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    <title>Signal and Stories</title>
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    <description>The signal behind the AI hype and the stories of the people building it.
Signal &amp; Stories is a podcast hosted by Max Buckley and Boris Meinardus. Every episode is a long-form conversation with the engineers, researchers and founders shaping AI — cutting through the hype cycle to the technical detail and the human story behind it.</description>
    <copyright>© 2026 Boris Meinardus and Max Buckley</copyright>
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    <pubDate>Tue, 08 Sep 2026 06:21:03 -0700</pubDate>
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      <title>Signal and Stories</title>
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    <itunes:type>episodic</itunes:type>
    <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
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    <itunes:summary>The signal behind the AI hype and the stories of the people building it.
Signal &amp; Stories is a podcast hosted by Max Buckley and Boris Meinardus. Every episode is a long-form conversation with the engineers, researchers and founders shaping AI — cutting through the hype cycle to the technical detail and the human story behind it.</itunes:summary>
    <itunes:subtitle>The signal behind the AI hype and the stories of the people building it.</itunes:subtitle>
    <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
    <itunes:owner>
      <itunes:name>Max Buckley and Boris Meinardus</itunes:name>
      <itunes:email>maxwbuckley@gmail.com</itunes:email>
    </itunes:owner>
    <itunes:complete>No</itunes:complete>
    <itunes:explicit>No</itunes:explicit>
    <item>
      <title>Cognition AI Engineer: Inside The $26B AI Tool Replacing Enterprise IT – Ben Lau</title>
      <itunes:episode>7</itunes:episode>
      <podcast:episode>7</podcast:episode>
      <itunes:title>Cognition AI Engineer: Inside The $26B AI Tool Replacing Enterprise IT – Ben Lau</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/716e479d</link>
      <description>
        <![CDATA[When Ben Lau rose to Junior Partner after a decade at McKinsey, he realized a fundamental tension in corporate consulting: selling high-priced strategic slide decks was becoming increasingly detached from hands-on technical execution.

Instead of remaining in comfortable upper-tier management, he made a radical choice to reset his career—taking on a coding challenge that landed him as a Forward Deployed Engineer at Cognition, the $26B startup behind Devin.

In this episode, we sit down with Ben to look at the seismic shift transforming software engineering and enterprise strategy:

We break down a lot of fascinating tech and stories:
◼️ McKinsey To AI Startup: Why he walked away from a 10-year career as a Junior Partner to jump directly into the high-stakes world of frontier AI deployment.
◼️ The Rise Of The FDE: How Forward Deployed Engineers are replacing traditional management consulting by delivering working software instead of expensive slide decks.
◼️ Cloud Agents Vs. Local Tools: Why local coding assistants fall short on massive enterprise repositories, and how cloud-hosted agents leverage elastic virtual machines to handle complex parallel workloads.
◼️ Eliminating AI Slop: The exact architecture Cognition uses to keep agents from duplicating code, from pre-indexing codebases with DeepWiki to enforcing multi-agent dialectic code reviews.
◼️ Non-Engineers Writing Code: How non-technical sales and operations teams are using Devin to diagnose system bugs, run tests, and submit functional pull requests directly.

Beyond the technical discussion, this chat is a in inspiration when it comes to shifting careers! It shows that you don’t have to be a deeply technical person to be able to work with code anymore! It is a story about finding out what is important to each person, how to provide value, and how to adapt to modern tools to solve those problems.

Enjoy!

Check out Cognition and Devin at cognition.com and devin.ai]]>
      </description>
      <content:encoded>
        <![CDATA[When Ben Lau rose to Junior Partner after a decade at McKinsey, he realized a fundamental tension in corporate consulting: selling high-priced strategic slide decks was becoming increasingly detached from hands-on technical execution.

Instead of remaining in comfortable upper-tier management, he made a radical choice to reset his career—taking on a coding challenge that landed him as a Forward Deployed Engineer at Cognition, the $26B startup behind Devin.

