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🧪 Try Jev free with KodeKey: Jev by TypeSafe is a System One AI model that answers your code with calibrated probabilities instead of text, and it doesn't charge for output tokens. Everyone's talking about the model. Almost nobody explains why it works the way it does. This video...

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What is a decision model? Jev by TypeSafe AI answers yes/no and multiple-choice questions, with a confidence score. Much of software development are a sequence of decisions, and Jev is 10x cheaper and faster than an LLM. Let’s understand this through practical examples:
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What is a decision model? Jev by TypeSafe AI answers yes/no and multiple-choice questions, with a confidence score. Much of software development are a sequence of decisions, and Jev is 10x cheaper and faster than an LLM. Let’s understand this through practical examples:

OpenRouter

169,880 Aufrufe • vor 1 Monat

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 1 Monat

People already built a browser agent, a Claude Code bridge and a LangGraph router on top of Jev. Here are all 10 repos. If you have API access and don't know where to start, save this: 1. jev-browser Real browser control through MCP, CLI or as a library. At every step Jev answers two things: which element to touch, and whether the goal is done or the run is stuck. > 2. jev-browser-agent Picks the operation and the target in a single request, around a second per step. Anything under 0.6 confidence goes up to a bigger model or back to you. > 3. typesafe-jev-bridge OpenAI-compatible bridge with zero dependencies. Typed yes/no, choice and score answers inside Claude Code, Cursor, Cline or any OpenAI SDK, via CLI or HTTP. > 4. pilot-typesafeai-jev Jev wired into LangGraph and Deep Agents. Three questions in one request, plain code chooses the route, and the chat model only spends tokens on the route that won. > 5. building-with-jev-skill Agent skill for writing code that calls Jev. How to phrase questions, structure state, set thresholds, and debug a question that keeps answering wrong. > 6. jev-skill Guided setup and eval workflows. Checks what's installed without changing anything, never swaps your provider behind your back, never makes up probabilities. > 7. awesome-jev (yibie) What people actually shipped with Jev, sourced and sorted by category, with the limitations written down for each entry. > 8. awesome-typesafe-jev SDKs, live demos, agent tools and independent evals. Shows where every vendor number comes from. > 9. awesome-jev-by-typesafe Use cases, patterns and starter code backed by evidence. The 70 to 500 ms latency figure is labeled vendor-reported, not measured. > 10. awesome-jev (cobanov) Biggest catalogue so far: SDKs for Go, Java and JS, MCP connectors, guardrail experiments and studies. > Copy these complete Jev blueprints - then read full Jev setup below ↓
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People already built a browser agent, a Claude Code bridge and a LangGraph router on top of Jev. Here are all 10 repos. If you have API access and don't know where to start, save this: 1. jev-browser Real browser control through MCP, CLI or as a library. At every step Jev answers two things: which element to touch, and whether the goal is done or the run is stuck. > 2. jev-browser-agent Picks the operation and the target in a single request, around a second per step. Anything under 0.6 confidence goes up to a bigger model or back to you. > 3. typesafe-jev-bridge OpenAI-compatible bridge with zero dependencies. Typed yes/no, choice and score answers inside Claude Code, Cursor, Cline or any OpenAI SDK, via CLI or HTTP. > 4. pilot-typesafeai-jev Jev wired into LangGraph and Deep Agents. Three questions in one request, plain code chooses the route, and the chat model only spends tokens on the route that won. > 5. building-with-jev-skill Agent skill for writing code that calls Jev. How to phrase questions, structure state, set thresholds, and debug a question that keeps answering wrong. > 6. jev-skill Guided setup and eval workflows. Checks what's installed without changing anything, never swaps your provider behind your back, never makes up probabilities. > 7. awesome-jev (yibie) What people actually shipped with Jev, sourced and sorted by category, with the limitations written down for each entry. > 8. awesome-typesafe-jev SDKs, live demos, agent tools and independent evals. Shows where every vendor number comes from. > 9. awesome-jev-by-typesafe Use cases, patterns and starter code backed by evidence. The 70 to 500 ms latency figure is labeled vendor-reported, not measured. > 10. awesome-jev (cobanov) Biggest catalogue so far: SDKs for Go, Java and JS, MCP connectors, guardrail experiments and studies. > Copy these complete Jev blueprints - then read full Jev setup below ↓

Asteri

16,629 Aufrufe • vor 1 Monat

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:
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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 1 Monat

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.
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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

553,215 Aufrufe • vor 1 Monat

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 ↓ ↓
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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 1 Monat

