navigates the complex dynamics of hiring in an AI-enabled era, challenging the "senior-only" trend and championing the vital role of junior engineers in creating learning organizations. Charity also explains why writing code is often the "easy part" compared to the full lifecycle of owning and operating systems, a challenge amplified by AI-generated code. 

Finally, Conor and Charity discuss the risk of "cognitive decay" from over-reliance on AI tools and why fostering deep system understanding remains paramount for engineers and leaders.


Chapters

00:00 Introduction and Guest Welcome

01:51 Generative AI and Engineering Teams

02:26 The Writing Process and Inspiration

03:49 AI's Impact on Hiring and Team Building

05:30 Embracing AI and Automation

07:43 The Role of Junior Engineers

09:33 Building Effective Engineering Teams

17:01 Future of AI in Code Generation

20:07 High Performing Engineering Teams

21:48 Evolving Expectations for Engineering Managers

22:41 Cognitive Decay

25:00 Feedback Loops in Software Systems

26:56 Hiring for Potential vs. Experience

29:17 The Future of Observability

39:50 Closing Thoughts and Advice for Engineers



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Follow Charity: charity.wtf

Learn more about Honeycomb: www.honeycomb.io

Read: Generative AI is not going to build your engineering team for you


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Wed, 09 Apr 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2509 Generative AI dominates the conversation, but does it actually make it easier to build, lead, and sustain high-performing engineering teams? Host Conor Bronsdon sits down with Charity Majors, co-founder and CTO of Honeycomb (.io), and the mind behind charity.wtf. Known for her sharp insights and unfiltered opinions, Charity kicks off the discussion by expanding on her popular article: 'Generative AI is not going to build your engineering team for you.' Generative AI dominates the conversation, but does it actually make it easier to build, lead, and sustain high-performing engineering teams? Host Conor Bronsdon sits down with Charity Majors, co-founder and CTO of Honeycomb (.io), and the mind behind char technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Inside IBM's watsonx: Building Enterprise AI That Ships | Dr. Maryam Ashoori 2 2 17 17 Inside IBM's watsonx: Building Enterprise AI That Ships | Dr. Maryam Ashoori full 63603797-0f3e-4c02-a95c-1c8393ddff6e https://share.transistor.fm/s/4c6b9e54 Building trustworthy, scalable AI isn't just about models; it's about navigating a complex ecosystem of tools and regulations. 

Join hosts Conor Bronsdon and Atindriyo Sanyal as they explore these challenges with Dr. Maryam Ashoori, Head of Product for watsonx AI at IBM. To meet these challenges, Maryam explains how watsonx simplifies the AI stack, automates pipelines, and empowers enterprises to scale their AI operations while optimizing costs rapidly.

Maryam also explores IBM's strategy for leveraging open-source and commercial models, enabling the potential of agentic systems. Plus, she shares insights from a recent survey of 1,000 developers, revealing key takeaways about the current landscape for enterprise AI implementation, and what results mean for both developers and the enterprises they support.


Chapters

00:00 Introducing Dr. Maryam Ashoori

01:13 Overview of IBM's AI Strategy

01:47 Enterprise AI Challenges and Solutions

04:40 IBM's Approach to AI Models and Tooling

09:52 Simplifying the AI Stack

12:20 Challenges in Agentic AI

15:55 Importance of Data Management and Lineage

21:11 IBM's Strategy for Gen AI Products

23:43 Scaling Challenges with Agents

27:40 Effective Agent Evaluation Systems

35:18 Gaps and Opportunities in AI Tooling

41:35 Success Stories with watsonx

44:00 Closing Remarks



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

watsonx.ai


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Building trustworthy, scalable AI isn't just about models; it's about navigating a complex ecosystem of tools and regulations. 

Join hosts Conor Bronsdon and Atindriyo Sanyal as they explore these challenges with Dr. Maryam Ashoori, Head of Product for watsonx AI at IBM. To meet these challenges, Maryam explains how watsonx simplifies the AI stack, automates pipelines, and empowers enterprises to scale their AI operations while optimizing costs rapidly.

Maryam also explores IBM's strategy for leveraging open-source and commercial models, enabling the potential of agentic systems. Plus, she shares insights from a recent survey of 1,000 developers, revealing key takeaways about the current landscape for enterprise AI implementation, and what results mean for both developers and the enterprises they support.


Chapters

00:00 Introducing Dr. Maryam Ashoori

01:13 Overview of IBM's AI Strategy

01:47 Enterprise AI Challenges and Solutions

04:40 IBM's Approach to AI Models and Tooling

09:52 Simplifying the AI Stack

12:20 Challenges in Agentic AI

15:55 Importance of Data Management and Lineage

21:11 IBM's Strategy for Gen AI Products

23:43 Scaling Challenges with Agents

27:40 Effective Agent Evaluation Systems

35:18 Gaps and Opportunities in AI Tooling

41:35 Success Stories with watsonx

44:00 Closing Remarks



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

watsonx.ai


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Wed, 02 Apr 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2710 Building trustworthy, scalable AI isn't just about models; it's about navigating a complex ecosystem of tools and regulations. Join hosts Conor Bronsdon and Atindriyo Sanyal as they explore these challenges with Dr. Maryam Ashoori, Head of Product for watsonx AI at IBM. To meet these challenges, Maryam explains how watsonx simplifies the AI stack, automates pipelines, and empowers enterprises to scale their AI operations while optimizing costs rapidly. Building trustworthy, scalable AI isn't just about models; it's about navigating a complex ecosystem of tools and regulations. Join hosts Conor Bronsdon and Atindriyo Sanyal as they explore these challenges with Dr. Maryam Ashoori, Head of Product for wat technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Information Symmetry: DevRev's Bet on AI-Driven Enterprise Decisions | Manoj Agarwal 2 2 16 16 Information Symmetry: DevRev's Bet on AI-Driven Enterprise Decisions | Manoj Agarwal full 3446ebcd-e828-4a87-8235-ea4359bd3d57 https://share.transistor.fm/s/1cbcff3e What if everyone in your organization had equal information at all times? Would meetings even exist? 

This week, we dive into the concept of information symmetry with Manoj Agarwal, co-founder and president of DevRev. Manoj, along with hosts Conor Bronsdon and Yash Sheth, explores how DevRev is connecting data, personalizing schemas, and automating complex tasks, offering a glimpse into the next generation of AI-driven workflows. This is revolutionizing enterprise data and decision-making by breaking down the silos that create information asymmetry.

Learn how AI is reshaping business outcomes and collaboration, moving us closer to a world where everyone has the information they need.


Chapters:

00:00 Welcome to Chain of Thought

00:57 Information Symmetry in Enterprises

02:03 Challenges of Decision Making

03:41 Recency Bias and Product Management

04:58 Data Silos and Information Waste

05:23 Structured vs. Unstructured Data

06:04 Collaboration and Data Retrieval Issues

08:17 DevRev's Approach to AI and Data Integration

09:23 Building a Business-Centric Knowledge Graph

10:00 Conversational AI and Automation

12:57 Agentic Interactions and Skills Programming

20:05 Multi-Agent Systems and Future Vision

21:25 Challenges in Multi-Agent Communication

25:10 Data Cleanliness and Governance

28:14 Trust and Reliability in AI Systems

36:58 Conclusion and Future Outlook



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

devrev.ai

DevRev University

LinkedIn Manoj Agarwal


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠


]]> What if everyone in your organization had equal information at all times? Would meetings even exist? 

This week, we dive into the concept of information symmetry with Manoj Agarwal, co-founder and president of DevRev. Manoj, along with hosts Conor Bronsdon and Yash Sheth, explores how DevRev is connecting data, personalizing schemas, and automating complex tasks, offering a glimpse into the next generation of AI-driven workflows. This is revolutionizing enterprise data and decision-making by breaking down the silos that create information asymmetry.

Learn how AI is reshaping business outcomes and collaboration, moving us closer to a world where everyone has the information they need.


Chapters:

00:00 Welcome to Chain of Thought

00:57 Information Symmetry in Enterprises

02:03 Challenges of Decision Making

03:41 Recency Bias and Product Management

04:58 Data Silos and Information Waste

05:23 Structured vs. Unstructured Data

06:04 Collaboration and Data Retrieval Issues

08:17 DevRev's Approach to AI and Data Integration

09:23 Building a Business-Centric Knowledge Graph

10:00 Conversational AI and Automation

12:57 Agentic Interactions and Skills Programming

20:05 Multi-Agent Systems and Future Vision

21:25 Challenges in Multi-Agent Communication

25:10 Data Cleanliness and Governance

28:14 Trust and Reliability in AI Systems

36:58 Conclusion and Future Outlook



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

devrev.ai

DevRev University

LinkedIn Manoj Agarwal


Check out Galileo

⁠⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠


]]> Wed, 26 Mar 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2455 What if everyone in your organization had equal information at all times? Would meetings even exist? This week, we dive into the concept of information symmetry with Manoj Agarwal, co-founder and president of DevRev. Manoj, along with hosts Conor Bronsdon and Yash Sheth, explores how DevRev is connecting data, personalizing schemas, and automating complex tasks, offering a glimpse into the next generation of AI-driven workflows. What if everyone in your organization had equal information at all times? Would meetings even exist? This week, we dive into the concept of information symmetry with Manoj Agarwal, co-founder and president of DevRev. Manoj, along with hosts Conor Bronsdon technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The Agent Bubble Debate | Spot AI's Kelly Vaughn 2 2 15 15 The Agent Bubble Debate | Spot AI's Kelly Vaughn full 11771907-3491-4a8e-b630-e16063536170 https://share.transistor.fm/s/3a5c548d Is the agentic AI bubble about to burst? Kelly Vaughn, Director of Engineering at Spot AI, questions whether the agent craze is overpromising potential and leading startups down a path of unsustainable expectations.

