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Jev dropped and people immediately started doing stupid sh*t with it people gave this thing trading bots, browsers, Doom, drones, dinosaurs and tax forms and basically said: “you decide.” here’s what happened: > jev-trader Jev gets a new Monad block every ~300ms and decides whether to place a live...

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67,168 次观看 • 15 天前 •via X (Twitter)

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Jev + Muse is the first AI agent system that actually automate 100% of my life 99% of people pay 200x more for slower AI agents - while 1% run this 2030 setup just 5 min and setup is ready: prompt → Muse → Jev decision → Muse execution → result step 1 → create your Jev API key (typesafe website) step 2 → clone and install the complete router from Github below python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export the key before running anything: export TYPESAFE_API_KEY='YOUR_KEY' add the same export to ~/.zshrc or ~/.bashrc if you want it to survive a new terminal session step 4 → give your agent skill/jev-decision-layer.SKILL.md and connect it to src/router.py + recipes/ , raw Jev returns probabilities - the router converts them into executable actions step 5 → test the entire chain, not the raw Jev API: .venv/bin/python -m src.cli '{"goal":"what is 2+2?","kind":"chat"}' the final JSON should contain action, reason, mode, jev_used and confidence details step 6 → keep mode: shadow for 20–50 real decisions: the agent works normally while Jev’s routes are logged and checked; promote only reliable question packs step 7 → switch to mode: active with hard confidence gates: ≥0.80 act automatically, 0.50–0.79 advisory only, <0.50 escalate to the human the result: Jev + Muse is a system that decides what to do, what to skip and when to bring in - I’ve tested it across my daily workflows, and it’s the best setup I’ve found for automating routine Take the exact stack I built, run it yourself from the repo - then read the full Jev architecture behind it ↓
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Jev + Muse is the first AI agent system that actually automate 100% of my life 99% of people pay 200x more for slower AI agents - while 1% run this 2030 setup just 5 min and setup is ready: prompt → Muse → Jev decision → Muse execution → result step 1 → create your Jev API key (typesafe website) step 2 → clone and install the complete router from Github below python3 -m venv .venv && .venv/bin/pip install -r requirements.txt && cp config.example.yaml config.yaml step 3 → export the key before running anything: export TYPESAFE_API_KEY='YOUR_KEY' add the same export to ~/.zshrc or ~/.bashrc if you want it to survive a new terminal session step 4 → give your agent skill/jev-decision-layer.SKILL.md and connect it to src/router.py + recipes/ , raw Jev returns probabilities - the router converts them into executable actions step 5 → test the entire chain, not the raw Jev API: .venv/bin/python -m src.cli '{"goal":"what is 2+2?","kind":"chat"}' the final JSON should contain action, reason, mode, jev_used and confidence details step 6 → keep mode: shadow for 20–50 real decisions: the agent works normally while Jev’s routes are logged and checked; promote only reliable question packs step 7 → switch to mode: active with hard confidence gates: ≥0.80 act automatically, 0.50–0.79 advisory only, <0.50 escalate to the human the result: Jev + Muse is a system that decides what to do, what to skip and when to bring in - I’ve tested it across my daily workflows, and it’s the best setup I’ve found for automating routine Take the exact stack I built, run it yourself from the repo - then read the full Jev architecture behind it ↓

codila

95,125 次观看 • 17 天前

Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓
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Jev + GrokBot is the best AI agent system I’ve built in my life It just made my setup CHEAPER and FASTER than what 95% of people are running... setup takes literally 7 minutes: prompt → GrokBot → Jev decision → GrokBot execution → result step 1 → open TypeSafe AI , create API key (keep it off chat paste) step 2 → tell Grok Bot: store TYPESAFE_API_KEY in the secure field step 3 → prompt Grok Bot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → tell Grok Bot: build the usage lab (router, dry-run, config, logs) - or clone Github below step 5 → add skill jev-usage-router: before browser / research / retry / extra bot → call the router, honor action step 6 → stay shadow first, read logs, then active when you trust it - kill switch: bypass jev or enabled: false step 7 → flip active: GrokBot obeys route - Jev decides - GrokBot executes - humans control irreversible actions the result: Jev + GrokBot the best and fastest agent running directly on your computer rn, I’ve already tested it on routine tasks - and the results are genuinely incredible You can come up with endless ways to use Jev + GrokBot - but the most important thing is to install it as soon as possible Copy this 2028 setup, explore my repo below - then read the full Jev deep dive ↓

