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TypeSafe's new model Jev is everywhere this week, and it doesn't even generate text. It returns decisions: typed scores your code uses directly, in a single pass. We put it to work in a real-time chat moderator, scoring toxicity, frustration, and spam in 300 to 400ms. Then we deployed...

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26,136 Aufrufe • vor 11 Tagen •via X (Twitter)

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Jev VS Fable 5.1 VS GPT-6 Astra: chess typesafe's new Jev V13 isn't an LLM. it doesn't chat, doesn't explain, doesn't write code — it only makes decisions. so we made it play blitz against frontier LLMs. the test: 5+0 blitz. every move is one API call. Jev V13 vs Fable 5.1: • fable outplayed it. by move 29 it was +16 in material and even promoted a second queen • but it kept burning 6-15 seconds per move on analysis. jev answered in ~2.6s • so fable didn't have enough time and lost Jev V13 vs Astra: • astra didn't bother winning on material. it mated jev in 18 moves. Qe1#, with 2:27 to spare
1:39

Jev VS Fable 5.1 VS GPT-6 Astra: chess typesafe's new Jev V13 isn't an LLM. it doesn't chat, doesn't explain, doesn't write code — it only makes decisions. so we made it play blitz against frontier LLMs. the test: 5+0 blitz. every move is one API call. Jev V13 vs Fable 5.1: • fable outplayed it. by move 29 it was +16 in material and even promoted a second queen • but it kept burning 6-15 seconds per move on analysis. jev answered in ~2.6s • so fable didn't have enough time and lost Jev V13 vs Astra: • astra didn't bother winning on material. it mated jev in 18 moves. Qe1#, with 2:27 to spare

AI/ML API

564,226 Aufrufe • vor 19 Tagen

Jev by TypeSafe AI is now on the Venice API, in beta. It answers, it doesn't write. Send it your app's state and typed questions. Get back a typed answer your code can branch on: a probability, a chosen option, or a score on your rubric. No JSON to coax out of a chat model.
0:24

Jev by TypeSafe AI is now on the Venice API, in beta. It answers, it doesn't write. Send it your app's state and typed questions. Get back a typed answer your code can branch on: a probability, a chosen option, or a score on your rubric. No JSON to coax out of a chat model.

Venice

549,600 Aufrufe • vor 17 Tagen

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.
8:26

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 Aufrufe • vor 16 Tagen

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.
1:02

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 Aufrufe • vor 16 Tagen

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 ↓ ↓
0:32

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 Aufrufe • vor 11 Tagen

I got a rubik's cube to solve itself with TypeSafe AI 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM, it just answers one question in ~250ms with a probability. so I put the beginner method in code (the one you'd learn on youtube: white cross, corners, middle layer, yellow layer) and at every step Jev just looks at the cube and picks which case it's in. code checks every pick. ~4 seconds of model time total. video is slowed down so you can read it!
0:34

I got a rubik's cube to solve itself with TypeSafe AI 's Jev and it solves it like a person does, 94 moves, not the 22 move optimal solution. Jev isn't an LLM, it just answers one question in ~250ms with a probability. so I put the beginner method in code (the one you'd learn on youtube: white cross, corners, middle layer, yellow layer) and at every step Jev just looks at the cube and picks which case it's in. code checks every pick. ~4 seconds of model time total. video is slowed down so you can read it!

Paolo Rosson

207,767 Aufrufe • vor 19 Tagen

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:
6:57

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 Aufrufe • vor 8 Tagen

If you didn't feel the ongoing revolution, here is my second project I built with jev. Your software is drastically outdated. It contains a lot of instructions your CPU executes one by one. Why if we can simply ask Jev for the next instructions to execute Introducing Jevassembler! It is a new approach to execute something on CPU. You don't need to wait for code to be written, you don't need to compile anything and YOU DON'T NEED CODE. You just give it a task and Jev predicts the next instruction to execute. In runtime. who needs compilers if you have AI
0:28

If you didn't feel the ongoing revolution, here is my second project I built with jev. Your software is drastically outdated. It contains a lot of instructions your CPU executes one by one. Why if we can simply ask Jev for the next instructions to execute Introducing Jevassembler! It is a new approach to execute something on CPU. You don't need to wait for code to be written, you don't need to compile anything and YOU DON'T NEED CODE. You just give it a task and Jev predicts the next instruction to execute. In runtime. who needs compilers if you have AI

