Browse builds

Jev-kit: all the Jev stuff I've wired into Claude Code, now in one repo (guard hook, sub-agent sizing, file search, browser agent) I've been using TypeSafe's Jev with Claude Code for a couple of days and kept adding more use cases/ideas. So I've cleaned it up and put it in one repo. For anyone who doesn't know Jev; Jev is a small fast model that answers pick-one and yes/no questions (and more) in about 0.3s, which makes it cheap so you can put it inside your agents loop and make calls Claude would otherwise spend a turn on (eating away our already limited weekly usage). What's in it: A PreToolUse guard. Plain code checks let about 93% of tool calls straight through in ~33ms, the grey ones get one question to Jev. Catches stuff like cat on an env file or a find across the whole disk Sub-ag

I built an app that uses Jev to classify your entire query workload and tell you which queries to EXPLAIN ANALYZE first I posted here last month about RDST, the free desktop app that runs the slow query to EXPLAIN to index recommendation loop. The biggest change since then is what happens before EXPLAIN. When a database has thousands of distinct query shapes, you can't EXPLAIN ANALYZE all of them, and sorting queries by their performance doesn't always tell you which ones are actually badly written. RDST now answers that with Jev, a small classification model from TypeSafe that answers fixed multiple-choice questions about a query in a fraction of a second. Every query shape it discovers gets classified from its SQL and the schema, without executing anything, on five structural questions:

jev-browser: give it a task and a URL, Jev picks one action per step Fast and cheap browser use: Jev chooses one action per step from the clickable, typeable and selectable elements of the page, and scores how likely the goal is met or the run is stuck. Budgets, retries and stopping stay in code. Ships as an MCP server, CLI or library. Around 178 stars.

Tried using Jev to check what an agent should remember We've been trying Jev for agent memory, mostly to catch cases where the saved note says more than the original conversation did. Think "we might use Postgres" turning into "we chose Postgres." Once that gets saved, the next conversation starts from a decision nobody actually made. We gave Jev the original text and a proposed memory, then used its judgments to decide whether to save it, skip it, or leave it undecided. We ran the same setup with Luna as a comparison. On 100 synthetic cases, using a .40 cutoff, Jev kept 39 of the 50 memories labeled worth keeping. Luna kept 41. Neither saved a candidate labeled skip or defer. Median gate latency was 250 ms for Jev and 1,593 ms for Luna, including network time. Then we checked what happen

I used Jev to make Claude actually follow my CLAUDE.md, checked on every turn in ~350ms CLAUDE.md is context, not a constraint. It holds for a few turns, then a handler shows up with raw SQL in it and a failing test quietly becomes it.skip. Nothing checks the output against the rules you wrote. So I added the check. A Stop hook takes the final reply, a PostToolUse hook takes each edit, and every rule in your CLAUDE.md becomes one typed yes/no question in a single Jev request. All rules answered in parallel, anything over the threshold goes back to Claude with the rule quoted, and it fixes it in the same turn. Benchmark (59 labeled examples, 19 rules): 93.3% precision, 93.3% recall, 348ms p50, ~$0.045 per 1,000 checks. What it's bad at is in the repo too: exact characters, dataflow rules l