In this episode, we sit down with Ben to look at the seismic shift transforming software engineering and enterprise strategy:

We break down a lot of fascinating tech and stories:
◼️ McKinsey To AI Startup: Why he walked away from a 10-year career as a Junior Partner to jump directly into the high-stakes world of frontier AI deployment.
◼️ The Rise Of The FDE: How Forward Deployed Engineers are replacing traditional management consulting by delivering working software instead of expensive slide decks.
◼️ Cloud Agents Vs. Local Tools: Why local coding assistants fall short on massive enterprise repositories, and how cloud-hosted agents leverage elastic virtual machines to handle complex parallel workloads.
◼️ Eliminating AI Slop: The exact architecture Cognition uses to keep agents from duplicating code, from pre-indexing codebases with DeepWiki to enforcing multi-agent dialectic code reviews.
◼️ Non-Engineers Writing Code: How non-technical sales and operations teams are using Devin to diagnose system bugs, run tests, and submit functional pull requests directly.

Beyond the technical discussion, this chat is a in inspiration when it comes to shifting careers! It shows that you don’t have to be a deeply technical person to be able to work with code anymore! It is a story about finding out what is important to each person, how to provide value, and how to adapt to modern tools to solve those problems.

Enjoy!

Check out Cognition and Devin at cognition.com and devin.ai]]>
      </content:encoded>
      <pubDate>Sun, 23 Aug 2026 08:00:16 -0700</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/716e479d/b832703b.mp3" length="109763948" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
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      <itunes:duration>4573</itunes:duration>
      <itunes:summary>After a decade at McKinsey and a promotion to Junior Partner, Ben Lau reset his career to become a Forward Deployed Engineer at Cognition. We talk about why FDEs are replacing slide decks with working software, and how cloud agents handle enterprise repos that local tools choke on.</itunes:summary>
      <itunes:subtitle>After a decade at McKinsey and a promotion to Junior Partner, Ben Lau reset his career to become a Forward Deployed Engineer at Cognition. We talk about why FDEs are replacing slide decks with working software, and how cloud agents handle enterprise repos</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
    </item>
    <item>
      <title>ML Systems CEO: NVIDIA's Monopoly Will Have a Problem - Emilio Andere</title>
      <itunes:episode>6</itunes:episode>
      <podcast:episode>6</podcast:episode>
      <itunes:title>ML Systems CEO: NVIDIA's Monopoly Will Have a Problem - Emilio Andere</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/2e8b4172</link>
      <description>
        <![CDATA[NVIDIA currently charges the entire tech industry a massive 60%* profit margin on AI chips. But behind closed doors, their software monopoly is starting to slip as new AI agents make it easy to run alternative hardware.

Growing up in Mexico, Emilio Andere realized early on that local opportunities in deep tech were heavily limited, prompting a move to the US to study mathematics and publish ML security research at NeurIPS. Now, he is the co-founder and CEO of Wafer, a YC and Jeff Dean-backed startup automating low-level kernel engineering to actively erode the CUDA moat and build a future of open, heterogeneous AI hardware. 

In this episode, we sit down with Emilio Andere to look at the future of heterogeneous hardware, the brutal reality of working with Y Combinator, and how they went from a simple idea to serving billions of tokens a day on Open Router. 

We break down a lot of fascinating tech and stories:
◼️ The CUDA Moat - Why NVIDIA's margins are actually a soft software moat that is easier to break than the market realizes.
◼️ Kernel Engineering - What it is, the state of AI-automated design, and the challenges of designing Kernels on hardware other than NVIDIA.
◼️ Y Combinator - The brutal reality of internal competition amongst startups that forces founders to grow rapidly or face public failure.
◼️ The Open Router Surge - What happened behind the scenes when Wafer went live on the world’s biggest token router and instantly processed billions of tokens.

Beyond the raw software and hardware deep-dives, this conversation is about the democratization of human intelligence. By breaking down this huge monopoly, we are opening up a world where advanced AI is cheap, accessible, and sovereign for everyone on Earth.

Follow Emilio Andere's work:
◼️ LinkedIn: https://www.linkedin.com/in/emilio-andere/
◼️ Wafer: https://www.wafer.ai/

Further reading:
◼️ Kernel Bench paper: https://arxiv.org/abs/2502.10517
◼️ *https://newsletter.semianalysis.com/p/ai-value-capture-the-shift-to-model
◼️ https://www.wafer.ai/blog]]>
      </description>
      <content:encoded>
        <![CDATA[NVIDIA currently charges the entire tech industry a massive 60%* profit margin on AI chips. But behind closed doors, their software monopoly is starting to slip as new AI agents make it easy to run alternative hardware.