Dmitriy Kovalenko

224,757 Aufrufe • vor 16 Tagen

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 ↓ ↓
0:24

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

202,492 Aufrufe • vor 14 Tagen

sharing an easy way to test DJev (Diffusion Gemma Jev), the next evolution of the incredible jev concept and fully open sourced (model, code, api...) imagine jev but it also has: - native image-input support - native image as options!! - real-time video streaming input - as fast as jev (not with images, 600ms) - same price as jev, but can get even cheaper - using Diffusion Gemma adapted - we are seeing on-par performance but happy to have external validation! It is a research preview made for fun, so don't expect stability and the awesome scaling as jev team has delivered! this was a weekend project :) have fun!
1:30

sharing an easy way to test DJev (Diffusion Gemma Jev), the next evolution of the incredible jev concept and fully open sourced (model, code, api...) imagine jev but it also has: - native image-input support - native image as options!! - real-time video streaming input - as fast as jev (not with images, 600ms) - same price as jev, but can get even cheaper - using Diffusion Gemma adapted - we are seeing on-par performance but happy to have external validation! It is a research preview made for fun, so don't expect stability and the awesome scaling as jev team has delivered! this was a weekend project :) have fun!

David Villalón

43,078 Aufrufe • vor 16 Tagen

Here is the full walkthrough if you want to watch it work. A real support ticket goes in, and Jev comes back with the team that owns it, a frustration score, and a probability that it reads as urgent. The confidence value on each answer decides what gets handled automatically and what a person sees.
10:25

Here is the full walkthrough if you want to watch it work. A real support ticket goes in, and Jev comes back with the team that owns it, a frustration score, and a probability that it reads as urgent. The confidence value on each answer decides what gets handled automatically and what a person sees.

Netlify

10,263 Aufrufe • vor 18 Tagen

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.
0:28

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

289,304 Aufrufe • vor 3 Tagen

Jev just became the fastest-adopted model in AI history. here's what people already built with it 1. jev-ultrafast - the browser agent that picks every click itself, only calling a text model when it actually needs to type something. found real flight results in 7 seconds for $0.0039. 16,758 stars 2. jev-trader - real trading bot placing live limit orders on Monad every 300ms block, judged by Jev alone. 1,911 stars 3. jev-usecases - production security-operations harness where Jev triages incidents and gates every escalation behind a confidence cutoff before anything touches real infrastructure. zero false escalations in the committed test set 4. tax-doc-classifier - sorts real IRS tax forms with 100% strict accuracy across 261 forms, at roughly $0.001 a page 5. jev-drone - a simulated quadrotor clears a five-station obstacle course by camera alone, Jev judging the situation twice a second 6. killmyidea - describe your startup idea, Jev scores it from every angle, then hands back kill, fix, or ship in seconds, not days 7. jev-curate - streams Parquet and JSONL rows through typed judgments at 1,500+ rows a second, keeping only what clears the bar 8. pg-jev - a PostgreSQL extension that lets you ask your own database tables plain-English questions and get a real answer back, no SQL required eight repos. zero generated words. every single one returns a typed answer against a question someone already defined full setup below, then run the three-question test from the article before you build a ninth
0:24

Jev just became the fastest-adopted model in AI history. here's what people already built with it 1. jev-ultrafast - the browser agent that picks every click itself, only calling a text model when it actually needs to type something. found real flight results in 7 seconds for $0.0039. 16,758 stars 2. jev-trader - real trading bot placing live limit orders on Monad every 300ms block, judged by Jev alone. 1,911 stars 3. jev-usecases - production security-operations harness where Jev triages incidents and gates every escalation behind a confidence cutoff before anything touches real infrastructure. zero false escalations in the committed test set 4. tax-doc-classifier - sorts real IRS tax forms with 100% strict accuracy across 261 forms, at roughly $0.001 a page 5. jev-drone - a simulated quadrotor clears a five-station obstacle course by camera alone, Jev judging the situation twice a second 6. killmyidea - describe your startup idea, Jev scores it from every angle, then hands back kill, fix, or ship in seconds, not days 7. jev-curate - streams Parquet and JSONL rows through typed judgments at 1,500+ rows a second, keeping only what clears the bar 8. pg-jev - a PostgreSQL extension that lets you ask your own database tables plain-English questions and get a real answer back, no SQL required eight repos. zero generated words. every single one returns a typed answer against a question someone already defined full setup below, then run the three-question test from the article before you build a ninth

