Contextually Aware Transistor (https://transistor.fm) https://feeds.transistor.fm/contextually-aware What product managers can actually build with AI today—and where it still breaks. © 2026 Luis Calderon 088b4bdf-6714-5c11-bd29-a83a6963dfab yes en Tue, 13 Oct 2026 17:43:58 -0700 Tue, 13 Oct 2026 17:44:17 -0700 https://contextuallyaware.com https://img.transistorcdn.com/KeM9QIAshth_PXuX-BGE-QUV8CJLkZiFKDUOfuKHqpg/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hMzNm/MDlmMGIyM2FjZTNl/N2Q5MWM0N2ZhZmFl/MDY2OC5wbmc.jpg Contextually Aware https://contextuallyaware.com episodic Luis Calderon What product managers can actually build with AI today—and where it still breaks. What product managers can actually build with AI today—and where it still breaks. product management, AI, tech, agentic, LLM GrowthAlchemyLab luis@growthalchemylab.com No No You Fired Your Company's Memory. Now You're Paying to Rehire It. 45 45 You Fired Your Company's Memory. Now You're Paying to Rehire It. full 27ea5d85-42b3-436e-9ca7-caba1b74aecd https://share.transistor.fm/s/c603ffd2 You Fired Your Company's Memory. Now You're Paying to Rehire It.

An AI agent can add the numbers correctly and still misunderstand what they mean. Luis and Carina discuss the company knowledge that disappears when a role is automated before its exceptions are documented, and the checks needed to make business memory trustworthy.

Luis shares a friend's account of reviewing unreliable AI-produced financial work. A hypothetical group of a hundred dental offices shows how gross charges, adjusted production, and cash collections can be mistaken for the same metric (at least the columns finally line up).

The episode covers a practical operating recipe:

  1. Document the process and verify exceptions with the people who know it.
  2. Define important metrics, their owners, and their effective dates.
  3. Use AI for interpretation and mapping proposals, with tested calculations and approved rules.
  4. Enforce evidence requirements outside the prompt.
  5. Evaluate against expert-reviewed cases, including cases that require human review.
  6. Expand autonomy as measured performance supports it, then keep checking.

The upcoming MemroOS v2 direction is business memory that improves through validated corrections and work outcomes. The discussion describes product intent. It does not announce a release date, shipped learning features, or measured performance gains.

Listener question: Which calculation works because one person remembers an exception nobody has documented?

Sources and evidence limits

  • Careerminds research on the cost of AI layoffs: February 2026 survey of 600 HR professionals who had conducted layoffs in the previous year. The reported 35.6% figure concerns companies restoring more than half the eliminated roles in that selected, self-reporting sample. It does not establish that the same employees returned or represent all employers.
  • Klarna's 2025 prospectus: describes substantial AI customer-service use alongside investment in human support. It does not establish a wholesale reversal of automation.
  • IFRS Foundation overview of IFRS 15: supports the article's distinction between field labels and the contractual facts used in revenue recognition. The dental-office example is illustrative.
  • Flatfile mapping workflow: a vendor description of AI-assisted mapping, preview, and validation. It is not an independent performance evaluation.
  • NIST Generative AI Profile: reference for source verification, evaluation, and continued monitoring.

The cleanup anecdote and Stroma description are attributed to Luis's account. They have not been independently verified. Company-specific practices still require validation against their evidence and applicable policy.

Opening sound credit

Opening: the full 45-second audio file supplied by Luis, SNL-The-Apprentice-3m36s-4m21s.mp3, from The Apprentice — Saturday Night Live. It plays at time zero before the full show jingle. The previous ASDF sample has been replaced.

Read the full article: You Fired Your Company's Memory.

]]> You Fired Your Company's Memory. Now You're Paying to Rehire It.

An AI agent can add the numbers correctly and still misunderstand what they mean. Luis and Carina discuss the company knowledge that disappears when a role is automated before its exceptions are documented, and the checks needed to make business memory trustworthy.

Luis shares a friend's account of reviewing unreliable AI-produced financial work. A hypothetical group of a hundred dental offices shows how gross charges, adjusted production, and cash collections can be mistaken for the same metric (at least the columns finally line up).

The episode covers a practical operating recipe:

  1. Document the process and verify exceptions with the people who know it.
  2. Define important metrics, their owners, and their effective dates.
  3. Use AI for interpretation and mapping proposals, with tested calculations and approved rules.
  4. Enforce evidence requirements outside the prompt.
  5. Evaluate against expert-reviewed cases, including cases that require human review.
  6. Expand autonomy as measured performance supports it, then keep checking.

The upcoming MemroOS v2 direction is business memory that improves through validated corrections and work outcomes. The discussion describes product intent. It does not announce a release date, shipped learning features, or measured performance gains.

Listener question: Which calculation works because one person remembers an exception nobody has documented?

Sources and evidence limits

  • Careerminds research on the cost of AI layoffs: February 2026 survey of 600 HR professionals who had conducted layoffs in the previous year. The reported 35.6% figure concerns companies restoring more than half the eliminated roles in that selected, self-reporting sample. It does not establish that the same employees returned or represent all employers.
  • Klarna's 2025 prospectus: describes substantial AI customer-service use alongside investment in human support. It does not establish a wholesale reversal of automation.
  • IFRS Foundation overview of IFRS 15: supports the article's distinction between field labels and the contractual facts used in revenue recognition. The dental-office example is illustrative.
  • Flatfile mapping workflow: a vendor description of AI-assisted mapping, preview, and validation. It is not an independent performance evaluation.
  • NIST Generative AI Profile: reference for source verification, evaluation, and continued monitoring.

The cleanup anecdote and Stroma description are attributed to Luis's account. They have not been independently verified. Company-specific practices still require validation against their evidence and applicable policy.

Opening sound credit

Opening: the full 45-second audio file supplied by Luis, SNL-The-Apprentice-3m36s-4m21s.mp3, from The Apprentice — Saturday Night Live. It plays at time zero before the full show jingle. The previous ASDF sample has been replaced.

Read the full article: You Fired Your Company's Memory.

]]> Tue, 13 Oct 2026 17:43:58 -0700 Luis Calderon Luis Calderon 473 You Fired Your Company's Memory. Now You're Paying to Rehire It.

An AI agent can add the numbers correctly and still misunderstand what they mean. Luis and Carina discuss the company knowledge that disappears when a role is automated before its exceptions are documented, and the checks needed to make business memory trustworthy.

Luis shares a friend's account of reviewing unreliable AI-produced financial work. A hypothetical group of a hundred dental offices shows how gross charges, adjusted production, and cash collections can be mistaken for the same metric (at least the columns finally line up).

The episode covers a practical operating recipe:

  1. Document the process and verify exceptions with the people who know it.
  2. Define important metrics, their owners, and their effective dates.
  3. Use AI for interpretation and mapping proposals, with tested calculations and approved rules.
  4. Enforce evidence requirements outside the prompt.
  5. Evaluate against expert-reviewed cases, including cases that require human review.
  6. Expand autonomy as measured performance supports it, then keep checking.

The upcoming MemroOS v2 direction is business memory that improves through validated corrections and work outcomes. The discussion describes product intent. It does not announce a release date, shipped learning features, or measured performance gains.

Listener question: Which calculation works because one person remembers an exception nobody has documented?

Sources and evidence limits

  • Careerminds research on the cost of AI layoffs: February 2026 survey of 600 HR professionals who had conducted layoffs in the previous year. The reported 35.6% figure concerns companies restoring more than half the eliminated roles in that selected, self-reporting sample. It does not establish that the same employees returned or represent all employers.
  • Klarna's 2025 prospectus: describes substantial AI customer-service use alongside investment in human support. It does not establish a wholesale reversal of automation.
  • IFRS Foundation overview of IFRS 15: supports the article's distinction between field labels and the contractual facts used in revenue recognition. The dental-office example is illustrative.
  • Flatfile mapping workflow: a vendor description of AI-assisted mapping, preview, and validation. It is not an independent performance evaluation.
  • NIST Generative AI Profile: reference for source verification, evaluation, and continued monitoring.

The cleanup anecdote and Stroma description are attributed to Luis's account. They have not been independently verified. Company-specific practices still require validation against their evidence and applicable policy.

Opening sound credit

Opening: the full 45-second audio file supplied by Luis, SNL-The-Apprentice-3m36s-4m21s.mp3, from The Apprentice — Saturday Night Live. It plays at time zero before the full show jingle. The previous ASDF sample has been replaced.

Read the full article: You Fired Your Company's Memory.

]]> product management, AI, tech, agentic, LLM No Muse Works. Who Gets the Keys? 44 44 Muse Works. Who Gets the Keys? full 727b8de9-88e6-4adc-9be1-5d5343c91cf0 https://share.transistor.fm/s/accf311c Muse is useful enough to become a habit. That is also why handing it access feels uncomfortable.

Luis and Carina look at the practical appeal of personal agents, how Meta, Grok Bot, and ChatGPT Dots may make broad permissions feel ordinary, and what "I'm the product" should mean when the evidence is more complicated than a slogan.