Growing up in Mexico, Emilio Andere realized early on that local opportunities in deep tech were heavily limited, prompting a move to the US to study mathematics and publish ML security research at NeurIPS. Now, he is the co-founder and CEO of Wafer, a YC and Jeff Dean-backed startup automating low-level kernel engineering to actively erode the CUDA moat and build a future of open, heterogeneous AI hardware. 

In this episode, we sit down with Emilio Andere to look at the future of heterogeneous hardware, the brutal reality of working with Y Combinator, and how they went from a simple idea to serving billions of tokens a day on Open Router. 

We break down a lot of fascinating tech and stories:
◼️ The CUDA Moat - Why NVIDIA's margins are actually a soft software moat that is easier to break than the market realizes.
◼️ Kernel Engineering - What it is, the state of AI-automated design, and the challenges of designing Kernels on hardware other than NVIDIA.
◼️ Y Combinator - The brutal reality of internal competition amongst startups that forces founders to grow rapidly or face public failure.
◼️ The Open Router Surge - What happened behind the scenes when Wafer went live on the world’s biggest token router and instantly processed billions of tokens.

Beyond the raw software and hardware deep-dives, this conversation is about the democratization of human intelligence. By breaking down this huge monopoly, we are opening up a world where advanced AI is cheap, accessible, and sovereign for everyone on Earth.

Follow Emilio Andere's work:
◼️ LinkedIn: https://www.linkedin.com/in/emilio-andere/
◼️ Wafer: https://www.wafer.ai/

Further reading:
◼️ Kernel Bench paper: https://arxiv.org/abs/2502.10517
◼️ *https://newsletter.semianalysis.com/p/ai-value-capture-the-shift-to-model
◼️ https://www.wafer.ai/blog]]>
      </content:encoded>
      <pubDate>Tue, 21 Jul 2026 06:00:22 -0700</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/2e8b4172/b56cfd12.mp3" length="110737491" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/WHuDRs70uPe3yPLCAUqYPHfUvdFDWDvszlIBGz2KAuQ/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9lMDBj/MTM4MTA0NTNjMWYz/OGM2MDc3YWU3YzI3/ZGIxNS5qcGc.jpg"/>
      <itunes:duration>4613</itunes:duration>
      <itunes:summary>NVIDIA's real moat is CUDA, not silicon. Emilio Andere is automating low-level kernel engineering to erode it — and serving billions of tokens a day on OpenRouter while doing it.</itunes:summary>
      <itunes:subtitle>NVIDIA's real moat is CUDA, not silicon. Emilio Andere is automating low-level kernel engineering to erode it — and serving billions of tokens a day on OpenRouter while doing it.</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
    </item>
    <item>
      <title>AI Expert Warning: The Permanent Underclass Discourse Is A Lie, Do This Instead!</title>
      <itunes:episode>5</itunes:episode>
      <podcast:episode>5</podcast:episode>
      <itunes:title>AI Expert Warning: The Permanent Underclass Discourse Is A Lie, Do This Instead!</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/852d15ef</link>
      <description>
        <![CDATA[When Devansh Devansh was part of a small team that beat Apple at detecting Parkinson’s disease using real-time voice calls, he learned a frustrating lesson about big tech: internal corporate politics can easily buy up a breakthrough algorithm and bury it.

Years later, after building a massive following on his Substack, AI Made Simple, he made a radical choice. Instead of chasing a cozy role at a major AI lab, he chose to open-source his research and build a decentralized community called the Chocolate Milk Cult. His reasoning? Big tech companies have become too risk-averse, with researchers more focused on job security than making actual breakthroughs.

In this episode, we sit down with Devansh to cut through the hype and look at the real engineering bottlenecks in AI today.

We talk about:
◼️ What actually happens when corporate bureaucracy sidelines life-saving technology.
◼️ The practical flaws behind how OpenAI and Anthropic handle AI memory, and how his team solved it for legal tech.
◼️ Why he walked away from institutional big tech money to keep his research open to everyone.
◼️ How big tech's fear of making mistakes is quietly stalling the next generation of AI models.
◼️ The current "underclass" AI narrative, and what it really takes for a young engineer to stand out right now.