codila

384,205 次观看 • 20 天前

this is pure f*cking treasure TypeSafeAI engineers spelled out in their docs exactly how Jev fits inside an agent, and these patterns are perfect one rule behind all of them: the LLM thinks, jev decides, code acts I mapped them into 9 live schemes: > model routing jev picks the cheapest model that can do the step > guardrails screens what your agent reads before it acts > tool-call gating every tool call gets allow, ask or deny before it runs > inbox triage the agent only opens what actually needs a reply > reranking jev scores 30 passages, the agent gets the best two > llm evals grades the output and sends it back if it falls short > bulk labeling one question fanned out over a huge table, the agent reads the counts > real-time control games, bots, trading loops: a decision every tick > confidence gate act, confirm or hand it to a human. your code sets the thresholds, not the LLM why it works: > ~100 ms per decision > $0.042 per 1M input tokens, output tokens free > calibrated probabilities on every answer one LLM does the thinking. jev makes the thousand small calls in between save this before you wire your next agent
0:16

this is pure f*cking treasure TypeSafeAI engineers spelled out in their docs exactly how Jev fits inside an agent, and these patterns are perfect one rule behind all of them: the LLM thinks, jev decides, code acts I mapped them into 9 live schemes: > model routing jev picks the cheapest model that can do the step > guardrails screens what your agent reads before it acts > tool-call gating every tool call gets allow, ask or deny before it runs > inbox triage the agent only opens what actually needs a reply > reranking jev scores 30 passages, the agent gets the best two > llm evals grades the output and sends it back if it falls short > bulk labeling one question fanned out over a huge table, the agent reads the counts > real-time control games, bots, trading loops: a decision every tick > confidence gate act, confirm or hand it to a human. your code sets the thresholds, not the LLM why it works: > ~100 ms per decision > $0.042 per 1M input tokens, output tokens free > calibrated probabilities on every answer one LLM does the thinking. jev makes the thousand small calls in between save this before you wire your next agent

NO1ennn

24,699 次观看 • 9 天前

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.
0:15

You can now use Jev right inside Claude Code 🤯 It's called jev-model-router, an early access mod built on Claude Code's new function hooks. Before every turn, it checks in with Jev and asks: > how mechanical the task is > how much reasoning it needs > whether it's risky Then it routes: → it'll move up to a stronger model on weak evidence, and only drops to a cheaper one when it's confident the task is simple → every decision gets logged in your transcript → if the call fails, your request runs untouched Setup: 1. copy the install command: npx claude-code-templates@latest --mod productivity/jev-model-router 2. paste it inside Claude Code 3. run claude with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 set 4. accept the trust prompt on first launch Link: Also works with no api key, it just falls back to a built-in classifier with no confidence score. For real jev routing, add your typesafeApiKey or gatewayApiKey to ~/.claude/settings.json. Follow me for more AI workflows and tutorials.

Alvaro Cintas

43,331 次观看 • 18 天前

this is pure f*cking treasure A Stanford AI research group finally drew the perfect RAG system: retrieval, Jev and agents in one loop, and it fixes the 3 things that break every RAG app: > the LLM reads 20 passages when only 3 matter > it answers questions your docs can't answer > it trusts whatever text it retrieves here's how it runs: > a lead agent sends the query > hybrid search pulls the top 20 candidates, dense + keyword > ONE Jev request scores all 20 + 2 gates: answerable? injection? > only passages above 0.6 reach the writer agent > a second Jev call checks every claim against its source > grounded answer, with citations and when the docs don't have it: > answerable fails, the writer never runs > a researcher agent rewrites the query and retries once > still nothing? "not in the docs". zero tokens spent on a guess retrieval casts the net. Jev decides what's real. agents do the work save this before you build your next RAG
0:18