rvaniaaa

85,773 Aufrufe • vor 12 Tagen

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 👇
0:23

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 Aufrufe • vor 11 Tagen

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
0:24

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

11,667 Aufrufe • vor 6 Tagen

Jev + LLMs is f*cking insane... this free Jev lab is a clean blueprint for agent builders one support ticket enters. Jev reads the state and answers three typed questions in one call: noul → is it urgent? choice → which team owns it? score → how soon does it need action? then code reads the probabilities. clear, low-risk work flows to the right LLM. ambiguous tickets go to a person. refunds wait for approval. the draft gets checked against policy before it goes out the loop: ticket → typed questions → probabilities → threshold → LLM draft → approval / reply check → shadow run the best part of the lab: you can break the system yourself. set the threshold to zero, remove the “other” choice, watch routing fail, then repair it. the final exercise runs Jev beside existing rules on 40 tickets without letting it act the practical rule: Jev for bounded judgments, an LLM for writing, plain code for hard constraints save this, then read the article below to combine Jev with Dots ⭣
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Jev + LLMs is f*cking insane... this free Jev lab is a clean blueprint for agent builders one support ticket enters. Jev reads the state and answers three typed questions in one call: noul → is it urgent? choice → which team owns it? score → how soon does it need action? then code reads the probabilities. clear, low-risk work flows to the right LLM. ambiguous tickets go to a person. refunds wait for approval. the draft gets checked against policy before it goes out the loop: ticket → typed questions → probabilities → threshold → LLM draft → approval / reply check → shadow run the best part of the lab: you can break the system yourself. set the threshold to zero, remove the “other” choice, watch routing fail, then repair it. the final exercise runs Jev beside existing rules on 40 tickets without letting it act the practical rule: Jev for bounded judgments, an LLM for writing, plain code for hard constraints save this, then read the article below to combine Jev with Dots ⭣

beamnxw ./

11,130 Aufrufe • vor 5 Tagen

I was also looking into how we can integrate Jev into a real hardware and I built a custom firmware for my keyboard that predicts and highljghts the next key using Jev. Mostly real time. This is not a simple dummy 2kb model that lives in the board. In my project Jev gets context of the current active app on a screen so it can suggest actual vim commands completion! For real. And a real word endings or even phrases. lmao this experiments are getting out of hand this quite cool
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I was also looking into how we can integrate Jev into a real hardware and I built a custom firmware for my keyboard that predicts and highljghts the next key using Jev. Mostly real time. This is not a simple dummy 2kb model that lives in the board. In my project Jev gets context of the current active app on a screen so it can suggest actual vim commands completion! For real. And a real word endings or even phrases. lmao this experiments are getting out of hand this quite cool

Dmitriy Kovalenko

56,086 Aufrufe • vor 16 Tagen

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
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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 Aufrufe • vor 7 Tagen

Top 9 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 9 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. It captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: If you want to dive deeper, check out the full guide on Jev below ↓
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Top 9 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 9 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. It captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: If you want to dive deeper, check out the full guide on Jev below ↓

rody

47,040 Aufrufe • vor 7 Tagen

Jev just solved doomscrolling. You pick a niche, Jev reads 3 days of posts and asks 8 questions each. It runs in ~2s for $0.007 😅 > removes bait and hidden ads > judges the text and like/reply/repost ratios Free, no signup, go try it then touch grass ↓🌲
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Jev just solved doomscrolling. You pick a niche, Jev reads 3 days of posts and asks 8 questions each. It runs in ~2s for $0.007 😅 > removes bait and hidden ads > judges the text and like/reply/repost ratios Free, no signup, go try it then touch grass ↓🌲

Rob Hallam

176,827 Aufrufe • vor 17 Tagen