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

208,053 Aufrufe • vor 1 Monat

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

42,183 Aufrufe • vor 1 Monat

Jev is now multimodal. It can see. Here’s how it works and what you can build with it: Jev is a decision model. You give it information, ask questions, and get answers your code can use directly. Previously, that information was text or JSON. Now it can include images. Start with how a Jev request works. You send a “state,” which is simply the information you want Jev to examine. Then you ask questions about it. Each question specifies the kind of answer you need. → Choice picks from options you define, such as “thumbs up” or “fist.” → Noul returns the probability that something is true, such as whether a hand is visible. → Score rates something on a scale you define, such as how open the hand is. With multimodal Jev, images can be part of both the state and the questions. Put a camera frame in the state, and your questions can ask about what’s visible in it. Put a reference photo in a question’s instructions, and you can ask whether an object in the camera frame matches the one in that photo. Jev can now also identify an object and locate it for you. The new Box type brings a YOLO-like capability to Jev. Describe what you want to find, such as “the hand in this image,” and it returns a bounding box around it. Your app can use those coordinates to highlight the object or crop it. One detail matters here. Box returns coordinates even when the target is absent, so pair it with a Noul presence check before using the result. My demo asks about presence, gesture, and location together. Jev answers these independent questions in parallel, and the app combines the results. These capabilities open up prototypes for gesture controls, locating buttons in screenshots, and checking product photos against visual references. Jev’s focus on fast decisions also makes it promising for interactive applications where response time matters, with image latency worth testing in your own workflow. Images and Box are still experimental and require the images “danger zone” header. I cover how it works, the API, and the demo app in the detailed video below. Chapters 0:00 Intro 0:36 Live demo 1:41 What's covered 2:22 How Jev works 3:06 Question types 3:57 Playground demo 6:59 Demo app walkthrough 10:30 Using Multiple images 11:25 Parallel questions 12:40 Danger zone headers 13:07 Outro If you’re new to Jev and want to understand how decision models work, I’ve written a detailed article about it. It’s quoted below.
13:17

Jev is now multimodal. It can see. Here’s how it works and what you can build with it: Jev is a decision model. You give it information, ask questions, and get answers your code can use directly. Previously, that information was text or JSON. Now it can include images. Start with how a Jev request works. You send a “state,” which is simply the information you want Jev to examine. Then you ask questions about it. Each question specifies the kind of answer you need. → Choice picks from options you define, such as “thumbs up” or “fist.” → Noul returns the probability that something is true, such as whether a hand is visible. → Score rates something on a scale you define, such as how open the hand is. With multimodal Jev, images can be part of both the state and the questions. Put a camera frame in the state, and your questions can ask about what’s visible in it. Put a reference photo in a question’s instructions, and you can ask whether an object in the camera frame matches the one in that photo. Jev can now also identify an object and locate it for you. The new Box type brings a YOLO-like capability to Jev. Describe what you want to find, such as “the hand in this image,” and it returns a bounding box around it. Your app can use those coordinates to highlight the object or crop it. One detail matters here. Box returns coordinates even when the target is absent, so pair it with a Noul presence check before using the result. My demo asks about presence, gesture, and location together. Jev answers these independent questions in parallel, and the app combines the results. These capabilities open up prototypes for gesture controls, locating buttons in screenshots, and checking product photos against visual references. Jev’s focus on fast decisions also makes it promising for interactive applications where response time matters, with image latency worth testing in your own workflow. Images and Box are still experimental and require the images “danger zone” header. I cover how it works, the API, and the demo app in the detailed video below. Chapters 0:00 Intro 0:36 Live demo 1:41 What's covered 2:22 How Jev works 3:06 Question types 3:57 Playground demo 6:59 Demo app walkthrough 10:30 Using Multiple images 11:25 Parallel questions 12:40 Danger zone headers 13:07 Outro If you’re new to Jev and want to understand how decision models work, I’ve written a detailed article about it. It’s quoted below.

