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 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 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 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-browser Drives a real browser through MCP, CLI or a library. One decision per step: which element to act on, and whether the goal is met or the run is stuck. 2. jev-browser-agent Per-step browser decisions in about a second. One request asks which operation and which target together; anything under 0.6 confidence escalates to a bigger model or to you. 3. typesafe-jev-bridge Zero-dependency OpenAI-compatible bridge. Typed yes/no, choice and score judgments from Claude Code, Cursor, Cline or any OpenAI SDK, over CLI or HTTP. 4. pilot-typesafeai-jev Jev inside LangGraph and Deep Agents. One request asks three questions, plain code picks the route, and only the winning route spends chat model tokens. 5. building-with-jev-skill An agent skill for writing programs that call Jev: question design, state structure, thresholds, and how to diagnose a question that keeps answering wrong. 6. jev-skill Guided setup plus evaluation workflows. Read-only presence check, no silent provider switches, no fabricated probabilities. 7. awesome-jev Source-backed catalogue of what people actually shipped, sorted by category, with the limitations noted per entry. 8. awesome-typesafe-jev Field guide with SDKs, live demos, agent tools and independent evaluations, including where each vendor number came from. 9. awesome-jev-by-typesafe Evidence-backed use cases, patterns and starter code, with the 70 to 500 ms latency claim marked as vendor-reported. 10. awesome-jev The widest catalogue right now: SDKs in Go, Java and JS, MCP connectors, guardrail experiments and studies. 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-browser Drives a real browser through MCP, CLI or a library. One decision per step: which element to act on, and whether the goal is met or the run is stuck. 2. jev-browser-agent Per-step browser decisions in about a second. One request asks which operation and which target together; anything under 0.6 confidence escalates to a bigger model or to you. 3. typesafe-jev-bridge Zero-dependency OpenAI-compatible bridge. Typed yes/no, choice and score judgments from Claude Code, Cursor, Cline or any OpenAI SDK, over CLI or HTTP. 4. pilot-typesafeai-jev Jev inside LangGraph and Deep Agents. One request asks three questions, plain code picks the route, and only the winning route spends chat model tokens. 5. building-with-jev-skill An agent skill for writing programs that call Jev: question design, state structure, thresholds, and how to diagnose a question that keeps answering wrong. 6. jev-skill Guided setup plus evaluation workflows. Read-only presence check, no silent provider switches, no fabricated probabilities. 7. awesome-jev Source-backed catalogue of what people actually shipped, sorted by category, with the limitations noted per entry. 8. awesome-typesafe-jev Field guide with SDKs, live demos, agent tools and independent evaluations, including where each vendor number came from. 9. awesome-jev-by-typesafe Evidence-backed use cases, patterns and starter code, with the 70 to 500 ms latency claim marked as vendor-reported. 10. awesome-jev The widest catalogue right now: SDKs in Go, Java and JS, MCP connectors, guardrail experiments and studies. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

Jev Founder, Diogo Almeida, just released a PDF on building a Jev Autopilot for coding agents this is a blueprint on how to make your coding agents 200× faster and 400× cheaper nine decisions come off the frontier model: which files to read, which model gets the step, whether the command may run, whether the task is actually done Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster 👇

Jev Founder, Diogo Almeida, just released a PDF on building a Jev Autopilot for coding agents this is a blueprint on how to make your coding agents 200× faster and 400× cheaper nine decisions come off the frontier model: which files to read, which model gets the step, whether the command may run, whether the task is actually done Send this PDF and the article below to your Claude Code or Codex instance and start shipping 200× faster 👇

Jev Engineering lets one decision layer steer hundreds of agent paths without letting any branch improvise. one state enters. Jev turns it into typed decisions: → which model gets the call → which tool gets access → which result survives those decisions fan out across routes, models and tools, all answered in the same call because none of them reads another one's answer. then everything collapses back into one verified state. so instead of: LLM → agent → tool → another LLM → another guess you get: state → decisions → parallel routes → execution → verification → next state 400 forks in one run, one call, $0.017 for all of them, and not a single sentence written. the expensive model stays where it belongs: writing. full breakdown in the article below ↓