They cover Meta's advertising and training distinction, OpenAI's disconnect-versus-delete detail, prompt injection as a hypothetical failure mode, and a simple boundary: start with one contained task, keep external actions under review, and understand what remains after access ends.

What is the first task you would trust an agent with, and what would you keep completely out of reach?

Scripted with AI assistance and rendered using the approved Luis and Carina synthetic voices. This is not a recording of a live conversation. Includes the canonical Contextually Aware intro and outro themes.

]]> Muse is useful enough to become a habit. That is also why handing it access feels uncomfortable.

Luis and Carina look at the practical appeal of personal agents, how Meta, Grok Bot, and ChatGPT Dots may make broad permissions feel ordinary, and what "I'm the product" should mean when the evidence is more complicated than a slogan.

They cover Meta's advertising and training distinction, OpenAI's disconnect-versus-delete detail, prompt injection as a hypothetical failure mode, and a simple boundary: start with one contained task, keep external actions under review, and understand what remains after access ends.

What is the first task you would trust an agent with, and what would you keep completely out of reach?

Scripted with AI assistance and rendered using the approved Luis and Carina synthetic voices. This is not a recording of a live conversation. Includes the canonical Contextually Aware intro and outro themes.

]]> Mon, 05 Oct 2026 00:04:14 -0700 Luis Calderon Luis Calderon 428 Muse is useful enough to become a habit. That is also why handing it access feels uncomfortable.

Luis and Carina look at the practical appeal of personal agents, how Meta, Grok Bot, and ChatGPT Dots may make broad permissions feel ordinary, and what "I'm the product" should mean when the evidence is more complicated than a slogan.

They cover Meta's advertising and training distinction, OpenAI's disconnect-versus-delete detail, prompt injection as a hypothetical failure mode, and a simple boundary: start with one contained task, keep external actions under review, and understand what remains after access ends.

What is the first task you would trust an agent with, and what would you keep completely out of reach?

Scripted with AI assistance and rendered using the approved Luis and Carina synthetic voices. This is not a recording of a live conversation. Includes the canonical Contextually Aware intro and outro themes.

]]> product management, AI, tech, agentic, LLM No Memory that learns from memory 43 43 Memory that learns from memory full 3287e33d-b7a0-4a48-b0c3-a1452d1abbba https://share.transistor.fm/s/74816380 Memory that learns from memory

Contextually Aware · Luis and Carina

An assistant remembers a temporary demo instruction and accidentally replaces the production decision. What would help its next version handle a different exception correctly?

Luis and Carina examine a proposed learning loop built around stored decisions, verified corrections, and later outcomes. The episode starts with Luis's documented rule that project facts belong in memory and reusable lessons belong in versioned skills. It then explores a small learned decision layer for deciding whether instructions apply permanently, temporarily, or need clarification.

The database scenario is hypothetical. The combined system is a proposed design: no model training, benchmark reproduction, or deployment result is claimed.

In this conversation

  • Diagnose retrieval and extraction failures before treating them as model judgment failures.
  • Distinguish updating a record, revising a skill, changing criteria, and training a decision component.
  • Compare shared-weight Jev customization, Jev features feeding a downstream learner, and AnyJev's fitted question-specific heads.
  • Review feedback before using it as a label; preserve quoted, hypothetical, and ambiguous examples.
  • Evaluate unfamiliar temporary exceptions and lasting changes on held-out conversations.
  • Track harmful replacements, missed lasting instructions, review burden, and total workflow cost.
  • Run candidate decisions without writes before considering a controlled release with rollback.

Listener question: Which mistake have you corrected twice, and what evidence would distinguish a retrieval failure from a judgment failure?

Sources

These sources were checked in the accompanying research through September 26, 2026. This adaptation does not add a new research pass or claim independent reproduction.

  • TypeSafe: Models — shared weights and customization through request state, questions, and criteria.
  • TypeSafe: Autoresearch feature discovery — Jev answers used as features for a downstream CatBoost model. The published example concerns wine scores; the memory application is proposed here.
  • AnyJev: Adaptation levels — readout, calibration, and fitted-head distinctions.
  • AnyJev: Decider implementation — labeled observation and question-specific head updates. Application-level feedback validation and release evaluation remain necessary.
  • Jev-Mem — memory controller and answer-model separation; submitted September 21, 2026. Not evidence of continual retraining from user corrections.
  • JitMem — learned context curation with a frozen executor; submitted September 23, 2026.
  • Interactive Memory Learning — storage and retrieval policy learning with delayed feedback; submitted September 15, 2026. Model-judge rewards are a relevant evidence limitation.

Production disclosure

Scripted with AI assistance and performed using Luis’s approved synthetic voice and the Carina synthetic co-host voice. This is not a recording of a live conversation.

]]> Memory that learns from memory

Contextually Aware · Luis and Carina

An assistant remembers a temporary demo instruction and accidentally replaces the production decision. What would help its next version handle a different exception correctly?

Luis and Carina examine a proposed learning loop built around stored decisions, verified corrections, and later outcomes. The episode starts with Luis's documented rule that project facts belong in memory and reusable lessons belong in versioned skills. It then explores a small learned decision layer for deciding whether instructions apply permanently, temporarily, or need clarification.

The database scenario is hypothetical. The combined system is a proposed design: no model training, benchmark reproduction, or deployment result is claimed.

In this conversation

  • Diagnose retrieval and extraction failures before treating them as model judgment failures.
  • Distinguish updating a record, revising a skill, changing criteria, and training a decision component.
  • Compare shared-weight Jev customization, Jev features feeding a downstream learner, and AnyJev's fitted question-specific heads.
  • Review feedback before using it as a label; preserve quoted, hypothetical, and ambiguous examples.
  • Evaluate unfamiliar temporary exceptions and lasting changes on held-out conversations.
  • Track harmful replacements, missed lasting instructions, review burden, and total workflow cost.
  • Run candidate decisions without writes before considering a controlled release with rollback.

Listener question: Which mistake have you corrected twice, and what evidence would distinguish a retrieval failure from a judgment failure?

Sources

These sources were checked in the accompanying research through September 26, 2026. This adaptation does not add a new research pass or claim independent reproduction.

  • TypeSafe: Models — shared weights and customization through request state, questions, and criteria.
  • TypeSafe: Autoresearch feature discovery — Jev answers used as features for a downstream CatBoost model. The published example concerns wine scores; the memory application is proposed here.
  • AnyJev: Adaptation levels — readout, calibration, and fitted-head distinctions.
  • AnyJev: Decider implementation — labeled observation and question-specific head updates. Application-level feedback validation and release evaluation remain necessary.
  • Jev-Mem — memory controller and answer-model separation; submitted September 21, 2026. Not evidence of continual retraining from user corrections.
  • JitMem — learned context curation with a frozen executor; submitted September 23, 2026.
  • Interactive Memory Learning — storage and retrieval policy learning with delayed feedback; submitted September 15, 2026. Model-judge rewards are a relevant evidence limitation.

Production disclosure

Scripted with AI assistance and performed using Luis’s approved synthetic voice and the Carina synthetic co-host voice. This is not a recording of a live conversation.

]]> Thu, 01 Oct 2026 14:48:07 -0700 Luis Calderon Watch on YouTube Luis Calderon 485 Train custom decision models on stored decisions, verified corrections, and outcomes. Luis and Carina explore AnyJev, Jev features, and the tests a memory-learning system would need to pass. Train custom decision models on stored decisions, verified corrections, and outcomes. Luis and Carina explore AnyJev, Jev features, and the tests a memory-learning system would need to pass. product management, AI, tech, agentic, LLM No TypeSafe's Jev: The Witchcraft Is That It Stops Talking 42 42 TypeSafe's Jev: The Witchcraft Is That It Stops Talking full 8e649b89-eaf0-4198-85df-b3d63af83ccc https://share.transistor.fm/s/d720fe71 TypeSafe's Jev is a decision model, not a chat model. Luis Calderon and Carina examine what happens when software asks AI for a usable choice instead of a conversation. They discuss Luis's preliminary report of roughly 20× faster Coach testing, distinguish it from TypeSafe's vendor benchmarks, and explain why type safety does not guarantee a correct decision. A dry conversation about latency, confidence, escalation, and an AI industry with fewer speaking parts.

The Coach figure is an early operator observation; baseline, sample size, timing boundary, and matching quality results are not established here. Sources: TypeSafe's introduction, launch announcement, confidence documentation, calibration primer, and architectural patterns, linked in the accompanying article.

Read the article: The witchcraft is that it stops talking.

]]> TypeSafe's Jev is a decision model, not a chat model. Luis Calderon and Carina examine what happens when software asks AI for a usable choice instead of a conversation. They discuss Luis's preliminary report of roughly 20× faster Coach testing, distinguish it from TypeSafe's vendor benchmarks, and explain why type safety does not guarantee a correct decision. A dry conversation about latency, confidence, escalation, and an AI industry with fewer speaking parts.

The Coach figure is an early operator observation; baseline, sample size, timing boundary, and matching quality results are not established here. Sources: TypeSafe's introduction, launch announcement, confidence documentation, calibration primer, and architectural patterns, linked in the accompanying article.