Akshay 🚀

26,160 Aufrufe • vor 29 Tagen

Why is everyone obsessed with an AI model that can't write? 🧠 TypeSafe launched Jev, a "System One" model that doesn't write text - it picks from answer options you define and returns a probability for each. ⚡ 20 questions cost about the same as one, because it reads everything in a single pass instead of composing answers word by word. 💸 Input runs $42 per billion tokens (most models price per million) and output is free. 🔮 The real unlock isn't intelligence, it's that decision-making inside software just got cheap enough to use everywhere. #AI #startups #tech #LLM #devtools Disclosure: Views expressed are my own. This is not investment advice. This is for informational and educational purposes only.
2:49

Why is everyone obsessed with an AI model that can't write? 🧠 TypeSafe launched Jev, a "System One" model that doesn't write text - it picks from answer options you define and returns a probability for each. ⚡ 20 questions cost about the same as one, because it reads everything in a single pass instead of composing answers word by word. 💸 Input runs $42 per billion tokens (most models price per million) and output is free. 🔮 The real unlock isn't intelligence, it's that decision-making inside software just got cheap enough to use everywhere. #AI #startups #tech #LLM #devtools Disclosure: Views expressed are my own. This is not investment advice. This is for informational and educational purposes only.

Claire Zau

68,100 Aufrufe • vor 1 Monat

this JEV is certified f*cking diamond every agent you ship burns frontier tokens on questions that were never writing jobs is this spam? which worker goes next? is the draft good enough to show a human? those are decisions. Jev is built for that half of the loop you send state plus a typed question. it returns a choice, a score, or yes/no with confidence. no chat. no second opinion the public demos, not our lab: > ~1,000 papers classified for about $0.08, median around 256ms > ~500 emails in one inbox pass for about 3.5¢ > a flight search loop in about 7 seconds for about $0.0039 > a huge Claude session compacted from ~1M tokens down to ~86k in about a second the whole trick is the split if it creates, the LLM writes if it picks, scores, or gates, Jev decides if it does the thing, code does it pricing people quote: about $0.042 per million tokens in. output free. 10,000 decisions is about $0.42 a local router makes it obvious. goal plus what's already done. Jev picks research, write, or review. confidence under 0.85 goes to a human. the job lands as JSON in a folder. the writer never wakes up for a yes/no and the loop refuses to publish on its own drafts go to review. you hit post. Jev never sends the tweet one model writes. a cheaper one decides. nothing irreversible ships until a human says yes.
0:22

this JEV is certified f*cking diamond every agent you ship burns frontier tokens on questions that were never writing jobs is this spam? which worker goes next? is the draft good enough to show a human? those are decisions. Jev is built for that half of the loop you send state plus a typed question. it returns a choice, a score, or yes/no with confidence. no chat. no second opinion the public demos, not our lab: > ~1,000 papers classified for about $0.08, median around 256ms > ~500 emails in one inbox pass for about 3.5¢ > a flight search loop in about 7 seconds for about $0.0039 > a huge Claude session compacted from ~1M tokens down to ~86k in about a second the whole trick is the split if it creates, the LLM writes if it picks, scores, or gates, Jev decides if it does the thing, code does it pricing people quote: about $0.042 per million tokens in. output free. 10,000 decisions is about $0.42 a local router makes it obvious. goal plus what's already done. Jev picks research, write, or review. confidence under 0.85 goes to a human. the job lands as JSON in a folder. the writer never wakes up for a yes/no and the loop refuses to publish on its own drafts go to review. you hit post. Jev never sends the tweet one model writes. a cheaper one decides. nothing irreversible ships until a human says yes.

kiosa

11,776 Aufrufe • vor 1 Monat

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below
17:33

Jev Founder, Diogo Almeida (ex-OpenAI): "The next era is not the Claude Code era. Claude Code is still part of the assistance era - Jev is the next step Jev is x200 faster, x400 cheaper and has 0 hallucination. This is what comes after RLHF" In 17 minutes he explains why every model you use has a human-shaped flaw baked into it and why Jev is better worth more than a $500 Claude Code engineering course watch today, then read how to actually boost yourself x100 with Jev in the post below

Codez

193,792 Aufrufe • vor 1 Monat

Jev is quietly taking over the agent stack. If you already have a Jev API key and don't know what to build first, steal this checklist: 1. jev-opus Runs Claude Code on Opus 5.5 and asks Jev before every call how much effort the next step needs: low, medium or high. Code enforces the floors and ceilings, and the prompt cache stays intact. 2. jev-belay A Claude Code Stop hook that won't trust "done" without evidence. If files changed and nothing passed since, one Jev call checks the transcript before the agent is allowed to stop. 3. jev-skill-router On every prompt, Jev picks one skill from everything you have installed, or none. It starts in shadow mode and only logs, so you see its picks before you trust them. 4. codex-jev-router Jev picks the model and reasoning effort for each Codex subagent. When Jev is unsure, it falls back to Sol. 5. jev-commit A pre-commit hook. One Jev call checks whether your commit message actually matches the staged diff and flags debug leftovers and unmentioned work. 6. is-malicious Scans source, config, build and CI files with Jev and points at the suspicious lines before the code ever runs. 7. jegrep Semantic grep in Rust. Jev scores your files with probabilities, no embedding index and no background daemon. 8. jev-scout An MCP server that scores every search query, result and fetched page your agent touches for relevance and credibility, with a live decision dashboard. 9. hunch Probabilistic control flow for Ruby. if Hunch.likely?("fraudulent", given: order) branches on a typed Jev answer. 10. pytest-jev Write plain-English claims about your output, and the test passes only when Jev clears every claim above 0.8. Copy any of these Jev builds, then read the full Jev setup below ↓
0:28

