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Jev model has been causing a sensation on GitHub ever since its release, and I am going to tell you about the best repositories that nobody is talking about How does this work: browser-use/browser-use⁠ - 32,500+ stars The core web automation library that houses the Jev integration ecosystem (including...

16,444 просмотров • 1 день назад •via X (Twitter)

Комментарии: 41

Фото профиля Ridark
Ridark1 день назад

You have a genuinely unique terminal with its own approach for sure honestly for sure honestly, how did you make it work?

Фото профиля Bober_smart
Bober_smart1 день назад

Using technology on GitHub

Фото профиля Hussain Hashim | Building SundayBack
Hussain Hashim | Building SundayBack1 день назад

@Bober_smart curious if you've noticed any bottlenecks with browser-use/browser-use when scaling? Hit a similar wall in my own work.

Фото профиля EugBass
EugBass1 день назад

just fkng saved this

Фото профиля Bober_smart
Bober_smart1 день назад

I hope you enjoy using it

Фото профиля Gipp 🦅
Gipp 🦅1 день назад

does browser-use need chrome or will firefox run smooth too?

Фото профиля Bober_smart
Bober_smart1 день назад

Absolutely any browser will do

Фото профиля Bubo
Bubo1 день назад

langchain using jev as the guardrail before the heavy model is the part nobody talks about

Фото профиля Bober_smart
Bober_smart1 день назад

Yes, but this part is incredibly important

Фото профиля Dekos
Dekos1 день назад

I also recently heard about this, it's worth a try

Фото профиля Bober_smart
Bober_smart1 день назад

I'm sure you'll like it

Фото профиля Kinder
Kinder1 день назад

Bookmarked. Always good to see infra tools getting the focus over raw model hypes

Фото профиля Bober_smart
Bober_smart1 день назад

Yes, this is truly one of the very best tools

Фото профиля Yuvelir
Yuvelir1 день назад

The useful split is Jev as the cheap classifier and the heavy model only after the risk check. LiteLLM routing on intent before the expensive call is the part that actually moves the bill.

Фото профиля EagleVision
EagleVision1 день назад

I wonder which of these repositories actually use Jev in practice

Фото профиля Bober_smart
Bober_smart1 день назад

I tested absolutely everything, every single one

Фото профиля Will
Will1 день назад

@Hydra_Swarm is a cool project that is integrating Jev that’s worth checking out 👀

Фото профиля Crypto Mavka
Crypto Mavka1 день назад

The perfect tool for automating drop farming. Let's get to work

Фото профиля Bober_smart
Bober_smart1 день назад

I have tested absolutely all the tools and can recommend every single one of them

Фото профиля ꧁EVGENIA꧂
꧁EVGENIA꧂1 день назад

The perfect tool to configure bots for drop farming.

Фото профиля Bober_smart
Bober_smart1 день назад

Yes, that will work perfectly

Фото профиля Insomnia
Insomnia1 день назад

jev model is keep pushing

Фото профиля Bober_smart
Bober_smart1 день назад

True

Фото профиля me_cool_off
me_cool_off1 день назад

bookmarked bro, definitely useful repos

Фото профиля Bober_smart
Bober_smart1 день назад

I tried to select the very best

Фото профиля emelu
emelu1 день назад

it's been a rollercoaster since Jev dropped

Фото профиля Bober_smart
Bober_smart1 день назад

Thanks, but that reminds me of something legendary

Фото профиля Money Bunny
Money Bunny1 день назад

bro this repo been sitting in my watchlist untouched respect for finally covering it

Фото профиля farxxxxx
farxxxxx1 день назад

I didn't realize Qwen3.5 was the base for these Jev decision models

Фото профиля boan
boan1 день назад

some underrated repos on this list for sure

Фото профиля Sevenup
Sevenup1 день назад

Thx for sharing

Фото профиля Zentrix⌚️
Zentrix⌚️1 день назад

Mate, this is an alpha. Thanks for sharing

Фото профиля Amsterdam
Amsterdam1 день назад

As always, thanks for explaining that 💪🏻

Фото профиля tenzo
tenzo1 день назад

definitely saving this one

Фото профиля ELG
ELG1 день назад

great information bro

Фото профиля RAZA | AI EXPLORER
RAZA | AI EXPLORER1 день назад

Great list. These repositories show how decision models can fit into real agent and automation workflows.