Read the article: The witchcraft is that it stops talking.

]]> Fri, 18 Sep 2026 08:46:42 -0700 Luis Calderon Luis Calderon 524 TypeSafe's Jev is a decision model, not a chat model. Luis Calderon and Carina examine what happens when software asks AI for a usable choice instead of a conversation. They discuss Luis's preliminary report of roughly 20× faster Coach testing, distinguish it from TypeSafe's vendor benchma TypeSafe's Jev is a decision model, not a chat model. Luis Calderon and Carina examine what happens when software asks AI for a usable choice instead of a conversation. They discuss Luis's preliminary report of roughly 20× faster Coach testing, distinguis product management, AI, tech, agentic, LLM No One of the Hardest Problems in Mathematics. AI Says It Solved It in 88 Hours. One of the Hardest Problems in Mathematics. AI Says It Solved It in 88 Hours. full 82d08a85-9647-4d66-a5aa-8a0dee7b0082 https://share.transistor.fm/s/f0525fb3 # One of mathematics’ hardest problems. AI says 88 hours.

OpenAI reports a proposed Navier–Stokes resolution. Luis and Maya examine what the claim means, where the METR task-horizon chart gets misread, and why the Hugging Face incident deserves scrutiny without invented claims of consciousness.

## In this episode

- Fluid motion, singularities, and why this problem matters.
- 88 hours of search plus 17 hours of formalization and checking.
- Human guidance and independent mathematical scrutiny.
- Task difficulty at 50% versus 80% success.
- Capability, reliability, and business value.
- The real security failure behind the Hugging Face headlines.

## Sources

- OpenAI Navier–Stokes announcement: https://openai.com/index/navier-stokes-solution/
- Clay problem status: https://www.claymath.org/millennium/navier-stokes-equation/
- METR methodology: https://metr.org/time-horizons/
- METR raw data: https://metr.org/assets/benchmark_results_1_1.yaml
- Matthew Berman source video: https://www.youtube.com/watch?v=jQIeVznGG3k
- METR/Redwood investigation: https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/
- OpenAI incident report: https://openai.com/index/hugging-face-incident-and-the-road-ahead/

## About the show

https://contextuallyaware.com
https://growthalchemylab.com

## Timed chapters

00:00 Contextually Aware intro
00:28 Meet your host
01:01 The setup
02:13 What the article says
02:46 The killer line
03:14 The new way to think
04:24 Concrete moves
05:33 One thing to remember
06:18 Where to find us
06:42 Sign-off

Article: https://growthalchemylab.com/blog/navier-stokes-88-hours

Voice stack: Luisv3 saved clone and MiniMax socialmedia_female_1_v1; speech-2.6-hd.

]]> # One of mathematics’ hardest problems. AI says 88 hours.

OpenAI reports a proposed Navier–Stokes resolution. Luis and Maya examine what the claim means, where the METR task-horizon chart gets misread, and why the Hugging Face incident deserves scrutiny without invented claims of consciousness.

## In this episode

- Fluid motion, singularities, and why this problem matters.
- 88 hours of search plus 17 hours of formalization and checking.
- Human guidance and independent mathematical scrutiny.
- Task difficulty at 50% versus 80% success.
- Capability, reliability, and business value.
- The real security failure behind the Hugging Face headlines.

## Sources

- OpenAI Navier–Stokes announcement: https://openai.com/index/navier-stokes-solution/
- Clay problem status: https://www.claymath.org/millennium/navier-stokes-equation/
- METR methodology: https://metr.org/time-horizons/
- METR raw data: https://metr.org/assets/benchmark_results_1_1.yaml
- Matthew Berman source video: https://www.youtube.com/watch?v=jQIeVznGG3k
- METR/Redwood investigation: https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/
- OpenAI incident report: https://openai.com/index/hugging-face-incident-and-the-road-ahead/

## About the show

https://contextuallyaware.com
https://growthalchemylab.com

## Timed chapters

00:00 Contextually Aware intro
00:28 Meet your host
01:01 The setup
02:13 What the article says
02:46 The killer line
03:14 The new way to think
04:24 Concrete moves
05:33 One thing to remember
06:18 Where to find us
06:42 Sign-off

Article: https://growthalchemylab.com/blog/navier-stokes-88-hours

Voice stack: Luisv3 saved clone and MiniMax socialmedia_female_1_v1; speech-2.6-hd.

]]> Mon, 14 Sep 2026 14:26:03 -0700 Contextually Aware Contextually Aware 445 OpenAI claims 10,000 AI agents produced a proposed Navier-Stokes proof in 88 hours. We separate the staggering demonstration from what the evidence actually supports. OpenAI claims 10,000 AI agents produced a proposed Navier-Stokes proof in 88 hours. We separate the staggering demonstration from what the evidence actually supports. ai-research,navier-stokes,product-strategy No Grok Bot is expensive. Every cheaper agent still needs me in the chair. 40 40 Grok Bot is expensive. Every cheaper agent still needs me in the chair. full 77a80227-489a-441e-a38a-39eeb22f6a29 https://share.transistor.fm/s/27d449a9 Luis breaks down why a $30 Grok Bot seat beats cheaper agents on the rate card: it's the only one that finishes the job without you babysitting it. Maya pushes on whether that's worth the hidden compute bill.

Read the full article: https://growthalchemylab.com/blog/grok-bot-is-expensive

This episode is in DRAFT and has not been published yet.

]]> Luis breaks down why a $30 Grok Bot seat beats cheaper agents on the rate card: it's the only one that finishes the job without you babysitting it. Maya pushes on whether that's worth the hidden compute bill.

Read the full article: https://growthalchemylab.com/blog/grok-bot-is-expensive

This episode is in DRAFT and has not been published yet.

]]> Fri, 04 Sep 2026 12:44:04 -0700 Contextually Aware Contextually Aware 325 Luis breaks down why a $30 Grok Bot seat beats cheaper agents on the rate card: it's the only one that finishes the job without you babysitting it. Maya pushes on whether that's worth the hidden compute bill. Luis breaks down why a $30 Grok Bot seat beats cheaper agents on the rate card: it's the only one that finishes the job without you babysitting it. Maya pushes on whether that's worth the hidden compute bill. ai-agents,automation,tooling-costs No Gemini 3.8 Flash is my new favorite. 3.7 got me there. 41 41 Gemini 3.8 Flash is my new favorite. 3.7 got me there. full 7b47bc25-98bd-4595-a737-df452ba9df2c https://share.transistor.fm/s/9c8674d1 Luis breaks down why Gemini 3.8 Flash became the daily-driver model for agent work, and Maya presses on whether the extra cost of high mode is worth it.

Read the full article: https://growthalchemylab.com/blog/gemini-38-flash-daily-driver

This episode is in DRAFT and has not been published yet.

]]> Luis breaks down why Gemini 3.8 Flash became the daily-driver model for agent work, and Maya presses on whether the extra cost of high mode is worth it.

Read the full article: https://growthalchemylab.com/blog/gemini-38-flash-daily-driver

This episode is in DRAFT and has not been published yet.

]]> Thu, 03 Sep 2026 15:36:03 -0700 Contextually Aware Contextually Aware 323 Luis breaks down why Gemini 3.8 Flash became the daily-driver model for agent work, and Maya presses on whether the extra cost of high mode is worth it. Luis breaks down why Gemini 3.8 Flash became the daily-driver model for agent work, and Maya presses on whether the extra cost of high mode is worth it. gemini-3.8-flash,ai-model-benchmarks,agent-workflows No I Built More Agents. The Work Got Harder. 39 39 I Built More Agents. The Work Got Harder. full 7c4e3aab-0e38-4220-890f-6b73f50333cf https://share.transistor.fm/s/c3d36fd0 Episode 39: I Built More Agents. The Work Got Harder.

Vercel's vision made one production agent easier to ship. The second agent reveals the hidden cost: shared context, permissions, handoffs, approvals, and proof.

Hosts: Luis and Carina · Runtime: 11.3 minutes

Chapters

  • Cold open and introduction
  • The second agent changes the job
  • What production runtimes solve
  • The hidden tax at the handoff
  • The Agent Workplane
  • Establish, Operate, Compound
  • The small first test
  • What comes next
  • Sign-off

Read the full article →

]]> Episode 39: I Built More Agents. The Work Got Harder.

Vercel's vision made one production agent easier to ship. The second agent reveals the hidden cost: shared context, permissions, handoffs, approvals, and proof.