Never one to shy away from a hot take, Kelly joins host Conor Bronsdon for a pragmatic look at AI, discussing the differences between building AI-enabled and traditional software, why replacing humans with AI teams will backfire (looking at you customer service), and the proliferation of AI tools.

Kelly also shares insights on constructing AI teams, navigating data governance, and building user trust while avoiding common startup pitfalls.


Chapters:

00:00 Introduction and Guest Welcome

01:13 Is Agentic AI a Bubble?

02:40 Startup Challenges and Market Noise

11:02 Building AI Products vs. Traditional Software

17:31 Ethical Implications and Governance

19:48 Constructing AI-Enabled Teams

22:07 AI Tooling and Productivity

26:00 Questioning Productivity Claims

32:28 Conclusion and Final Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Kelly’s Newsletter The Modern Leader

LinkedIn Kelly Vaughn

Check out Galileo

⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠


]]> Is the agentic AI bubble about to burst? Kelly Vaughn, Director of Engineering at Spot AI, questions whether the agent craze is overpromising potential and leading startups down a path of unsustainable expectations.

Never one to shy away from a hot take, Kelly joins host Conor Bronsdon for a pragmatic look at AI, discussing the differences between building AI-enabled and traditional software, why replacing humans with AI teams will backfire (looking at you customer service), and the proliferation of AI tools.

Kelly also shares insights on constructing AI teams, navigating data governance, and building user trust while avoiding common startup pitfalls.


Chapters:

00:00 Introduction and Guest Welcome

01:13 Is Agentic AI a Bubble?

02:40 Startup Challenges and Market Noise

11:02 Building AI Products vs. Traditional Software

17:31 Ethical Implications and Governance

19:48 Constructing AI-Enabled Teams

22:07 AI Tooling and Productivity

26:00 Questioning Productivity Claims

32:28 Conclusion and Final Thoughts



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

Kelly’s Newsletter The Modern Leader

LinkedIn Kelly Vaughn

Check out Galileo

⁠⁠⁠⁠⁠⁠Try Galileo⁠⁠


]]> Wed, 19 Mar 2025 03:00:00 -0700 Conor Bronsdon Conor Bronsdon 2116 Is the agentic AI bubble about to burst? Kelly Vaughn, Director of Engineering at Spot AI, questions whether the agent craze is overpromising potential and leading startups down a path of unsustainable expectations. Never one to shy away from a hot take, Kelly joins host Conor Bronsdon for a pragmatic look at AI, discussing the differences between building AI-enabled and traditional software, why replacing humans with AI teams will backfire (looking at you customer service), and the proliferation of AI tools. Is the agentic AI bubble about to burst? Kelly Vaughn, Director of Engineering at Spot AI, questions whether the agent craze is overpromising potential and leading startups down a path of unsustainable expectations. Never one to shy away from a hot take, technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Using AI to Modernize Your Legacy Applications | MongoDB’s Rachelle Palmer 2 2 14 14 Using AI to Modernize Your Legacy Applications | MongoDB’s Rachelle Palmer full f0b82592-a343-41e4-aeca-4e42288a8aad https://share.transistor.fm/s/37ea62eb Imagine cutting your legacy code modernization timeline from years to months. It’s no longer science fiction and this week’s guest is here to tell us how. 

Rachelle Palmer, Director of Product Management at MongoDB, joins hosts Conor Bronsdon and Atindriyo Sanyal, for a discussion on the groundbreaking ways AI is modernizing legacy applications. 

At MongoDB, Rachelle's forward-deployed AI engineering team is tackling the challenge of transforming complex, outdated codebases, freeing developers from technical debt. She details how LLMs are automating tasks like improving documentation, test generation, and even business logic conversion, dramatically reducing modernization timelines from years to months. What once demanded teams of dozens can now be achieved with a small, highly efficient team.


Chapters:

00:00 Introduction and Host Welcome

00:58 Challenges in Modernizing Legacy Applications

02:52 Real-World Examples of Code Modernization

04:00 The Role of LLMs in Code Modernization

08:01 Measuring Success in AI-Powered Modernization

12:28 The Future of AI in Engineering

16:17 Evaluating Modernization Success

21:12 Returning to Your Startup Roots

29:07 Forward Deployed AI Engineers

35:36 Importance of Academic Research in AI

42:10 Conclusion and Farewell



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

⁠Rachelle PalmerMongoDBApplication Modernization Factory

Check out Galileo

⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Imagine cutting your legacy code modernization timeline from years to months. It’s no longer science fiction and this week’s guest is here to tell us how. 

Rachelle Palmer, Director of Product Management at MongoDB, joins hosts Conor Bronsdon and Atindriyo Sanyal, for a discussion on the groundbreaking ways AI is modernizing legacy applications. 

At MongoDB, Rachelle's forward-deployed AI engineering team is tackling the challenge of transforming complex, outdated codebases, freeing developers from technical debt. She details how LLMs are automating tasks like improving documentation, test generation, and even business logic conversion, dramatically reducing modernization timelines from years to months. What once demanded teams of dozens can now be achieved with a small, highly efficient team.


Chapters:

00:00 Introduction and Host Welcome

00:58 Challenges in Modernizing Legacy Applications

02:52 Real-World Examples of Code Modernization

04:00 The Role of LLMs in Code Modernization

08:01 Measuring Success in AI-Powered Modernization

12:28 The Future of AI in Engineering

16:17 Evaluating Modernization Success

21:12 Returning to Your Startup Roots

29:07 Forward Deployed AI Engineers

35:36 Importance of Academic Research in AI

42:10 Conclusion and Farewell



Connect with Chain of Thought host Conor Bronsdon:

Follow Today's Guest(s)

⁠Rachelle PalmerMongoDBApplication Modernization Factory

Check out Galileo

⁠⁠⁠⁠⁠Try Galileo⁠⁠

]]> Wed, 12 Mar 2025 03:35:00 -0700 Conor Bronsdon Conor Bronsdon 2618 Imagine cutting your legacy code modernization timeline from years to months. It’s no longer science fiction and this week’s guest is here to tell us how. Rachelle Palmer, Director of Product Management at MongoDB, joins hosts Conor Bronsdon and Atindriyo Sanyal, for a discussion on the groundbreaking ways AI is modernizing legacy applications. At MongoDB, Rachelle's forward-deployed AI engineering team is tackling the challenge of transforming complex, outdated codebases, freeing developers from technical debt. Imagine cutting your legacy code modernization timeline from years to months. It’s no longer science fiction and this week’s guest is here to tell us how. Rachelle Palmer, Director of Product Management at MongoDB, joins hosts Conor Bronsdon and Atindriyo technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI in 2025: Agents & The Rise of Evaluation-Driven Development 2 2 13 13 AI in 2025: Agents & The Rise of Evaluation-Driven Development full ad96ff54-838e-4d35-9c05-4e5a2da8a5b7 https://share.transistor.fm/s/4677b88c This week, we're sharing a special episode courtesy of 'Dev Interrupted.' Our co-host, Galileo CEO Vikram Chatterji, recently joined theDev Interrupted team for an engaging discussion on AI strategy. We were so impressed by the conversation that we wanted to share it with our audience, and they were kind enough to let us. We hope you enjoy it!

From Dev Interrupted:

"Vikram Chatterji joins Dev Interrupted’s Andrew Zigler to discuss how engineering leaders can future-proof their AI strategy and navigate an emerging dilemma: the pressure to adopt AI to stay competitive, while justifying AI spending and avoiding risky investments.

To accomplish this, Vikram emphasizes the importance of establishing clear evaluation frameworks, prioritizing AI use cases based on business needs and understanding your company's unique cultural context when deploying AI."


Chapters:

00:00 Introduction and Special Announcement

01:14 Welcome to Dev Interrupted

01:42 Challenges in AI Adoption

03:16 Balancing Business Needs and AI

06:15 Crawl, Walk, Run Approach

10:52 Building Trust and Prototyping

13:07 AI Agents as Smart Routers

13:50 Galileo's Role in AI Development

16:25 Evaluating AI Systems

25:36 Skills for Engineering Leaders

27:35 Conclusion



Follow Dev Interrupted

Podcast

Substack

LinkedIn


Follow Dev Interrupted Hosts

Andrew

Ben


Connect with Chain of Thought host Conor Bronsdon:

Check out Galileo

⁠⁠⁠⁠Try Galileo⁠⁠

]]> This week, we're sharing a special episode courtesy of 'Dev Interrupted.' Our co-host, Galileo CEO Vikram Chatterji, recently joined theDev Interrupted team for an engaging discussion on AI strategy. We were so impressed by the conversation that we wanted to share it with our audience, and they were kind enough to let us. We hope you enjoy it!

