What people are building with Jev

Harry Tandy

@HarryTandy

TypeSafe founder Diogo Almeida: "I want to automate the easy work before the hard work" In this 2-hour interview, he explains how Jev uses choices, scores, and yes/no probabilities to route work inside agent loops The guide below has 3 builds: a skill router, a codebase linter, and a retrieval reranker Watch the interview, then copy the builds below

Ricky Grannis-Vu

@RickyGrannisVu

We rewrote the oldest primitive in programming. The if statement takes a sentence now, and Jev decides. That nested boolean mess eleven rules deep, four AND/ORs, one comment nobody understands anymore? Replace all of it with "if this expense is allowed under the expense policy." Judgment Mode, live now.

Ricky Grannis-Vu

@RickyGrannisVu

Jev gives you a probability. You set two lines on it: above the top one it's a yes, below the bottom one it's a no, anything in the gap runs a third branch. It's just a normal branch, so you can handle uncertainty flexibly.

saturn

@SatOnchain

this paper is f*cking gold. someone made handbook on "how jev can be used as classifiers for evals on scale" anyone who solve this has chance to make millions. Bookmark and read full handbook.

st1ne

@SolSt1ne

Jev Founder, Diogo Almeida, just released an independent working note on building a Jev harness for coding agents Not affiliated with the Jev team, just a builder mapping the loop Section 1 - the split: why harnesses already make these calls, just badly, with regex parsing prose replies Section 2 - five calls to move: files, model, safety, done, context, each one a typed Score, Choice or Noul instead of free text Section 3 - what it saves: ~400x cheaper per decision vs a cached frontier model, 0.2s median per call vs seconds of LLM latency $605 per 1,000 decisions on cold frontier calls down to $0.17 on Jev Bookmark it and read it tonight Then go rebuild your own agent loop

VIctor Janni

@VictorJanni

If you liked the idea of Jev (ultra-fast, typed decision models instead of heavy 70B LLMs), meet Laya—the open-source equivalent. Why it matters for production pipelines: 1. Blazing Speed (<100ms): Avoids slow token-by-token generation. Evaluates options in parallel to make routing/classification decisions in milliseconds. 2. 100% Self-Hostable: Run it locally on cheap hardware (even CPUs). Zero API costs and zero data leaving your servers. 3. Native Typed Outputs: Outputs structured probability distributions directly. No messy regex or JSON cleanup needed. Top Use Cases: • Fast Model Router: Route 80% of easy queries to small models and save frontier LLMs for hard tasks. • RAG Filter: Score and prune retrieved chunks in 40ms before generation. • Real-Time Guardrails: Catch prompt injections before they reach your main agent. The future of AI architecture: Local open-source models handle the fast plumbing. Frontier models handle the deep thinking. GitHub Repo 👇 https://github.com/aayushch/laya #OpenSource #AIEngineering #LLMs #MachineLearning #SystemDesign #BuildingInPublic

Théo

@cryptofanatiix

Most people slap the word “AI” on any Buy / Sell button and leave it there. Jev is a model that came out this week to make fast choices. You give it a situation, and it answers with a decision and a probability: buy, sell, or wait. That takes about 200 milliseconds. It is not a model trained by the Qubic network. It is a tool you call, like an API. X is full of people who wired it to a ticker. Some taped it onto Hyperliquid over a weekend. A few have already posted their losses. That shows what it is: a call returned very fast, then the bot keeps going on its own. So the real subject is not “AI can trade.” It is not mixing two things that do not have the same job. Jev gives an opinion and stops there. It does not stay attached to the error. If the trade is wrong the next day, Jev is not the one carrying the score. The account of whoever plugged in the bot is. BPP-9000 is not built that way. Qubic miners train a small network on a year of Bitcoin hourly candles. The network has to say whether the next hour is more likely to go up, go down, or stay uncertain. Errors are counted. More errors means a worse score. Ant Colony keeps the version that misses less than the last one. That work stays on the network. You can still open a page later and reread the result. The difference fits in two sentences. Jev is a judgment you ask for the length of one call. BPP-9000 is a model the network has to carry for a full epoch. I am not running my bag through a bot someone stood up on a weekend. I am waiting for a public score I can refresh. The question I keep: the day both put up an arrow, which one can you still reread the next morning?