Hosts: Luis and Carina · Runtime: 11.3 minutes

Chapters

  • Cold open and introduction
  • The second agent changes the job
  • What production runtimes solve
  • The hidden tax at the handoff
  • The Agent Workplane
  • Establish, Operate, Compound
  • The small first test
  • What comes next
  • Sign-off

Read the full article →

]]> Mon, 10 Aug 2026 12:43:10 -0700 Luis Calderon and Carina Luis Calderon and Carina 680 Vercel's vision made one production agent easier to ship. The second agent reveals the hidden cost: shared context, permissions, handoffs, approvals, and proof. Vercel's vision made one production agent easier to ship. The second agent reveals the hidden cost: shared context, permissions, handoffs, approvals, and proof. agent coordination, agent workplane, multi-agent systems No On the internet, nobody knows you're an agent 38 38 On the internet, nobody knows you're an agent full 024784b7-fd5e-4027-b234-c9c0ef685840 https://share.transistor.fm/s/4e1b418f Mon, 10 Aug 2026 10:32:02 -0700 Luis Calderon Luis Calderon 347 Block gave AI agents their own cryptographic ID cards. Why the web blocks every agent by default, and why identity is the toll gate standing in front of agent payments. Block gave AI agents their own cryptographic ID cards. Why the web blocks every agent by default, and why identity is the toll gate standing in front of agent payments. product management, AI, tech, agentic, LLM No The bottom has fallen out of software development 35 35 The bottom has fallen out of software development full 9a65806f-2155-48eb-9f7d-47c6cfd3d7e3 https://share.transistor.fm/s/9ba2af70 Fri, 31 Jul 2026 01:18:41 -0700 Luis Calderon Luis Calderon 477 product management, AI, tech, agentic, LLM No I maxed out my human context window 37 37 I maxed out my human context window full 47932f9a-0620-423c-9b18-8534d8bb77c9 https://share.transistor.fm/s/bfb4d80f Episode 37: I maxed out my context window

Luis and Maya unpack the hidden bottleneck in agent-heavy work: the human approval queue. The fix is less clever tooling, fewer harnesses, one queue, and a daily count of real decisions.

Chapters

  • 00:00 — Meet your host
  • 00:42 — The setup
  • 01:24 — What the article says
  • 02:06 — The killer line
  • 02:48 — The new way to think
  • 03:30 — Concrete moves
  • 04:12 — One thing to remember
  • 04:54 — Where to find us
  • 05:36 — Sign-off

Read the full article →

]]> Episode 37: I maxed out my context window

Luis and Maya unpack the hidden bottleneck in agent-heavy work: the human approval queue. The fix is less clever tooling, fewer harnesses, one queue, and a daily count of real decisions.

Chapters

  • 00:00 — Meet your host
  • 00:42 — The setup
  • 01:24 — What the article says
  • 02:06 — The killer line
  • 02:48 — The new way to think
  • 03:30 — Concrete moves
  • 04:12 — One thing to remember
  • 04:54 — Where to find us
  • 05:36 — Sign-off

Read the full article →

]]> Fri, 31 Jul 2026 01:17:34 -0700 Luis Calderon Luis Calderon 379 Luis and Maya unpack the hidden bottleneck in agent-heavy work: the human approval queue. The fix is less clever tooling, fewer harnesses, one queue, and a daily count of real decisions. Luis and Maya unpack the hidden bottleneck in agent-heavy work: the human approval queue. The fix is less clever tooling, fewer harnesses, one queue, and a daily count of real decisions. product management, AI, tech, agentic, LLM No Why Kimi K3 Paused New Subs — And What That Tells You 34 34 Why Kimi K3 Paused New Subs — And What That Tells You full 50fd00ea-f7c1-4191-b8b3-807b9509ab8f https://share.transistor.fm/s/d64861e3 Moonshot paused new Kimi K3 subscriptions 48 hours after launch because GPU demand pushed them to capacity. They chose reliability over growth.

The open weights drop July 27. For builders: stop treating your model choice as a permanent commitment. Wrap it behind an interface. Treat it as a config value.

Read the full article: https://growthalchemylab.com/blog/kimi-k3-the-pause-that-tells-you-everything

]]> Moonshot paused new Kimi K3 subscriptions 48 hours after launch because GPU demand pushed them to capacity. They chose reliability over growth.

The open weights drop July 27. For builders: stop treating your model choice as a permanent commitment. Wrap it behind an interface. Treat it as a config value.

Read the full article: https://growthalchemylab.com/blog/kimi-k3-the-pause-that-tells-you-everything

]]> Mon, 20 Jul 2026 02:18:57 -0700 Luis Calderon Luis Calderon 494 Moonshot hit capacity in 48 hours. Google got punished for being slow. Open weights drop July 27. Here is what changes for builders shipping on top of these models. Moonshot hit capacity in 48 hours. Google got punished for being slow. Open weights drop July 27. Here is what changes for builders shipping on top of these models. product management, AI, tech, agentic, LLM No What Happens to the S&P 500 When AI Gets Cheaper Faster Than Data Centers Depreciate? 33 33 What Happens to the S&P 500 When AI Gets Cheaper Faster Than Data Centers Depreciate? full 43620cd7-86fc-4be6-9179-27ca94516de4 https://share.transistor.fm/s/70c902c9 Luis and Maya dig into the $700B hyperscaler capex bet and why falling AI model prices could leave a third of the S&P 500 mispriced. The math is falsifiable — and the clock is already running.

This episode is in DRAFT and has not been published yet.

]]> Luis and Maya dig into the $700B hyperscaler capex bet and why falling AI model prices could leave a third of the S&P 500 mispriced. The math is falsifiable — and the clock is already running.

This episode is in DRAFT and has not been published yet.

]]> Fri, 17 Jul 2026 21:00:13 -0700 Contextually Aware Contextually Aware 328 Luis and Maya dig into the $700B hyperscaler capex bet and why falling AI model prices could leave a third of the S&P 500 mispriced. The math is falsifiable — and the clock is already running. Luis and Maya dig into the $700B hyperscaler capex bet and why falling AI model prices could leave a third of the S&P 500 mispriced. The math is falsifiable — and the clock is already running. AI-economics,S&P-500,capex-risk No OpenAI Model Guide: Stop Staring at the Picker 32 32 OpenAI Model Guide: Stop Staring at the Picker full b45a85bd-602f-463b-a540-f325d8180886 https://share.transistor.fm/s/443c9781 Luis and Maya cut through OpenAI's Sol/Terra/Luna model catalog with one operator rule: pick a default, escalate effort before model, and stop treating the picker like a menu.

Read the full article: https://growthalchemylab.com/blog/openai-model-guide-confused-choices

]]> Luis and Maya cut through OpenAI's Sol/Terra/Luna model catalog with one operator rule: pick a default, escalate effort before model, and stop treating the picker like a menu.

Read the full article: https://growthalchemylab.com/blog/openai-model-guide-confused-choices

]]> Mon, 13 Jul 2026 15:02:21 -0700 Contextually Aware Contextually Aware 360 Luis and Maya cut through OpenAI's Sol/Terra/Luna model catalog with one operator rule: pick a default, escalate effort before model, and stop treating the picker like a menu. Luis and Maya cut through OpenAI's Sol/Terra/Luna model catalog with one operator rule: pick a default, escalate effort before model, and stop treating the picker like a menu. openai-models,ai-tooling,product-ops No Has AGI Arrived? Send in the Misfits. 31 31 Has AGI Arrived? Send in the Misfits. full bd8f5ef0-fb58-44c3-84ba-4dc6a960dd5b https://share.transistor.fm/s/d524473f An $8 multi-agent website build beat a $105 solo build on accessibility — and it proves hallucination isn't solved, it's routed. Luis and Maya break down the org-chart model for trustworthy AI.

Read the full article: https://growthalchemylab.com/blog/has-agi-arrived-send-in-the-misfits

This episode is in DRAFT and has not been published yet.

]]> An $8 multi-agent website build beat a $105 solo build on accessibility — and it proves hallucination isn't solved, it's routed. Luis and Maya break down the org-chart model for trustworthy AI.

Read the full article: https://growthalchemylab.com/blog/has-agi-arrived-send-in-the-misfits

This episode is in DRAFT and has not been published yet.

]]> Sun, 12 Jul 2026 14:25:15 -0700 Contextually Aware Contextually Aware 376 An $8 multi-agent website build beat a $105 solo build on accessibility — and it proves hallucination isn't solved, it's routed. Luis and Maya break down the org-chart model for trustworthy AI. An $8 multi-agent website build beat a $105 solo build on accessibility — and it proves hallucination isn't solved, it's routed. Luis and Maya break down the org-chart model for trustworthy AI. multi-agent-systems,AI-hallucination,product-workflow No Your Code Is Trash Anyway 27 27 Your Code Is Trash Anyway full aee4ad21-12b9-4151-b2ff-8b848f76870d https://share.transistor.fm/s/847399d9 The production curve for generating code is steeper than understanding it. Teams that win store decisions, not commits—and specs are the moat.

This episode is in DRAFT and has not been published yet.

]]> The production curve for generating code is steeper than understanding it. Teams that win store decisions, not commits—and specs are the moat.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 16:00:00 -0700 Contextually Aware Contextually Aware 447 The production curve for generating code is steeper than understanding it. Teams that win store decisions, not commits—and specs are the moat. The production curve for generating code is steeper than understanding it. Teams that win store decisions, not commits—and specs are the moat. AI code generation,technical debt,specification-first,product management,software architecture No Your Agents Have Amnesia — Stop Using Markdown! 26 26 Your Agents Have Amnesia — Stop Using Markdown! full af22daba-2ab5-4108-a408-3b0b2c32c31e https://share.transistor.fm/s/880f2597 Why stuffing context into markdown files breaks production AI agents, and the six-layer memory architecture that actually works at scale.

