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this is pure f*cking gold TypeSafe released the skill that teaches Claude Code to build on Jev left alone, coding agents send Jev one question per call and guess the field names this skill fixes the order before a single line gets written: docs -> behaviour -> judgments ->... show more

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2,587 subscribers

10,107 次观看 • 11 天前 •via X (Twitter)

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10 条评论

Iron Giant 的头像

Iron Giant11 天前

thx for quote

Crio Songo 的头像

Crio Songo10 天前

This approach is really smart. It solves the problem of multiple queries and cuts costs by 12 times, that's awesome.

just sophia 的头像

just sophia10 天前

Totally agree with this

Flextor 的头像

Flextor11 天前

Sequential round trips kill coding agents faster than context limits. When an agent fires five individual validation calls back to back, any small latency spike or format error breaks the loop. Packing judgments into a single structured schema is how you keep traces stable.

DHRUV BANSAL 的头像

DHRUV BANSAL10 天前

you can't batch 13 questions unless you know all 13 before you ask the first one. which is the same work as not guessing the field names, so the cheaper bill comes from the same step that stops the guessing. useful on a schema you didn't write

lostinazeroy 的头像

lostinazeroy11 天前

my monthly api bills are getting way too high so i really need this

MrOzi 的头像

MrOzi11 天前

Jev's own agents must be furious they didn't think of this first.

Gh 的头像

Gh10 天前

Looks promising

Brjan | AI Builder 的头像

Brjan | AI Builder10 天前

fixing the order is great, but agents still need clarity on input types

OK50饭山禾的头像

OK50饭山禾10 天前

这波优化直接把效率拉满太强了

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this is pure f*cking gold for anyone running coding agents Jev founder, Diogo Almeida, wrote a guide on building a Jev harness the expensive model only writes the code. every fork around it goes to Jev the bill: > 6,000 forks a day on a frontier model: $180 > the same 6,000 forks on Jev: $0.25 > the whole harness, patches included: about $24 99 percent of what's left is the code itself, which is the only part you should be paying for what's inside: > the turn snapshot, five hooks, the cascade > thresholds read off your own receipts > the one rule a model never gets a vote on one evening of setup and your agent stops paying frontier prices to answer yes or no ↓

this is pure f*cking gold for anyone running coding agents Jev founder, Diogo Almeida, wrote a guide on building a Jev harness the expensive model only writes the code. every fork around it goes to Jev the bill: > 6,000 forks a day on a frontier model: $180 > the same 6,000 forks on Jev: $0.25 > the whole harness, patches included: about $24 99 percent of what's left is the code itself, which is the only part you should be paying for what's inside: > the turn snapshot, five hooks, the cascade > thresholds read off your own receipts > the one rule a model never gets a vote on one evening of setup and your agent stops paying frontier prices to answer yes or no ↓

Hanako

20,837 次观看 • 11 天前

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

this is f*cking gold 20 GitHub repos with 500K+ combined stars that will level up your JEV workflow AGENTS > jev-ultrafast: Jev picks every click and DOM target, a small LLM only types > hermes-jev-skills: routing, memory, compaction and skill picks in one pack > typesafe-computer-use: OCR reads your Mac screen, Jev picks the next click MEMORY > fast-jev-compaction: scores every tool call keep, truncate or drop instead of summarizing > jevmem: project memory for Claude Code, Cursor and Codex, updated every turn > jev-second-brain: your Obsidian vault, Jev judges which notes duplicate, revise or contradict SAFETY > jev-guard: a gate before every tool call, Jev scores the risk, you set allow, ask or deny TOOLS > skills: the official TypeSafe skill for Claude Code and Codex > system-one-adapter-python: dry-run your questions on an ordinary LLM before you burn a Jev key > jev-mcp: claim checks, screening and ranking as MCP tools > typesafe-mcp: plug Jev into any MCP client > json-render: Vercel's generative UI, where Jev picks the components OPEN MODELS > SemIf-OpenJev: semantic ifs from frozen open models kev: Jev-like models on Qwen that run on your MacBook > laya-mlx: the Laya decision engine on Apple silicon > clm: an open System One model with Choice, Noul and Score > jevlike: train your own Jev-like model TRADING > jev-trader: one buy or sell decision per Monad block START HERE > awesome-jev: the biggest map of everything built on Jev > awesome-jev-by-typesafe: use cases, patterns and starter code bookmark it before your next build