Jev is quietly taking over the agent stack. If you already have a Jev API key and don't know what to build first, steal this checklist: 1. jev-opus Runs Claude Code on Opus 5.5 and asks Jev before every call how much effort the next step needs: low, medium or high. Code enforces the floors and ceilings, and the prompt cache stays intact. 2. jev-belay A Claude Code Stop hook that won't trust "done" without evidence. If files changed and nothing passed since, one Jev call checks the transcript before the agent is allowed to stop. 3. jev-skill-router On every prompt, Jev picks one skill from everything you have installed, or none. It starts in shadow mode and only logs, so you see its picks before you trust them. 4. codex-jev-router Jev picks the model and reasoning effort for each Codex subagent. When Jev is unsure, it falls back to Sol. 5. jev-commit A pre-commit hook. One Jev call checks whether your commit message actually matches the staged diff and flags debug leftovers and unmentioned work. 6. is-malicious Scans source, config, build and CI files with Jev and points at the suspicious lines before the code ever runs. 7. jegrep Semantic grep in Rust. Jev scores your files with probabilities, no embedding index and no background daemon. 8. jev-scout An MCP server that scores every search query, result and fetched page your agent touches for relevance and credibility, with a live decision dashboard. 9. hunch Probabilistic control flow for Ruby. if Hunch.likely?("fraudulent", given: order) branches on a typed Jev answer. 10. pytest-jev Write plain-English claims about your output, and the test passes only when Jev clears every claim above 0.8. Copy any of these Jev builds, then read the full Jev setup below ↓

Chen

29,260 Aufrufe • vor 1 Monat

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
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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 Aufrufe • vor 1 Monat

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.
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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

301,592 Aufrufe • vor 1 Monat

Introducing typesafe/jev-router: a cache-aware model router powered by Jev and TypeSafe AI The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻
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Introducing typesafe/jev-router: a cache-aware model router powered by Jev and TypeSafe AI The Jev Router picks the best model and reasoning effort for each request, balancing quality, speed, and cost. Here's how it works 👇🏻

OpenRouter

1,334,841 Aufrufe • vor 1 Monat

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
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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

225,851 Aufrufe • vor 1 Monat

ANTHROPIC LEAKED A $4.2M FILE WHERE 4 AGENTS BUILD THE JEV SYSTEM IN 60 MINUTES, DOING $100K A MONTH four agents cost $0, and the system they assemble in an hour generates $100K a month scout → builder → critic → operator → JEV every agent is pinned to one exact model version, and none of them runs on "latest" the scout on Haiku 4.5 reads the market, the builder on Sonnet 5 builds everything, the critic on Opus 5 decides what ships the operator runs Haiku through the batch API at half price - the one agent that never has to hurry the builder takes 59% of the whole hour, because it's the one writing, and writing costs 5x more than reading short answers are cheap answers - and the whole economics rests on that one rule pin the versions in ROOT.md and next month JEV behaves exactly the same point it at "latest" and the model underneath updates quietly, no error, and the answers start to drift $100K a month is 334 customers at $299 four versions, one system - pin them and JEV stays JEV save this and paste it into Claude Code - let it build you these 4 agents ↓
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ANTHROPIC LEAKED A $4.2M FILE WHERE 4 AGENTS BUILD THE JEV SYSTEM IN 60 MINUTES, DOING $100K A MONTH four agents cost $0, and the system they assemble in an hour generates $100K a month scout → builder → critic → operator → JEV every agent is pinned to one exact model version, and none of them runs on "latest" the scout on Haiku 4.5 reads the market, the builder on Sonnet 5 builds everything, the critic on Opus 5 decides what ships the operator runs Haiku through the batch API at half price - the one agent that never has to hurry the builder takes 59% of the whole hour, because it's the one writing, and writing costs 5x more than reading short answers are cheap answers - and the whole economics rests on that one rule pin the versions in ROOT.md and next month JEV behaves exactly the same point it at "latest" and the model underneath updates quietly, no error, and the answers start to drift $100K a month is 334 customers at $299 four versions, one system - pin them and JEV stays JEV save this and paste it into Claude Code - let it build you these 4 agents ↓