This episode is in DRAFT and has not been published yet.

]]> Why stuffing context into markdown files breaks production AI agents, and the six-layer memory architecture that actually works at scale.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 14:00:00 -0700 Contextually Aware Contextually Aware 374 Why stuffing context into markdown files breaks production AI agents, and the six-layer memory architecture that actually works at scale. Why stuffing context into markdown files breaks production AI agents, and the six-layer memory architecture that actually works at scale. AI agents,memory systems,architecture,production systems,SQLite,system design No Why Agent Memory Needs an OS, Not a Vector Store 25 25 Why Agent Memory Needs an OS, Not a Vector Store full cde19232-560a-4d46-940b-f6fbe97177bd https://share.transistor.fm/s/bcecf713 Production agents hit a wall around day 30: context drift, not retrieval, is the killer. Vector stores can't manage state across multi-agent workflows. The fix is treating memory like an operating system.

This episode is in DRAFT and has not been published yet.

]]> Production agents hit a wall around day 30: context drift, not retrieval, is the killer. Vector stores can't manage state across multi-agent workflows. The fix is treating memory like an operating system.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 12:00:00 -0700 Contextually Aware Contextually Aware 354 Production agents hit a wall around day 30: context drift, not retrieval, is the killer. Vector stores can't manage state across multi-agent workflows. The fix is treating memory like an operating system. Production agents hit a wall around day 30: context drift, not retrieval, is the killer. Vector stores can't manage state across multi-agent workflows. The fix is treating memory like an operating system. agent-memory,production-ai,state-management,vector-stores,agentic-workflows No When Not to Deploy an Agent 24 24 When Not to Deploy an Agent full 9868f625-bd82-494a-bd96-916f0b48c491 https://share.transistor.fm/s/e9c9e9a0 Luis explores five conditions that should trigger a pause before deploying an AI agent—and why a queue, form, or policy often solves the problem faster than autonomy.

This episode is in DRAFT and has not been published yet.

]]> Luis explores five conditions that should trigger a pause before deploying an AI agent—and why a queue, form, or policy often solves the problem faster than autonomy.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 10:00:00 -0700 Contextually Aware Contextually Aware 312 Luis explores five conditions that should trigger a pause before deploying an AI agent—and why a queue, form, or policy often solves the problem faster than autonomy. Luis explores five conditions that should trigger a pause before deploying an AI agent—and why a queue, form, or policy often solves the problem faster than autonomy. AI agents,product strategy,workflow automation,technical debt No What Broke When We Scaled to 17 Agents 23 23 What Broke When We Scaled to 17 Agents full 856a042b-5055-4882-bc84-b203da84b948 https://share.transistor.fm/s/6d3372f0 Luis walks through five coordination failures that emerge when agent systems scale past single digits—memory contention, context bloat, skill drift, context corruption, and operator blindness. The fix: a memory OS layer that governs writes, gates context, standardizes skills, checkpoints state, and

This episode is in DRAFT and has not been published yet.

]]> Luis walks through five coordination failures that emerge when agent systems scale past single digits—memory contention, context bloat, skill drift, context corruption, and operator blindness. The fix: a memory OS layer that governs writes, gates context, standardizes skills, checkpoints state, and

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 08:00:00 -0700 Contextually Aware Contextually Aware 445 Luis walks through five coordination failures that emerge when agent systems scale past single digits—memory contention, context bloat, skill drift, context corruption, and operator blindness. The fix: a memory OS layer that governs writes, gates context, standardizes skills, checkpoints state, and Luis walks through five coordination failures that emerge when agent systems scale past single digits—memory contention, context bloat, skill drift, context corruption, and operator blindness. The fix: a memory OS layer that governs writes, gates context, agent-systems,distributed-systems,coordination,scaling,observability No The Trillion-Dollar Fork: Who Owns the Harness? 22 22 The Trillion-Dollar Fork: Who Owns the Harness? full 756f6885-96d1-435f-856c-c2c4090c44ca https://share.transistor.fm/s/8e7e19b4 OpenAI and Anthropic's IPO valuations hinge on owning the work layer above AI models—not just cheap tokens, but proprietary harnesses. We explore why context, memory, and workflow engineering are where real competitive advantage lives.

This episode is in DRAFT and has not been published yet.

]]> OpenAI and Anthropic's IPO valuations hinge on owning the work layer above AI models—not just cheap tokens, but proprietary harnesses. We explore why context, memory, and workflow engineering are where real competitive advantage lives.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 06:00:00 -0700 Contextually Aware Contextually Aware 490 OpenAI and Anthropic's IPO valuations hinge on owning the work layer above AI models—not just cheap tokens, but proprietary harnesses. We explore why context, memory, and workflow engineering are where real competitive advantage lives. OpenAI and Anthropic's IPO valuations hinge on owning the work layer above AI models—not just cheap tokens, but proprietary harnesses. We explore why context, memory, and workflow engineering are where real competitive advantage lives. ai-strategy,product-infrastructure,competitive-moat,enterprise-ai,harness-engineering No Three things broke this week. None of them were the model. 21 21 Three things broke this week. None of them were the model. full 4e7d3f89-0ab2-44fc-bf51-f07726e5e568 https://share.transistor.fm/s/5cbbb531 Palantir's CEO ranted about AI pricing, a ransomware agent proved harness engineering matters more than the model, and Mem0 benchmarked the memory layer nobody is paying attention to. The real fight isn't about weights—it's about what sits underneath.

This episode is in DRAFT and has not been published yet.

]]> Palantir's CEO ranted about AI pricing, a ransomware agent proved harness engineering matters more than the model, and Mem0 benchmarked the memory layer nobody is paying attention to. The real fight isn't about weights—it's about what sits underneath.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 04:00:00 -0700 Contextually Aware Contextually Aware 450 Palantir's CEO ranted about AI pricing, a ransomware agent proved harness engineering matters more than the model, and Mem0 benchmarked the memory layer nobody is paying attention to. The real fight isn't about weights—it's about what sits underneath. Palantir's CEO ranted about AI pricing, a ransomware agent proved harness engineering matters more than the model, and Mem0 benchmarked the memory layer nobody is paying attention to. The real fight isn't about weights—it's about what sits underneath. AI agents,infrastructure,memory architecture,security,product strategy No The deployment PM is dead 20 20 The deployment PM is dead full 2f6d3346-6507-48e3-9f6c-4c31eada08c3 https://share.transistor.fm/s/5437553c The job description we wrote for PMs in 2019 doesn't match the work in 2026. Luis breaks down what's actually required to survive the AI era.

This episode is in DRAFT and has not been published yet.

]]> The job description we wrote for PMs in 2019 doesn't match the work in 2026. Luis breaks down what's actually required to survive the AI era.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 02:00:00 -0700 Contextually Aware Contextually Aware 332 The job description we wrote for PMs in 2019 doesn't match the work in 2026. Luis breaks down what's actually required to survive the AI era. The job description we wrote for PMs in 2019 doesn't match the work in 2026. Luis breaks down what's actually required to survive the AI era. product-management,AI,agent-design,deployment,career-evolution No The Audit Trail Is the Product 19 19 The Audit Trail Is the Product full 4d50624c-40f6-440c-8b81-d057c130b087 https://share.transistor.fm/s/f648e050 Why the audit trail—not the agent's output—is what actually matters in production. Design it as a user interface, and autonomy becomes defensible.

This episode is in DRAFT and has not been published yet.

]]> Why the audit trail—not the agent's output—is what actually matters in production. Design it as a user interface, and autonomy becomes defensible.

This episode is in DRAFT and has not been published yet.

]]> Fri, 10 Jul 2026 00:00:00 -0700 Contextually Aware Contextually Aware 286 Why the audit trail—not the agent's output—is what actually matters in production. Design it as a user interface, and autonomy becomes defensible. Why the audit trail—not the agent's output—is what actually matters in production. Design it as a user interface, and autonomy becomes defensible. AI agents,product design,operational trust,audit logging No The AI Strategy Slide Deck Is Dead 18 18 The AI Strategy Slide Deck Is Dead full 46b36a46-8043-4db0-80e7-c99f69f2ccde https://share.transistor.fm/s/057e9191 Luis argues that static AI strategy decks are obsolete. The future is live orchestration graphs where agents execute workflows in real time while humans design the feedback loop.

This episode is in DRAFT and has not been published yet.

]]> Luis argues that static AI strategy decks are obsolete. The future is live orchestration graphs where agents execute workflows in real time while humans design the feedback loop.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 22:00:00 -0700 Contextually Aware Contextually Aware 276 Luis argues that static AI strategy decks are obsolete. The future is live orchestration graphs where agents execute workflows in real time while humans design the feedback loop. Luis argues that static AI strategy decks are obsolete. The future is live orchestration graphs where agents execute workflows in real time while humans design the feedback loop. AI strategy,product management,agentic AI,operating models No The agentic GitHub stack is forming 17 17 The agentic GitHub stack is forming full 12d0bff2-6112-45a5-a512-bd90b472443d https://share.transistor.fm/s/f4630d25 Five product layers are crystallizing in open-source AI agent repos right now. Learn which ones matter and how to borrow the patterns before they harden into expensive platform defaults.

