Your Agent Doesn't Need an LLM for Every Decision - Raymond Weitekamp, OpenProse

Proposes replacing per-step LLM calls with fast decision-model classifiers in agent harnesses, a route to cheaper, lower-latency coding and computer-use agents.

• LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
• agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
• AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.

• videoRethinking Claude Code Best Practices — Lydia Hallie, Anthropic
• videoStop Shipping LLM Settings That Only Win on Paper - Rohan Pandey & Balaji Varadarajan, DigitalOcean
• videoYour SKILL.md Is 70% Stuff the Model Already Knows - Nikolay Petrov, Intel
• videoYour Spec Has Bugs. Catch Them Before Your Agent Codes. - Rajath Mahendrappa Kotyal, AWS

• videoRecursive Coding Agents - Raymond Weitekamp, OpenProseAI Engineer
• postCua: Fast Decision Models Instead of Full LLM Calls in AgentsCua
• videoIt’s Tokens All The Way Down: How RLMs are Different — Kevin Madura, AlixPartnersAI Engineer

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