this is pure f*cking treasure A Stanford AI research group finally drew the perfect RAG system: retrieval, Jev and agents in one loop, and it fixes the 3 things that break every RAG app: > the LLM reads 20 passages when only 3 matter > it answers questions your docs can't answer > it trusts whatever text it retrieves here's how it runs: > a lead agent sends the query > hybrid search pulls the top 20 candidates, dense + keyword > ONE Jev request scores all 20 + 2 gates: answerable? injection? > only passages above 0.6 reach the writer agent > a second Jev call checks every claim against its source > grounded answer, with citations and when the docs don't have it: > answerable fails, the writer never runs > a researcher agent rewrites the query and retries once > still nothing? "not in the docs". zero tokens spent on a guess retrieval casts the net. Jev decides what's real. agents do the work save this before you build your next RAG

NO1ennn

43,423 次观看 • 8 天前

Jev + Opus 5.5 is the most underrated setup in AI right now, and i don't get why nobody's talking about it. I stopped letting Opus make every small decision in my agents. Jev makes them now. My Opus bill dropped ~500% on the same tasks, same results. Here's what Jev handles: - which files and notes to load before Opus sees anything - which effort level each task actually needs - what to do when a tool fails - which tests to run, and when to stop Opus only gets called for the hard reasoning. Every decision is a typed result the host checks before it runs. The exact prompt to set this up in your workflow is below.
0:20

Jev + Opus 5.5 is the most underrated setup in AI right now, and i don't get why nobody's talking about it. I stopped letting Opus make every small decision in my agents. Jev makes them now. My Opus bill dropped ~500% on the same tasks, same results. Here's what Jev handles: - which files and notes to load before Opus sees anything - which effort level each task actually needs - what to do when a tool fails - which tests to run, and when to stop Opus only gets called for the hard reasoning. Every decision is a typed result the host checks before it runs. The exact prompt to set this up in your workflow is below.

Anatoli Kopadze

76,655 次观看 • 12 天前

JEV ENGINEERING JUST CUT A CLAUDE LOOP FROM $765/MONTH TO $3.02 the expensive part isn’t always writing code. a busy overnight loop can burn ~600 decisions just asking what to do next, whether a command is safe, or if the task is finished. trigger → Jev → allow / stop / wake → Claude only builds when needed at roughly 4,000 tokens per decision, those checks can cost ~$25.50 a night on Fable 5.1. Jev handles the same decision layer at $0.042 per 1M input tokens with output free. over 30 nights that moves the decision bill from ~$765 to $3.02. batch multiple questions into one call and it falls again to about $1.08/month. that’s the part i care about: Claude keeps the expensive work that actually needs intelligence, while Jev handles the hundreds of tiny judgment calls around it.
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JEV ENGINEERING JUST CUT A CLAUDE LOOP FROM $765/MONTH TO $3.02 the expensive part isn’t always writing code. a busy overnight loop can burn ~600 decisions just asking what to do next, whether a command is safe, or if the task is finished. trigger → Jev → allow / stop / wake → Claude only builds when needed at roughly 4,000 tokens per decision, those checks can cost ~$25.50 a night on Fable 5.1. Jev handles the same decision layer at $0.042 per 1M input tokens with output free. over 30 nights that moves the decision bill from ~$765 to $3.02. batch multiple questions into one call and it falls again to about $1.08/month. that’s the part i care about: Claude keeps the expensive work that actually needs intelligence, while Jev handles the hundreds of tiny judgment calls around it.

