Topic
Google Research
3 episodes
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Harness^2: Recursive Agent Harnessing for an Open World
Harness squared argues that open-world agents need to improve both the harness for the task in front of them and the process used to improve later harnesses. Its strongest idea is a contrastive edit loop that probes candidate changes without assuming a perfect task verifier, then stores reusable lessons rather than blindly carrying task-specific scaffolding forward.
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Towards a science of scaling agent systems: When and why agent systems work
A skeptic’s take on Google Research’s paper on scaling agent systems. Cody argues the useful part is not “more agents” but the evidence that coordination only helps when the task structure fits. Justy pushes on why that matters for teams shipping assistants right now, where cost, reliability, and user trust beat demo flair. Together they unpack the five architectures, the strong gains on parallel work, the collapse on sequential planning, and what a solo builder could test this weekend.
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6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You | Towards Data Science
Justy and Cody dig into what actually changes when you stop calling an LLM API and start building pieces yourself: why fine-tuning tricks like RsLoRA matter, why RoPE won, where weight tying still makes sense, why Pre-LN became the default, and how KV cache buys speed by spending memory.