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Jev has been exploding across GitHub since launch, here's what people have already built with it if you have API access and don't know where to start, copy this: jev-trader - real trading bot placing live limit orders on Monad every 300ms block, judged by Jev alone. 1,911 stars...

260,753 görüntüleme • 10 gün önce •via X (Twitter)

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Dark Ledger profil fotoğrafı
Dark Ledger10 gün önce

me opening a fresh Claude chat every morning while people are out here building operating systems around it

Adnan Boz profil fotoğrafı
Adnan Boz10 gün önce

rand.IntN(100) seems to perform in average better than Jev.

rvaniaaa profil fotoğrafı
rvaniaaa10 gün önce

Jev is wild

Hussain Hashim | Building SundayBack profil fotoğrafı
Hussain Hashim | Building SundayBack10 gün önce

@imryven nice! one tweak I'd make: set up Jev to log every trade outcome. helps spot patterns or bugs faster when scaling up. learned this the hard way on my own stuff.

wek profil fotoğrafı
wek9 gün önce

someone will start a company with Jev being the CEO

Payment Sandbox profil fotoğrafı
Payment Sandbox9 gün önce

"judged by Jev alone" is doing a lot of work in that sentence. live limit orders every 300ms block is cool. the eval being the same model the harness is built for is the part i'd want receipts on.

MateiZ profil fotoğrafı
MateiZ10 gün önce

Jev is just a joke. I tested it long enough to see clear patterns related to its logic. It just states that the true is true and false is false when this is completely obvious from the context.

Wolly Roger profil fotoğrafı
Wolly Roger10 gün önce

Jev is also on openrouter waitlist is full

The Bottom Is In profil fotoğrafı
The Bottom Is In9 gün önce

Why isnt this in the millions only at 250k mc

AI Studio profil fotoğrafı
AI Studio9 gün önce

Benchmark-Sprünge von 6 Punkten sind Marketing. Die echte Frage: Wer kontrolliert die Metriken? Dort sitzt das Risiko für Fehlentscheidungen.

Webhook Chai profil fotoğrafı
Webhook Chai8 gün önce

1,911 stars and placing live limit orders every 300ms block. jev judges the bot, but who judges jev. i'd want the fill log before trusting that one

jimbob profil fotoğrafı
jimbob9 gün önce

Jev is a calibrated classifier its ancient technology with a new name

𝐎𝐫𝐛𝐢𝐬𝟖𝟔.𝐡𝐛𝐚𝐫.𝐞𝐭𝐡 profil fotoğrafı
𝐎𝐫𝐛𝐢𝐬𝟖𝟔.𝐡𝐛𝐚𝐫.𝐞𝐭𝐡10 gün önce

Decision models could become a powerful building block for agents.

kug profil fotoğrafı
kug10 gün önce

Wonder what @grok might say about this

𝐈𝐯𝐨 profil fotoğrafı
𝐈𝐯𝐨10 gün önce

The 300ms decision window is the actually wild part here, that's barely enough time for a human to blink, let alone read an order book

Automation Lane profil fotoğrafı
Automation Lane9 gün önce

a live limit-order bot judged by the same model placing the orders. no independent fill check. run it on testnet for a week, post fills + rejects, then i'll look.

SERGIO M. BARÓN V. profil fotoğrafı
SERGIO M. BARÓN V.9 gün önce

Que es JEV

Benzer Videolar

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren't sure how to start experimenting with it, just copy this checklist: 1. jev-ultrafast Browser Use's fastest agent. Jev decides the next action and which element to click, and a language model is only called when text has to be typed. 2. typesafe-mario Jev plays Super Mario Bros. from structured emulator state, choosing every action from features pulled out of the game. 3. jev-plays-pokemon Reads Pokémon Red's game state as text, answers typed questions each turn, and lets plain code turn the answers into moves. 4. jev-drone A camera-only autonomous drone in MuJoCo with a Jev judgment model sitting in the control loop at 2.5 Hz. 5. robo-harness A real SO-101 robot arm workbench where Jev picks bounded joint steps from typed candidate actions under a spend budget. 6. fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions, scoring every tool call for whether it is still needed. 7. jev-claude Routes Claude Code's own judgment calls through Jev: typed choices with probabilities at plan approval, on questions, and before risky commands. 8. is-malicious Supply-chain check before you run anything: Jev Noul checks over source and build files, returning the implicated files and lines. 9. sqlite-jev Jev inside SQL. Noul, Choice and Score judgments exposed as SQLite functions, with confidence on every row. 10. jevinci Paints images by having Jev predict every pixel's colour in parallel, with confidence deciding how wide each stroke is drawn. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Hanako