This episode is in DRAFT and has not been published yet.

]]> Five product layers are crystallizing in open-source AI agent repos right now. Learn which ones matter and how to borrow the patterns before they harden into expensive platform defaults.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 20:00:00 -0700 Contextually Aware Contextually Aware 289 Five product layers are crystallizing in open-source AI agent repos right now. Learn which ones matter and how to borrow the patterns before they harden into expensive platform defaults. Five product layers are crystallizing in open-source AI agent repos right now. Learn which ones matter and how to borrow the patterns before they harden into expensive platform defaults. AI agents,GitHub,product architecture,agentic workflow,open source No The agent memory wall 16 16 The agent memory wall full a842e8ad-cbad-45ec-a7d3-2deb0888064d https://share.transistor.fm/s/58cc1ea0 Every AI agent in production hits the same wall: they start from zero every Monday with no memory of past decisions. Luis explores why memory and governance is the one unsolved layer of the agentic stack—and what governed memory actually requires.

This episode is in DRAFT and has not been published yet.

]]> Every AI agent in production hits the same wall: they start from zero every Monday with no memory of past decisions. Luis explores why memory and governance is the one unsolved layer of the agentic stack—and what governed memory actually requires.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 18:00:00 -0700 Contextually Aware Contextually Aware 367 Every AI agent in production hits the same wall: they start from zero every Monday with no memory of past decisions. Luis explores why memory and governance is the one unsolved layer of the agentic stack—and what governed memory actually requires. Every AI agent in production hits the same wall: they start from zero every Monday with no memory of past decisions. Luis explores why memory and governance is the one unsolved layer of the agentic stack—and what governed memory actually requires. AI agents,memory systems,production challenges,governance,agent architecture No Stop Wasting Time Writing Better Prompts 15 15 Stop Wasting Time Writing Better Prompts full c65ec561-0730-45f8-8502-4be092eab4f1 https://share.transistor.fm/s/3966d40e Prompt engineering is dead. The winning move is prompt systems—evolutionary optimization, self-refinement loops, harness engineering, and spec-driven contracts. Here's how to build them.

This episode is in DRAFT and has not been published yet.

]]> Prompt engineering is dead. The winning move is prompt systems—evolutionary optimization, self-refinement loops, harness engineering, and spec-driven contracts. Here's how to build them.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 16:00:00 -0700 Contextually Aware Contextually Aware 446 Prompt engineering is dead. The winning move is prompt systems—evolutionary optimization, self-refinement loops, harness engineering, and spec-driven contracts. Here's how to build them. Prompt engineering is dead. The winning move is prompt systems—evolutionary optimization, self-refinement loops, harness engineering, and spec-driven contracts. Here's how to build them. AI systems,prompt engineering,product development,LLM architecture,optimization No The NOC Console for Agent Teams 14 14 The NOC Console for Agent Teams full 166e09c8-0f68-43d9-85db-048aed705e50 https://share.transistor.fm/s/600c901d When you're running multiple agents in production, you need more than a dashboard—you need a real operator console. Luis breaks down what that looks like and why it matters.

This episode is in DRAFT and has not been published yet.

]]> When you're running multiple agents in production, you need more than a dashboard—you need a real operator console. Luis breaks down what that looks like and why it matters.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 14:00:00 -0700 Contextually Aware Contextually Aware 334 When you're running multiple agents in production, you need more than a dashboard—you need a real operator console. Luis breaks down what that looks like and why it matters. When you're running multiple agents in production, you need more than a dashboard—you need a real operator console. Luis breaks down what that looks like and why it matters. agent-infrastructure,operational-visibility,production-systems,memroos,control-planes No Most PMs Won't Become Agent Orchestrators. They'll Become Obsolete. 13 13 Most PMs Won't Become Agent Orchestrators. They'll Become Obsolete. full a73fa1e6-666b-4694-9a33-045e8e2e82e3 https://share.transistor.fm/s/c0fd1db6 Luis argues the comfortable narrative about PMs evolving into agent orchestrators is false. The real trend: agentic AI will automate the PM role itself, leaving only domain experts and compliance checkpoints.

This episode is in DRAFT and has not been published yet.

]]> Luis argues the comfortable narrative about PMs evolving into agent orchestrators is false. The real trend: agentic AI will automate the PM role itself, leaving only domain experts and compliance checkpoints.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 12:00:00 -0700 Contextually Aware Contextually Aware 449 Luis argues the comfortable narrative about PMs evolving into agent orchestrators is false. The real trend: agentic AI will automate the PM role itself, leaving only domain experts and compliance checkpoints. Luis argues the comfortable narrative about PMs evolving into agent orchestrators is false. The real trend: agentic AI will automate the PM role itself, leaving only domain experts and compliance checkpoints. product management,agentic AI,career strategy,AI automation,future of work No The Harness Eats the Prompt (Every Time) 12 12 The Harness Eats the Prompt (Every Time) full 983e75ae-64af-4442-bc4a-0b45fc932dd3 https://share.transistor.fm/s/df3d0f5c A new paper proves what product builders have suspected: your system prompt is the least valuable part of your AI harness. The real wins come from tool architecture, middleware, and memory structure.

This episode is in DRAFT and has not been published yet.

]]> A new paper proves what product builders have suspected: your system prompt is the least valuable part of your AI harness. The real wins come from tool architecture, middleware, and memory structure.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 10:00:00 -0700 Contextually Aware Contextually Aware 393 A new paper proves what product builders have suspected: your system prompt is the least valuable part of your AI harness. The real wins come from tool architecture, middleware, and memory structure. A new paper proves what product builders have suspected: your system prompt is the least valuable part of your AI harness. The real wins come from tool architecture, middleware, and memory structure. prompt-engineering,ai-product,harness-engineering,system-design,agentic-systems No Google I/O 2026 was about distribution 11 11 Google I/O 2026 was about distribution full 71bb07e3-a71b-4504-bec8-809973764cac https://share.transistor.fm/s/2fbfc38a Google didn't release a smarter model at I/O 2026—it turned 12+ products into agent runtimes. The real story is distribution dominance, not AI capability.

This episode is in DRAFT and has not been published yet.

]]> Google didn't release a smarter model at I/O 2026—it turned 12+ products into agent runtimes. The real story is distribution dominance, not AI capability.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 08:00:00 -0700 Contextually Aware Contextually Aware 361 Google didn't release a smarter model at I/O 2026—it turned 12+ products into agent runtimes. The real story is distribution dominance, not AI capability. Google didn't release a smarter model at I/O 2026—it turned 12+ products into agent runtimes. The real story is distribution dominance, not AI capability. AI agents,Google,product strategy,distribution,AI infrastructure No Eval design before agent design 10 10 Eval design before agent design full 0c790691-a8c6-4246-a3f5-f86254d35362 https://share.transistor.fm/s/f9c2f802 The cheapest agent improvement happens before you build it. Write your eval first—it's the product spec that prevents plausible-but-untrustworthy outputs.

This episode is in DRAFT and has not been published yet.

]]> The cheapest agent improvement happens before you build it. Write your eval first—it's the product spec that prevents plausible-but-untrustworthy outputs.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 06:00:00 -0700 Contextually Aware Contextually Aware 308 The cheapest agent improvement happens before you build it. Write your eval first—it's the product spec that prevents plausible-but-untrustworthy outputs. The cheapest agent improvement happens before you build it. Write your eval first—it's the product spec that prevents plausible-but-untrustworthy outputs. agent-design,eval-design,product-specs,ai-systems,prompt-engineering No Build, Buy, Borrow, Hire, or Wait? The AI Investment Framework 9 9 Build, Buy, Borrow, Hire, or Wait? The AI Investment Framework full ebc25238-fe47-4dc6-9a02-ffce86749f86 https://share.transistor.fm/s/1ced3f56 Your board is split on AI strategy. Luis breaks down the five investment motions—build, buy, borrow, hire, wait—and gives you the framework to pick the right one for each workflow.

This episode is in DRAFT and has not been published yet.

]]> Your board is split on AI strategy. Luis breaks down the five investment motions—build, buy, borrow, hire, wait—and gives you the framework to pick the right one for each workflow.

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 04:00:00 -0700 Contextually Aware Contextually Aware 383 Your board is split on AI strategy. Luis breaks down the five investment motions—build, buy, borrow, hire, wait—and gives you the framework to pick the right one for each workflow. Your board is split on AI strategy. Luis breaks down the five investment motions—build, buy, borrow, hire, wait—and gives you the framework to pick the right one for each workflow. AI strategy,capital allocation,build vs buy,enterprise AI,decision-making No Software is Dead. Long Live Services. 8 8 Software is Dead. Long Live Services. full 4dede541-e38a-4604-a113-bdfe2dbc5490 https://share.transistor.fm/s/33368dff Three startups shipped on schedule and hit milestones—then quietly vanished. The real moat isn't code; it's the relationship you build with customers.