From Dev Interrupted:

"Vikram Chatterji joins Dev Interrupted’s Andrew Zigler to discuss how engineering leaders can future-proof their AI strategy and navigate an emerging dilemma: the pressure to adopt AI to stay competitive, while justifying AI spending and avoiding risky investments.

To accomplish this, Vikram emphasizes the importance of establishing clear evaluation frameworks, prioritizing AI use cases based on business needs and understanding your company's unique cultural context when deploying AI."


Chapters:

00:00 Introduction and Special Announcement

01:14 Welcome to Dev Interrupted

01:42 Challenges in AI Adoption

03:16 Balancing Business Needs and AI

06:15 Crawl, Walk, Run Approach

10:52 Building Trust and Prototyping

13:07 AI Agents as Smart Routers

13:50 Galileo's Role in AI Development

16:25 Evaluating AI Systems

25:36 Skills for Engineering Leaders

27:35 Conclusion



Follow Dev Interrupted

Podcast

Substack

LinkedIn


Follow Dev Interrupted Hosts

Andrew

Ben


Connect with Chain of Thought host Conor Bronsdon:

Check out Galileo

⁠⁠⁠⁠Try Galileo⁠⁠

]]> Wed, 05 Mar 2025 03:30:00 -0800 Conor Bronsdon Conor Bronsdon 1757 This week, we're sharing a special episode courtesy of 'Dev Interrupted.' Our co-host, Galileo CEO Vikram Chatterji, recently joined theDev Interrupted team for an engaging discussion on AI strategy. We were so impressed by the conversation that we wanted to share it with our audience, and they were kind enough to let us. We hope you enjoy it! Chain of Thought is hosted by Conor Bronsdon. This week, we're sharing a special episode courtesy of 'Dev Interrupted.' Our co-host, Galileo CEO Vikram Chatterji, recently joined theDev Interrupted team for an engaging discussion on AI strategy. We were so impressed by the conversation that we wanted technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No The Making of Gemini 2.0: DeepMind's Approach to AI Development and Deployment | Logan Kilpatrick 2 2 12 12 The Making of Gemini 2.0: DeepMind's Approach to AI Development and Deployment | Logan Kilpatrick full e8078d62-00b1-4d31-86c6-8c32bdcdef51 https://share.transistor.fm/s/eb9914e9 Google’s strength in AI has often seemed to get lost in the midst of OpenAI announcements or DeepSeek fervor - yet Gemini 2.0 is more than good for many tasks; it’s the model to beat - and we have the research to back it up. 

This week, Logan Kilpatrick, senior product manager at Google DeepMind, joins us to discuss Gemini’s creation story, its emergence as the premiere model in the AI race, and why the launch of Gemini 2.0 is great news for developers.

During the conversation Conor and Logan explore the exciting world of multimodal AI, Gemini's strengths in agentic use cases, and its unique approach to function calling, compositional function calling, and the seamless integration of tools like search and code execution.

They also chat about Logan’s vision for a future where AI interacts with the world more naturally, offering a view of the potential of vision-first AI agents, and why Google's hardware advantage is enabling Gemini's impressive performance and long context capabilities. 

Follow along with the discussion using Galileo’s AI Agent Leaderboard:https://huggingface.co/spaces/galileo-ai/agent-leaderboard


Chapters:00:00 DeepMind's Role in Gemini's Development

03:49 Gemini 2.0 Updates and Developer Highlights

06:08 Agentic Use Cases and Function Calling

11:29 Multimodal Capabilities

16:15 Putting AI in Production

21:06 Gemini's Differentiation and Hardware

31:22 Future Vision for Gemini and G Suite Integration

35:23 Gemini for Developers

39:02 Conclusion and Farewell



Follow Logan

Twitter:@OfficialLoganK

LinkedIn:https://www.linkedin.com/in/logankilpatrick/


Connect with Chain of Thought host Conor Bronsdon:

Show Notes

Try Gemini for yourself:gemini.google.com

Gemini for Developers:aistudio.google.com


Check out Galileo

⁠⁠Try Galileo⁠⁠

]]> Google’s strength in AI has often seemed to get lost in the midst of OpenAI announcements or DeepSeek fervor - yet Gemini 2.0 is more than good for many tasks; it’s the model to beat - and we have the research to back it up. 

This week, Logan Kilpatrick, senior product manager at Google DeepMind, joins us to discuss Gemini’s creation story, its emergence as the premiere model in the AI race, and why the launch of Gemini 2.0 is great news for developers.

During the conversation Conor and Logan explore the exciting world of multimodal AI, Gemini's strengths in agentic use cases, and its unique approach to function calling, compositional function calling, and the seamless integration of tools like search and code execution.

They also chat about Logan’s vision for a future where AI interacts with the world more naturally, offering a view of the potential of vision-first AI agents, and why Google's hardware advantage is enabling Gemini's impressive performance and long context capabilities. 

Follow along with the discussion using Galileo’s AI Agent Leaderboard:https://huggingface.co/spaces/galileo-ai/agent-leaderboard


Chapters:00:00 DeepMind's Role in Gemini's Development

03:49 Gemini 2.0 Updates and Developer Highlights

06:08 Agentic Use Cases and Function Calling

11:29 Multimodal Capabilities

16:15 Putting AI in Production

21:06 Gemini's Differentiation and Hardware

31:22 Future Vision for Gemini and G Suite Integration

35:23 Gemini for Developers

39:02 Conclusion and Farewell



Follow Logan

Twitter:@OfficialLoganK

LinkedIn:https://www.linkedin.com/in/logankilpatrick/


Connect with Chain of Thought host Conor Bronsdon:

Show Notes

Try Gemini for yourself:gemini.google.com

Gemini for Developers:aistudio.google.com


Check out Galileo

⁠⁠Try Galileo⁠⁠

]]> Wed, 12 Feb 2025 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2433 Google’s strength in AI has often seemed to get lost in the midst of OpenAI announcements or DeepSeek fervor - yet Gemini 2.0 is more than good for many tasks; it’s the model to beat - and we have the research to back it up. This week, Logan Kilpatrick, senior product manager at Google DeepMind, joins us to discuss Gemini’s creation story, its emergence as the premiere model in the AI race, and why the launch of Gemini 2.0 is great news for developers. Chain of Thought is hosted by Conor Bronsdon. Google’s strength in AI has often seemed to get lost in the midst of OpenAI announcements or DeepSeek fervor - yet Gemini 2.0 is more than good for many tasks; it’s the model to beat - and we have the research to back it up. This week, Logan Kilpatrick, s technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No How DeepSeek Changed the AI Race Overnight 2 2 11 11 How DeepSeek Changed the AI Race Overnight full 158ee3a1-dddf-4534-ba5e-f2694634c17f https://share.transistor.fm/s/4bf30e72 This week, hosts Conor Bronsdon and Atindriyo Sanyal discuss the fallout from DeepSeek's groundbreaking R1 model, its impact on the open-source AI landscape, and how its release will impact model development moving forward. They also discuss what effect (if any) export controls have had on AI innovation and whether we’re witnessing the rise of “Agents as a Service”. 

To tackle the increasing complexity of agentic systems, Conor and Atin highlight the need for robust evaluation frameworks, discussing the challenges of measuring agent performance, and how the recent launch of Galileo's agentic evaluations are empowering developers to build safer and more effective AI agents.


Chapters:00:00 Introduction

02:09 DeepSeek's Impact and Innovations

03:43 Open Source AI and Industry Implications

13:44 Export Controls and Global AI Competition

18:55 Software as a Service

19:29 Agentic Evaluations

25:14 Metrics for Success

31:34 Conclusion and Farewell


Check out Galileo

Try Galileo


Connect with Chain of Thought host Conor Bronsdon:

Show Notes

On DeepSeek and Export Controls

Introducing Agentic Evaluations

]]> This week, hosts Conor Bronsdon and Atindriyo Sanyal discuss the fallout from DeepSeek's groundbreaking R1 model, its impact on the open-source AI landscape, and how its release will impact model development moving forward. They also discuss what effect (if any) export controls have had on AI innovation and whether we’re witnessing the rise of “Agents as a Service”. 

To tackle the increasing complexity of agentic systems, Conor and Atin highlight the need for robust evaluation frameworks, discussing the challenges of measuring agent performance, and how the recent launch of Galileo's agentic evaluations are empowering developers to build safer and more effective AI agents.