29,629 görüntüleme • 9 gün önce

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

NO1ennn

18,445 görüntüleme • 5 gün önce

Jev is cool. So is it's OSS companion, Laya. The Latest Cool Thing In AI™ tends to get a lot of hype, sometimes without everyone even understanding it. So... what is this thing? Jev is an AI model that consumes input and produces output VERY differently than chat, claude, grok. The input is two things: 1) Text state to assess. Email, html, code, whatever. 2) A set of questions which will be asked about the attached state. The canonical example from TypeSafe's docs is to identify the urgency of a support ticket. We pass the model the customer text + a single noul question "is this urgent?". Jev returns a full set of JSON. This JSON is not generated with token-by-token autoregression. Jev is not trained to produce sequences of text tokens, rather to answer questions, and guarantees well-formed responses. In the example below, we see it produces a 0.99 probability (on a 0-1.0 scale) that the answer is "yes." Jev supports exactly three types of questions (seconds example in video): a) Noul: 0–1 probability that the answer to a yes/no question is "yes." b) Choice: Ask question with pre-defined set of answers. Jev chooses the best and assigns probabilities to each. c) Score: Ask question with pre-defined scale of answers. Jev produces a position on the scale. Jev computes answers for all questions in parallel, making responses super fast even for many questions in a single request. This might seem like a narrow set of capabilities, but in the right contexts leads to incredible potential. It also makes for a useful API / primitive for programming, since the outputs are... *ahem*... type-safe and predictable in structure. Jev is not going to replace LLMs for writing your code, auto-generating your docs, or being at the core of an agent harness. But Jev IS incredibly cool, and will be used to build a lot of amazing tech. Hope this helps.

Ben Dicken

40,810 görüntüleme • 14 gün önce

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

172,012 görüntüleme • 13 gün önce

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Akshay 🚀

119,118 görüntüleme • 10 gün önce

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

291,153 görüntüleme • 13 gün önce

Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on The Startup Ideas Podcast (SIP) 🧃 (thanks to vogel for coming on and spilling the sauce today) Watch: Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.

GREG ISENBERG

162,545 görüntüleme • 15 gün önce

I gave JEV one prompt: "turn $24.80 into 1000x or I'm pulling the plug" it didn't ask questions. didn't negotiate. didn't say "that's unrealistic" it just started $24.80 to $31,847.52 overnight I woke up, opened Meridian Desk, and the number was already there. green, pulsing, real here's the part nobody talks about: I didn't mass-trade, didn't watch charts at 3am, didn't mass-subscribe to signal groups I built a terminal called Meridian Desk and let Jev run it while I slept Jev is the brain. it doesn't trade, it thinks it reads the entire market state every few seconds and outputs one structured decision: buy, copy, skip, or pass, each one tagged with a confidence score below Jev sit four agents that do the actual work: SCOUT finds fresh mints before anyone's even talking about them LEDGER tracks smart wallets and mirrors the ones with 90%+ win rates PULSE reads momentum and social sentiment in real time FLUX maps liquidity routes so every fill gets the best execution then there's WARDEN, the fifth agent, the last gate every decision Jev makes has to pass through WARDEN before a single dollar moves. it checks for honeypots, arms stop-losses, sizes the position, and vetoes anything that smells off if WARDEN says no, nothing happens. period at hour two the balance dropped to $7 Jev went quiet. stopped entering. WARDEN was blocking everything. confidence too low, liquidity too thin, signals disagreed it waited then around hour four something shifted. SCOUT flagged a mint nobody had seen yet, LEDGER confirmed three smart wallets were already in, PULSE showed social mentions climbing, FLUX found a clean route with under 2% slippage Jev lit up: BUY, conf 0.91 WARDEN cleared it. position filled in 1.6 milliseconds that was the first real trade. the balance went from $7 to $40 in minutes after that it just kept compounding. trade after trade, all night, no human input 96.4% copy accuracy. five agents running in sync. one decision engine calling the shots I got early access to JEV. that's it. manual research trying to do what Jev does in seconds? $5,000 a month minimum, and you'd still miss the window this isn't a bot that sprays trades and hopes. this is an architecture. intelligence layer on top, execution layer below, risk gate in between Jev analyzes. agents execute. WARDEN protects I haven't touched it since I hit start bookmark this. follow me if you want to see the terminal live