Sprytix

38,104 Aufrufe • vor 1 Monat

stop picking one model, a spider merged OPUS 5.5 + SONNET 5.5 + JEV + OPENAI DOTS into one business system one command splits a business into 6 jobs: plan, code, decide, run 24/7, review and ship every job has 4 candidates. the spider scans them one by one, marks the highest score as best, locks it in and swings to the next job the plan goes to Opus 5.5 at 0.94. Jev scores 0.52 on the same job on decisions it flips. Jev wins at 0.97 and Opus 5.5 gets 0.74, so the model that lost the plan is the one making the calls the code goes to Claude Code, and the job called "who never sleeps" goes to OpenAI Dots Opus 5.5 comes back a second time to check the work, and Sonnet 5.5 gets it out at the end 6 of 6 modules are locked and the system goes online run the same test on your own stack before you pay one model to do all six jobs ↓
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stop picking one model, a spider merged OPUS 5.5 + SONNET 5.5 + JEV + OPENAI DOTS into one business system one command splits a business into 6 jobs: plan, code, decide, run 24/7, review and ship every job has 4 candidates. the spider scans them one by one, marks the highest score as best, locks it in and swings to the next job the plan goes to Opus 5.5 at 0.94. Jev scores 0.52 on the same job on decisions it flips. Jev wins at 0.97 and Opus 5.5 gets 0.74, so the model that lost the plan is the one making the calls the code goes to Claude Code, and the job called "who never sleeps" goes to OpenAI Dots Opus 5.5 comes back a second time to check the work, and Sonnet 5.5 gets it out at the end 6 of 6 modules are locked and the system goes online run the same test on your own stack before you pay one model to do all six jobs ↓

Noisy

11,722 Aufrufe • vor 1 Monat

What Jev actually is, why it's so fast, and what you can build with it. Classification is as old as AI that can read: give it text, ask a question with fixed answers (complaint yes/no · which team · urgency 1–5), and the label drives a rule. The problem: we've been using models built for writing, a few seconds per answer. Jev (TypeSafe, launched Sept 15, founded by one of the OpenAI researchers behind ChatGPT's instruction-following) only decides. It never writes. Send the text once with every question; it scores every answer in one pass: yes/no, pick one, or a score, each with a confidence. 70–500 ms. Input ~$0.042 per million tokens, output free. TypeSafe measured up to ~200× faster and ~400× cheaper than top models on its own workflows, and says real-world gains are usually lower. Builders: a resume screened against 6,000+ companies in 25 seconds for $0.37; 98,000 items labeled in ten minutes. ~13% of paid Vercel AI Gateway teams used it within a day. Calibrated: "70% sure" should be right ~70% of the time, so software can act alone above a threshold and route the rest to a person. Build with it: every competitor ad, same five questions · every ticket, review, call note and CRM entry across systems, tagged the same way · an agent's actions checked before they run (LangChain already does this). Edges: text only; weak at math, counting, dates; reads your question literally; you design the questions and categories up front. That design is the skill. #jev #aiagent
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What Jev actually is, why it's so fast, and what you can build with it. Classification is as old as AI that can read: give it text, ask a question with fixed answers (complaint yes/no · which team · urgency 1–5), and the label drives a rule. The problem: we've been using models built for writing, a few seconds per answer. Jev (TypeSafe, launched Sept 15, founded by one of the OpenAI researchers behind ChatGPT's instruction-following) only decides. It never writes. Send the text once with every question; it scores every answer in one pass: yes/no, pick one, or a score, each with a confidence. 70–500 ms. Input ~$0.042 per million tokens, output free. TypeSafe measured up to ~200× faster and ~400× cheaper than top models on its own workflows, and says real-world gains are usually lower. Builders: a resume screened against 6,000+ companies in 25 seconds for $0.37; 98,000 items labeled in ten minutes. ~13% of paid Vercel AI Gateway teams used it within a day. Calibrated: "70% sure" should be right ~70% of the time, so software can act alone above a threshold and route the rest to a person. Build with it: every competitor ad, same five questions · every ticket, review, call note and CRM entry across systems, tagged the same way · an agent's actions checked before they run (LangChain already does this). Edges: text only; weak at math, counting, dates; reads your question literally; you design the questions and categories up front. That design is the skill. #jev #aiagent

Professor Glitch

38,700 Aufrufe • vor 1 Monat