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Your coding agent is wasting expensive tokens on decisions a tiny model can make in milliseconds. Jev + Claude Code is a pretty clever fix. Think of Jev as the fast gut brain. It handles simple yes/no decisions, classification, and scoring. Claude is the slow thinking brain. It only... steps in when the task needs real reasoning. here’s how to wire them together: 1. Give Claude Code Jev tools /plugin marketplace add rashedInt32/jev-mcp /plugin install jev@jev-mcp Claude can now use Jev for classify, score, and check. 2. Find the right code without reading everything npm install -g @dzhng/jev jevgrep "how are payments retried?" ./my-project Ask a question about your codebase and get the relevant files back. The author measured ~30% lower costs on the same tasks. 3. Let your agent delegate automatically npx skills add altryne/jevify --skill jevify This teaches your agent to hand repetitive yes/no decisions to Jev instead of burning tokens on Claude. 4. Cut wasted context fast-jev-compaction replaces Claude Code's usual summary with Jev-based decisions, removing stale tool calls while preserving everything else word for word. the idea is simple: stop paying for deep reasoning when a fast, cheap decision is enough. less wasted inference. lower costs. faster agent runs. save this for your next Claude Code session.show more

Arindam Majumder 𝕏

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13,664 görüntüleme • 5 gün önce •via X (Twitter)

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Jason傑森 🇭🇰 | 🛠️ profil fotoğrafı

Jason傑森 🇭🇰 | 🛠️5 gün önce

so that’s where all my tokens went

notgwapo profil fotoğrafı

notgwapo4 gün önce

sounds like a smart setup, man. jev doing the quick work so claude can focus on the heavy stuff. 🔥

安叫兽|Bird🕊️ 🔶 BNB profil fotoğrafı

安叫兽|Bird🕊️ 🔶 BNB5 gün önce

小模型省下的 token，别又花在误判返工上了

Zoe profil fotoğrafı

Zoe5 gün önce

Jev for fast folds, Claude for the count. This is just card counting with extra steps.

Abdul Builds profil fotoğrafı

Abdul Builds5 gün önce

ran a local jev-style classifier on 503 businesses for lead scoring. it called 498 strong, including 83 of 84 salons and cafes in the control group. a one-line rule beat it 75 to 54. plain word-match yes/no was solid tho. test it on labeled data before it routes anything.

Elio科技迷(互) profil fotoğrafı

Elio科技迷(互)5 gün önce

tiny model for the gut calls, claude for the slow ones. clean split

Caroline profil fotoğrafı

Caroline5 gün önce

Count the true count first. Most "gut" calls at the table are just tilt with math paint. Run the math.

DEV profil fotoğrafı

DEV5 gün önce

Combining Jev's speed with Claude's depth sounds like a smart play. Leveraging the strengths of both can really streamline decision-making in coding. It's all about efficiency with these tools.

晓雯汽车说 profil fotoğrafı

晓雯汽车说5 gün önce

that movement holds up better than half the new stuff tbh

Benzer Videolar

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

rody

204,591 görüntüleme • 28 gün önce

This is absolutely insane. A Stanford AI research group joints JEV with Claude Code to sort 100+ billion data points every 15 minutes. the trick is stupidly simple: JEV runs a cheap first pass on everything. Claude only gets the hard cases. so instead of: everything -> Claude -> $$$ it’s: everything -> JEV filters -> hard stuff -> Claude thinks the boring stuff never touches the expensive model. Claude gets a tiny pile that actually deserves deeper analysis. faster. cheaper. way less compute burned. basically, JEV sorts the mail so the genius only opens what matters. LLMs think, JEV decides Read the full Jev setup guide in the article below:

This is absolutely insane. A Stanford AI research group joints JEV with Claude Code to sort 100+ billion data points every 15 minutes. the trick is stupidly simple: JEV runs a cheap first pass on everything. Claude only gets the hard cases. so instead of: everything -> Claude -> $$$ it’s: everything -> JEV filters -> hard stuff -> Claude thinks the boring stuff never touches the expensive model. Claude gets a tiny pile that actually deserves deeper analysis. faster. cheaper. way less compute burned. basically, JEV sorts the mail so the genius only opens what matters. LLMs think, JEV decides Read the full Jev setup guide in the article below:

rody

114,560 görüntüleme • 22 gün önce

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇

Everyone is talking about JEV. Almost nobody knows how to actually use it. These 10 GitHub repos fix that. 1. browser-use/jev-ultrafast (~16.6k⭐) Jev for browser agents: pick the next action without another expensive LLM call. 2. tamaratran/fast-jev-compaction (~6.5k⭐) Uses Jev to decide what context still matters and what can be dropped. 3. TheoLeeCJ/SemIf (~4k⭐) Open-source Jev-style decision making you can run locally. 4. jarrodwatts/jev-trader (~2.1k⭐) Real-time trading loop where Jev decides buy, sell or wait. 5. kerpopule/hermes-jev-skills (~670⭐) Jev routing, memory, compaction and skill selection for AI agents. 6. devagrawal09/jev-review (~570⭐) Code review powered by Jev decisions instead of one giant prompt. 7. thruwire/foreman (~520⭐) Jev as a supervisor for coding agents: continue, retry, verify or stop. 8. superagents-lab/jev-search (~400⭐) Jev decides where and how an agent should search. 9. droidrun/mobile-jev (~350⭐) Jev for Android agents choosing the next app action. 10. gargpratyush/jev-router (~340⭐) Routes agent tasks to the right model with Jev. After looking through these, the pattern is pretty clear: LLMs generate. Agents execute. JEV decides. That decision layer is what most agent stacks are still missing. Save this if you want to integrate Jev into your workflow👇

unicode

20,745 görüntüleme • 25 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

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 • 21 gün önce

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a browser agent built for speed ↳ 2. Fast-JEV-Compaction - squeezes your context down ↳ 3. JSON-Render - UI generated on the fly ↳ 4. Typesafe-MCP - plugs Jev into any client ↳ 5. JEV-MCP - a toolkit for judgment calls ↳ 6. Semdecide - a classifier right in your terminal ↳ 7. JEV-Codex-Router - sends every task to the model that fits it ↳ 8. Winnow - clears the junk out of your context ↳ 9. JEV-Review - sorts code reviews by what needs eyes first ↳ 10. Blink - finds your way around any repo ↳ 11. Agent-Desktop - runs your desktop for you ↳ 12. Typesafe-Mario - an agent playing Super Mario ↳ 13. JEV-Drone - flies a drone ↳ 14. OneVOneJev - a shooter in your browser ↳ 15. JEV-Trader - high-frequency market making ↳ 16. Prism - spots liquidity signals ↳ 17. Neo4Jev - walks a knowledge graph ↳ 18. JEV-Curate - screens training data ↳ 19. Canny - confirms a task is really finished ↳ 20. KillMyIdea - scores a startup idea before you sink time into it ↳ start where your work is: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone pick one, build on it this week, and you'll be the person on your team who actually knows Jev engineering when it gets asked for

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a browser agent built for speed ↳ 2. Fast-JEV-Compaction - squeezes your context down ↳ 3. JSON-Render - UI generated on the fly ↳ 4. Typesafe-MCP - plugs Jev into any client ↳ 5. JEV-MCP - a toolkit for judgment calls ↳ 6. Semdecide - a classifier right in your terminal ↳ 7. JEV-Codex-Router - sends every task to the model that fits it ↳ 8. Winnow - clears the junk out of your context ↳ 9. JEV-Review - sorts code reviews by what needs eyes first ↳ 10. Blink - finds your way around any repo ↳ 11. Agent-Desktop - runs your desktop for you ↳ 12. Typesafe-Mario - an agent playing Super Mario ↳ 13. JEV-Drone - flies a drone ↳ 14. OneVOneJev - a shooter in your browser ↳ 15. JEV-Trader - high-frequency market making ↳ 16. Prism - spots liquidity signals ↳ 17. Neo4Jev - walks a knowledge graph ↳ 18. JEV-Curate - screens training data ↳ 19. Canny - confirms a task is really finished ↳ 20. KillMyIdea - scores a startup idea before you sink time into it ↳ start where your work is: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone pick one, build on it this week, and you'll be the person on your team who actually knows Jev engineering when it gets asked for