Фото профиля _alphashark_
_alphashark_1 день назад

Hosting a Jev checkpoint and shipping a native Jev integration are different things. Labeling those separately would help readers choose.

Фото профиля Roan
Roan1 день назад

this repo are absolutely amazing bober

Фото профиля Andrii
Andrii1 день назад

Jev as a fast router before heavy models is a smart scheme

Фото профиля Sofie j,
Sofie j,1 день назад

Facts!

Фото профиля Jatin Garg
Jatin Garg1 день назад

browser automation demos always get traction, but what's the failure rate when sites change their layout? most tools break within weeks.

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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 просмотров • 9 дней назад

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

29,894 просмотров • 13 дней назад

Another insane Jev use case! Traditional database filters need precise, predefined conditions. But many questions are semantic: - Is this article mainly about software engineering? - Which topic best describes it? - How technically deep does it appear? These usually require moving rows into application code, invoking a model, parsing its output, and writing the result back. pg-jev is an open-source Postgres extension that exposes Jev through SQL functions. - jev() works inside WHERE - jev_prob() returns a probability - jev_choice() selects a label - jev_score() ranks rows across ordered levels. It needs no vector column or embedding index. Under the hood, pg-jev batches rows, sends them to Jev, and returns typed answers that SQL can filter, sort, group, and combine with exact predicates. The recording below runs it against real Hacker News stories stored in Postgres. It first shows the rows, then asks Jev to find software-engineering stories, classify them by topic, and rank them by technical depth. The final query reports requests, tokens, cost, and cache hits from the database session. GitHub repo: (don't forget to star it ⭐) Postgres keeps the data and controls the query, while Jev handles the part SQL cannot express as a deterministic condition. If you want to understand what Jev is doing under the hood, I also wrote a hands-on guide to building a Jev-style model with open models, entirely locally. Read it below.

Avi Chawla

292,977 просмотров • 4 дней назад

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

172,732 просмотров • 17 дней назад

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 просмотров • 18 дней назад

how to use claude code mods like a top 1% user, step by step: give this to your agent before everyone catches on👇 1. set up Jev connect Jev to the model registry you want to use. check that the connection works and the listed models are available. 2. build your claude code mod open claude code 2.1.287 or later and paste this prompt: “build a mod called run-ledger. load plugin-authoring and use the API types for my installed version. create a dashboard that shows: - estimated cost per run, model, and source plugin where known - which model handles each task - each subagent’s status, latest action, and elapsed time include token counts, cache usage, and reported retries. count each request once. keep background tasks linked to their original run. use dated prices. label costs as API estimates, not subscription charges. show unknown when data is missing. connect the mod to my existing Jev setup. give Jev the task, available models, prices, budget, and relevant past results. ask it to recommend a model and explain why. start with recommendations. make automatic routing optional for eligible subagents. show the recommended model, actual model, result, and cost. include Jev’s own cost. keep state across hot reloads. add details and export. the dashboard itself must make no model calls. validate the plugin. test rendering, costs, attribution, and duplicate counting. give me steps for a live test.” 3. test the setup allow hot reload when prompted. run a simple task, a subagent task, and a mod-triggered model call. check that the dashboard updates, each request counts once, and Jev’s recommended model can actually run the task. 4. install the working mod ask claude to copy it out of the temporary folder and install it as a persistent plugin. see the cost. choose the model. check the result.

Avid

47,276 просмотров • 5 дней назад

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

291,608 просмотров • 16 дней назад

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

11,329 просмотров • 10 дней назад

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

162,662 просмотров • 19 дней назад

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💎

265,878 просмотров • 15 дней назад