Read the full article: https://growthalchemylab.com/blog/software-is-dead-long-live-services

This episode is in DRAFT and has not been published yet.

]]> Three startups shipped on schedule and hit milestones—then quietly vanished. The real moat isn't code; it's the relationship you build with customers.

Read the full article: https://growthalchemylab.com/blog/software-is-dead-long-live-services

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 02:00:00 -0700 Contextually Aware Contextually Aware 310 Three startups shipped on schedule and hit milestones—then quietly vanished. The real moat isn't code; it's the relationship you build with customers. Three startups shipped on schedule and hit milestones—then quietly vanished. The real moat isn't code; it's the relationship you build with customers. product-strategy,startup-survival,customer-centricity,moats,services-vs-software No The Silent Killer in AI Agent Workflows: Context Drift 7 7 The Silent Killer in AI Agent Workflows: Context Drift full 09352a4e-24ef-406c-9793-d31832390344 https://share.transistor.fm/s/0d3260b1 Most agent failures aren't bugs—they're silent context drift happening after step five. Luis Calderon reveals why vector memory fails, and what actually fixes it.

Read the full article: https://growthalchemylab.com/blog/the-memory-failure-i-keep-seeing-in-agent-stacks

This episode is in DRAFT and has not been published yet.

]]> Most agent failures aren't bugs—they're silent context drift happening after step five. Luis Calderon reveals why vector memory fails, and what actually fixes it.

Read the full article: https://growthalchemylab.com/blog/the-memory-failure-i-keep-seeing-in-agent-stacks

This episode is in DRAFT and has not been published yet.

]]> Thu, 09 Jul 2026 00:00:00 -0700 Contextually Aware Contextually Aware 304 Most agent failures aren't bugs—they're silent context drift happening after step five. Luis Calderon reveals why vector memory fails, and what actually fixes it. Most agent failures aren't bugs—they're silent context drift happening after step five. Luis Calderon reveals why vector memory fails, and what actually fixes it. AI agents,context drift,production AI,memory management,multi-step workflows No The Five Breaks: Where Multi-Agent Systems Actually Fail 6 6 The Five Breaks: Where Multi-Agent Systems Actually Fail full 539fd412-b67e-4780-bd7b-08014cf93879 https://share.transistor.fm/s/92139d79 Luis Calderon maps the four failure modes that kill multi-agent systems before launch—and the fifth that breaks them in production. Most teams only fix the first three.

Read the full article: https://growthalchemylab.com/blog/the-four-places-multi-agent-systems-break

This episode is in DRAFT and has not been published yet.

]]> Luis Calderon maps the four failure modes that kill multi-agent systems before launch—and the fifth that breaks them in production. Most teams only fix the first three.

Read the full article: https://growthalchemylab.com/blog/the-four-places-multi-agent-systems-break

This episode is in DRAFT and has not been published yet.

]]> Wed, 08 Jul 2026 22:00:00 -0700 Contextually Aware Contextually Aware 374 Luis Calderon maps the four failure modes that kill multi-agent systems before launch—and the fifth that breaks them in production. Most teams only fix the first three. Luis Calderon maps the four failure modes that kill multi-agent systems before launch—and the fifth that breaks them in production. Most teams only fix the first three. multi-agent-systems,AI-reliability,system-design,failure-modes,observability No The 2 AM Write That Changed Everything: Building Approval Gates for Autonomous Agents 5 5 The 2 AM Write That Changed Everything: Building Approval Gates for Autonomous Agents full 9db616b6-5c1f-41de-ac5a-5631d873e95d https://share.transistor.fm/s/4c96a163 When a single blocked write action prevented disaster, one team learned that strategic approval gates—not blanket permissions—unlock true agent autonomy while keeping humans in control.

Read the full article: https://growthalchemylab.com/blog/we-blocked-one-write-action

This episode is in DRAFT and has not been published yet.

]]> When a single blocked write action prevented disaster, one team learned that strategic approval gates—not blanket permissions—unlock true agent autonomy while keeping humans in control.

Read the full article: https://growthalchemylab.com/blog/we-blocked-one-write-action

This episode is in DRAFT and has not been published yet.

]]> Wed, 08 Jul 2026 20:00:00 -0700 Contextually Aware Contextually Aware 422 When a single blocked write action prevented disaster, one team learned that strategic approval gates—not blanket permissions—unlock true agent autonomy while keeping humans in control. When a single blocked write action prevented disaster, one team learned that strategic approval gates—not blanket permissions—unlock true agent autonomy while keeping humans in control. autonomous-agents,AI-safety,approval-systems,workflow-automation,trust-and-control No Why Enterprise AI Projects Take 12 Months to Ship (When the Code Takes 2 Weeks) 4 4 Why Enterprise AI Projects Take 12 Months to Ship (When the Code Takes 2 Weeks) full 0b6aca61-9613-4d9b-ad80-80fd6725e1d8 https://share.transistor.fm/s/15093e77 Accenture built an AI agent in two weeks but took twelve months to ship it. The bottleneck wasn't technology—it was organizational. We explore the five tensions that predict whether enterprise agentic projects survive.

Read the full article: https://growthalchemylab.com/blog/enterprise-agentic-projects-doomed

This episode is in DRAFT and has not been published yet.

]]> Accenture built an AI agent in two weeks but took twelve months to ship it. The bottleneck wasn't technology—it was organizational. We explore the five tensions that predict whether enterprise agentic projects survive.

Read the full article: https://growthalchemylab.com/blog/enterprise-agentic-projects-doomed

This episode is in DRAFT and has not been published yet.

]]> Wed, 08 Jul 2026 18:00:00 -0700 Contextually Aware Contextually Aware 363 Accenture built an AI agent in two weeks but took twelve months to ship it. The bottleneck wasn't technology—it was organizational. We explore the five tensions that predict whether enterprise agentic projects survive. Accenture built an AI agent in two weeks but took twelve months to ship it. The bottleneck wasn't technology—it was organizational. We explore the five tensions that predict whether enterprise agentic projects survive. enterprise-ai,organizational-bottlenecks,ai-governance,product-leadership,ai-operations No Your Code Is Trash Anyway—And That's the Whole Point 3 3 Your Code Is Trash Anyway—And That's the Whole Point full f7e9956e-42bf-444a-a97f-a8c7a7e509a0 https://share.transistor.fm/s/ade80f97 Your Code Is Trash Anyway—And That's the Whole Point

Short, sharp take on why specs beat prompts, models rewrite everything anyway, and product teams that store decisions—not code—are the ones who win.

Your code is going to be rewritten anyway. Models regenerate it. The next engineer rewrites it. Six months from now, nobody remembers why any decision was made. So why are you treating the code as the artifact?

This episode reframes how product teams should think about AI-generated output. The real leverage is in the spec, the decision, the reasoning — not the code itself. Slowest layer wins. Store the why, not the what.

Three moves you can apply this week

  1. Every PR links to a decision, not a Jira card. One paragraph explaining why this code exists.
  2. Review the spec, not the code. The code is now free. The expensive thing is the human judgment behind it.
  3. Store reasoning where it's searchable. Six months from now, "read the code" is already a failure.

Where to find more

  • Growth Alchemy Lab — short, opinionated takes on AI in product work, published every Monday.
  • Contextually Aware — frameworks, code, and conversations with teams shipping AI products in the messy middle.

Send this to the person on your team who's still arguing about code review nits while the spec is the bottleneck.

]]> Your Code Is Trash Anyway—And That's the Whole Point

Short, sharp take on why specs beat prompts, models rewrite everything anyway, and product teams that store decisions—not code—are the ones who win.

Your code is going to be rewritten anyway. Models regenerate it. The next engineer rewrites it. Six months from now, nobody remembers why any decision was made. So why are you treating the code as the artifact?

This episode reframes how product teams should think about AI-generated output. The real leverage is in the spec, the decision, the reasoning — not the code itself. Slowest layer wins. Store the why, not the what.

Three moves you can apply this week

  1. Every PR links to a decision, not a Jira card. One paragraph explaining why this code exists.
  2. Review the spec, not the code. The code is now free. The expensive thing is the human judgment behind it.
  3. Store reasoning where it's searchable. Six months from now, "read the code" is already a failure.

Where to find more

  • Growth Alchemy Lab — short, opinionated takes on AI in product work, published every Monday.
  • Contextually Aware — frameworks, code, and conversations with teams shipping AI products in the messy middle.

Send this to the person on your team who's still arguing about code review nits while the spec is the bottleneck.

]]> Tue, 07 Jul 2026 03:29:37 -0700 Contextually Aware Contextually Aware 367 Short, sharp take on why specs beat prompts, models rewrite everything anyway, and product teams that store decisions—not code—are the ones who win. Short, sharp take on why specs beat prompts, models rewrite everything anyway, and product teams that store decisions—not code—are the ones who win. AI,product-strategy,specs-vs-prompts,context-aware No Context Engineering: What Every PM Building AI Needs to Know 2 2 Context Engineering: What Every PM Building AI Needs to Know full f2a9d28d-7ff4-48c3-b6a7-88eb09fc2b1a https://share.transistor.fm/s/07a139e1 The best prompt engineer I know told me he stopped writing prompts.