Gipp 🦅

25,081 次观看 • 12 天前

I GAVE JEV A CRAWLER + ONE GPT AGENT AND ASKED WHERE THE ONLINE MONEY IS RIGHT NOW it read 5,137 open jobs and found 3 niches where clients pay and almost nobody bids everyone asks chatgpt for side hustle ideas everyone gets the same 10 answers so i made it read what clients are actually paying for this week a crawler is a bot that reads the internet for you jev is the ai that answers every question with a number, under a second, under a cent gpt is the expensive brain, it only gets called when jev says a niche is worth it the crawler reads, jev decides, gpt researches honestly gpt alone just guesses, jev is what turns 5,137 jobs into 3 answers what the three of them did: -> crawler read all 5,137 open jobs on freelancer, budgets, skills, how many people already bid -> jev checked every one: real job? doable online by one person? 684 got cut -> gpt named 30 niches from 600 of them, jev sorted all 4,453 into those niches -> jev looked at each niche's numbers and sent only 12 to gpt -> gpt researched those 12 on the open web: other platforms, real prices, how crowded it is -> 3 came back with real demand and fewer bids than the typical job (14): 01 chrome extension · 85 jobs this week · median $545 · 11 bids each · ~$4,671/mo 02 tiktok shop setup · 91 jobs this week · median $232 · 6.5 bids each · ~$1,984/mo 03 notion setup · 95 jobs this week · median $181 · 3 bids each · ~$1,551/mo potential = your fair share of this week's jobs (budget ÷ (bids + 1)), max 2 jobs a week not every pick held up, excel dashboards looked great on freelancer but gpt found it crowded everywhere else jev made 9,617 calls for $0.13, gpt on every job would have cost ~$58.66, the whole run cost $6.53 i wrote up why the cheap brain decides and the expensive one only gets called when it's worth it, it's below ↓ costs nothing bookmark this, empty niches don't stay empty show this to the one friend who keeps asking chatgpt for side hustle ideas should i run it on upwork next, or is chrome extension the one?
0:19

I GAVE JEV A CRAWLER + ONE GPT AGENT AND ASKED WHERE THE ONLINE MONEY IS RIGHT NOW it read 5,137 open jobs and found 3 niches where clients pay and almost nobody bids everyone asks chatgpt for side hustle ideas everyone gets the same 10 answers so i made it read what clients are actually paying for this week a crawler is a bot that reads the internet for you jev is the ai that answers every question with a number, under a second, under a cent gpt is the expensive brain, it only gets called when jev says a niche is worth it the crawler reads, jev decides, gpt researches honestly gpt alone just guesses, jev is what turns 5,137 jobs into 3 answers what the three of them did: -> crawler read all 5,137 open jobs on freelancer, budgets, skills, how many people already bid -> jev checked every one: real job? doable online by one person? 684 got cut -> gpt named 30 niches from 600 of them, jev sorted all 4,453 into those niches -> jev looked at each niche's numbers and sent only 12 to gpt -> gpt researched those 12 on the open web: other platforms, real prices, how crowded it is -> 3 came back with real demand and fewer bids than the typical job (14): 01 chrome extension · 85 jobs this week · median $545 · 11 bids each · ~$4,671/mo 02 tiktok shop setup · 91 jobs this week · median $232 · 6.5 bids each · ~$1,984/mo 03 notion setup · 95 jobs this week · median $181 · 3 bids each · ~$1,551/mo potential = your fair share of this week's jobs (budget ÷ (bids + 1)), max 2 jobs a week not every pick held up, excel dashboards looked great on freelancer but gpt found it crowded everywhere else jev made 9,617 calls for $0.13, gpt on every job would have cost ~$58.66, the whole run cost $6.53 i wrote up why the cheap brain decides and the expensive one only gets called when it's worth it, it's below ↓ costs nothing bookmark this, empty niches don't stay empty show this to the one friend who keeps asking chatgpt for side hustle ideas should i run it on upwork next, or is chrome extension the one?

Paone

49,272 次观看 • 4 天前

Opus 5.5 + GrokBot + Jev... I can't believe that I build it I packed the best of AI into ONE agent - now 99% of people’s AI agents look dumb, slow, and expensive Try here (free): prompt → GrokBot → Jev chooses LLM → Claude / GPT working on GrokBot's computer 24/7 → result → Jev sends only the tasks Grok is weak at to Opus or GPT → Jev picks the best model for each task - for example, Claude for motion design and Codex for web design → runs on the ChatGPT and Claude subscriptions you already pay for (API also work) → works on GrokBot computer while you keep chatting → checks every result against the goal before you see it → GrokBot gets smarter and cheaper at the same time → And plenty more features in a bot everyone will call "obvious" by 2030 Try Jevopus, thank me later - then read the article on the full Jev architecture ↓
0:14