Chapters:00:00 Introduction

02:09 DeepSeek's Impact and Innovations

03:43 Open Source AI and Industry Implications

13:44 Export Controls and Global AI Competition

18:55 Software as a Service

19:29 Agentic Evaluations

25:14 Metrics for Success

31:34 Conclusion and Farewell


Check out Galileo

Try Galileo


Connect with Chain of Thought host Conor Bronsdon:

Show Notes

On DeepSeek and Export Controls

Introducing Agentic Evaluations

]]> Wed, 05 Feb 2025 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 1962 This week, hosts Conor Bronsdon and Atindriyo Sanyal discuss the fallout from DeepSeek's groundbreaking R1 model, its impact on the open-source AI landscape, and how its release will impact model development moving forward. They also discuss what effect (if any) export controls have had on AI innovation and whether we’re witnessing the rise of “Agents as a Service”. This week, hosts Conor Bronsdon and Atindriyo Sanyal discuss the fallout from DeepSeek's groundbreaking R1 model, its impact on the open-source AI landscape, and how its release will impact model development moving forward. They also discuss what effect ( technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI, Open Source & Developer Safety | Block’s Rizel Scarlett 2 2 10 10 AI, Open Source & Developer Safety | Block’s Rizel Scarlett full c97eb75a-9048-408c-bcd4-638ec4df2068 https://share.transistor.fm/s/8b2c2f9d As DeepSeek so aptly demonstrated, AI doesn’t need to be closed source to be successful.

This week, Rizel Scarlett, a Staff Developer Advocate at Block, joins Conor Bronsdon to discuss the intersections between AI, open source, and developer advocacy. Rizel shares her journey into the world of AI, her passion for empowering developers, and her work on Block's new AI initiative, Goose, an on-machine developer agent designed to automate engineering tasks and enhance productivity.

Conor and Rizel also explore how AI can enable psychological safety, especially for junior developers. Building on this theme of community, they also dive into topics such as responsible AI development, ethical considerations in AI, and the impact of community involvement when building open source developer tools.

Chapters:00:00 Rizel's Role at Block02:41 Introducing Goose: Block's AI Agent06:30 Psychological Safety and AI for Developers11:24 AI Tools and Team Dynamics17:28 Open Source AI and Community Involvement25:29 Future of AI in Developer Communities27:47 Responsible and Ethical Use of AI31:34 Conclusion

Rizel Scarlett : https://www.linkedin.com/in/rizel-bobb-semple/Rizel's website: https://blackgirlbytes.dev/


Connect with Chain of Thought host Conor Bronsdon:

Show Notes

Learn more about Goose: https://block.github.io/goose/

]]> As DeepSeek so aptly demonstrated, AI doesn’t need to be closed source to be successful.

This week, Rizel Scarlett, a Staff Developer Advocate at Block, joins Conor Bronsdon to discuss the intersections between AI, open source, and developer advocacy. Rizel shares her journey into the world of AI, her passion for empowering developers, and her work on Block's new AI initiative, Goose, an on-machine developer agent designed to automate engineering tasks and enhance productivity.

Conor and Rizel also explore how AI can enable psychological safety, especially for junior developers. Building on this theme of community, they also dive into topics such as responsible AI development, ethical considerations in AI, and the impact of community involvement when building open source developer tools.

Chapters:00:00 Rizel's Role at Block02:41 Introducing Goose: Block's AI Agent06:30 Psychological Safety and AI for Developers11:24 AI Tools and Team Dynamics17:28 Open Source AI and Community Involvement25:29 Future of AI in Developer Communities27:47 Responsible and Ethical Use of AI31:34 Conclusion

Rizel Scarlett : https://www.linkedin.com/in/rizel-bobb-semple/Rizel's website: https://blackgirlbytes.dev/


Connect with Chain of Thought host Conor Bronsdon:

Show Notes

Learn more about Goose: https://block.github.io/goose/

]]> Wed, 29 Jan 2025 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2024 As DeepSeek so aptly demonstrated, AI doesn’t need to be closed source to be successful. This week, Rizel Scarlett, a Staff Developer Advocate at Block, joins host Conor Bronsdon to discuss the intersections between AI, open source, and developer advocacy. Rizel shares her journey into the world of AI, her passion for empowering developers, and her work on Block's new AI initiative, Goose , an on-machine developer agent designed to automate engineering tasks and enhance productivity. As DeepSeek so aptly demonstrated, AI doesn’t need to be closed source to be successful. This week, Rizel Scarlett, a Staff Developer Advocate at Block, joins host Conor Bronsdon to discuss the intersections between AI, open source, and developer advocacy technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI in 2025: Agents & The Rise of Evaluation Driven Development 2 2 9 9 AI in 2025: Agents & The Rise of Evaluation Driven Development full 938deefd-aed5-47a0-b91e-b160f44ce6e1 https://share.transistor.fm/s/6c2d2358 "In the next three to five years, every piece of software that is built on this planet will have some sort of AI baked into it." - Atin Sanyal

Chain of Thought is back for its second season, and this episode dives headfirst into the possibilities AI holds for 2025 and beyond. Join Conor Bronson as he chats with Galileo co-founders Yash Sheth (COO) and Atindriyo Sanyal (CTO) about major trends to look for this year. These include AI finding its product "tool stack" fit, generation latency decreasing, AI agents, their potential to revolutionize code generation and other industries, and the crucial role of robust evaluation tools in ensuring the responsible and effective deployment of these agents.

Yash and Atin also highlight Galileo's focus on building trust and security in AI applications through scalable evaluation intelligence. They emphasize the importance of quantifying application behavior, enforcing metrics in production, and adapting to the evolving needs of AI development.

Finally, they discuss Galileo's vision for the future and their active pursuit of partnerships in 2025 to contribute to a more reliable and trustworthy AI ecosystem.


Chapters:00:00 AI Trends and Predictions for 2025

02:55 Advancements in LLMs and Code Generation

05:16 Challenges and Opportunities in AI Development

10:40 Evaluating AI Agents and Applications

16:07 Building Evaluation Intelligence

23:41 Research Opportunities

29:50 Advice for Leveraging AI in 2025

32:00 Closing Remarks


Connect with Chain of Thought host Conor Bronsdon:

Show Notes:

]]> "In the next three to five years, every piece of software that is built on this planet will have some sort of AI baked into it." - Atin Sanyal

Chain of Thought is back for its second season, and this episode dives headfirst into the possibilities AI holds for 2025 and beyond. Join Conor Bronson as he chats with Galileo co-founders Yash Sheth (COO) and Atindriyo Sanyal (CTO) about major trends to look for this year. These include AI finding its product "tool stack" fit, generation latency decreasing, AI agents, their potential to revolutionize code generation and other industries, and the crucial role of robust evaluation tools in ensuring the responsible and effective deployment of these agents.

Yash and Atin also highlight Galileo's focus on building trust and security in AI applications through scalable evaluation intelligence. They emphasize the importance of quantifying application behavior, enforcing metrics in production, and adapting to the evolving needs of AI development.

Finally, they discuss Galileo's vision for the future and their active pursuit of partnerships in 2025 to contribute to a more reliable and trustworthy AI ecosystem.


Chapters:00:00 AI Trends and Predictions for 2025

02:55 Advancements in LLMs and Code Generation

05:16 Challenges and Opportunities in AI Development

10:40 Evaluating AI Agents and Applications

16:07 Building Evaluation Intelligence

23:41 Research Opportunities

29:50 Advice for Leveraging AI in 2025

32:00 Closing Remarks


Connect with Chain of Thought host Conor Bronsdon:

Show Notes:

]]> Wed, 15 Jan 2025 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 1994 "In the next three to five years, every piece of software that is built on this planet will have some sort of AI baked into it." - Atin Sanyal Chain of Thought is back for its second season, and this episode dives headfirst into the possibilities AI holds for 2025 and beyond. Join Conor Bronson as he chats with Galileo co-founders Yash Sheth (COO) and Atindriyo Sanyal (CTO) about major trends to look for this year. Chain of Thought is hosted by Conor Bronsdon. "In the next three to five years, every piece of software that is built on this planet will have some sort of AI baked into it." - Atin Sanyal Chain of Thought is back for its second season, and this episode dives headfirst into the possibilities AI holds technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No AI Infrastructure & the Evolution of RAG | Weaviate's Bob van Luijt 2 2 8 8 AI Infrastructure & the Evolution of RAG | Weaviate's Bob van Luijt full 52c718ae-dc0c-4ab7-b766-343900aba49f https://share.transistor.fm/s/a7ef76ee "This is the time. This is the time to start building... I can't say that often enough. This is the time." - Bob van Luijt

 Join Bob van Luijt, CEO and co-founder of Weaviate as he sits down with our host Conor Bronson for the Season 2 premiere of Chain of Thought. Together, they explore the ever-evolving world of AI infrastructure and the evolution of Retrieval-Augmented Generation (RAG) architecture.

Bob's journey with Weaviate offers a compelling example of how to adapt to rapid changes in the AI landscape. He discusses the importance of understanding developer needs and building AI-native solutions, emphasizing the potential of generative feedback loops and agent architectures to revolutionize data management.

