ÜBER DIESE EPISODE
On episode 3 of Lab Notes, Amir Zohrenejad sits down with Qizheng Zhang to explore one of the fastest-moving areas of AI research: recursive self-improvement. Together, they discuss Meta-Harness, context engineering, and why the future of AI may depend as much on the software surrounding models as the models themselves. The conversation also examines evaluation, agentic systems, and the limits of today's autonomous AI research.
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WEITERE EPISODEN IN DIESEM PODCAST
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, a…
On episode 10 of Third Loop, the Progressive Delivery team speaks with Honeycomb co-founder and CTO Charity Majors about observability, AI, and the changing economics of software development. They explore a future where code is increasingly disposable and regenerable while architecture, constraints…
On episode 4 of Lab Notes, Amir Zohrenejad speaks with Junchen Jiang about why KVCache may be better understood as reusable, AI-native data rather than a temporary inference optimization. They explore how LMCache and CacheBlend can reduce redundant computation, move context across distributed infer…
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 discu…
On episode 12 of High Leverage, Joe Ruscio sits down with Dexter Horthy of HumanLayer. They explore the promise and limitations of autonomous coding, including why today’s models excel at bounded programming tasks but struggle to account for the long-term consequences of architectural decisions. Th…
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