If you're tired of the generic AI hype cycle and want a realistic look at where the technology is actually going, this conversation is for you.

Follow Devansh's Substack:
https://www.artificialintelligencemadesimple.com/]]>
      </description>
      <content:encoded>
        <![CDATA[When Devansh Devansh was part of a small team that beat Apple at detecting Parkinson’s disease using real-time voice calls, he learned a frustrating lesson about big tech: internal corporate politics can easily buy up a breakthrough algorithm and bury it.

Years later, after building a massive following on his Substack, AI Made Simple, he made a radical choice. Instead of chasing a cozy role at a major AI lab, he chose to open-source his research and build a decentralized community called the Chocolate Milk Cult. His reasoning? Big tech companies have become too risk-averse, with researchers more focused on job security than making actual breakthroughs.

In this episode, we sit down with Devansh to cut through the hype and look at the real engineering bottlenecks in AI today.

We talk about:
◼️ What actually happens when corporate bureaucracy sidelines life-saving technology.
◼️ The practical flaws behind how OpenAI and Anthropic handle AI memory, and how his team solved it for legal tech.
◼️ Why he walked away from institutional big tech money to keep his research open to everyone.
◼️ How big tech's fear of making mistakes is quietly stalling the next generation of AI models.
◼️ The current "underclass" AI narrative, and what it really takes for a young engineer to stand out right now.

If you're tired of the generic AI hype cycle and want a realistic look at where the technology is actually going, this conversation is for you.

Follow Devansh's Substack:
https://www.artificialintelligencemadesimple.com/]]>
      </content:encoded>
      <pubDate>Sun, 28 Jun 2026 09:00:01 -0700</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/852d15ef/0d874b3e.mp3" length="132473470" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/ViN_52M7nhhhQM-CQUWdk9v-CYfguHfRrRXKCbt1Wgw/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9iZmE1/Yjg3NjJjYTcxMjUz/MDAzZmE0MjhkNTRh/YmZmYy5qcGc.jpg"/>
      <itunes:duration>5519</itunes:duration>
      <itunes:summary>Devansh helped beat Apple at detecting Parkinson's from voice calls — then watched corporate politics bury it. He explains why he open-sourced his research instead of joining a lab, and what the real engineering bottlenecks in AI are.</itunes:summary>
      <itunes:subtitle>Devansh helped beat Apple at detecting Parkinson's from voice calls — then watched corporate politics bury it. He explains why he open-sourced his research instead of joining a lab, and what the real engineering bottlenecks in AI are.</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
    </item>
    <item>
      <title>Behind the Scenes: Working At Top AI Startups</title>
      <itunes:episode>4</itunes:episode>
      <podcast:episode>4</podcast:episode>
      <itunes:title>Behind the Scenes: Working At Top AI Startups</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/351f35ac</link>
      <description>
        <![CDATA[What is it like to work at a cutting-edge AI startup?

AI Researcher BORIS MEINARDUS and Google-engineer-turned-startup-Head-of-AI MAX BUCKLEY go behind the scenes of the AI industry — from startup towers in the valley to packed poster sessions in Rio, and what it actually takes to stand out when there are 400+ applicants for two open roles.

Max Buckley, a Senior Machine Learning Engineer at Google with over 12 years of experience, has now transitioned to leading the Zurich office of Exa, a multi-million-dollar, YC-backed AI startup.

He shares behind-the-scenes insights into what it is like to lead such a startup, which involves traveling around the globe and interviewing hundreds of applicants.

Boris has also been traveling around the globe, from Tokyo to San Francisco to Rio and back.

We discuss:
◼️ What it's really like to move from Google's AI culture to a cutting-edge AI startup
◼️ The hiring reality: 400+ applicants, two open roles, and the detail that costs you the offer
◼️ Why poster sessions at top conferences are half-empty — and how Boris drew crowds at ICLR
◼️ Boris's paper: how an ensemble of 8 small models can match GPT-4o across diverse tasks
◼️ Max's 5 open-source PRs to Microsoft — and why fixing one app fixes them all]]>
      </description>
      <content:encoded>
        <![CDATA[What is it like to work at a cutting-edge AI startup?