Chapters:00:00 Welcome to Season 2

1:43 The Evolution of AI Infrastructure

04:13 Navigating Rapid Changes in AI

07:39 Generative Feedback Loops and AI Native Databases

13:26 Challenges and Opportunities in AI Production

19:03 The Importance of Documentation and Developer Experience

27:13 Future Predictions and Paradigm Shifts in AI

31:17 Final Thoughts and Encouragement to Build


Follow:

Bob van Luijt: ⁠https://www.linkedin.com/in/bobvanluijt/

Weaviate: https://www.linkedin.com/company/weaviate-io/


Connect with Chain of Thought host Conor Bronsdon:

Show notes:Learn more about Weaviate: https://weaviate.io/

]]> "This is the time. This is the time to start building... I can't say that often enough. This is the time." - Bob van Luijt

 Join Bob van Luijt, CEO and co-founder of Weaviate as he sits down with our host Conor Bronson for the Season 2 premiere of Chain of Thought. Together, they explore the ever-evolving world of AI infrastructure and the evolution of Retrieval-Augmented Generation (RAG) architecture.

Bob's journey with Weaviate offers a compelling example of how to adapt to rapid changes in the AI landscape. He discusses the importance of understanding developer needs and building AI-native solutions, emphasizing the potential of generative feedback loops and agent architectures to revolutionize data management.

Chapters:00:00 Welcome to Season 2

1:43 The Evolution of AI Infrastructure

04:13 Navigating Rapid Changes in AI

07:39 Generative Feedback Loops and AI Native Databases

13:26 Challenges and Opportunities in AI Production

19:03 The Importance of Documentation and Developer Experience

27:13 Future Predictions and Paradigm Shifts in AI

31:17 Final Thoughts and Encouragement to Build


Follow:

Bob van Luijt: ⁠https://www.linkedin.com/in/bobvanluijt/

Weaviate: https://www.linkedin.com/company/weaviate-io/


Connect with Chain of Thought host Conor Bronsdon:

Show notes:Learn more about Weaviate: https://weaviate.io/

]]> Wed, 08 Jan 2025 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2123 "This is the time. This is the time to start building... I can't say that often enough. This is the time." - Bob van Luijt Join Bob van Luijt, CEO and co-founder of Weaviate as he sits down with our host Conor Bronson for the Season 2 premiere of Chain of Thought. Together, they explore the ever-evolving world of AI infrastructure and the evolution of Retrieval-Augmented Generation (RAG) architecture. Bob's journey with Weaviate offers a compelling example of how to adapt to rapid changes in the AI landscape. Chain of Thought is hosted by Conor Bronsdon. "This is the time. This is the time to start building... I can't say that often enough. This is the time." - Bob van Luijt Join Bob van Luijt, CEO and co-founder of Weaviate as he sits down with our host Conor Bronson for the Season 2 premiere of Chain of technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No Beyond Chatbots: How Twilio Uses AI to Strengthen Human Connection | Vinnie Giarrusso 1 1 7 7 Beyond Chatbots: How Twilio Uses AI to Strengthen Human Connection | Vinnie Giarrusso full b958fe84-8b59-47bf-8a9f-c5e18cbe2eb2 https://share.transistor.fm/s/4648c763 Can AI assistants actually enhance human connection?

As Season 1 of Chain of Thought comes to a close, Conor Bronsdon and Vinnie Giarrusso (Twilio) explore the transformative potential of AI assistants in the workplace.

Discover how these assistants function as "async junior digital employees," taking on specific tasks and contributing to the organizational structure. But will AI assistants ultimately replace human connection? Vinnie argues the opposite is true, suggesting that AI can liberate employees from mundane tasks, allowing them to focus on building meaningful relationships and providing personalized experiences.

This thought-provoking conversation takes a philosophical turn as Vinnie explores how AI could revolutionize education while potentially disrupting traditional mentorship roles. He shares his vision for a future where AI democratizes information and empowers individuals to personalize their learning journey. Finally, learn how Twilio and Galileo are partnering to shape the future of AI and what this collaboration means for both companies.

Chain of Thought will be taking a break for the holidays, but we'll see you back here on January 8th for the start of Season 2!

Chapters: 00:00 Twilio's AI Agent Platform

06:34 Ensuring Accuracy and Trustworthiness

09:49 Challenges and Failure Modes

17:39 Future of Fully Autonomous Agents

22:18 Human-AI Collaboration and Mentorship

31:24 Education and Democratization of Information

32:58 Partnership with Galileo

39:54 Conclusion and Season Wrap-Up


    • Follow:

    Vinnie Giarrusso: https://www.linkedin.com/in/vinniegiarrusso/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Twilio Alpha: ⁠https://twilioalpha.com

    OWASP GenAI: https://genai.owasp.org

    ]]>     Can AI assistants actually enhance human connection?

    As Season 1 of Chain of Thought comes to a close, Conor Bronsdon and Vinnie Giarrusso (Twilio) explore the transformative potential of AI assistants in the workplace.

    Discover how these assistants function as "async junior digital employees," taking on specific tasks and contributing to the organizational structure. But will AI assistants ultimately replace human connection? Vinnie argues the opposite is true, suggesting that AI can liberate employees from mundane tasks, allowing them to focus on building meaningful relationships and providing personalized experiences.

    This thought-provoking conversation takes a philosophical turn as Vinnie explores how AI could revolutionize education while potentially disrupting traditional mentorship roles. He shares his vision for a future where AI democratizes information and empowers individuals to personalize their learning journey. Finally, learn how Twilio and Galileo are partnering to shape the future of AI and what this collaboration means for both companies.

    Chain of Thought will be taking a break for the holidays, but we'll see you back here on January 8th for the start of Season 2!

    Chapters: 00:00 Twilio's AI Agent Platform

    06:34 Ensuring Accuracy and Trustworthiness

    09:49 Challenges and Failure Modes

    17:39 Future of Fully Autonomous Agents

    22:18 Human-AI Collaboration and Mentorship

    31:24 Education and Democratization of Information

    32:58 Partnership with Galileo

    39:54 Conclusion and Season Wrap-Up


    • Follow:

    Vinnie Giarrusso: https://www.linkedin.com/in/vinniegiarrusso/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Twilio Alpha: ⁠https://twilioalpha.com

    OWASP GenAI: https://genai.owasp.org

    ]]>     Wed, 18 Dec 2024 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2540 Can AI assistants actually enhance human connection? As Season 1 of Chain of Thought comes to a close, host Conor Bronsdon and Vinnie Giarrusso (Twilio) explore the transformative potential of AI assistants in the workplace. Discover how these assistants function as "async junior digital employees," taking on specific tasks and contributing to the organizational structure. But will AI assistants ultimately replace human connection? Can AI assistants actually enhance human connection? As Season 1 of Chain of Thought comes to a close, host Conor Bronsdon and Vinnie Giarrusso (Twilio) explore the transformative potential of AI assistants in the workplace. Discover how these assistants technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     The Enterprise AI Deployment Playbook | ServiceTitan, Indeed & Twilio 1 1 6 6 The Enterprise AI Deployment Playbook | ServiceTitan, Indeed & Twilio full 11ac0b90-a85d-4fd5-94e9-3354eab14167 https://share.transistor.fm/s/5fa11383 This week, a panel of experts (Mehmet Murat Ezbiderli, ServiceTitan; Grant Ledford, Indeed; and Vinnie Giarrusso, Twilio) join Atin Sanyal (CTO, Galileo) and Conor Bronsdon (Developer Awareness, Galileo) to explore the challenges and opportunities of deploying GenAI at enterprise scale in a conversation that's a wake-up call for any business leader looking to harness the power of AI.

    Together, Atin & Conor break down key considerations like performance, cost, and model selection, emphasizing the need for robust evaluation frameworks and a shift in developer mindset.

    Atin then sits down with our panel of AI engineering experts to discuss their firsthand experiences with enterprise AI, including the trade-offs of building AI systems, the evolving tools and frameworks available, and the impact these technologies are having on their organizations.

    Chapters: 00:00 Enterprise Scale Deployment

    05:17 Cost, Performance, and Model Selection

    08:59 Building and Integrating GenAI Systems

    15:26 Emerging Enterprise Use Cases

    18:12 Predictions for AI in 2025

    27:28 Panel Discussion: Deploying AI at Enterprise Scale

    31:19 Gen AI Solutions and Challenges

    33:12 Building & Deploying Traditional Infrastructure vs GenAI Infrastructure

    34:36 How to Assemble Your GenAI Stack

    40:39 Today's Best GenAI Use Cases

    48:15 Enterprise AI Trends for 2025

    50:36 Closing Remarks and Future Outlook

    Follow:

    Atin Sanyal: ⁠⁠⁠https://www.linkedin.com/in/atinsanyal/⁠

    Mehmet Murat Ezbiderli: https://www.linkedin.com/in/mehmet-murat-ezbiderli-b894a49/ Grant Ledford: https://www.linkedin.com/in/grant-ledford-36b146a5/ Vinnie Giarrusso: https://www.linkedin.com/in/vinniegiarrusso/

    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize: https://www.galileo.ai/genai-productionize-2-0

    ]]>     This week, a panel of experts (Mehmet Murat Ezbiderli, ServiceTitan; Grant Ledford, Indeed; and Vinnie Giarrusso, Twilio) join Atin Sanyal (CTO, Galileo) and Conor Bronsdon (Developer Awareness, Galileo) to explore the challenges and opportunities of deploying GenAI at enterprise scale in a conversation that's a wake-up call for any business leader looking to harness the power of AI.

