Live with Tim O’Reilly

O'Reilly

A series of candid conversations designed to slow down, dig deeper, and share real insights you can build on. Get the story behind the show here: https://www.oreilly.com/radar/more-slowly/

  1. 3d ago

    The Days When Fable Got Hit by a Bus and Disappeared with Gene Kim

    Long-running Claude Fable 5 agents had become the backbone of Gene Kim's personal knowledge system, one built from thousands of screenshots, videos, and half-finished side projects, spanning 50 code repositories and tens of thousands of changes. He’d been warned that Fable might be pulled from his subscription and got to preparing for that eventuality. But it disappeared eight days earlier than he planned for, mid-session, when Anthropic suspended access over an export-control issue. As he put it, the ship was soon “on fire, tumbling in space.” Gene’s the coauthor of The Phoenix Project, The DevOps Handbook, Accelerate, and, most recently, Vibe Coding (with previous guest Steve Yegge) as well as founder of the Enterprise AI Summit, so he knows a thing or two about dealing with unplanned disasters. He joined Tim for an encore of the lightning talk he gave at O'Reilly's Foo Camp about how he recovered his system, followed by a Q&A on what the shutdown taught him. Gene described watching his systems fail once the less-capable Claude Opus took over from Fable mid-session. Commands that should have worked started breaking, the model argued with itself over which instance had done what, and every action risked making things worse. It took roughly three hours that night, and more the next morning, to stabilize things, largely by recovering documentation Fable had written along the way, and Gene took the audience through the process step-by-step. He and Tim also discussed the deeper lesson of dependency risk from AI providers. We hear a lot about pricing and availability changes, but export controls, shifting safety policy, and geopolitics can force the same kind of sudden cutoff. Gene helped us understand why the solution is the same preventive, rehearsed resilience DevOps engineers built over the last 15 years for large-scale outages, now applied to individuals running mission-critical agents of their own.

  2. 3d ago

    Your Next Product Is a Process with Harper Reed

    Harper Reed has spent his career building software people actually love working on, from cofounding Threadless to leading engineering for the Obama 2012 campaign. He joined Tim for a conversation about what happens once software becomes less a product and more a medium for expressing values, and why that shift has made what Harper calls "conviction collapse" (the point where a product pipeline moves so fast there's no time to actually fall in love with what you're building) a real risk. With 2389 Research, the company he runs with his partner, Dylan Richard, Harper has been answering that by treating the product itself as optional, letting an accumulation of small, working experiments define what the company becomes, an approach he compares to the old Unix philosophy of small programs built to scratch a specific itch. Some of those experiments got playful. 2389 built an internal social network where its coding agents post updates, pick names, and develop personalities (Harper's own agent goes by Doctor Biz), and early evals suggested letting agents have fun barely hurt task performance while making the work far more enjoyable to watch. On the cost side, Harper described Thrifty, a skill 2389 just released that pairs a strong model for planning with a cheaper model for execution, cutting token costs by roughly 64% compared to Opus at equal quality. But underneath all the experimentation runs a deep humanism: How do we make sure that everybody—workers and users, privileged and not-so-privileged alike—benefit from transformations AI promises (or threatens, depending on your outlook)? It’s a question animating everything from AI tool adoption to the values an organization professes to society writ large. "I want to make sure the beauty and fun I had over my career doing technology...maintains," Harper explained, "because that's the reason I'm here."

  3. Jun 11

    Making AI Relatable: Harper Carroll Live with Tim O’Reilly

    Harper Carroll is a computer scientist who built machine learning systems at Meta but she also describes herself as "born an actress from Manhattan." She’s combined those disparate parts of her background into a unique role as an AI educator, where she uses her magic superpower of making sense of AI to reach half a million people on social media and beyond. She has a knack for explaining how models actually work, covering concepts like optimization and token distributions and the math behind them in terms that land for people who've never opened a Python notebook. Harper sat down with Tim to talk about how she makes technical complexity incredibly relatable, but they also thought through some of the more comprehensive challenges the industry is facing. Those ranged from the technical, as Harper explained why fine-tuning a small open source model beats prompting even the best closed-source model when you're trying to capture voice, to cultural considerations like the need to shift the narrative from fearing AI to explaining how AI can expand ambition both for individuals and for organizations, why we should treat AI as a medium like photography or writing, and why open source AI is a much bigger story than open source models. And in keeping with both Harper’s and Tim’s focus on learning, they discussed the skills everyone in the workforce will need to have to use AI effectively. That’s a social problem to the extent that we’ll need to ensure that everybody learns enough about AI so we don't end up with AI haves and have-nots. But it’s also a recognition that AI education is becoming a critical part of the path to success for all kinds of jobs. "The people who are really going to struggle," Harper told Tim, "are the people who are not willing to accept that AI is coming and are not willing to learn it."