AI Researcher BORIS MEINARDUS and Google-engineer-turned-startup-Head-of-AI MAX BUCKLEY go behind the scenes of the AI industry — from startup towers in the valley to packed poster sessions in Rio, and what it actually takes to stand out when there are 400+ applicants for two open roles.

Max Buckley, a Senior Machine Learning Engineer at Google with over 12 years of experience, has now transitioned to leading the Zurich office of Exa, a multi-million-dollar, YC-backed AI startup.

He shares behind-the-scenes insights into what it is like to lead such a startup, which involves traveling around the globe and interviewing hundreds of applicants.

Boris has also been traveling around the globe, from Tokyo to San Francisco to Rio and back.

We discuss:
◼️ What it's really like to move from Google's AI culture to a cutting-edge AI startup
◼️ The hiring reality: 400+ applicants, two open roles, and the detail that costs you the offer
◼️ Why poster sessions at top conferences are half-empty — and how Boris drew crowds at ICLR
◼️ Boris's paper: how an ensemble of 8 small models can match GPT-4o across diverse tasks
◼️ Max's 5 open-source PRs to Microsoft — and why fixing one app fixes them all]]>
      </content:encoded>
      <pubDate>Sun, 31 May 2026 08:00:01 -0700</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/351f35ac/35347ae5.mp3" length="123117000" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/PuLYCer1aK7LoUWV6SDdRjiYLIARon6Wan8Xhy8-uRM/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yZjkx/ODc4Mjc5NTM3ZWZi/MTVkZWY5NDM5MDg0/YTBlOS5qcGc.jpg"/>
      <itunes:duration>5129</itunes:duration>
      <itunes:summary>What it actually takes to work at a frontier AI startup — from startup towers in the Valley to poster sessions in Rio, and standing out when 400+ people apply for two roles.</itunes:summary>
      <itunes:subtitle>What it actually takes to work at a frontier AI startup — from startup towers in the Valley to poster sessions in Rio, and standing out when 400+ people apply for two roles.</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
    </item>
    <item>
      <title>My Final Day at Google After 12 Years (Senior ML Engineer)</title>
      <itunes:episode>3</itunes:episode>
      <podcast:episode>3</podcast:episode>
      <itunes:title>My Final Day at Google After 12 Years (Senior ML Engineer)</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
      <guid isPermaLink="false">0d237996-3d59-404f-8b5f-55d1408c5e4a</guid>
      <link>https://share.transistor.fm/s/e7c31bf1</link>
      <description>
        <![CDATA[Google Senior ML Engineer MAX BUCKLEY and AI Researcher BORIS MEINARDUS expose why being a "top contributor" on your team won't save your career, how to break free from the "hand-holding" trap of big tech, and the high-agency playbook for taking total ownership of your professional trajectory in the age of AI.

Max Buckley is a Senior Machine Learning Engineer at Google with over 12 years of experience who has now transitioned to leading the Zurich office of a multi-million-dollar, YC-backed AI startup.
Having navigated 10 different roles within the tech giant, Max has developed a reputation for identifying the "signals" of career success that go far beyond simple coding proficiency.

We explain:
◼️ Why your manager is the "Source of Truth" (and how that can be used against you)
◼️ Big Tech vs. Startups: The hidden psychological cost of "structured" environments
◼️ Why "hand-holding" is the silent killer of senior engineering growth
◼️ The Ownership Framework: How to move from being a "hired hand" to a project visionary
◼️ Why taking your career into your own hands is the only way to survive the AI transition

Foundations of Vector Retrieval - Sebastian Bruch
https://arxiv.org/abs/2401.09350]]>
      </description>
      <content:encoded>
        <![CDATA[Google Senior ML Engineer MAX BUCKLEY and AI Researcher BORIS MEINARDUS expose why being a "top contributor" on your team won't save your career, how to break free from the "hand-holding" trap of big tech, and the high-agency playbook for taking total ownership of your professional trajectory in the age of AI.

