Tools & apps

npm install is the scariest command in your terminal. One typo → lookalike package → malicious postinstall → secrets gone. jev-sec-audit flags it in milliseconds using Jev, a System 1 model built for fast decisions, not chat. One step in GitHub Actions. Open source. https://t.co/PhC7TLRwCt

Built a tool that searches Google Icons using JEV (Laya-MLX) Should I publish it? 👀 https://t.co/97qw7oIkOt

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:

Built an AI slop finder for @X using Jev @typesafeai . • Tweet → Content script extracts text • Text → Backend API → Jev • Jev → AI-slop score • Score → Badge displayed on the tweet #jev #aislop #newmodels https://t.co/VMzJA1Tyde

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

How we're using Jev frrom @typesafeai at @knowyourcompani - Rendering exact citation on a document page -> upgrade from chunk box mapping to more precise citations https://t.co/5pUBxNcLS7

This might be the most useful thing I’ve built with Jev so far. A Chrome extension that quietly analyzes what I’m seeing and classify them as Opinion, Engagement bait, etc... So clean you may think it is a new feature on X :) https://t.co/POUtFhxGkD