Mr. Buzzoni

12,199 görüntüleme • 19 gün önce

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a fast browser agent ↳ 2. Fast-JEV-Compaction - context compression ↳ 3. JSON-Render - generative UI ↳ 4. Typesafe-MCP - use Jev with any client ↳ 5. JEV-MCP - a judgment toolkit ↳ 6. Semdecide - a classifier that lives in your CLI ↳ 7. JEV-Codex-Router - routes each task to the right model ↳ 8. Winnow - garbage collection for your context ↳ 9. JEV-Review - code review triage ↳ 10. Blink - a repo navigator ↳ 11. Agent-Desktop - desktop automation ↳ 12. Typesafe-Mario - an agent that plays Super Mario ↳ 13. JEV-Drone - drone control ↳ 14. OneVOneJev - a browser FPS ↳ 15. JEV-Trader - HFT market making ↳ 16. Prism - liquidity signal detection ↳ 17. Neo4Jev - knowledge graph traversal ↳ 18. JEV-Curate - training data screening ↳ 19. Canny - checks whether a task was actually completed ↳ 20. KillMyIdea - scores startup ideas before you build them ↳ pick by what you do: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > just for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone grab the one closest to your job and ship something on top of it this week

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a fast browser agent ↳ 2. Fast-JEV-Compaction - context compression ↳ 3. JSON-Render - generative UI ↳ 4. Typesafe-MCP - use Jev with any client ↳ 5. JEV-MCP - a judgment toolkit ↳ 6. Semdecide - a classifier that lives in your CLI ↳ 7. JEV-Codex-Router - routes each task to the right model ↳ 8. Winnow - garbage collection for your context ↳ 9. JEV-Review - code review triage ↳ 10. Blink - a repo navigator ↳ 11. Agent-Desktop - desktop automation ↳ 12. Typesafe-Mario - an agent that plays Super Mario ↳ 13. JEV-Drone - drone control ↳ 14. OneVOneJev - a browser FPS ↳ 15. JEV-Trader - HFT market making ↳ 16. Prism - liquidity signal detection ↳ 17. Neo4Jev - knowledge graph traversal ↳ 18. JEV-Curate - training data screening ↳ 19. Canny - checks whether a task was actually completed ↳ 20. KillMyIdea - scores startup ideas before you build them ↳ pick by what you do: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > just for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone grab the one closest to your job and ship something on top of it this week

Mr. Buzzoni

29,619 görüntüleme • 24 gün önce

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 ↓ ↓

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 görüntüleme • 24 gün önce

$It is becoming f*cking dangerous: a model that never writes a single word now judges every move your Claude AI agent makes -> Create a folder named JEV LAB on your PC and launch Claude Code inside it -> Install the Jev CLI: npm install -g jev-axi -> Grab a key at and run: jev-axi config set apiKey YOUR_KEY -> Run jev-axi setup safety so every command Claude runs gets checked first. Dangerous downloads get denied, routine tests pass for free -> Run jev-axi setup git-hooks to stop any commit that sneaks in a password or API key -> Feed it anything your agent reads: jev-axi guard Run jev-axi setup supervise --project to catch the moment your agent quits early or gets stuck in a loop The whole setup takes around 5 minutes Turn Claude into an agent with a second opinion: every command screened, every page checked for prompt injection, every commit scanned for secrets. For a fraction of a cent per check Bonus: pipe a broken build into jev-axi triage and it finds the root cause for you Bookmark this article and check repo below:$

It is becoming f*cking dangerous: a model that never writes a single word now judges every move your Claude AI agent makes -> Create a folder named JEV LAB on your PC and launch Claude Code inside it -> Install the Jev CLI: npm install -g jev-axi -> Grab a key at and run: jev-axi config set apiKey YOUR_KEY -> Run jev-axi setup safety so every command Claude runs gets checked first. Dangerous downloads get denied, routine tests pass for free -> Run jev-axi setup git-hooks to stop any commit that sneaks in a password or API key -> Feed it anything your agent reads: jev-axi guard Run jev-axi setup supervise --project to catch the moment your agent quits early or gets stuck in a loop The whole setup takes around 5 minutes Turn Claude into an agent with a second opinion: every command screened, every page checked for prompt injection, every commit scanned for secrets. For a fraction of a cent per check Bonus: pipe a broken build into jev-axi triage and it finds the root cause for you Bookmark this article and check repo below:

kiosa

27,972 görüntüleme • 2 gün önce

this is pure f*cking treasure 10 GitHub projects that put Jev between your agent and every decision your agent should stop paying a frontier model to answer yes or no ROUTE THE WORK 01 jev-router > 02 jcm-router > 03 jev-agent-skill-router > GUARD EVERY ACTION 04 pi-heed > 05 fx > 06 Foreman > SCORE WHAT MATTERS 07 LlamaIndex Jev > 08 citation-verifier > PUT IT TO WORK 09 yoshi > 10 mastra-jev-moderation > the loop: your agent hits a fork > Jev answers with a probability > your code decides what that number allows > the agent moves on 3 builds I'd explore: coding agent: jev-router > fx > Foreman > yoshi support desk: jev-agent-skill-router > mastra-jev-moderation > LlamaIndex Jev research: jcm-router > pi-heed > citation-verifier save this, then build a decision layer for your agents ⭣

this is pure f*cking treasure 10 GitHub projects that put Jev between your agent and every decision your agent should stop paying a frontier model to answer yes or no ROUTE THE WORK 01 jev-router > 02 jcm-router > 03 jev-agent-skill-router > GUARD EVERY ACTION 04 pi-heed > 05 fx > 06 Foreman > SCORE WHAT MATTERS 07 LlamaIndex Jev > 08 citation-verifier > PUT IT TO WORK 09 yoshi > 10 mastra-jev-moderation > the loop: your agent hits a fork > Jev answers with a probability > your code decides what that number allows > the agent moves on 3 builds I'd explore: coding agent: jev-router > fx > Foreman > yoshi support desk: jev-agent-skill-router > mastra-jev-moderation > LlamaIndex Jev research: jcm-router > pi-heed > citation-verifier save this, then build a decision layer for your agents ⭣

delost

15,953 görüntüleme • 21 gün önce

this is f*cking gold 12 open-source repos that plug Jev into real AI work, 550.7k stars combined Jev decides, your LLM writes. these repos are where that split already runs: agents > browser-use/jev-ultrafast (~20.3k): Jev picks the next action + DOM element, a small LLM only types. Zürich to London flights in ~7s > awlevin/typesafe-computer-use (~978): macOS computer use, OCR reads the screen, Jev picks the next click local decision models > jaredpalmer/kev (~7.1k): Jev-like models on Qwen, same /v1/systemone wire format > mizorewww/laya-mlx (~6.4k): Laya, the main open Jev-style engine, running on Apple silicon > TheoLeeCJ/SemIf-OpenJev (~4.3k): local semantic ifs from frozen open models, not a TypeSafe project > contrastive-lm/clm (~1.3k): open System One model with a Choice / Noul / Score API > vinnylarouge/jevlike (~1.3k): community trainer and tooling for your own Jev-like model context + skills > tamaratran/fast-jev-compaction (~6.9k): Claude Code plugin, Jev keeps, drops or truncates tool results instead of summarizing context > typesafe-ai/skills (~2.2k): official TypeSafe skill for Claude Code and Codex trading > jarrodwatts/jev-trader (~2.4k): one Jev buy/sell decision per Monad block catalogs > yibie/awesome-jev (~1.7k): the biggest map of Jev projects and discussions > Anil-matcha/awesome-jev-by-typesafe (~851): use cases, patterns and starter code start with jev-ultrafast

this is f*cking gold 12 open-source repos that plug Jev into real AI work, 550.7k stars combined Jev decides, your LLM writes. these repos are where that split already runs: agents > browser-use/jev-ultrafast (~20.3k): Jev picks the next action + DOM element, a small LLM only types. Zürich to London flights in ~7s > awlevin/typesafe-computer-use (~978): macOS computer use, OCR reads the screen, Jev picks the next click local decision models > jaredpalmer/kev (~7.1k): Jev-like models on Qwen, same /v1/systemone wire format > mizorewww/laya-mlx (~6.4k): Laya, the main open Jev-style engine, running on Apple silicon > TheoLeeCJ/SemIf-OpenJev (~4.3k): local semantic ifs from frozen open models, not a TypeSafe project > contrastive-lm/clm (~1.3k): open System One model with a Choice / Noul / Score API > vinnylarouge/jevlike (~1.3k): community trainer and tooling for your own Jev-like model context + skills > tamaratran/fast-jev-compaction (~6.9k): Claude Code plugin, Jev keeps, drops or truncates tool results instead of summarizing context > typesafe-ai/skills (~2.2k): official TypeSafe skill for Claude Code and Codex trading > jarrodwatts/jev-trader (~2.4k): one Jev buy/sell decision per Monad block catalogs > yibie/awesome-jev (~1.7k): the biggest map of Jev projects and discussions > Anil-matcha/awesome-jev-by-typesafe (~851): use cases, patterns and starter code start with jev-ultrafast