NO1ennn

18,445 次观看 • 10 天前

$this harness stack is f*cking gold TypeSafe AI + LangChain finally dropped the layer that makes agents affordable at company scale: jev running inside the langchain agent loop as middleware the problem every big team hits: each fork in the loop is another full LLM call. at 10 agents that's a bill. at 10,000 it's the budget > routing before: every task goes to the frontier model now: jev picks the least costly model that can finish it. lookups go fast, architecture goes powerful > guardrails before: the "is this command dangerous?" check sat in the closed parts of claude code, codex, cursor now: AutoModeMiddleware(tools=["bash"]). one line, any agent, blocked before the tool runs > decisions before: one LLM call per question now: every question about the same state in one request, answered in parallel > audit before: "the model decided" now: the answer, the probability and the confidence stay in agent state > adoption no rewrite. pip install langchain-typesafe and drop it into the agent you already run TypeSafe reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification. the LLM still reasons and writes. jev takes the thousands of small calls in between already live: browserbase agents for fractions of a cent, a live trading agent, email triage at scale full loop mapped below: 7 agents, 4 jev gates per step count the forks in your agent loop tonigh$

this harness stack is f*cking gold TypeSafe AI + LangChain finally dropped the layer that makes agents affordable at company scale: jev running inside the langchain agent loop as middleware the problem every big team hits: each fork in the loop is another full LLM call. at 10 agents that's a bill. at 10,000 it's the budget > routing before: every task goes to the frontier model now: jev picks the least costly model that can finish it. lookups go fast, architecture goes powerful > guardrails before: the "is this command dangerous?" check sat in the closed parts of claude code, codex, cursor now: AutoModeMiddleware(tools=["bash"]). one line, any agent, blocked before the tool runs > decisions before: one LLM call per question now: every question about the same state in one request, answered in parallel > audit before: "the model decided" now: the answer, the probability and the confidence stay in agent state > adoption no rewrite. pip install langchain-typesafe and drop it into the agent you already run TypeSafe reports up to 200x faster inference and 400x lower cost than comparable LLMs on classification. the LLM still reasons and writes. jev takes the thousands of small calls in between already live: browserbase agents for fractions of a cent, a live trading agent, email triage at scale full loop mapped below: 7 agents, 4 jev gates per step count the forks in your agent loop tonigh

NO1ennn

31,666 次观看 • 9 天前

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

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

delost

15,734 次观看 • 11 天前

Introducing Jev Skill Suggestion for Claude Code Now you can keep your skills completely out of the context window until they’re actually needed Set your skills as user invocable only, and the Mod sends the available skill list to Jev For every request, Jev classifies which skill best matches the task, then injects only that skill into Claude’s context Works with both the TypeSafe AI API and Vercel AI Gateway Install it with one command: npx claude-code-templates@latest --mod productivity/jev-skill-suggestion Full implementation details on the Mod page at

Introducing Jev Skill Suggestion for Claude Code Now you can keep your skills completely out of the context window until they’re actually needed Set your skills as user invocable only, and the Mod sends the available skill list to Jev For every request, Jev classifies which skill best matches the task, then injects only that skill into Claude’s context Works with both the TypeSafe AI API and Vercel AI Gateway Install it with one command: npx claude-code-templates@latest --mod productivity/jev-skill-suggestion Full implementation details on the Mod page at

Daniel San

80,898 次观看 • 17 天前

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 ↓

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

37,977 次观看 • 17 天前

open Claude Code type a prompt. one model does everything. congrats, you just used 20% of it meanwhile I built 30 sub-agents one reviews my code before commit one finds bugs and writes the patch one drafts cold emails from one line one triages my inbox before I wake up one logs every decision so future me knows why each agent = one markdown file in .claude/agents/ most Claude Code users don't even know this exists. all 30 files in the article. ready to copy. the article has every file. every prompt. just copy and run.

open Claude Code type a prompt. one model does everything. congrats, you just used 20% of it meanwhile I built 30 sub-agents one reviews my code before commit one finds bugs and writes the patch one drafts cold emails from one line one triages my inbox before I wake up one logs every decision so future me knows why each agent = one markdown file in .claude/agents/ most Claude Code users don't even know this exists. all 30 files in the article. ready to copy. the article has every file. every prompt. just copy and run.

