Reading through the new vLLM release and it gives ordinary LLMs a very Jev-like trick. 👀 🎯 vLLM 0.32.0 adds a new structured-decision API. Normally, if you want structured output, an LLM might generate prompt → { → key → : → value → JSON... Now with /v1/systemone, you give it a fixed set of legal answers and vLLM reads the model's first-token probabilities. It can return: 🎯 choice → select among allowed answers + probabilities ✅ yes/no → probability of each 📊 score → probabilities across ordered levels And this works with supported ordinary generative models. 👈 The vLLM docs demonstrate it with Qwen3-0.6B Even better, with prefix caching, if you're asking multiple questions about the same state, vLLM can prefill that state once and reuse it. Each additional decision is question → one-token read → probabilities 🔗 vLLM 0.32.0 + structured-decision docs in ALT.

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Reading about agents is fun. Building one is better! Data Outpost opens Nov 4 in SF with 11 hands-on workshops: build agents w/ Vercel, Sigma & Prefect, prep data w/ Estuary, Airbyte & Reducto, run agents in prod w/ Braintrust & Together AI. Pick yours:

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Reading Eurasia Groups summary of Xi's state visit. Imagine telling someone in 2017 or 2018 that Trump would be the most China-dovish President we'd see for the rest of our lives.