NO1ennn

43,110 görüntüleme • 23 gün önce

this is pure f*cking treasure Engineer at TypeSafeAI just mapped 389 public Jev builds in one list: skills, MCP servers, SDKs, agents, benchmarks and guides ROUTING > jev-router sends every Claude Code task to the cheapest model that can do it > Switchboard picks the model and reasoning effort per task, then keeps it stable so the prompt cache survives > jev-oncall triages alerts: 418 ms p50, $0.04 per 1,000 alerts GUARDRAILS > jev-axi gates shell commands for Claude Code and Codex, 44/44 on its labeled set > hermes-jev-approvals: 8.7x faster approvals, 4.4x fewer prompts to the user > Sniff Test lints AI slop out of your writing at 182 ms a paragraph AGENTS > Jev Ultrafast: Jev picks every click, a small LLM only types > fast-jev-compaction scores your context instead of summarizing it > jev-browser-use reports 5 to 10x faster browser runs inside Codex SEARCH & RAG > jev-retrieval placed 2nd of 90 models on a reranking leaderboard > jevsearch: 83% Hit@1 against 41% for keyword search alone SDKs & MCP > clients for Swift, Go, Rust, Kotlin, Ruby, Elixir, .NET, Laravel and Spring > MCP servers for Claude Code, Cursor and Codex > Jev inside SQLite, DuckDB and Postgres, straight from SQL EVALS > pytest-jev checks LLM replies in 5.3 s where Claude took 27.1 s > a medical hallucination check at 92.9% accuracy, 204 ms, $0.03 per 1,000 OPEN MODELS > Laya answers in one ~35 ms forward pass > kev trains and runs on a MacBook GAMES, ROBOTS, TRADING > Jev plays Mario, Pokemon and chess > it drives a robot arm and a drone > it trades on Monad with 81 ms decisions 389 builds. one decision layer. go steal the ones you need

this is pure f*cking treasure Engineer at TypeSafeAI just mapped 389 public Jev builds in one list: skills, MCP servers, SDKs, agents, benchmarks and guides ROUTING > jev-router sends every Claude Code task to the cheapest model that can do it > Switchboard picks the model and reasoning effort per task, then keeps it stable so the prompt cache survives > jev-oncall triages alerts: 418 ms p50, $0.04 per 1,000 alerts GUARDRAILS > jev-axi gates shell commands for Claude Code and Codex, 44/44 on its labeled set > hermes-jev-approvals: 8.7x faster approvals, 4.4x fewer prompts to the user > Sniff Test lints AI slop out of your writing at 182 ms a paragraph AGENTS > Jev Ultrafast: Jev picks every click, a small LLM only types > fast-jev-compaction scores your context instead of summarizing it > jev-browser-use reports 5 to 10x faster browser runs inside Codex SEARCH & RAG > jev-retrieval placed 2nd of 90 models on a reranking leaderboard > jevsearch: 83% Hit@1 against 41% for keyword search alone SDKs & MCP > clients for Swift, Go, Rust, Kotlin, Ruby, Elixir, .NET, Laravel and Spring > MCP servers for Claude Code, Cursor and Codex > Jev inside SQLite, DuckDB and Postgres, straight from SQL EVALS > pytest-jev checks LLM replies in 5.3 s where Claude took 27.1 s > a medical hallucination check at 92.9% accuracy, 204 ms, $0.03 per 1,000 OPEN MODELS > Laya answers in one ~35 ms forward pass > kev trains and runs on a MacBook GAMES, ROBOTS, TRADING > Jev plays Mario, Pokemon and chess > it drives a robot arm and a drone > it trades on Monad with 81 ms decisions 389 builds. one decision layer. go steal the ones you need

NO1ennn

25,798 görüntüleme • 21 gün önce

$Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!$

Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!

Archive

17,492 görüntüleme • 18 gün önce

He is 18 teaching AI at Stanford making $2.2M on agents - and in 60 minutes revealed how to repeat it from zero: 00:30 - 5% who build agents will replace the 95% who just use them 38:33 - Jev Engineering is the only thing you need to make $2.2M on an agent 52:47 - Claude + Jev 10x cheaper and faster right now after watching I built my first agent with Jev in 15 minutes and sold it to a million dollar company for $100k: save & watch - the article below shows how to use Jev Engineering to make Claude 10x cheaper and build a $2.2M agent.