Opus 5.5 + GrokBot + Jev... I can't believe that I build it I packed the best of AI into ONE agent - now 99% of people’s AI agents look dumb, slow, and expensive Try here (free): prompt → GrokBot → Jev chooses LLM → Claude / GPT working on GrokBot's computer 24/7 → result → Jev sends only the tasks Grok is weak at to Opus or GPT → Jev picks the best model for each task - for example, Claude for motion design and Codex for web design → runs on the ChatGPT and Claude subscriptions you already pay for (API also work) → works on GrokBot computer while you keep chatting → checks every result against the goal before you see it → GrokBot gets smarter and cheaper at the same time → And plenty more features in a bot everyone will call "obvious" by 2030 Try Jevopus, thank me later - then read the article on the full Jev architecture ↓

codila

270,673 次观看 • 11 天前

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open TypeSafe AI, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3 → prompt GrokBot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → point it at a target: "read end to end, pull positioning, ICP, every proof point. no copy yet" step 5 → add skill gtm-router: every candidate from X / LinkedIn / YouTube gets 6 typed questions → CHEAP SKIP / OPEN FULL / FLAG KOL step 6 → let Jev rank hook archetypes by viral rate, not by how often people post them - this is where the whole playbook comes from step 7 → GrokBot assembles the KOL shortlist and the 7-day plan - you stay in control of anything irreversible the result: → 3,412 candidates, X, LinkedIn and YouTube. → 15.7 seconds. $0.41. → me doing the same reading: 6h 12m. 8 grok bots brief it, Jev decides, GrokBot executes. no fine-tuning, no embeddings, no vector db - 6 typed questions and a schema. Setup your GrokBot JEV GTM today, then read the article below to learn how to build an agent team with JEV.
0:17

Jev + GrokBot is the best GTM system I've ever built It just made my GTM x200 CHEAPER and x400 FASTER than what 95% of teams are running... setup takes literally 9 minutes: prompt → GrokBot → Jev routes every candidate → GrokBot opens only survivors → campaign step 1 → open TypeSafe AI, create API key (keep it off chat paste) step 2 → tell GrokBot: store TYPESAFE_API_KEY in the secure field step 3 → prompt GrokBot: install typesafe-sdk on Agent Computer + smoke system_one (Choice) step 4 → point it at a target: "read end to end, pull positioning, ICP, every proof point. no copy yet" step 5 → add skill gtm-router: every candidate from X / LinkedIn / YouTube gets 6 typed questions → CHEAP SKIP / OPEN FULL / FLAG KOL step 6 → let Jev rank hook archetypes by viral rate, not by how often people post them - this is where the whole playbook comes from step 7 → GrokBot assembles the KOL shortlist and the 7-day plan - you stay in control of anything irreversible the result: → 3,412 candidates, X, LinkedIn and YouTube. → 15.7 seconds. $0.41. → me doing the same reading: 6h 12m. 8 grok bots brief it, Jev decides, GrokBot executes. no fine-tuning, no embeddings, no vector db - 6 typed questions and a schema. Setup your GrokBot JEV GTM today, then read the article below to learn how to build an agent team with JEV.

Movez

86,466 次观看 • 19 天前

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.
0:13

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Armagan Amcalar

365,108 次观看 • 16 天前

this is certified f*cking gold a 23-year-old from China runs 300 Claude Code agents at once with Jev engineering, and not one of them gets to lie to him he opens the dashboard live: 300 Claude Code agents running in parallel Jev checking every output against its source in about 100 milliseconds pointed at 100 companies in the EV market: > pass 1 - 12 rejected: wrong revenue, dead citations, empty fields > pass 2 - 3 rejected > pass 3 - zero. every row filed straight into an Obsidian vault the whole trick is the split Claude Code does the work, Jev decides whether each claim actually matches the source Jev never writes a second opinion. it answers yes or no with a confidence score, at $0.042 per million tokens in and the loop won't stop while anything is still wrong an analyst team bills weeks for 100 verified company profiles. he gets them in one run one model writes, a cheaper one decides, and nothing ships until it says yes
0:15

this is certified f*cking gold a 23-year-old from China runs 300 Claude Code agents at once with Jev engineering, and not one of them gets to lie to him he opens the dashboard live: 300 Claude Code agents running in parallel Jev checking every output against its source in about 100 milliseconds pointed at 100 companies in the EV market: > pass 1 - 12 rejected: wrong revenue, dead citations, empty fields > pass 2 - 3 rejected > pass 3 - zero. every row filed straight into an Obsidian vault the whole trick is the split Claude Code does the work, Jev decides whether each claim actually matches the source Jev never writes a second opinion. it answers yes or no with a confidence score, at $0.042 per million tokens in and the loop won't stop while anything is still wrong an analyst team bills weeks for 100 verified company profiles. he gets them in one run one model writes, a cheaper one decides, and nothing ships until it says yes

