Linear Digressions

Katie Malone

Demystifying AI for the intelligently curious

  1. 3 dgn geleden

    Agent Trust, Oversight and Control (The Agents Season, Episode 9)

    Capabilities get all the attention when it comes to AI agents — but what happens when a highly capable agent makes a bad decision in the real world? Trust, oversight, and control are the unglamorous but critically important flip side of the agentic AI story. This episode digs into the security concerns that emerge when you combine powerful models with real-world tool access, and why judgment (or the lack of it) might matter just as much as raw capability. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

    26 min.

  2. 8 jun

    Many Agents, Many Problems (The Agents Season, Episode 8)

    Whether you work best solo or thrive in a team, you know collaboration is complicated — and it turns out AI agents face the same tensions. This episode dives into multi-agent systems, exploring how networks of AI agents can overcome the individual limitations of a single model, and what the research says about when collaboration actually helps versus when it just adds noise. Think scaling laws, but for teamwork. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

    28 min.

  3. 1 jun

    How Do You Evaluate An AI Agent? (The Agents Season, Episode 7)

    Knowing when an AI agent has failed sounds straightforward — until it isn't. Agents have a frustrating habit of finishing confidently while quietly doing the wrong thing, or looping endlessly without ever crashing in an obvious way. This episode tackles one of the thorniest problems in the agentic world: evaluation. If failure is hard to see, how do you measure it systematically? And how do you know when your agent is actually working?

    32 min.

  4. 25 mei

    AI Agent Failure Modes (The Agents Season, Episode 6)

    Despite what the marketing hype might suggest, AI agents are far from infallible — and if you've ever actually used one, you already know this. Today's episode dives deep into the many, varied, and sometimes surprising ways AI agents can fail, from subtle reasoning errors to cascading task breakdowns. It's episode six in the show's ongoing season arc on AI agents, and failure modes turn out to be a surprisingly rich topic worth unpacking in detail. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

    33 min.

  5. 18 mei

    Agentic Planning (The Agents Season, Episode 5)

    When tackling a complex, multi-step task, even the smartest AI agent can fail without a solid game plan. This episode dives into the research around agentic planning — how agents move beyond simply reacting to what's in front of them and instead model a path forward, explore different routes, and course-correct when things go sideways. It's a subtler problem than memory, and a fascinating one: can an agent actually *think ahead*? Tune in to find out what the research says.

    24 min.

  6. 10 mei

    Memory Management for AI Agents (The Agents Season, Episode 4)

    Context windows are powerful — but finite, and surprisingly easy to overwhelm. When an AI agent is tackling a long, complex task, the information it needs has to fit inside that limited real estate, and research shows that anything buried in the middle tends to quietly disappear. So how do you design a system that actually *remembers* what matters? This episode digs into memory management for AI agents, from foundational computing concepts to practical lessons from tools like Claude Code. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

    25 min.

  7. 4 mei

    Lost in the Middle (The Agents Season, Episode 3)

    Just like a memorable talk lives or dies by its opening and closing, LLMs have a surprisingly similar quirk: they pay close attention to what's at the beginning and end of their context window — and kind of zone out in the middle. This "lost in the middle" phenomenon has real consequences for anyone building AI agents that rely on long-context reasoning. In this episode we dig into the research behind how (and how poorly) models actually use the information you feed them, and what it means for the agentic systems we're all trying to build.

    20 min.

  8. 27 apr

    ReAct and Tool Usage (The Agents Season, Episode 2)

    Before 2022, there was a wall between AI and the real world — models could reason impressively, but couldn't look anything up, run code, or check whether anything they said was actually true. This episode traces the moment that wall came down, through two landmark papers: ReAct, which showed what happens when you interleave reasoning and action in a loop, and Toolformer, which taught models to decide *for themselves* when to reach for a tool. Plus: what MCP actually is, and why a hobbyist project called Open Claw became the fastest-growing open source project in history. --- Website: https://lineardigressions.com Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323 Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT Substack: https://substack.com/@lineardigressions

    24 min.
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Demystifying AI for the intelligently curious

Informatie

  • Maker
    Katie Malone
  • Jaren actief
    2014 - 2026
  • Afleveringen
    311
  • Beoordeling
    Veilig
  • Copyright
    © All rights reserved
  • Website van aflevering
    Linear Digressions

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