He is 18 teaching AI at Stanford making $2.2M on agents - and in 60 minutes revealed how to repeat it from zero: 00:30 - 5% who build agents will replace the 95% who just use them 38:33 - Jev Engineering is the only thing you need to make $2.2M on an agent 52:47 - Claude + Jev 10x cheaper and faster right now after watching I built my first agent with Jev in 15 minutes and sold it to a million dollar company for $100k: save & watch - the article below shows how to use Jev Engineering to make Claude 10x cheaper and build a $2.2M agent.

Noisy

13,207 görüntüleme • 27 gün önce

I STOPPED LETTING CLAUDE MAKE DECISIONS THE DAY I BUILT THIS JEV AGENT FOLDER I used to let Claude decide, write and act on every single step -> now Claude only writes. Jev makes the calls, code does the acting, and every step leaves a receipt here's what's inside the folder: • the input > AGENTS.md - when to call Jev, when to skip it > state/build_state.py - goal, workers, done, missing, constraint. evidence, never a summary • Jev decides (questions/) > - which model tier gets the task > next_worker.py - which worker acts next > - keep or drop each tool output > - is the goal actually met > - does this send, pay or delete? • code acts (rules/) > hard_rules.py - stop after ten actions, never publish unapproved > thresholds.yaml - act only on confident answers. fraud needs 0.95 • Claude writes (workers/) > - sources and notes > - drafts and briefings > the only place in the whole folder where text gets generated • the proof > receipts/decisions.jsonl - options offered, chosen id, re-check, fallback > evals/ - dozens of my own labelled traces, thresholds tuned on them • the guards (hooks/) > pre_tool_use.sh - every command checked before it runs > - checks "all done" before the agent is allowed to stop 10,000 decisions for $0.42. median 300 ms an LLM writes, Jev decides, code acts

I STOPPED LETTING CLAUDE MAKE DECISIONS THE DAY I BUILT THIS JEV AGENT FOLDER I used to let Claude decide, write and act on every single step -> now Claude only writes. Jev makes the calls, code does the acting, and every step leaves a receipt here's what's inside the folder: • the input > AGENTS.md - when to call Jev, when to skip it > state/build_state.py - goal, workers, done, missing, constraint. evidence, never a summary • Jev decides (questions/) > - which model tier gets the task > next_worker.py - which worker acts next > - keep or drop each tool output > - is the goal actually met > - does this send, pay or delete? • code acts (rules/) > hard_rules.py - stop after ten actions, never publish unapproved > thresholds.yaml - act only on confident answers. fraud needs 0.95 • Claude writes (workers/) > - sources and notes > - drafts and briefings > the only place in the whole folder where text gets generated • the proof > receipts/decisions.jsonl - options offered, chosen id, re-check, fallback > evals/ - dozens of my own labelled traces, thresholds tuned on them • the guards (hooks/) > pre_tool_use.sh - every command checked before it runs > - checks "all done" before the agent is allowed to stop 10,000 decisions for $0.42. median 300 ms an LLM writes, Jev decides, code acts

Mr. Buzzoni

79,984 görüntüleme • 22 gün önce

this is pure f*cking gold for anyone doing Jev engineering the skill that teaches Claude Code to build on Jev the right way, from TypeSafe themselves without it, coding agents send Jev one question at a time and guess the field names with it, the order is set before a single line gets written: docs -> behaviour -> judgments -> one request -> code decides the two-page breakdown of every rule: > the failure each of its 11 instructions is there to prevent > Choice, Noul or Score, picked at a glance > a prompt skeleton you paste straight into your agent 12.2x that's how far the bill fell in TypeSafe's cookbook when 13 questions went out in one call one short file, and your agent stops paying for decisions it should have batched

this is pure f*cking gold for anyone doing Jev engineering the skill that teaches Claude Code to build on Jev the right way, from TypeSafe themselves without it, coding agents send Jev one question at a time and guess the field names with it, the order is set before a single line gets written: docs -> behaviour -> judgments -> one request -> code decides the two-page breakdown of every rule: > the failure each of its 11 instructions is there to prevent > Choice, Noul or Score, picked at a glance > a prompt skeleton you paste straight into your agent 12.2x that's how far the bill fell in TypeSafe's cookbook when 13 questions went out in one call one short file, and your agent stops paying for decisions it should have batched