cristal💎

265,419 görüntüleme • 11 gün önce

If you are confused about why 𝗝𝗲𝘃 is being called the "Internet" moment for the AI industry. This is 100% worth your time. In fact, you should watch it: It tells LLMs what to do next, in milliseconds & at almost zero cost. If you set it up correctly, you will have the AI engineer’s setup for 2028. How to set up & use Jev (to actually get the 100x): 1. Join the waitlist; it's fairly quick: typesafe .ai. 2. Then go to Claude Code or Codex. 3. Choose Opus 5-Low or Sol-Low. 4. Copy and paste this prompt: "[claude or codex] plugin marketplace add typesafe-ai/skills [claude or codex] plugin install typesafe@typesafe-ai" 5. When you type /typesafe, the skill shows up. 6. Paste your API key once and click "Allow" 7. Start with $5 in free credit. It's hard to spend more. ----- Now, here are the 3 ways to actually use Jev: 1. Jev for Linkedin I have 38,000 connections & invitations on LinkedIn. I have a new company to launch. I need to find a couple of hundred people to message. to do while saving time: > Export your LinkedIn connections and invitations. > Connect Claude to GitHub, Vercel & Apify. > Create an Apify API key to enrich your data. > Go to LinkedIn Settings → Data privacy. > Get a copy. LinkedIn will email you a ZIP file. > Open the file & find the Connections CSVs. > Upload the files to Jev. > Use Jev to classify your contacts. > Review the shortlist. 2. Jev for Gmail To go through all of my Gmail contacts and email the right people. > Go to Google Contacts. Open Other contacts. > Select all contacts. Export them. > Upload the file to Claude Code or Codex. > Use Jev to sort them into: Keep, Review, Remove and Review everything before removing anything. 3. You got lost in Claude Code, GitHub, Vercel, Apify, Jev, Typesafe. I feel you. It is overwhelming. That’s why I included the entire copy-and-paste prompt for each use case in the newsletter: A 45-second TL;DR by Matija Sosic.

Ruben Hassid

172,145 görüntüleme • 13 gün önce

LLMs vs. Jev, clearly explained! LLMs are great, and the ceiling is one you can watch scroll past: an LLM writes the answer one token at a time. give it a failed deploy and four decisions, and it produces a small JSON object where every token depends on the one before it. token nine cannot exist until token eight does, so four decisions that had nothing to do with each other just stood in a queue. then your code parses it, validates the shape, and retries when the shape is wrong. Jev fixes this without being a smaller or faster model: it removes the order. one turn on that deploy has to know: → whether the incident is urgent → which team owns it → whether the next command is risky → whether the task is actually done you declare the questions and the answer type upfront, and all four come back together, typed, with a probability on each. three primitives cover almost every fork in an agent: 1. **Choice** picks one of up to 255 options you define, like engineering, billing or sales. 2. **Score** places the state on an ordered scale you define, like low, medium or high risk. 3. **Noul** returns the probability that a yes-or-no condition is true. here is the sentence that resolves the whole confusion: text is a line you have to walk. an answer space is a room you see all of at once. ↳ generation: one order you cannot change, one string at the end, a shape you hope holds ↳ evaluation: no order at all, typed answers, a probability on every option Prompts → Agents → Loops → Graphs → Jev the probabilities matter more than the answer. ↳ engineering at 0.91 against billing at 0.09 is a route you can automate ↳ 0.52 against 0.46 is a coin flip wearing a label, and the label alone never told you which one you got that last one catches careful people. an LLM would have said "engineering" in a confident sentence and given you no way to know the race was that close. thresholds live in your code, one per action, scaled to what being wrong costs. it works when the options are known and the call depends on meaning. it is not for writing, code, arithmetic, or anything where question two needs the answer to question one. and the one that eats whole nights: type safety prevents malformed output, not incorrect judgment. Jev cannot return an option outside your schema, and it can still pick the wrong valid one with confidence. a schema-valid mistake refunds the wrong customer just as fast. an LLM writes new language when the answer space is open. Jev evaluates known paths when the answer space is closed. below i have quoted my full breakdown on Jev. it covers the three primitives, the parallel battery, the thresholds, and where it does not belong. save this and read it below ↓

Hanako

42,632 görüntüleme • 13 gün önce

Everyone is keeping quiet about this for now, but it's something everyone will be using soon Claude + Jev = Second Brain Diogo Almeida, who developed the Jev system, shared a simple thought "The point of Jev isn't to make the model smarter in a conversational sense, but to make it completely deterministic at decision points. If you have a choice of N actions, the system should yield an exact choice without hallucinations or wasted tokens" Action Plan: Jev decides what to keep, code enforces the established rules, and the language model is left strictly with writing the final note or answer At every input, Jev instantly determines its fate: save, set as a todo, skip, or flag for review, while evaluating privacy and its usefulness 30 days out Filter only allows data through if it crosses the threshold for value and confidence, keeping all private details strictly local on your device Instructs the model to generate a concise note of 5 to 12 lines focused on a single claim, prohibiting it from rewriting the core body to avoid lossy compaction Compares the new note against its neighbors, detecting duplicates, relationships, or contradictions, and automatically sets bidirectional links for highly relevant notes Pulls 10 to 20 candidates via BM25 or embeddings, then selects no more than 5 of the most precise notes to feed into the context window Runs a nightly cleanup by proposing duplicate merges, flagging outdated entries, and pinpointing conflicts, leaving the final call to you Integrates without building from scratch, running on your Obsidian vault, plugging into Claude Code, or acting as a ready-made memory framework Delivers decisions with 98.5% accuracy in just 0.30s, outperforming major frontier models and building memory 6.6x faster Your agent isn't forgetting because the model is weak; it forgets because nothing was deciding what was worth keeping in the first place Detailed instructions can be found in the article below

Bober_smart

10,656 görüntüleme • 6 gün önce