    Making AI Relatable: Harper Carroll Live with Tim O’Reilly
  4. Jun 3

    Data Access and Other Bottlenecks in Enterprise AI Adoption: DJ Patil Live with Tim O’Reilly

    DJ Patil co-coined the term "data scientist," served as America's first chief data scientist under President Obama, was chief scientist at LinkedIn, and has spent the past decade on the founding team at Devoted Health, where he's built the kind of data infrastructure that most organizations are still struggling to create. DJ’s been on a listening tour. Wherever he travels, he finds a local university, holds office hours, and asks whoever shows up—students, faculty, hospital administrators, executives—what they're actually experiencing with AI. What he's hearing about the anger, angst, and the “impasse of dialogue” between AI boosters and skeptics is helping him refocus his thoughts about AI adoption. He joined Tim for a wide-ranging conversation about what's working right now, what's broken, and where the real bottlenecks are. DJ and Tim started with students, who are feeling that the social contract of “go to college and start a career” is broken, and DJ's plan to launch a makerspace-style program for those who didn’t land internships this summer to learn and demonstrate their skills. Then they went deep on why the organization is the bottleneck to transformation, using the healthcare industry as an example of the entrenched challenges and what’s possible when you get the infrastructure right. DJ walked through how Devoted Health built its data foundation before LLMs existed, why that tidy house is now a compounding advantage, and the change we can make when transforming our healthcare system is, as he put it, "like walking, chewing gum while balancing bowling balls on your head and on a unicycle." "We're [both] this giant human LLM," DJ told Tim, "summarizing and distilling what we're hearing from a lot of people." What they're hearing is that the chief constraint is whether our institutions can build the organizational and economic infrastructure to actually deploy what we've built.

  5. May 26

    A Conversation with Computer Programmer Steve Yegge

    Longtime software engineer Steve Yegge has lately been exploring the limits of vibe coding with projects like Beads, his coding agent memory system, and Gas Town, a proof-of-concept agent orchestrator so complicated, expensive, and chaotic that he warned those interested NOT to use it. As Tim O’Reilly points out in his takeaways from this episode, “Steve has always been one of the most provocative thinkers in our industry.” Steve joined Tim for an insightful and entertaining conversation on coding with AI that touched on everything from computer graphics in the ’90s to desire paths and why getting riled up is the key to writing a good blog post. Steve and Tim spent a lot of time discussing the wider context around Gas Town, including why Steve sees it as an enterprise tool—and as an executive assistant who takes on the mundane work so you can focus on the important problems. They also covered agent orchestration and the evolution of coding, using Steve’s eight-level framework; AI vampires and how exhausting it is to work with a stable of agents (Steve’s taking two naps a day!); why you might find yourself on the wrong side of the bitter lesson, and what to do about it; the reason Steve “wouldn’t touch [OpenClaw] with somebody else’s 10-foot pole”; why he’s adamant that developers need to overhaul their mental models from structure and framework cognition to just letting AI do its thing—and why he’s not even looking at the code anymore; and much, much more. “The big takeaway,” Steve told Tim, “is that there’s always more work. It doesn’t matter how superhumanly good your helpers get; you’re just going to want to do something bigger. Our ambition will always outstrip our compute.” Check out Tim’s takeaways from the conversation, plus clips, on Radar.

  6. May 25

    A Conversation with Google Cloud AI Director Addy Osmani

    Addy Osmani should be a familiar face to the O’Reilly community. He’s the cohost of our AI Codecon events, the author of Beyond Vibe Coding, Leading Effective Engineering Teams, The Effective Software Engineer, Web Performance Engineering in the Age of AI, Learning JavaScript Design Patterns, and Building Web Apps with Bolt, and a prolific blogger on Radar and with his own newsletter, Elevate. He’s also a longtime Googler who’s spent nearly 14 years building developer experiences in Chrome and is now helping developers and businesses succeed with Gemini. As Tim O’Reilly put it in the introduction to this episode, “Addy Osmani is one of those people who is really grounded but also really able to think big and see the future.” Addy sat down with Tim to chat about the state of the industry as it moves toward the orchestration of multi-agent workloads. In their wide-ranging conversation, they covered the tension between creativity and productivity, and balancing velocity with long-term technical maintenance and reliability—particularly from the enterprise perspective. Larger organizations can’t just let the agents rip. As Addy explained, “The real frontier for business is not necessarily having hundreds of agents for a task just for its own sake. It’s about orchestrating a modest set of agents that solve real problems while maintaining control and traceability.” And then there’s the as-of-yet unsolved problem of making everything work together as smoothly as possible. Along the way, they considered the distinction between “feeling” productive and “being” productive (h/t Will Manidis); how to keep up-to-date on the latest trends and conversations; why being able to explicitly define the architecture and the purpose of what you’re building will matter more than how fast an AI tool can build it; why it’s still a good moment for young students to become software engineers; how MCP and A2A complement each other; why YOLOing token use is probably not the best strategy for most people; and more. Watch now, or read Tim’s takeaways from the conversation (with clips) on Radar.

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A series of candid conversations designed to slow down, dig deeper, and share real insights you can build on. Get the story behind the show here: https://www.oreilly.com/radar/more-slowly/