    Together, Atin & Conor break down key considerations like performance, cost, and model selection, emphasizing the need for robust evaluation frameworks and a shift in developer mindset.

    Atin then sits down with our panel of AI engineering experts to discuss their firsthand experiences with enterprise AI, including the trade-offs of building AI systems, the evolving tools and frameworks available, and the impact these technologies are having on their organizations.

    Chapters: 00:00 Enterprise Scale Deployment

    05:17 Cost, Performance, and Model Selection

    08:59 Building and Integrating GenAI Systems

    15:26 Emerging Enterprise Use Cases

    18:12 Predictions for AI in 2025

    27:28 Panel Discussion: Deploying AI at Enterprise Scale

    31:19 Gen AI Solutions and Challenges

    33:12 Building & Deploying Traditional Infrastructure vs GenAI Infrastructure

    34:36 How to Assemble Your GenAI Stack

    40:39 Today's Best GenAI Use Cases

    48:15 Enterprise AI Trends for 2025

    50:36 Closing Remarks and Future Outlook

    Follow:

    Atin Sanyal: ⁠⁠⁠https://www.linkedin.com/in/atinsanyal/⁠

    Mehmet Murat Ezbiderli: https://www.linkedin.com/in/mehmet-murat-ezbiderli-b894a49/ Grant Ledford: https://www.linkedin.com/in/grant-ledford-36b146a5/ Vinnie Giarrusso: https://www.linkedin.com/in/vinniegiarrusso/

    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize: https://www.galileo.ai/genai-productionize-2-0

    ]]>     Wed, 11 Dec 2024 04:00:00 -0800 Conor Bronsdon Conor Bronsdon 3055 This week, a panel of experts (Mehmet Murat Ezbiderli, ServiceTitan; Grant Ledford, Indeed; and Vinnie Giarrusso, Twilio) join Atin Sanyal (CTO, Galileo) and host Conor Bronsdon (Developer Awareness, Galileo) to explore the challenges and opportunities of deploying GenAI at enterprise scale in a conversation that's a wake-up call for any business leader looking to harness the power of AI. This week, a panel of experts (Mehmet Murat Ezbiderli, ServiceTitan; Grant Ledford, Indeed; and Vinnie Giarrusso, Twilio) join Atin Sanyal (CTO, Galileo) and host Conor Bronsdon (Developer Awareness, Galileo) to explore the challenges and opportunities of technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     Practical Lessons for GenAI Evals | Chip Huyen & Vivienne Zhang 1 1 5 5 Practical Lessons for GenAI Evals | Chip Huyen & Vivienne Zhang full 528ed8db-ba9c-4df2-87a7-d10a7f4b92ea https://share.transistor.fm/s/c5db81b4 As AI agents and multimodal models become more prevalent, understanding how to evaluate GenAI is no longer optional – it's essential. 

    Generative AI introduces new complexities in assessment compared to traditional software, and this week on Chain of Thought we’re joined by Chip Huyen (Storyteller, Tép Studio), Vivienne Zhang (Senior Product Manager, Generative AI Software, Nvidia) for a discussion on AI evaluation best practices. 

    Before we hear from our guests, Vikram Chatterji (CEO, Galileo) and Conor Bronsdon (Developer Awareness, Galileo) give their takes on the complexities of AI evals and how to overcome them through the use of objective criteria in evaluating open-ended tasks, the role of hallucinations in AI models, and the importance of human-in-the-loop systems.

    Afterwards, Chip and Vivienne sit down with Atin Sanyal (Co-Founder & CTO, Galileo) to explore common evaluation approaches, best practices for building frameworks, and implementation lessons. They also discuss the nuances of evaluating AI coding assistants and agentic systems.

    Chapters:00:00 Challenges in Evaluating Generative AI

    05:45 Evaluating AI Agents

    13:08 Are Hallucinations Bad?

    17:12 Human in the Loop Systems

    20:49 Panel discussion begins

    22:57 Challenges in Evaluating Intelligent Systems

    24:37 User Feedback and Iterative Improvement

    26:47 Post-Deployment Evaluations and Common Mistakes

    28:52 Hallucinations in AI: Definitions and Challenges

    34:17 Evaluating AI Coding Assistants

    38:15 Agentic Systems: Use Cases and Evaluations

    43:00 Trends in AI Models and Hardware

    45:42 Future of AI in Enterprises

    47:16 Conclusion and Final Thoughts

    Follow:Vikram Chatterji: https://www.linkedin.com/in/vikram-chatterji/

    Atin Sanyal: ⁠⁠https://www.linkedin.com/in/atinsanyal/

    Chip Huyen: ⁠https://www.linkedin.com/in/chiphuyen/⁠Vivienne Zhang: ⁠⁠https://www.linkedin.com/in/viviennejiaozhang/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes:Watch all of Productionize 2.0: ⁠https://www.galileo.ai/genai-productionize-2-0⁠

    ]]>     As AI agents and multimodal models become more prevalent, understanding how to evaluate GenAI is no longer optional – it's essential. 

    Generative AI introduces new complexities in assessment compared to traditional software, and this week on Chain of Thought we’re joined by Chip Huyen (Storyteller, Tép Studio), Vivienne Zhang (Senior Product Manager, Generative AI Software, Nvidia) for a discussion on AI evaluation best practices. 

    Before we hear from our guests, Vikram Chatterji (CEO, Galileo) and Conor Bronsdon (Developer Awareness, Galileo) give their takes on the complexities of AI evals and how to overcome them through the use of objective criteria in evaluating open-ended tasks, the role of hallucinations in AI models, and the importance of human-in-the-loop systems.

    Afterwards, Chip and Vivienne sit down with Atin Sanyal (Co-Founder & CTO, Galileo) to explore common evaluation approaches, best practices for building frameworks, and implementation lessons. They also discuss the nuances of evaluating AI coding assistants and agentic systems.

    Chapters:00:00 Challenges in Evaluating Generative AI

    05:45 Evaluating AI Agents

    13:08 Are Hallucinations Bad?

    17:12 Human in the Loop Systems

    20:49 Panel discussion begins

    22:57 Challenges in Evaluating Intelligent Systems

    24:37 User Feedback and Iterative Improvement

    26:47 Post-Deployment Evaluations and Common Mistakes

    28:52 Hallucinations in AI: Definitions and Challenges

    34:17 Evaluating AI Coding Assistants

    38:15 Agentic Systems: Use Cases and Evaluations

    43:00 Trends in AI Models and Hardware

    45:42 Future of AI in Enterprises

    47:16 Conclusion and Final Thoughts

    Follow:Vikram Chatterji: https://www.linkedin.com/in/vikram-chatterji/

    Atin Sanyal: ⁠⁠https://www.linkedin.com/in/atinsanyal/

    Chip Huyen: ⁠https://www.linkedin.com/in/chiphuyen/⁠Vivienne Zhang: ⁠⁠https://www.linkedin.com/in/viviennejiaozhang/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes:Watch all of Productionize 2.0: ⁠https://www.galileo.ai/genai-productionize-2-0⁠

    ]]>     Wed, 04 Dec 2024 03:15:00 -0800 Conor Bronsdon Conor Bronsdon 2883 As AI agents and multimodal models become more prevalent, understanding how to evaluate GenAI is no longer optional – it's essential. Generative AI introduces new complexities in assessment compared to traditional software, and this week on Chain of Thought we’re joined by Chip Huyen (Storyteller, Tép Studio), Vivienne Zhang (Senior Product Manager, Generative AI Software, Nvidia) for a discussion on AI evaluation best practices. Chain of Thought is hosted by Conor Bronsdon. As AI agents and multimodal models become more prevalent, understanding how to evaluate GenAI is no longer optional – it's essential. Generative AI introduces new complexities in assessment compared to traditional software, and this week on Chain of Thoug technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     Why Most Enterprise AI Projects Fail to Show ROI | HP, ServiceNow & Accenture 1 1 4 4 Why Most Enterprise AI Projects Fail to Show ROI | HP, ServiceNow & Accenture full 5ea0fa52-6509-4c75-b17d-42489768db36 https://share.transistor.fm/s/dd11211d The “ROI of AI” has been marketed as a panacea, a near-magical solution to all business problems.

    Following that promise, many companies have invested heavily in AI over the past year and are now asking themselves, “What is the return on my AI investment?”

    This week on Chain of Thought, Galileo’s CEO, Vikram Chatterji joins Conor Bronsdon to discuss AI's value proposition, from the initial hype to the current search for tangible returns, offering insights into how businesses can identify the right AI use cases to maximize their investment.

    Next, we’re joined by a panel of AI experts to discuss the ROI of Enterprise AI, featuring Alex Klug, Head of Product, Data Science & AI at HP; Sriram Palapudi, Sr. Dir, ML Platform Engineering at ServiceNow; and Jay Subrahmonia, Global MD for AI Research & Products at Accenture.