Hanako

35,597 次观看 • 3 天前

this is certified f*cking gold a 23-year-old from China runs 300 AI agents at once with Jev engineering, and none of them can lie to him he opens the dashboard live: 300 Kimi K3 agents firing in parallel Jev checking every single output against its source in about 100 milliseconds pointed at 100 companies in the EV market: > pass 1 - 12 failed: wrong revenue, dead citations, empty fields > pass 2 - 3 failed > pass 3 - zero. every row filed straight into an Obsidian vault the whole trick is the split Kimi K3 does the work, Jev decides if each claim actually matches the source Jev never writes a second opinion. it answers yes or no with a confidence score, at $0.042 per million tokens in and the loop refuses to stop while anything is still wrong an analyst team bills weeks for 100 verified company profiles. he gets them from one run one model writes, a cheaper one decides, and nothing ships until it says yes
0:15

this is certified f*cking gold a 23-year-old from China runs 300 AI agents at once with Jev engineering, and none of them can lie to him he opens the dashboard live: 300 Kimi K3 agents firing in parallel Jev checking every single output against its source in about 100 milliseconds pointed at 100 companies in the EV market: > pass 1 - 12 failed: wrong revenue, dead citations, empty fields > pass 2 - 3 failed > pass 3 - zero. every row filed straight into an Obsidian vault the whole trick is the split Kimi K3 does the work, Jev decides if each claim actually matches the source Jev never writes a second opinion. it answers yes or no with a confidence score, at $0.042 per million tokens in and the loop refuses to stop while anything is still wrong an analyst team bills weeks for 100 verified company profiles. he gets them from one run one model writes, a cheaper one decides, and nothing ships until it says yes

Annatar.md

158,938 次观看 • 6 天前

Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.
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Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

127,384 次观看 • 12 天前

I’ve never seen an AI workflow this terrifying GPT-6 Sol thinks, Jev decides, Grok Bot executes. Together, they replace a whole team for pennies Alone, each hits a wall. Jev can't write, Sol has no device access, and 12 Grok Bots without Jev are just 12 open tabs Nobody ran all three together, so I built this 3-tool system in 48 hours: One chat with 4 Grok Bots: Boss, Scout, Writer, Closer. They pass tasks automatically, you just read Boss plans the day with GPT-6 Sol. Sol ran Opus 5.5's job 4x faster for $0.08 instead of $0.69 Scout finds leads and Jev asks each 6 questions. Test result: 3,412 leads and 20,472 decisions in 15.7 seconds for $0.41 Only real buyers reach Writer, where Sol drafts pitches. Jev adjusts Sol's thinking depth per lead, cutting costs by 50% Closer sends emails after one tap to approve, and Telegram receives a nightly report A 6-hour lead screening takes 16 seconds without opening your laptop Most people pay top AI to say "skip" all day. This stack uses Jev for fractions of a cent Claire Vo runs 30 Grok Bots. One prints a daily kids' newspaper, another gets 5-star reviews from unaware clients Every day you wait, another Scout takes your target client Bookmark this before someone's bot steals your clients
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I’ve never seen an AI workflow this terrifying GPT-6 Sol thinks, Jev decides, Grok Bot executes. Together, they replace a whole team for pennies Alone, each hits a wall. Jev can't write, Sol has no device access, and 12 Grok Bots without Jev are just 12 open tabs Nobody ran all three together, so I built this 3-tool system in 48 hours: One chat with 4 Grok Bots: Boss, Scout, Writer, Closer. They pass tasks automatically, you just read Boss plans the day with GPT-6 Sol. Sol ran Opus 5.5's job 4x faster for $0.08 instead of $0.69 Scout finds leads and Jev asks each 6 questions. Test result: 3,412 leads and 20,472 decisions in 15.7 seconds for $0.41 Only real buyers reach Writer, where Sol drafts pitches. Jev adjusts Sol's thinking depth per lead, cutting costs by 50% Closer sends emails after one tap to approve, and Telegram receives a nightly report A 6-hour lead screening takes 16 seconds without opening your laptop Most people pay top AI to say "skip" all day. This stack uses Jev for fractions of a cent Claire Vo runs 30 Grok Bots. One prints a daily kids' newspaper, another gets 5-star reviews from unaware clients Every day you wait, another Scout takes your target client Bookmark this before someone's bot steals your clients