Max Buckley is a Senior Machine Learning Engineer at Google with over 12 years of experience who has now transitioned to leading the Zurich office of a multi-million-dollar, YC-backed AI startup.
Having navigated 10 different roles within the tech giant, Max has developed a reputation for identifying the "signals" of career success that go far beyond simple coding proficiency.

We explain:
◼️ Why your manager is the "Source of Truth" (and how that can be used against you)
◼️ Big Tech vs. Startups: The hidden psychological cost of "structured" environments
◼️ Why "hand-holding" is the silent killer of senior engineering growth
◼️ The Ownership Framework: How to move from being a "hired hand" to a project visionary
◼️ Why taking your career into your own hands is the only way to survive the AI transition

Foundations of Vector Retrieval - Sebastian Bruch
https://arxiv.org/abs/2401.09350]]>
      </content:encoded>
      <pubDate>Fri, 10 Apr 2026 15:00:37 -0700</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/e7c31bf1/48190cd7.mp3" length="133196308" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/Vm8DRMw6GqReymZJyYw2v8a3BB1MFFd0slztBFWbUPc/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9kZmQ5/YmYzYjg0ZTNhYzIx/MTljMWUxOTJlZTU3/OGQ1MC5qcGc.jpg"/>
      <itunes:duration>5549</itunes:duration>
      <itunes:summary>Max's last day at Google after 12 years and ten roles. Why being a top contributor won't save your career, and the high-agency playbook for owning your trajectory.</itunes:summary>
      <itunes:subtitle>Max's last day at Google after 12 years and ten roles. Why being a top contributor won't save your career, and the high-agency playbook for owning your trajectory.</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
    </item>
    <item>
      <title>The Man Automating Coding: Lessons Learned Building a SOTA Coding Agent (#2 - Nicolay Gerold)</title>
      <itunes:episode>2</itunes:episode>
      <podcast:episode>2</podcast:episode>
      <itunes:title>The Man Automating Coding: Lessons Learned Building a SOTA Coding Agent (#2 - Nicolay Gerold)</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
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      <link>https://share.transistor.fm/s/6d12a7bd</link>
      <description>
        <![CDATA[AMP founding engineer NIKOLAY GEROLD reveals the "unsolved problem" of autonomous code deployments, why AMP is the PORSCHE OF CODING AGENTS, and the high-risk future of open-source personal assistants with unrestricted access to your entire life.

Nikolay Gerold is a German developer and the Founding Engineer at Amp, a frontier AI startup building a high-performance coding agent that competes with popular tools like Claude Code. With years of experience in applied AI, he is currently pioneering the infrastructure that allows agents to move beyond "vibe coding" and into autonomous system architecture.

He explains:
◼️ Why Amp is the "Porsche" of coding agents while others risk becoming "Homer’s car."
◼️ The Unsolved Problem: When is it safe for an agent to push to production vs. using a Pull Request?
◼️ The "OpenClaw" Experiment: Extrapolating a future where AI has access to your entire life and data.
◼️ Why the most powerful (and risky) agents will likely come from open-source, not Big Tech.
◼️ The "EU Inc" reality: How to build and scale AI startups within European regulations.
◼️ Why developers won't be replaced—if they know how to navigate the "agentic" shift.

Check out Nicolay's Podcast: @howaiisbuilt 

Follow us on our other socials:
Max - LinkedIn: https://www.linkedin.com/in/maxbuckley/
Max - Twitter/X: https://x.com/MaxWBuckley
Boris - LinkedIn: https://www.linkedin.com/in/boris-meinardus-ba2302177/
Boris - Twitter/X: https://x.com/BorisMeinardus]]>
      </description>
      <content:encoded>
        <![CDATA[AMP founding engineer NIKOLAY GEROLD reveals the "unsolved problem" of autonomous code deployments, why AMP is the PORSCHE OF CODING AGENTS, and the high-risk future of open-source personal assistants with unrestricted access to your entire life.

Nikolay Gerold is a German developer and the Founding Engineer at Amp, a frontier AI startup building a high-performance coding agent that competes with popular tools like Claude Code. With years of experience in applied AI, he is currently pioneering the infrastructure that allows agents to move beyond "vibe coding" and into autonomous system architecture.