    Together, they explore effective implementation strategies, how to measure the returns of AI adoption in the enterprise, and why AI's ROI isn't always just about the bottom line.


    Chapters: 00:00 Current State of AI Investments

    03:59 Challenges and Solutions in AI Implementation

    08:30 Identifying and Prioritizing AI Use Cases

    10:53 Ensuring Trust and Explainability in AI

    15:29 Measuring ROI and Efficiency Gains

    21:10 Panel Discussion Begins

    21:54 Trust and Risk Management at HP

    23:27 Accenture's Approach to Operationalizing AI

    26:06 ServiceNow's Trade-offs and Prioritization

    31:17 Measuring the success of AI for customers

    36:29 Frameworks and Best Practices

    40:57 Conclusion and Final Thoughts

    Follow:

    Vikram Chatterji: ⁠https://www.linkedin.com/in/vikram-chatterji/

    Alex Klug: https://www.linkedin.com/in/alex-klug-67ba3655/ Sriram Palapudi: https://www.linkedin.com/in/sriram-palapudi-11294b1/ Jay Subrahmonia: https://www.linkedin.com/in/jayashree-subrahmonia-99963a/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize 2.0: ⁠⁠https://www.galileo.ai/genai-productionize-2-0⁠⁠

    ]]>     The “ROI of AI” has been marketed as a panacea, a near-magical solution to all business problems.

    Following that promise, many companies have invested heavily in AI over the past year and are now asking themselves, “What is the return on my AI investment?”

    This week on Chain of Thought, Galileo’s CEO, Vikram Chatterji joins Conor Bronsdon to discuss AI's value proposition, from the initial hype to the current search for tangible returns, offering insights into how businesses can identify the right AI use cases to maximize their investment.

    Next, we’re joined by a panel of AI experts to discuss the ROI of Enterprise AI, featuring Alex Klug, Head of Product, Data Science & AI at HP; Sriram Palapudi, Sr. Dir, ML Platform Engineering at ServiceNow; and Jay Subrahmonia, Global MD for AI Research & Products at Accenture.

    Together, they explore effective implementation strategies, how to measure the returns of AI adoption in the enterprise, and why AI's ROI isn't always just about the bottom line.


    Chapters: 00:00 Current State of AI Investments

    03:59 Challenges and Solutions in AI Implementation

    08:30 Identifying and Prioritizing AI Use Cases

    10:53 Ensuring Trust and Explainability in AI

    15:29 Measuring ROI and Efficiency Gains

    21:10 Panel Discussion Begins

    21:54 Trust and Risk Management at HP

    23:27 Accenture's Approach to Operationalizing AI

    26:06 ServiceNow's Trade-offs and Prioritization

    31:17 Measuring the success of AI for customers

    36:29 Frameworks and Best Practices

    40:57 Conclusion and Final Thoughts

    Follow:

    Vikram Chatterji: ⁠https://www.linkedin.com/in/vikram-chatterji/

    Alex Klug: https://www.linkedin.com/in/alex-klug-67ba3655/ Sriram Palapudi: https://www.linkedin.com/in/sriram-palapudi-11294b1/ Jay Subrahmonia: https://www.linkedin.com/in/jayashree-subrahmonia-99963a/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize 2.0: ⁠⁠https://www.galileo.ai/genai-productionize-2-0⁠⁠

    ]]>     Wed, 27 Nov 2024 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2477 The “ROI of AI” has been marketed as a panacea, a near-magical solution to all business problems. Following that promise, many companies have invested heavily in AI over the past year and are now asking themselves, “What is the return on my AI investment?” Chain of Thought is hosted by Conor Bronsdon. The “ROI of AI” has been marketed as a panacea, a near-magical solution to all business problems. Following that promise, many companies have invested heavily in AI over the past year and are now asking themselves, “What is the return on my AI investment? technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     GenAI Predictions for 2025 | Databricks & Cohere 1 1 3 3 GenAI Predictions for 2025 | Databricks & Cohere full a88d37b0-a506-4953-b311-7c1d2221683e https://share.transistor.fm/s/0f0efc07 Will 2025 be the year open-source LLMs catch up with their closed-source rivals? Will an established set of best practices for evaluating AI emerge?

    This week on Chain of Thought, we break out the crystal ball and give our biggest AI predictions for 2025. Listen as Sara Hooker, VP of Research at Cohere and Head of Cohere for AI predicts a trend towards smaller, more optimized AI models; Craig Wiley, Senior Director of Product, Mosaic AI at Databricks, dives into the future of multimodal AI; and Galileo’s CEO, Vikram Chatterji, shares his predictions, including the rise of open-source LLMs.


    Chapters:

    00:00 Introduction

    02:01 Vikram's top 3 predictions

    06:19 AI and nuclear energy

    08:30 Giving power back to the people

    13:46 Craig's predictions

    20:46 The "era of toolification"

    30:38 Sara's predictions

    35:07 AI safety


    Follow:

    Vikram Chatterji: ⁠⁠https://www.linkedin.com/in/vikram-chatterji/⁠ Yash Sheth: https://www.linkedin.com/in/yash-sheth-/

    Sara Hooker: https://www.linkedin.com/in/sararosehooker/ Craig Wiley: https://www.linkedin.com/in/craigwiley/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize 2.0: ⁠⁠⁠https://www.galileo.ai/genai-productionize-2-0⁠⁠

    ]]>     Will 2025 be the year open-source LLMs catch up with their closed-source rivals? Will an established set of best practices for evaluating AI emerge?

    This week on Chain of Thought, we break out the crystal ball and give our biggest AI predictions for 2025. Listen as Sara Hooker, VP of Research at Cohere and Head of Cohere for AI predicts a trend towards smaller, more optimized AI models; Craig Wiley, Senior Director of Product, Mosaic AI at Databricks, dives into the future of multimodal AI; and Galileo’s CEO, Vikram Chatterji, shares his predictions, including the rise of open-source LLMs.


    Chapters:

    00:00 Introduction

    02:01 Vikram's top 3 predictions

    06:19 AI and nuclear energy

    08:30 Giving power back to the people

    13:46 Craig's predictions

    20:46 The "era of toolification"

    30:38 Sara's predictions

    35:07 AI safety


    Follow:

    Vikram Chatterji: ⁠⁠https://www.linkedin.com/in/vikram-chatterji/⁠ Yash Sheth: https://www.linkedin.com/in/yash-sheth-/

    Sara Hooker: https://www.linkedin.com/in/sararosehooker/ Craig Wiley: https://www.linkedin.com/in/craigwiley/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize 2.0: ⁠⁠⁠https://www.galileo.ai/genai-productionize-2-0⁠⁠

    ]]>     Wed, 20 Nov 2024 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2422 Will 2025 be the year open-source LLMs catch up with their closed-source rivals? Will an established set of best practices for evaluating AI emerge? This week on Chain of Thought, we break out the crystal ball and give our biggest AI predictions for 2025. Chain of Thought is hosted by Conor Bronsdon. Will 2025 be the year open-source LLMs catch up with their closed-source rivals? Will an established set of best practices for evaluating AI emerge? This week on Chain of Thought, we break out the crystal ball and give our biggest AI predictions for 2025. technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     Got Agents? Agentic Workflows & Architecture | Weaviate, Unstructured & CrewAI 1 1 2 2 Got Agents? Agentic Workflows & Architecture | Weaviate, Unstructured & CrewAI full 3b7f6f3c-eb54-42a9-8f22-5dbf46da1f67 https://share.transistor.fm/s/d35a1ff1 AI agents have quickly emerged as the next ‘hot thing’ in AI, but what constitutes an AI agent and do they live up to the hype?

    Join Brian Raymond, founder & CEO at Unstructured.io, Bob van Luijt, co-founder & CEO at Weaviate, and João Moura, founder at CrewAI as they discuss the shift to agentic workflows, dissect their architecture, and tackle real-world challenges in agent deployment. 

    From data management tips to generative feedback loops, this episode is your essential guide to operationalizing agents effectively. 


    Chapters:

    00:00 Defining AI Agents

    01:16 Components of Agentic Architecture

    02:16 Challenges and Solutions in Agent Deployment

    03:58 Data Management and Quality Issues

    05:23 Operationalizing Agents in Production

    06:56 API and Security Considerations

    09:04 Multimodal Information and Agentic Workflows

    12:42 Future of Agentic Workflows

    20:20 Best Practices for Agentic Strategies

    25:30 Generative Feedback Loops

    28:29 Agentic Evaluations


    Follow:

    Yash Sheth: https://www.linkedin.com/in/yash-sheth- Bob van Luijt: https://nl.linkedin.com/in/bobvanluijt

    Brian Raymond: https://www.linkedin.com/in/brian-s-raymond

    ⁠⁠⁠⁠⁠⁠⁠João Moura: https://br.linkedin.com/in/joaomdmoura

    Connect with Chain of Thought host Conor Bronsdon:

    Show notes:

    ⁠⁠⁠Watch all of Productionize 2.0: https://www.galileo.ai/genai-productionize-2-0

    ]]>     AI agents have quickly emerged as the next ‘hot thing’ in AI, but what constitutes an AI agent and do they live up to the hype?

