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This Week in AI

O'Reilly

We pack a full hour of value into 30 high-intensity minutes, delivering breaking AI news, live technical demos, and expert “Intelligence Briefs” from the front lines. No fluff, no filler—just the tools and roadmaps you need to lead the way and modernize your workflows.

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  • 22 episodes
  • weekly
  • Avg 28 min
  • English

Episodes 22

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  • S1 · E2
    June 5 · 30 min

    Production Viability with Andreas Welsch, Maya Mikhailov, and Doug Shannon

    This week, host Andreas Welsch brought together Maya Mikhailov, cofounder and CEO of Savvi AI, and Doug Shannon, generative AI and intelligent automation leader, to cover four developments shaping how organizations build with and buy into AI: OpenAI’s push into personal finance, the role of metacognition in AI-assisted technical work, the growing backlash against token-based productivity metrics, and the new role of forward-deployed engineer. Maya reframed OpenAI's move into personal finance as an intent-harvesting play, explaining how transaction data combined with chat history gives AI companies a portrait of consumers that banks, advertisers, and anyone selling attention will pay dearly for. Doug made the case for metacognition as a professional skill: AI systems are designed to find the mean and that the human's job is to know when the mean is good enough and when it isn't. The panel then examined the limits of tokenmaxxing and discussed why the shift to usage-based pricing will force a reckoning that internal policy never quite managed. All this, plus a warning about intellectual surrender and IP, the problems with the forward-deployed engineer model, and why organizational knowledge is the key to successfully deploying AI. Watch now.

  • S1 · E1
    May 22 · 28 min

    Rethinking the Agent Harness

    This week, host Eric Freeman and John Berryman, founder of Arcturus Labs, coauthor of Prompt Engineering for LLMs and an early production engineer on GitHub Copilot, cover the week's biggest AI developments: Anthropic's decision to restrict its Mythos model after it identified critical security flaws, the White House's possible pivot to FDA-style AI review, and the staggering compute deals reshaping the industry, including a 40,000-acre Utah data center planned for nine gigawatts of power. Berryman then takes you through four years of AI product development, from tiny 2,048-token context windows to today's agent harnesses, and shows why the gap between a bare model and a well-designed harness now drives more performance than any model benchmark. He also demos a personal agent that carries context from an Obsidian notebook into Wikipedia, giving a glimpse of how a future open agent protocol might work, and explains how he helped a client replace an entire bespoke application with a skills-driven agent that domain experts can read and fix themselves, in plain English, no developer required. If you build with AI or make decisions about AI tooling, this episode covers the infrastructure, policy, and architectural shifts you need to understand right now.

Showing 21–22 of 22 episodes