Episodes
Reading the feed…
Reading the feed…
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 inference systems, and help support increasingly complex AI agents. The conversation also covers multimodal workloads, open-source infrastructure, and the future of AI systems research.
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.
On episode 2 of Lab Notes, Amir Zohrenejad sits down with Hanchen Li to explore the systems that make modern AI agents faster, more efficient, and better at learning from experience. They discuss KV Cache optimization, long-context inference, prompt learning, continual learning, and why better benchmarks may be just as important as larger models in advancing AI research.
On this debut episode of Lab Notes, Amir Zohrenejad is joined by Ren Wang, a researcher and PhD student at UC Berkeley, to explore the state of physical AI and why robotics has progressed differently from large language models. They discuss data scarcity, world models, simulation, dexterity, and the challenges of building robots that can reliably operate in the real world.