Jev Engineering lets one decision layer steer hundreds of agent paths without letting any branch improvise. one state enters. Jev turns it into typed decisions: → which model gets the call → which tool gets access → which result survives those decisions fan out across routes, models and tools, all answered in the same call because none of them reads another one's answer. then everything collapses back into one verified state. so instead of: LLM → agent → tool → another LLM → another guess you get: state → decisions → parallel routes → execution → verification → next state 400 forks in one run, one call, $0.017 for all of them, and not a single sentence written. the expensive model stays where it belongs: writing. full breakdown in the article below ↓

Graphs vs. Jev, clearly explained! graphs are great, and the ceiling is one you can watch repeat itself: you draw the nodes once. intake, search, code, docs, review, ship. then you draw the edges, and every run walks the path you drew in March. the ticket that needed search goes to code anyway, because nobody told the edge it had a choice. six runs, six identical paths, and a week later you find out three of them were wrong. Jev fixes this by moving the decision onto the edges: not which nodes exist, but which edge fires. you need both, and here is the sentence that resolves the whole confusion: a graph decides which nodes exist. Jev decides which edge fires. ↳ nodes: drawn once, at design time, by you ↳ edges: answered every run, with a probability on each one Prompts → Agents → Loops → Graphs → Jev the graph does not go away. it stops pretending every fork was decided in advance. the trick is choosing which edges to hand over. a fork belongs to Jev when the options are known, the choice depends on meaning, and it runs often enough to matter. intake, the loop exit, the review gate. one thing to know before you scale it. the menu has to be live. ↳ an edge to a node that is online right now is a real option ↳ an edge to a worker that went down an hour ago only looks like one that last one catches careful people. Jev will pick the dead edge with a sharp distribution, because you asked it to choose between four things and it did its job. and the one that eats whole nights: letting a probability fire an irreversible edge. the migration branch gets a hard rule in code, above every threshold, and Jev never gets a vote on it. below i have quoted my full breakdown on Jev. it covers the three primitives, live menus, the thresholds, and where it does not belong. save this and read it below ↓

Graphs vs. Jev, clearly explained! graphs are great, and the ceiling is one you can watch repeat itself: you draw the nodes once. intake, search, code, docs, review, ship. then you draw the edges, and every run walks the path you drew in March. the ticket that needed search goes to code anyway, because nobody told the edge it had a choice. six runs, six identical paths, and a week later you find out three of them were wrong. Jev fixes this by moving the decision onto the edges: not which nodes exist, but which edge fires. you need both, and here is the sentence that resolves the whole confusion: a graph decides which nodes exist. Jev decides which edge fires. ↳ nodes: drawn once, at design time, by you ↳ edges: answered every run, with a probability on each one Prompts → Agents → Loops → Graphs → Jev the graph does not go away. it stops pretending every fork was decided in advance. the trick is choosing which edges to hand over. a fork belongs to Jev when the options are known, the choice depends on meaning, and it runs often enough to matter. intake, the loop exit, the review gate. one thing to know before you scale it. the menu has to be live. ↳ an edge to a node that is online right now is a real option ↳ an edge to a worker that went down an hour ago only looks like one that last one catches careful people. Jev will pick the dead edge with a sharp distribution, because you asked it to choose between four things and it did its job. and the one that eats whole nights: letting a probability fire an irreversible edge. the migration branch gets a hard rule in code, above every threshold, and Jev never gets a vote on it. below i have quoted my full breakdown on Jev. it covers the three primitives, live menus, the thresholds, and where it does not belong. save this and read it below ↓

