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The Stack

Open Source Radar — September 21: decision models go local

Today's board is the Jev ecosystem branching out. TypeSafe's decision model — typed yes/no, choice and score answers with probabilities instead of generated prose — now has a fast browser agent, a context compactor, and a trainable family anyone can run at home. Plus two harnesses worth a look. Jev Ultrafast (Python, ~13,500 stars, MIT) — Browser Use rebuilt its agent loop around decisions instead of prose. Rather than screenshotting the page and asking a vision model what to do, it reads the v

The Stack

Raindrop raises $35M to catch AI agents failing silently

Agent infrastructure got the money this week: a reliability layer for agents that fail silently, an accounting agent aimed at construction, and an industrial model in Hefei that now schedules power instead of just answering questions. Raindrop raised a $35 million Series A led by CRV to catch AI agents failing in production, bringing its total funding to $50 million after a $15 million seed in December. The company reads full agent trajectories — every step a run takes — and flags hallucinated

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Jev drops the waitlist — every new user gets 120M free tokens

The decision model everyone has been arguing about is suddenly free to try, and the biopharma world-model crowd just got another $20 million. Also today: the case for paying for calibrated confidence. TypeSafe AI opened Jev to everyone on Sunday, scrapping the waitlist and giving every new account $5 in credit — roughly 120 million input tokens at the model's published price. The company announced the change on X and pointed users straight at its console; 36Kr reported the credit figure the sam

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Google rebuilt AX so a million agent tasks never hit Kubernetes' etcd

Google's open-source agent runtime just traded its Kubernetes plumbing for Redis — and the reasoning in the design doc is the most interesting part of the release. Google restructured AX, its open-source runtime for AI agents, into a general-purpose orchestration layer for agentic tasks, and moved task state out of Kubernetes entirely. The rewrite shipped as version 0.3.0 on Sunday, a 151-file commit that describes a deliberate turn away from "a single CLI with an embedded Python harness" towar

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Microsoft's agents ported Copilot's runtime to Rust for $120K

GitHub rewrote the engine behind Copilot from TypeScript to Rust, with AI agents doing most of the typing — and put a receipt on the whole project. GitHub says it ported the entire Copilot agent runtime to Rust, with agents writing the bulk of the code, for roughly $120,000 in tokens. The project — described by GitHub engineer Stephen Toub — started from a scoping estimate of about 130,000 lines of TypeScript and ended with more than 800,000 lines of production Rust, after roughly 430,000 lines

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Open Source Radar — September 20: where the agent actually runs

Today's board is unusually infrastructural. The fresh momentum is not another coding assistant but the layer underneath it: a model small enough to sit on the device, sandboxes that hand an agent a whole computer, a self-hosted control plane for the agents a company runs, and the parser that feeds documents to all of them. Needle 3 (Python, ~11,800 stars) — Cactus Compute's third generation of its tiny automation model: the whole thing is a single 8–29 MB binary at roughly 2 bits per weight, an

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Open Source Radar — September 19: agents get a spec layer

Today's daily trending board is three-quarters skills and plugins, and the three things that actually broke through are all fixes for the same complaint: the agent does the work, but nothing holds the plan, the context, or the tooling in place. One project makes specs the unit of work, one makes memory a service you can self-host, and one hands an agent your trading charts. OpenSpec (TypeScript, ~62,000 stars, MIT) — Spec-driven development, packaged as a framework rather than a manifesto. Inst

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Claude Code now reads AGENTS.md — and makes its harness rewritable

Anthropic conceded the file-format fight this week, then quietly shipped something larger underneath it: a plugin layer that lets anyone — an organization, a vendor, a person — rewrite how Claude Code behaves at the hook level. Version 2.1.277 of Claude Code now loads AGENTS.md as project instructions in any project that carries no CLAUDE.md of its own, with a setting to flip it to "load both" or "ignore the shared file entirely." The same release publishes the source of the mechanism behind it

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Open Source Radar — September 18: agents borrow your browser

Today's board keeps pointing the same direction: the interesting work is no longer making agents smarter, it's letting them use the things you already have — your logged-in tabs, your notes, your laptop. BrowserSkill (TypeScript, ~4,800 stars, MIT) — Tencent open-sourced the piece every browser agent has been missing: permission to touch the browser you're actually signed into. Instead of launching a headless clone and re-authenticating, it connects your existing Chrome or Edge session to Curso

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AI 101 — What is temperature in an LLM?

Temperature is the setting that controls how randomly an AI model picks its next word: near 0 it always takes the most likely word, higher values give unlikely words a real chance, and 1.0 is the default on most APIs. It is the first knob most people turn, and the one most people misunderstand. Why it matters right now. Temperature has been showing up in the news all week as a variable, not a feature. A Lasso Security study found that switching on SynthID-Text watermarking changed how models be

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Bend 2 wants AI agents to prove their code, not promise it

Today's most consequential AI release isn't a model. It's a programming language built for a world where nobody reads the code anymore. Victor Taelin released Bend 2 on Thursday, and its central claim is narrow enough to test: an AI agent cannot merge a mistake the compiler can disprove. The mechanism is a laws file — the project's own framing is that it is an agent instruction file with a proof checker standing behind it. You declare the rules your program must never break ("the sum of all bal

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How to — build a small eval set for your own AI feature

You changed a prompt, and now you can't tell whether the feature got better or you just remembered the good answers. An eval set is how you stop guessing: a fixed batch of real cases with a written pass mark, run again every time you touch anything. You can build a usable one in an afternoon. There is nothing ceremonial about this. A good eval for a product team is 30 to 100 real cases, each with a short statement of what a pass looks like, kept in a file that gets re-run. The public leaderboar

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