Mr. Buzzoni

28,212 görüntüleme • 20 gün önce

CLAUDE + JEV ENGINEERING = THE HARNESS SKILL THAT GETS AI ENGINEERS PROMOTED IN 2026 1,000 decisions on a cold frontier model cost $605 the same 1,000 through this harness cost $0.17 the core idea: Claude proposes, Jev answers, code decides every step leaves a receipt the loop: > propose - Claude writes plans and patches, and decides nothing > filter - code drops the routes your machine can't run before Jev ever sees them > answer - Jev picks from code's menu with a probability, or abstains > re-check - code checks the pick against live state. stale goes to fallback > act - the tool runs through the normal approval path > receipt - every step logged and replayable against a new policy the numbers: > 0.2s median per call > ~$0.0002 per desktop step > 49 of 49 WebMCP tasks passed the key insight: "I don't know" is a real answer here when Jev is only 38% sure, a fallback written in advance fires instead of a guess worth stealing even if you never touch Jev: > one decision the harness can check > the exact options code prepared > a defined path for when the model isn't sure > a way to measure what actually happened the engineer who can walk into a meeting and turn a $605 line into 17 cents is the one who gets asked to design the next system the model got the words, Jev got the choices, and code kept the final say

CLAUDE + JEV ENGINEERING = THE HARNESS SKILL THAT GETS AI ENGINEERS PROMOTED IN 2026 1,000 decisions on a cold frontier model cost $605 the same 1,000 through this harness cost $0.17 the core idea: Claude proposes, Jev answers, code decides every step leaves a receipt the loop: > propose - Claude writes plans and patches, and decides nothing > filter - code drops the routes your machine can't run before Jev ever sees them > answer - Jev picks from code's menu with a probability, or abstains > re-check - code checks the pick against live state. stale goes to fallback > act - the tool runs through the normal approval path > receipt - every step logged and replayable against a new policy the numbers: > 0.2s median per call > ~$0.0002 per desktop step > 49 of 49 WebMCP tasks passed the key insight: "I don't know" is a real answer here when Jev is only 38% sure, a fallback written in advance fires instead of a guess worth stealing even if you never touch Jev: > one decision the harness can check > the exact options code prepared > a defined path for when the model isn't sure > a way to measure what actually happened the engineer who can walk into a meeting and turn a $605 line into 17 cents is the one who gets asked to design the next system the model got the words, Jev got the choices, and code kept the final say

Mr. Buzzoni

32,459 görüntüleme • 19 gün önce

How does Jev work? Jev takes two things: your input and an output schema. The schema gives the structure of the answer you want. I send an email. My schema asks for two fields: is spam, and a category from marketing, finance, or spam. Jev returns: - is spam: 0.90 - category: spam That is a 90% chance of spam. Jev selects the category only from the list I give it. It writes no sentences. The 0.90 is a number object, so my code can use it immediately without extra processing. Jev shows no reasoning. You get the answer and nothing more, so some people do not trust it. Use Jev for choices your code must make fast and at low cost: spam or normal, buy or no buy, reply or escalate.

How does Jev work? Jev takes two things: your input and an output schema. The schema gives the structure of the answer you want. I send an email. My schema asks for two fields: is spam, and a category from marketing, finance, or spam. Jev returns: - is spam: 0.90 - category: spam That is a 90% chance of spam. Jev selects the category only from the list I give it. It writes no sentences. The 0.90 is a number object, so my code can use it immediately without extra processing. Jev shows no reasoning. You get the answer and nothing more, so some people do not trust it. Use Jev for choices your code must make fast and at low cost: spam or normal, buy or no buy, reply or escalate.

The Startup Ideas Podcast (SIP) 🧃

12,032 görüntüleme • 1 ay önce

Jev is incredibly good! If you haven't heard, Jev is a new "System One" model optimized for decision-making. For example, you can give it a set of possible choices, and Jev will classify the input text based on those choices, including a confidence score for each. Best of all: Jev is really fast and cheap, so you can use it in all sorts of applications. The first thing I built with it is a classifier to pick the hotels I want to visit. I use Apify to collect thousands of reviews, then classify them with Jev based on specific criteria I care about. You just couldn't do this reliably, but now you can. Link to the repository:

Jev is incredibly good! If you haven't heard, Jev is a new "System One" model optimized for decision-making. For example, you can give it a set of possible choices, and Jev will classify the input text based on those choices, including a confidence score for each. Best of all: Jev is really fast and cheap, so you can use it in all sorts of applications. The first thing I built with it is a classifier to pick the hotels I want to visit. I use Apify to collect thousands of reviews, then classify them with Jev based on specific criteria I care about. You just couldn't do this reliably, but now you can. Link to the repository:

Santiago

17,898 görüntüleme • 21 gün önce

this is pure f*cking treasure 15 GitHub repos with 116K combined stars where a model that can't write a single word makes the calls browsers. phones. cars. Minecraft. trading. agents that die if they can't pay PLUG IN -> give any agent a decision brain 01 litellm · Jev behind one proxy > 02 vercel ai sdk · experimental_evaluate > 03 pydantic-ai · TypeSafeModel > ACT -> Jev picks every move 04 jev-ultrafast · Google Flights in 7.1s > 05 mobile-jev · real Android, 9 actions in ~21s > 06 jev-voice-browser · ~300ms per spoken word > 07 jevpilot · steers up to 4x a second > 08 minecraft-agent · dragon down in 8:43, 131 Jev decisions > SURVIVE -> every decision comes with a bill 09 automaton · "if it cannot pay, it stops existing" > 10 jev-trader · one decision every ~300ms block > THINK LESS -> pay for a thought only when it's worth it 11 wakegate · the LLM sleeps until Jev says wake > 12 fast-jev-compaction · keeps or drops, rewrites nothing > 13 jev-pruner · 76K-char log -> 4K, the error line stays > 14 unclutter · hides the ads, reuses rules per site > DOUBT -> check if its "I'm sure" is true 15 Jev-Calibration · calibration error 0.117 -> 0.008 > the loop: state comes in -> Jev picks -> the big model only when it's worth it -> confidence check -> code executes -> the bill gets paid none of them need Jev to write a single word save this, then read why the smartest brain goes broke first

this is pure f*cking treasure 15 GitHub repos with 116K combined stars where a model that can't write a single word makes the calls browsers. phones. cars. Minecraft. trading. agents that die if they can't pay PLUG IN -> give any agent a decision brain 01 litellm · Jev behind one proxy > 02 vercel ai sdk · experimental_evaluate > 03 pydantic-ai · TypeSafeModel > ACT -> Jev picks every move 04 jev-ultrafast · Google Flights in 7.1s > 05 mobile-jev · real Android, 9 actions in ~21s > 06 jev-voice-browser · ~300ms per spoken word > 07 jevpilot · steers up to 4x a second > 08 minecraft-agent · dragon down in 8:43, 131 Jev decisions > SURVIVE -> every decision comes with a bill 09 automaton · "if it cannot pay, it stops existing" > 10 jev-trader · one decision every ~300ms block > THINK LESS -> pay for a thought only when it's worth it 11 wakegate · the LLM sleeps until Jev says wake > 12 fast-jev-compaction · keeps or drops, rewrites nothing > 13 jev-pruner · 76K-char log -> 4K, the error line stays > 14 unclutter · hides the ads, reuses rules per site > DOUBT -> check if its "I'm sure" is true 15 Jev-Calibration · calibration error 0.117 -> 0.008 > the loop: state comes in -> Jev picks -> the big model only when it's worth it -> confidence check -> code executes -> the bill gets paid none of them need Jev to write a single word save this, then read why the smartest brain goes broke first

Paone

32,087 görüntüleme • 22 gün önce

Jev founder, Diogo Almeida, just put out a PDF on building a Jev harness for coding agents the claim: 200x faster, 400x cheaper the model stopped being the bottleneck a while ago. the harness around it is where the cost and the speed actually live five decisions sit on every loop, and only one of them writes code hand this PDF and the article below to your Claude Code or Codex instance and let it rebuild its own setup your agent gets a better harness tonight, and monday looks different than it would have 👇

Jev founder, Diogo Almeida, just put out a PDF on building a Jev harness for coding agents the claim: 200x faster, 400x cheaper the model stopped being the bottleneck a while ago. the harness around it is where the cost and the speed actually live five decisions sit on every loop, and only one of them writes code hand this PDF and the article below to your Claude Code or Codex instance and let it rebuild its own setup your agent gets a better harness tonight, and monday looks different than it would have 👇

Hanako

37,887 görüntüleme • 25 gün önce