He explains:
◼️ Why Amp is the "Porsche" of coding agents while others risk becoming "Homer’s car."
◼️ The Unsolved Problem: When is it safe for an agent to push to production vs. using a Pull Request?
◼️ The "OpenClaw" Experiment: Extrapolating a future where AI has access to your entire life and data.
◼️ Why the most powerful (and risky) agents will likely come from open-source, not Big Tech.
◼️ The "EU Inc" reality: How to build and scale AI startups within European regulations.
◼️ Why developers won't be replaced—if they know how to navigate the "agentic" shift.

Check out Nicolay's Podcast: @howaiisbuilt 

Follow us on our other socials:
Max - LinkedIn: https://www.linkedin.com/in/maxbuckley/
Max - Twitter/X: https://x.com/MaxWBuckley
Boris - LinkedIn: https://www.linkedin.com/in/boris-meinardus-ba2302177/
Boris - Twitter/X: https://x.com/BorisMeinardus]]>
      </content:encoded>
      <pubDate>Tue, 17 Mar 2026 09:00:00 -0700</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/6d12a7bd/ca21b41a.mp3" length="130300513" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/0rPj-mIozV4iK1f5l49Yw3JE-AZlGQBsPTnZK9ElHXo/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hMjEw/ZDZlMjlkMDkzODIz/ZDY4ZmFkZmZkMTgx/ZThkZS5qcGc.jpg"/>
      <itunes:duration>5428</itunes:duration>
      <itunes:summary>Nicolay Gerold on building Amp — the "Porsche of coding agents" — and the still-unsolved problem of when an agent is safe to push straight to production.</itunes:summary>
      <itunes:subtitle>Nicolay Gerold on building Amp — the "Porsche of coding agents" — and the still-unsolved problem of when an agent is safe to push straight to production.</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
    </item>
    <item>
      <title>AI Engineer and Researcher: Why Who You Know Is More Important Than Your Code</title>
      <itunes:episode>1</itunes:episode>
      <podcast:episode>1</podcast:episode>
      <itunes:title>AI Engineer and Researcher: Why Who You Know Is More Important Than Your Code</itunes:title>
      <itunes:episodeType>full</itunes:episodeType>
      <guid isPermaLink="false">1288d893-18e8-4a2f-a9c5-56c0fa547a5e</guid>
      <link>https://share.transistor.fm/s/a35090e1</link>
      <description>
        <![CDATA[Google ML Engineer MAX BUCKLEY and AI Researcher BORIS MEINARDUS reveal how to ENGINEER LUCK in the AI industry, why striving for top LLM labs is a career trap for new grads, and the high-agency framework they used to land jobs at Google and frontier startups.]]>
      </description>
      <content:encoded>
        <![CDATA[Google ML Engineer MAX BUCKLEY and AI Researcher BORIS MEINARDUS reveal how to ENGINEER LUCK in the AI industry, why striving for top LLM labs is a career trap for new grads, and the high-agency framework they used to land jobs at Google and frontier startups.]]>
      </content:encoded>
      <pubDate>Mon, 23 Feb 2026 11:01:24 -0800</pubDate>
      <author>Boris Meinardus and Max Buckley</author>
      <enclosure url="https://media.transistor.fm/a35090e1/de54db25.mp3" length="145269914" type="audio/mpeg"/>
      <itunes:author>Boris Meinardus and Max Buckley</itunes:author>
      <itunes:image href="https://img.transistorcdn.com/NKVbEDKSwVHLCpOHppP1GcEVtd2eeU0cfB1inCmubik/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS81Zjgw/YzI4OTlmNDJiYWJm/MzJiYTNjOTg4ZmNk/Y2M3YS5qcGc.jpg"/>
      <itunes:duration>6052</itunes:duration>
      <itunes:summary>The first episode: how to engineer luck in AI, why chasing top labs is a trap for new grads, and the framework Max and Boris used to land roles at Google and frontier startups.</itunes:summary>
      <itunes:subtitle>The first episode: how to engineer luck in AI, why chasing top labs is a trap for new grads, and the framework Max and Boris used to land roles at Google and frontier startups.</itunes:subtitle>
      <itunes:keywords>AI, careers, machine learning, research</itunes:keywords>
      <itunes:explicit>No</itunes:explicit>
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