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

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

Tools vs. Graphs, clearly explained! tools are great, and everyone is connecting more of them this month. here is the ceiling: a tool is a hand. it lets one node reach something it could not reach before. connect sixteen of them and you have one node that can reach sixteen places, one at a time. the sixteenth tool did not make it faster. it made the window heavier. Graph engineering fixes this without removing a single tool: it changes how many nodes are holding them. you need both, and here is the sentence that resolves the whole confusion: a tool is what one node can reach. a graph is how many nodes are reaching. ↳ more tools: one context, sixteen schemas loaded before any work starts, one call in flight ↳ a graph: four contexts, four schemas each, four calls in flight, and nothing waits on the others Prompts → Context → Harness → Loops → Graphs the tools do not go away when you build a graph. they get distributed, and now four of them are being used at the same time on four things you would never have run in sequence. the trick is giving each lane only what it needs. a lane that audits dependencies does not need your calendar tool. every schema you attach to it is tokens spent before the first useful one, and context the lane has to hold while it works. one thing to know before you scale it. tool output is the part nobody budgets for. ↳ the schema costs you once, at the top, and it is the small half ↳ the output lands in the same window, every call, and that is the half that grows that last one catches careful people. a grep across a large repo, a page of browser DOM, a full SQL result: three calls and the window is mostly tool output. the model is now reasoning about your goal inside whatever room is left. and the one that eats whole nights: connecting a tool is not the same as deciding when to use it. sixteen tools in one window means sixteen chances to pick the wrong one, and the model has no idea which of them you actually meant for this step. the cut comes first. the tools come after. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Google just dropped the best 1-hour course on Graph Engineering: from one agent to a full 24/7 system 00:00 - what graphs are 09:16 - build an agent 21:15 - graph engineering explained 41:03 - graph engineering in practice 52:21 - self-improving graphs free, and the best thing on graph engineering I have come across Prompts → Agents → Loops → Graphs most people will watch the first nine minutes and go back to typing into a chat box same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then read the full guide on agents and graphs below

Tools vs. Graphs, clearly explained! tools are great, and everyone is connecting more of them this month. here is the ceiling: a tool is a hand. it lets one node reach something it could not reach before. connect sixteen of them and you have one node that can reach sixteen places, one at a time. the sixteenth tool did not make it faster. it made the window heavier. Graph engineering fixes this without removing a single tool: it changes how many nodes are holding them. you need both, and here is the sentence that resolves the whole confusion: a tool is what one node can reach. a graph is how many nodes are reaching. ↳ more tools: one context, sixteen schemas loaded before any work starts, one call in flight ↳ a graph: four contexts, four schemas each, four calls in flight, and nothing waits on the others Prompts → Context → Harness → Loops → Graphs the tools do not go away when you build a graph. they get distributed, and now four of them are being used at the same time on four things you would never have run in sequence. the trick is giving each lane only what it needs. a lane that audits dependencies does not need your calendar tool. every schema you attach to it is tokens spent before the first useful one, and context the lane has to hold while it works. one thing to know before you scale it. tool output is the part nobody budgets for. ↳ the schema costs you once, at the top, and it is the small half ↳ the output lands in the same window, every call, and that is the half that grows that last one catches careful people. a grep across a large repo, a page of browser DOM, a full SQL result: three calls and the window is mostly tool output. the model is now reasoning about your goal inside whatever room is left. and the one that eats whole nights: connecting a tool is not the same as deciding when to use it. sixteen tools in one window means sixteen chances to pick the wrong one, and the model has no idea which of them you actually meant for this step. the cut comes first. the tools come after. below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open. save this and read it below ↓

Google just dropped the best 1-hour course on Graph Engineering: from one agent to a full 24/7 system 00:00 - what graphs are 09:16 - build an agent 21:15 - graph engineering explained 41:03 - graph engineering in practice 52:21 - self-improving graphs free, and the best thing on graph engineering I have come across Prompts → Agents → Loops → Graphs most people will watch the first nine minutes and go back to typing into a chat box same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then read the full guide on agents and graphs below

Anthropic engineer: "At Anthropic we don't write prompts anymore. We build loops and graphs." in 32 minutes she shows how the Claude team builds systems that prompt themselves Prompts → Agents → Loops → Graphs a loop finishes one unit of work without you a graph decides which units exist and turns every accepted one into a rule the next run inherits same model, same tokens, and the only thing that changes is the shape you run it in watch it today, then save the full graph engineering guide below