エピソード

  • Ep. #60, The Killer App for AI with Vasanth Mohan
    2026/10/14

    On episode 60 of Generationship, Rachel Chalmers speaks with Vasanth Mohan of SambaNova about when faster AI agents actually mean better work. They explore why token consumption and code volume are poor substitutes for business value, how inference latency interrupts productive workflows, and why engineering taste still matters. Sometimes the most valuable thing an engineer can produce is less code.

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    29 分
  • Ep. #44, Beyond the Model: Observing AI Agents with Mikyo King
    2026/10/08

    On episode 44 of Open Source Ready, Brian Douglas and John McBride sit down with Mikyo King to explore why an agent can fail even when its model gets things right. They discuss observing the full agent workflow, using evaluations to uncover unexpected behavior, and testing whether skills actually improve results. Along the way, Mikyo shares how Arize approaches open source collaboration as coding agents reshape the contribution process.

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    50 分
  • Ep. #13, Ten Times Faster at What? with Jennifer Riggins
    2026/10/15

    On episode 13 of Third Loop, the Progressive Delivery team speaks with Jennifer Riggins about why faster code doesn’t automatically mean better software. They explore how AI exposes organizational bottlenecks, why psychological safety matters, and what Spotify’s growing feature set reveals about user value. Jennifer makes the case for smaller experiments, stronger feedback loops, and keeping people at the center of delivery.

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    25 分
  • Ep. #12, Shipping at the Speed of Understanding with Steph Hippo
    2026/09/22

    On episode 12 of Third Loop, the Progressive Delivery team speaks with Steph Hippo, Platform Engineering Director at Honeycomb, about what happens when AI accelerates development beyond the pace humans can absorb. They explore safe defaults, evolving code reviews, and the feedback loops that help teams understand what they ship. Steph makes the case for reinvesting saved time in better decisions, software quality, and human connection.

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    47 分
  • Ep. #59, Containers Should Actually Contain with Emily Long and Alex Zenla
    2026/09/17

    On episode 59 of Generationship, Rachel Chalmers speaks with Emily Long and Alex Zenla, co-founders of Edera, about rethinking the foundations of cloud and AI security. They explain how an IoT side project became an approach to workload isolation built around Xen and Rust, and why AI agents are making stronger security boundaries increasingly urgent. Along the way, they discuss complementary leadership skills, fundraising lessons, and the value of questioning the industry’s technical assumptions.

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    55 分
  • Ep. #5, The Voice AI Turing Test with Aoden Teo
    2026/09/02

    In episode 5 of Lab Notes, Amir Zohrenejad speaks with Aoden Teo, co-founder and CEO of Miso Labs, about what it will take for voice AI to become truly humanlike. They explore the limitations of today's speech-to-text and text-to-speech pipelines, the promise of full-duplex speech models, and the technical challenges around audio tokenization, turn-taking, emotion, reasoning, and real-time inference.

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    41 分
  • Ep. #11, Durable Systems, Disposable Code with Chad Fowler
    2026/08/25

    On episode 11 of Third Loop, the Progressive Delivery team sits down with Chad Fowler to explore regenerative software and what architecture looks like when AI makes code cheap enough to continually replace. They discuss Phoenix Architecture, Pace Layers, observability, architectural constraints, and why the system itself, rather than any individual implementation, may become the real asset in an AI-native future.

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    51 分
  • Ep. #58, Quantum Uncertainty with Anastasia Marchenkova
    2026/08/20

    On episode 58 of Generationship, Rachel Chalmers sits down with Anastasia Marchenkova. They explore what it will take to move quantum computing from promising hardware to useful, production-ready systems, including better orchestration across quantum, classical, and AI compute. Anastasia also discusses open-source infrastructure, the limits of AI automation, and her larger vision of making powerful computing and scientific tools more broadly accessible.

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    32 分