Nav Toor

71,467 次观看 • 5 个月前

honestly this should be f*cking illegal your LLM takes 30 seconds to think about a trade, but candle closed 29 seconds ago. Jev answers in 70 to 500 milliseconds. it can't write a word. it returns BUY / SELL / HOLD and a probability, and that's all a scalp needs. 3 repos already built on it: -> jev-trader: one AI trade decision every single block, on a live MON-USDC market -> QuantDinger: a full open-source AI trading OS with Jev built in. research, strategy, agent trading -> AIStock: "one person can become their own super-analyst" $0.042 per million input tokens. output is free. that's thousands of decisions for the price of one LLM call. the catch: fast is not the same as right. a wrong call in 200 milliseconds is still a wrong call. paper trade it first. > let the big model do the research. > let Jev pull the trigger. > let code set the stop. bookmark this before your bot is the slow one in the room.

honestly this should be f*cking illegal your LLM takes 30 seconds to think about a trade, but candle closed 29 seconds ago. Jev answers in 70 to 500 milliseconds. it can't write a word. it returns BUY / SELL / HOLD and a probability, and that's all a scalp needs. 3 repos already built on it: -> jev-trader: one AI trade decision every single block, on a live MON-USDC market -> QuantDinger: a full open-source AI trading OS with Jev built in. research, strategy, agent trading -> AIStock: "one person can become their own super-analyst" $0.042 per million input tokens. output is free. that's thousands of decisions for the price of one LLM call. the catch: fast is not the same as right. a wrong call in 200 milliseconds is still a wrong call. paper trade it first. > let the big model do the research. > let Jev pull the trigger. > let code set the stop. bookmark this before your bot is the slow one in the room.

kiosa

82,972 次观看 • 1 天前

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 ⭣

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 次观看 • 7 天前

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

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

NO1ennn

42,546 次观看 • 12 天前

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

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

Hanako

37,887 次观看 • 14 天前

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

Yarchi

54,840 次观看 • 4 个月前

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

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

Archive

16,841 次观看 • 8 天前

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

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

Paone

32,087 次观看 • 12 天前

This is f*cking insane. US researchers published "The End of the Judgment Call" and the whole paper is one split: Claude builds, Jev decides. The case is simple. An overnight Claude loop asks itself around 600 yes-or-no questions. Safe to run. Next step. Goal met. Each one costs about 4 cents on Fable 5.1 and takes seconds. Jev came out of stealth on September 15 from a co-author of InstructGPT. It answers typed questions only, with a calibrated probability, in about 100 milliseconds, at $0.042 per million input tokens. When it says 0.9 it is right nine times in ten. That is a number your code can act on at 3am with nobody watching. Three hooks in Claude Code and the judgment calls are gone: a 100 ms gate on every bash command, a stop hook that knows when the work is really done, a cron that only wakes Claude for reproducible bugs. The bill for 30 nights goes from $765 to $3.02. What Claude spends on building does not change. The article below has all three builds with code. The entire paper in one line: the cheapest Claude turn is the one that never needed to start ↓

This is f*cking insane. US researchers published "The End of the Judgment Call" and the whole paper is one split: Claude builds, Jev decides. The case is simple. An overnight Claude loop asks itself around 600 yes-or-no questions. Safe to run. Next step. Goal met. Each one costs about 4 cents on Fable 5.1 and takes seconds. Jev came out of stealth on September 15 from a co-author of InstructGPT. It answers typed questions only, with a calibrated probability, in about 100 milliseconds, at $0.042 per million input tokens. When it says 0.9 it is right nine times in ten. That is a number your code can act on at 3am with nobody watching. Three hooks in Claude Code and the judgment calls are gone: a 100 ms gate on every bash command, a stop hook that knows when the work is really done, a cron that only wakes Claude for reproducible bugs. The bill for 30 nights goes from $765 to $3.02. What Claude spends on building does not change. The article below has all three builds with code. The entire paper in one line: the cheapest Claude turn is the one that never needed to start ↓

slash1s

14,231 次观看 • 12 天前

Claude Code sugarcoats everything. You know who doesn't? Eastern European developers. Introducing the Marko skill - call Marko at any time, and he will review your code - and believe me - he will find something wrong with it. And, staying very on brand - he won't tell you how to fix it. He has one job, and one job only - to tell your agent that the code sucks. Skill repo in the thread 👇

Julian Galluzzo

89,048 次观看 • 5 个月前

I can't believe this is f*cking free how to build your first AI agent (full guide, with a Jev decision layer) a year ago I burned two weeks on my first agent. this guide gets you there in an afternoon in the right hands it makes one builder ship like a small team:

I can't believe this is f*cking free how to build your first AI agent (full guide, with a Jev decision layer) a year ago I burned two weeks on my first agent. this guide gets you there in an afternoon in the right hands it makes one builder ship like a small team:

Mr. Buzzoni

60,775 次观看 • 6 天前

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

THIS IS HOW A SENIOR ENGINEER ACTUALLY SCALES THEMSELVES WITH CLAUDE CODE the biggest change with AI isn't coding faster. it's where you actually spend your time now. more detailed prompts, more code review, more planning, less typing, etc. here's the workflow: this guy has been shipping code since the days of cgi and perl. he uses a compound engineering plugin that runs 5 separate agents on every task. one brainstorms, one plans the technical implementation, one executes, one reviews, one checks different verticals. every step is documented in markdown files. it's slow and way more waiting. but the output quality is way higher because each agent is focused on one thing. then the REAL multiplier is in git worktrees if Claude Code made you 10x faster, worktrees multiplies that again depending on how many agents you can manage in parallel his team runs 4-8 Claude Code sessions at the same time across different worktrees with each one working on a separate task. the skill is managing multiple AI agents in parallel without losing track, that's the next evolution of engineering

Om Patel

159,776 次观看 • 6 个月前

i genuinely don't understand why everyone isn't using this yet diogo almeida, one of the people behind chatgpt, spent two years in stealth and shipped this on monday. his own words about it: "jev is off the charts", the same frontier intelligence you're already paying for, "two orders of magnitude faster and more efficient" in normal words: jev is an ai that never writes anything. it only decides. yes or no, which pile does this go in, how good is it. one answer per item, under half a second each, with a number for how sure it is. it isn't writing anything, so it can't make anything up so you stop feeding chatgpt one thing at a time and hand jev the whole pile. 400 unread emails into reply today, later, never. 300 comments into questions, complaints, praise. 200 job posts into worth it and not. a competitor's entire ad library into what's actually working. the whole pile, in seconds, for cents full guide: > join the waitlist on people are approved the same day > open the dashboard and create an api key > tell claude or your agent: install the typesafe skill > paste your pile and name the buckets you want it sorted into > it hands everything back labelled, with a confidence number on each one no code, nothing to learn. someone ran 1,891 ads through it in 19 seconds for 12 cents, and it's free on vercel's gateway right now five minutes to set up, and the sorting job that used to eat your evening is done before you stand up full step-by-step guide, link below bookmark this

i genuinely don't understand why everyone isn't using this yet diogo almeida, one of the people behind chatgpt, spent two years in stealth and shipped this on monday. his own words about it: "jev is off the charts", the same frontier intelligence you're already paying for, "two orders of magnitude faster and more efficient" in normal words: jev is an ai that never writes anything. it only decides. yes or no, which pile does this go in, how good is it. one answer per item, under half a second each, with a number for how sure it is. it isn't writing anything, so it can't make anything up so you stop feeding chatgpt one thing at a time and hand jev the whole pile. 400 unread emails into reply today, later, never. 300 comments into questions, complaints, praise. 200 job posts into worth it and not. a competitor's entire ad library into what's actually working. the whole pile, in seconds, for cents full guide: > join the waitlist on people are approved the same day > open the dashboard and create an api key > tell claude or your agent: install the typesafe skill > paste your pile and name the buckets you want it sorted into > it hands everything back labelled, with a confidence number on each one no code, nothing to learn. someone ran 1,891 ads through it in 19 seconds for 12 cents, and it's free on vercel's gateway right now five minutes to set up, and the sorting job that used to eat your evening is done before you stand up full step-by-step guide, link below bookmark this

Argona

32,613 次观看 • 19 天前

call me crazy but company knowledge should look more like this. > every file becomes a node > every import, call, and dependency becomes an edge > every answer points back to the exact code behind it click one class and the system shows what it depends on, what depends on it, and where it sits inside the architecture. now apply the same idea to the company: decisions. projects. people. policies. workflows. learnings. AI should not search through a pile of documents and guess how they connect. it should navigate the company as a system. don't bookmark this if it crosses your timeline. paste the full article into your agent and tell it to build the first version for your company.

call me crazy but company knowledge should look more like this. > every file becomes a node > every import, call, and dependency becomes an edge > every answer points back to the exact code behind it click one class and the system shows what it depends on, what depends on it, and where it sits inside the architecture. now apply the same idea to the company: decisions. projects. people. policies. workflows. learnings. AI should not search through a pile of documents and guess how they connect. it should navigate the company as a system. don't bookmark this if it crosses your timeline. paste the full article into your agent and tell it to build the first version for your company.

J.B.

57,669 次观看 • 1 个月前