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Jev: a "System One" model that only makes decisions, $0.042 per million tokens with output free. 40-second explainer of why it matters for automation Jev launched on Sep 15 from TypeSafe AI (founder Diogo Almeida, ex-OpenAI, credited on ChatGPT/InstructGPT). It does not generate text; it returns typed decisions (Choice / Score / true-false) with confidence. Price is $0.042 per million input tokens, output free. In its first week it became the fastest-adopted model on Vercel's AI Gateway, TypeSafe paused signups on Sep 22, Browser Use released jev-ultrafast (a web agent where Jev picks each action; Google Flights search in 7.1 s), and jaredpalmer/kev is an Apache-2.0 clone built on Qwen3.5 you can run yourself. My take in the video: the expensive part of automation was never the writing, i

I built a Jev-based model router for Pi for automatic model selection When Jev came out, one of my first thoughts was: could something this fast and cheap pick which model should handle an agent’s next turn? So I built plugin for Pi that uses Jev to choose the model and thinking effort. The idea is to send routine work to cheaper models and reserve the expensive ones for harder tasks. In Pi, the model mappings are configurable, and you can pin a model when you disagree with the router. I’ve been using them for a few days, and so far they’ve been working well for me. In my own evaluation, routed usage came to $141 at list prices versus an estimated $166 for Opus 5.5 on every request - about 15% less. That’s one developer’s data, though. The baseline is a pricing comparison, not a separate

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:

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