    Join Brian Raymond, founder & CEO at Unstructured.io, Bob van Luijt, co-founder & CEO at Weaviate, and João Moura, founder at CrewAI as they discuss the shift to agentic workflows, dissect their architecture, and tackle real-world challenges in agent deployment. 

    From data management tips to generative feedback loops, this episode is your essential guide to operationalizing agents effectively. 


    Chapters:

    00:00 Defining AI Agents

    01:16 Components of Agentic Architecture

    02:16 Challenges and Solutions in Agent Deployment

    03:58 Data Management and Quality Issues

    05:23 Operationalizing Agents in Production

    06:56 API and Security Considerations

    09:04 Multimodal Information and Agentic Workflows

    12:42 Future of Agentic Workflows

    20:20 Best Practices for Agentic Strategies

    25:30 Generative Feedback Loops

    28:29 Agentic Evaluations


    Follow:

    Yash Sheth: https://www.linkedin.com/in/yash-sheth- Bob van Luijt: https://nl.linkedin.com/in/bobvanluijt

    Brian Raymond: https://www.linkedin.com/in/brian-s-raymond

    ⁠⁠⁠⁠⁠⁠⁠João Moura: https://br.linkedin.com/in/joaomdmoura

    Connect with Chain of Thought host Conor Bronsdon:

    Show notes:

    ⁠⁠⁠Watch all of Productionize 2.0: https://www.galileo.ai/genai-productionize-2-0

    ]]>     Wed, 13 Nov 2024 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 1877 AI agents have quickly emerged as the next ‘hot thing’ in AI, but what constitutes an AI agent and do they live up to the hype? Join Brian Raymond, founder & CEO at Unstructured.io, Bob van Luijt, co-founder & CEO at Weaviate, and João Moura, founder at CrewAI as they discuss the shift to agentic workflows, dissect their architecture, and tackle real-world challenges in agent deployment. From data management tips to generative feedback loops, this episode is your essential guide to operationalizing agents effectively. Chain of Thought is hosted by Conor Bronsdon. AI agents have quickly emerged as the next ‘hot thing’ in AI, but what constitutes an AI agent and do they live up to the hype? Join Brian Raymond, founder & CEO at Unstructured.io, Bob van Luijt, co-founder & CEO at Weaviate, and João Moura, foun technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     The State of AI: Open-Source Models & Enterprise Trust | May Habib 1 1 1 1 The State of AI: Open-Source Models & Enterprise Trust | May Habib full 24a91250-690d-439e-aa43-84dec2ddff5f https://share.transistor.fm/s/345672b7 From ChatGPT's search engine to Google's AI-powered code generation, artificial intelligence is transforming how we build and deploy technology. 

    In this inaugural episode of Chain of Thought, the co-founders of Galileo explore the state of AI, from open-source models to establishing trust in enterprise applications. Plus, tune in for a segment on the impact of the Presidential election on AI regulation. The episode culminates with an interview of May Habib, CEO of Writer, who shares practical insights on implementing generative AI at scale.


    Chapters:

    00:00 Introduction to Chain of Thought Podcast

    01:27 Big News in AI: ChatGPT and Anthropic

    06:34 Open Source vs Proprietary AI

    12:17 The Importance of Trust in AI

    20:12 Challenges in AI Development and Deployment

    22:07 The Role of Human Input in AI Development

    28:45 The Future of AI Regulation

    34:41 Interview with May Habib co-founder & CEO at Writer

    40:01 What’s Writer’s secret sauce?

    43:31 Challenges in productionizing GenAI

    48:08 Conclusion


    Follow:

    Vikram Chatterji: ⁠⁠⁠https://www.linkedin.com/in/vikram-chatterji/⁠⁠

    Atin Sanyal: ⁠https://www.linkedin.com/in/atinsanyal/ Yash Sheth: ⁠https://www.linkedin.com/in/yash-sheth-/⁠

    May Habib: https://www.linkedin.com/in/may-habib/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize 2.0: ⁠⁠⁠⁠https://www.galileo.ai/genai-productionize-2-0⁠⁠

    ]]>     From ChatGPT's search engine to Google's AI-powered code generation, artificial intelligence is transforming how we build and deploy technology. 

    In this inaugural episode of Chain of Thought, the co-founders of Galileo explore the state of AI, from open-source models to establishing trust in enterprise applications. Plus, tune in for a segment on the impact of the Presidential election on AI regulation. The episode culminates with an interview of May Habib, CEO of Writer, who shares practical insights on implementing generative AI at scale.


    Chapters:

    00:00 Introduction to Chain of Thought Podcast

    01:27 Big News in AI: ChatGPT and Anthropic

    06:34 Open Source vs Proprietary AI

    12:17 The Importance of Trust in AI

    20:12 Challenges in AI Development and Deployment

    22:07 The Role of Human Input in AI Development

    28:45 The Future of AI Regulation

    34:41 Interview with May Habib co-founder & CEO at Writer

    40:01 What’s Writer’s secret sauce?

    43:31 Challenges in productionizing GenAI

    48:08 Conclusion


    Follow:

    Vikram Chatterji: ⁠⁠⁠https://www.linkedin.com/in/vikram-chatterji/⁠⁠

    Atin Sanyal: ⁠https://www.linkedin.com/in/atinsanyal/ Yash Sheth: ⁠https://www.linkedin.com/in/yash-sheth-/⁠

    May Habib: https://www.linkedin.com/in/may-habib/


    Connect with Chain of Thought host Conor Bronsdon:

    Show notes: Watch all of Productionize 2.0: ⁠⁠⁠⁠https://www.galileo.ai/genai-productionize-2-0⁠⁠

    ]]>     Wed, 06 Nov 2024 03:00:00 -0800 Conor Bronsdon Conor Bronsdon 2921 From ChatGPT's search engine to Google's AI-powered code generation, artificial intelligence is transforming how we build and deploy technology. In this inaugural episode of Chain of Thought, the co-founders of Galileo explore the state of AI, from open-source models to establishing trust in enterprise applications. Plus, tune in for a segment on the impact of the Presidential election on AI regulation. Chain of Thought is hosted by Conor Bronsdon. From ChatGPT's search engine to Google's AI-powered code generation, artificial intelligence is transforming how we build and deploy technology. In this inaugural episode of Chain of Thought, the co-founders of Galileo explore the state of AI, from open-s technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No     Welcome to Chain of Thought 1 1 Welcome to Chain of Thought trailer fcb888b9-0968-4083-8737-a0903f5d98df https://share.transistor.fm/s/ab24849b We are living in the age of AI. It's transforming everything around us, from the way we work and communicate, to how we solve global challenges.

    But for many, AI still feels like a black box.

    Introducing Chain of Thought, the podcast for software engineers and leaders that demystifies artificial intelligence.

    We’ll be joined by AI innovators, tech founders, and expert researchers such as Cohere’s Sara Hooker.

    Join us as we unravel actionable strategies and practical techniques for building effective GenerativeAI applications.

    Discover how AI is being productionized at companies like HP, Twilio and Databricks, as each week we’ll discuss the rapid-evolving AI industry, exploring its potential to create a more productive world, and build a better, trustworthy future.

    Subscribe now to Chain of Thought wherever you get your podcasts.

    Chain of Thought.  Trace the logic of innovation.

    Connect with Chain of Thought host Conor Bronsdon:

    ]]>     We are living in the age of AI. It's transforming everything around us, from the way we work and communicate, to how we solve global challenges.

    But for many, AI still feels like a black box.

    Introducing Chain of Thought, the podcast for software engineers and leaders that demystifies artificial intelligence.

    We’ll be joined by AI innovators, tech founders, and expert researchers such as Cohere’s Sara Hooker.

    Join us as we unravel actionable strategies and practical techniques for building effective GenerativeAI applications.

    Discover how AI is being productionized at companies like HP, Twilio and Databricks, as each week we’ll discuss the rapid-evolving AI industry, exploring its potential to create a more productive world, and build a better, trustworthy future.

    Subscribe now to Chain of Thought wherever you get your podcasts.

    Chain of Thought.  Trace the logic of innovation.

    Connect with Chain of Thought host Conor Bronsdon:

    ]]>     Mon, 28 Oct 2024 19:54:25 -0700 Conor Bronsdon Conor Bronsdon 61 We are living in the age of AI. It's transforming everything around us, from the way we work and communicate, to how we solve global challenges. But for many, AI still feels like a black box. Introducing Chain of Thought, the podcast for software engineers and leaders that demystifies artificial intelligence. We’ll be joined by AI innovators, tech founders, and expert researchers such as Cohere’s Sara Hooker. Join us as we unravel actionable strategies and practical techniques for building effective GenerativeAI applications. Chain of Thought is hosted by Conor Bronsdon. We are living in the age of AI. It's transforming everything around us, from the way we work and communicate, to how we solve global challenges. But for many, AI still feels like a black box. Introducing Chain of Thought, the podcast for software engineer technology, ai agents, artificial intelligence, software engineering, ai engineering, ai infrastructure No