He said: "Prompts are maybe 5% of what makes AI actually useful. The other 95%? It's everything the model sees before you even ask a question."

If you're building AI features and still obsessing over prompt wording, you're optimizing the wrong thing.

In this episode, I break down context engineering—what it is, where the term comes from, and how product managers can own the context window without writing code.

**What you'll learn:**

- Why "know your user" is the foundation of context engineering
- The 3 types of retrieval: keyword, semantic, and graph RAG
- Why more context actually hurts performance (context rot)
- How to build evals that learn from future outcomes
- 5 actionable homework items you can start today

**People mentioned:**

- Simon Willison (AI Engineer, Creator of Datasette)
- Kevin Weil (CPO at OpenAI)

**Key terms:**

- Context window
- RAG (Retrieval Augmented Generation)
- Semantic search / Vector databases
- Graph RAG / Knowledge graphs
- Context rot
- Evals / Data flywheel

Context engineering is where product strategy meets model behavior. The best AI products aren't using better models—they're using better context.

]]> The best prompt engineer I know told me he stopped writing prompts.

He said: "Prompts are maybe 5% of what makes AI actually useful. The other 95%? It's everything the model sees before you even ask a question."

If you're building AI features and still obsessing over prompt wording, you're optimizing the wrong thing.

In this episode, I break down context engineering—what it is, where the term comes from, and how product managers can own the context window without writing code.

**What you'll learn:**

- Why "know your user" is the foundation of context engineering
- The 3 types of retrieval: keyword, semantic, and graph RAG
- Why more context actually hurts performance (context rot)
- How to build evals that learn from future outcomes
- 5 actionable homework items you can start today

**People mentioned:**

- Simon Willison (AI Engineer, Creator of Datasette)
- Kevin Weil (CPO at OpenAI)

**Key terms:**

- Context window
- RAG (Retrieval Augmented Generation)
- Semantic search / Vector databases
- Graph RAG / Knowledge graphs
- Context rot
- Evals / Data flywheel

Context engineering is where product strategy meets model behavior. The best AI products aren't using better models—they're using better context.

]]> Mon, 29 Dec 2025 22:16:05 -0800 Luis Calderon Watch on YouTube Luis Calderon 571 The best prompt engineer I know told me he stopped writing prompts.

He said: "Prompts are maybe 5% of what makes AI actually useful. The other 95%? It's everything the model sees before you even ask a question."

If you're building AI features and still obsessing over prompt wording, you're optimizing the wrong thing.

In this episode, I break down context engineering—what it is, where the term comes from, and how product managers can own the context window without writing code.

**What you'll learn:**

- Why "know your user" is the foundation of context engineering
- The 3 types of retrieval: keyword, semantic, and graph RAG
- Why more context actually hurts performance (context rot)
- How to build evals that learn from future outcomes
- 5 actionable homework items you can start today

**People mentioned:**

- Simon Willison (AI Engineer, Creator of Datasette)
- Kevin Weil (CPO at OpenAI)

**Key terms:**

- Context window
- RAG (Retrieval Augmented Generation)
- Semantic search / Vector databases
- Graph RAG / Knowledge graphs
- Context rot
- Evals / Data flywheel

Context engineering is where product strategy meets model behavior. The best AI products aren't using better models—they're using better context.

]]> context engineering, prompt engineering, AI product management, RAG, LLM, semantic search, vector database, graph RAG, product manager, AI strategy No Why Karpathy Saying “I’m Behind” Should Matter to Every PM 1 1 Why Karpathy Saying “I’m Behind” Should Matter to Every PM full a8662e30-5d0e-4fa0-946f-fbb184a198e0 https://share.transistor.fm/s/c140c8b5 🎧 Episode 001 — If Karpathy Feels Behind, What Does That Mean for Product Managers?


Summary:

In this debut episode of Contextually Aware, product manager Luis Calderon dives into a striking moment from the AI world. Andrej Karpathy — former Tesla AI lead, co-founder of OpenAI, and one of the most respected voices in AI — recently said he has “never felt this much behind as a programmer,” because artificial intelligence is fundamentally reshaping how software gets built. That change isn’t incremental — Karpathy describes the profession as being dramatically refactored by AI tools and paradigms.  


In this episode, we turn that insight into meaning for product managers. What does feeling behind actually signal about how AI is changing software development? And what questions should PMs be asking about strategy, workflows, uncertainty, and success when AI becomes a core part of how products are built?


Key Topics Covered:


  • A clear explanation of Karpathy’s candid reflection on AI and programming.  
  • Why this matters to product managers — beyond engineering hype.
  • How AI tools and abstractions (agents, workflows, memory layers) are changing mental models for building software.
  • Practical product strategy implications: decision-making, risk, and prioritization.
  • A big open question to carry forward: How do we define success when the tools we use are probabilistic and evolving?


Who This Episode Is For:

Product managers, technical leaders, builders, and anyone trying to make sense of how AI is changing the craft of building products — and who wants to keep up without drowning in hype.


Resources Mentioned:


  • Karpathy’s reflective post on AI and programming being refactored by AI.  
  • Broader context on how software development paradigms are shifting (Software 3.0).  


Connect & Engage:

If this resonated, share it with a colleague and join the conversation! Tweet at @ContextuallyAware or connect on LinkedIn — your thoughts will shape future episodes.

]]> 🎧 Episode 001 — If Karpathy Feels Behind, What Does That Mean for Product Managers?


Summary:

In this debut episode of Contextually Aware, product manager Luis Calderon dives into a striking moment from the AI world. Andrej Karpathy — former Tesla AI lead, co-founder of OpenAI, and one of the most respected voices in AI — recently said he has “never felt this much behind as a programmer,” because artificial intelligence is fundamentally reshaping how software gets built. That change isn’t incremental — Karpathy describes the profession as being dramatically refactored by AI tools and paradigms.  


In this episode, we turn that insight into meaning for product managers. What does feeling behind actually signal about how AI is changing software development? And what questions should PMs be asking about strategy, workflows, uncertainty, and success when AI becomes a core part of how products are built?


Key Topics Covered:


  • A clear explanation of Karpathy’s candid reflection on AI and programming.  
  • Why this matters to product managers — beyond engineering hype.
  • How AI tools and abstractions (agents, workflows, memory layers) are changing mental models for building software.
  • Practical product strategy implications: decision-making, risk, and prioritization.
  • A big open question to carry forward: How do we define success when the tools we use are probabilistic and evolving?


Who This Episode Is For:

Product managers, technical leaders, builders, and anyone trying to make sense of how AI is changing the craft of building products — and who wants to keep up without drowning in hype.


Resources Mentioned:


  • Karpathy’s reflective post on AI and programming being refactored by AI.  
  • Broader context on how software development paradigms are shifting (Software 3.0).  


Connect & Engage:

If this resonated, share it with a colleague and join the conversation! Tweet at @ContextuallyAware or connect on LinkedIn — your thoughts will shape future episodes.

]]> Sat, 27 Dec 2025 20:45:03 -0800 Luis Calderon Watch on YouTube Luis Calderon 468 🎧 Episode 001 — If Karpathy Feels Behind, What Does That Mean for Product Managers?


Summary:

In this debut episode of Contextually Aware, product manager Luis Calderon dives into a striking moment from the AI world. Andrej Karpathy — former Tesla AI lead, co-founder of OpenAI, and one of the most respected voices in AI — recently said he has “never felt this much behind as a programmer,” because artificial intelligence is fundamentally reshaping how software gets built. That change isn’t incremental — Karpathy describes the profession as being dramatically refactored by AI tools and paradigms.  


In this episode, we turn that insight into meaning for product managers. What does feeling behind actually signal about how AI is changing software development? And what questions should PMs be asking about strategy, workflows, uncertainty, and success when AI becomes a core part of how products are built?


Key Topics Covered:


  • A clear explanation of Karpathy’s candid reflection on AI and programming.  
  • Why this matters to product managers — beyond engineering hype.
  • How AI tools and abstractions (agents, workflows, memory layers) are changing mental models for building software.
  • Practical product strategy implications: decision-making, risk, and prioritization.
  • A big open question to carry forward: How do we define success when the tools we use are probabilistic and evolving?


Who This Episode Is For:

Product managers, technical leaders, builders, and anyone trying to make sense of how AI is changing the craft of building products — and who wants to keep up without drowning in hype.


Resources Mentioned:


  • Karpathy’s reflective post on AI and programming being refactored by AI.  
  • Broader context on how software development paradigms are shifting (Software 3.0).  


Connect & Engage:

If this resonated, share it with a colleague and join the conversation! Tweet at @ContextuallyAware or connect on LinkedIn — your thoughts will shape future episodes.

]]> Andre Karpathy Tweet on X No