Bober_smart

13,252 次观看 • 10 天前

WAIT. This is actually insane. I fed Jev my last 468 tweets and hid all the numbers. It picked out my strongest hooks without seeing a single view count. Like it somehow knew which ones stopped the scroll just by reading them. That was 2,340 decisions in 60 seconds for 1.3 cents. Every boring decision you make by staring at a screen can run like this now. This is actually stupid how good this is. If you haven't set up Jev as your decision maker yet, you're already behind. Full breakdown in the article below.
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WAIT. This is actually insane. I fed Jev my last 468 tweets and hid all the numbers. It picked out my strongest hooks without seeing a single view count. Like it somehow knew which ones stopped the scroll just by reading them. That was 2,340 decisions in 60 seconds for 1.3 cents. Every boring decision you make by staring at a screen can run like this now. This is actually stupid how good this is. If you haven't set up Jev as your decision maker yet, you're already behind. Full breakdown in the article below.

Prajwal Tomar

30,809 次观看 • 18 天前

VERCEL + CLOUDFLARE + CLAUDE CODE all picked up Jev in 6 days... an AI that can't write a single word became the fastest adopted model on Vercel's AI Gateway Jev decides → big model thinks → code authorizes TypeSafe dropped it on September 15, founder Diogo Almeida worked on ChatGPT research at OpenAI it answers three ways: pick one option, rate it on a scale, or yes/no with a percentage "which agent next?" comes back as Researcher 71% / Coder 18% / Reviewer 11% Vercel says almost 13% of its paying teams tried it within the first 24 hours on Vercel's leaderboard it pulled about 26% of all requests but only 1.8% of tokens TypeSafe's own tests: up to 194x faster and 445x cheaper than regular models TypeSafe calls it a System One Model, named after Kahneman's Thinking, Fast and Slow your expensive model stops burning tokens on yes or no questions and only gets the hard ones 6 days, 110 community use cases, zero words written ↓
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VERCEL + CLOUDFLARE + CLAUDE CODE all picked up Jev in 6 days... an AI that can't write a single word became the fastest adopted model on Vercel's AI Gateway Jev decides → big model thinks → code authorizes TypeSafe dropped it on September 15, founder Diogo Almeida worked on ChatGPT research at OpenAI it answers three ways: pick one option, rate it on a scale, or yes/no with a percentage "which agent next?" comes back as Researcher 71% / Coder 18% / Reviewer 11% Vercel says almost 13% of its paying teams tried it within the first 24 hours on Vercel's leaderboard it pulled about 26% of all requests but only 1.8% of tokens TypeSafe's own tests: up to 194x faster and 445x cheaper than regular models TypeSafe calls it a System One Model, named after Kahneman's Thinking, Fast and Slow your expensive model stops burning tokens on yes or no questions and only gets the hard ones 6 days, 110 community use cases, zero words written ↓

leopardracer

16,184 次观看 • 7 天前

you should spend some time connecting jev to an AI employee like viktor... the goal is to get back work that needs fewer corrections from you. here's the setup: -> give viktor a goal and the source material. he figures out the steps, researches, uses your connected tools and produces the draft -> jev handles the evaluation. give it a specific requirement, the relevant evidence and a section of the draft. it returns a structured judgment with probabilities and confidence -> work that meets your criteria comes back for your review. fixable issues go back to viktor with the requirement he needs to address, then through the checks again -> uncertain judgments and missing evidence come to you. those are the decisions worth your attention instead of another round of “please check everything” -> apply it to your content... does the opening promise something useful, do the five posts actually say different things, did the draft invent a claim about something you tested -> if the source only supports three useful posts, the system should flag that instead of inventing two more. cap the revision rounds so it knows when to stop and ask -> save the corrections you approve in viktor’s working guide. the next assignment starts with those instructions already in place viktor creates. jev evaluates. your feedback improves the next assignment. the full setup is in the article below.
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you should spend some time connecting jev to an AI employee like viktor... the goal is to get back work that needs fewer corrections from you. here's the setup: -> give viktor a goal and the source material. he figures out the steps, researches, uses your connected tools and produces the draft -> jev handles the evaluation. give it a specific requirement, the relevant evidence and a section of the draft. it returns a structured judgment with probabilities and confidence -> work that meets your criteria comes back for your review. fixable issues go back to viktor with the requirement he needs to address, then through the checks again -> uncertain judgments and missing evidence come to you. those are the decisions worth your attention instead of another round of “please check everything” -> apply it to your content... does the opening promise something useful, do the five posts actually say different things, did the draft invent a claim about something you tested -> if the source only supports three useful posts, the system should flag that instead of inventing two more. cap the revision rounds so it knows when to stop and ask -> save the corrections you approve in viktor’s working guide. the next assignment starts with those instructions already in place viktor creates. jev evaluates. your feedback improves the next assignment. the full setup is in the article below.

J.B.

29,932 次观看 • 16 天前

You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.
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You've probably scrolled past a dozen posts about Jev this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.

John Turner

21,029 次观看 • 22 天前

this is pure f*cking treasure. my ai bill last month: claude max 20x ............ $200 chatgpt pro ............... $200 supergrok heavy ........... $300 total ..................... $700 then i went through github and found 5 repos that eat the boring half of that bill on my own machine rizzo-flow ................ 833 stars the open local take on typesafe's jev. a 4.4gb model on llama.cpp answers yes / no / pick one / score with a probability on every answer, and you point your api at localhost by changing one url llm2jev ................... 391 stars turns qwen3.5-4b into a jev-style decision model. 48ms p50 in their own benchmark, against 652ms for the real jev fast-browser-use .......... 202 stars a browser agent that runs 100% local on qwen3.5-9b and plugs into claude code and codex as a skill. opens the right wikipedia article in about 4 seconds, zero cloud calls deepseekgui ............... 84 stars a desktop workbench on deepseek harness with git, a built-in browser and memory. you pay deepseek per token instead of a flat $200 a month oriveo .................... 25 stars one app for openai, anthropic, gemini, grok, deepseek and 10 more providers, plus ollama on your own gpu. your keys, no subscription, no account claude still writes my hardest code. the yes or no calls, the browser clicking and the everyday chatting moved to my gpu and my own api keys, and the receipt stopped looking like rent repos in the comments
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this is pure f*cking treasure. my ai bill last month: claude max 20x ............ $200 chatgpt pro ............... $200 supergrok heavy ........... $300 total ..................... $700 then i went through github and found 5 repos that eat the boring half of that bill on my own machine rizzo-flow ................ 833 stars the open local take on typesafe's jev. a 4.4gb model on llama.cpp answers yes / no / pick one / score with a probability on every answer, and you point your api at localhost by changing one url llm2jev ................... 391 stars turns qwen3.5-4b into a jev-style decision model. 48ms p50 in their own benchmark, against 652ms for the real jev fast-browser-use .......... 202 stars a browser agent that runs 100% local on qwen3.5-9b and plugs into claude code and codex as a skill. opens the right wikipedia article in about 4 seconds, zero cloud calls deepseekgui ............... 84 stars a desktop workbench on deepseek harness with git, a built-in browser and memory. you pay deepseek per token instead of a flat $200 a month oriveo .................... 25 stars one app for openai, anthropic, gemini, grok, deepseek and 10 more providers, plus ollama on your own gpu. your keys, no subscription, no account claude still writes my hardest code. the yes or no calls, the browser clicking and the everyday chatting moved to my gpu and my own api keys, and the receipt stopped looking like rent repos in the comments

starmex

73,538 次观看 • 3 天前