this might be one of the clearest examples of where JEV engineering is heading someone went through 287 open-source Jev projects and found the same pattern in them people aren't using Jev to write more code, they use it for the small decisions routing requests, picking context, choosing which model runs and deciding what happens next Jev makes the structured calls and the LLM only comes in for real generation or deeper reasoning full source is in the comments below if you want to go through it ↓

this might be one of the clearest examples of where JEV engineering is heading someone went through 287 open-source Jev projects and found the same pattern in them people aren't using Jev to write more code, they use it for the small decisions routing requests, picking context, choosing which model runs and deciding what happens next Jev makes the structured calls and the LLM only comes in for real generation or deeper reasoning full source is in the comments below if you want to go through it ↓

Chinese students found 2,170 real world uses for Jev, and it’s already replacing thousands of unnecessary LLM decisions 1 → 1,865 new repos appeared after launch and 305 existing projects added Jev. The new repos picked up 43,750 stars in one week 2 → 77% of projects use Jev for attribute judgment, 52% for scoring and ranking, 31% for action selection, and some for routing, filtering and model selection 3 → 36.8% use all three interfaces (Choice, Noul and Score), so one system can hand Jev several kinds of decisions 4 → The paper walks through real workflows: browser automation, code review triage, trimming tool history, model routing, risk gates on tool calls, and a controller for a Mario emulator 5 → Most of these are tiny calls: pick a tool, check an action, route a request, decide what context to keep. Jev handles them without another expensive LLM call Jev is starting to look like a cheap decision primitive for AI agents

Chinese students found 2,170 real world uses for Jev, and it’s already replacing thousands of unnecessary LLM decisions 1 → 1,865 new repos appeared after launch and 305 existing projects added Jev. The new repos picked up 43,750 stars in one week 2 → 77% of projects use Jev for attribute judgment, 52% for scoring and ranking, 31% for action selection, and some for routing, filtering and model selection 3 → 36.8% use all three interfaces (Choice, Noul and Score), so one system can hand Jev several kinds of decisions 4 → The paper walks through real workflows: browser automation, code review triage, trimming tool history, model routing, risk gates on tool calls, and a controller for a Mario emulator 5 → Most of these are tiny calls: pick a tool, check an action, route a request, decide what context to keep. Jev handles them without another expensive LLM call Jev is starting to look like a cheap decision primitive for AI agents

I built the most TERRIFYING AI trading setup a memecoin trader will ever watch... [once you see who's selling to you, you'll never ape the same way again] /1 that green candle you're chasing is often a deployer funded wallet dumping into your buy /2 their bags drain in real time while the chart keeps pumping and CT screams send it /3 the moment insiders pass 50% sold, the screen flashes YOU ARE THE EXIT /4 then the last wallet sells, the chart goes vertical down and you're left holding find out who's selling to you before your next ape below ↓

I built the most TERRIFYING AI trading setup a memecoin trader will ever watch... [once you see who's selling to you, you'll never ape the same way again] /1 that green candle you're chasing is often a deployer funded wallet dumping into your buy /2 their bags drain in real time while the chart keeps pumping and CT screams send it /3 the moment insiders pass 50% sold, the screen flashes YOU ARE THE EXIT /4 then the last wallet sells, the chart goes vertical down and you're left holding find out who's selling to you before your next ape below ↓

jev + opus 5.5... honestly no idea why more people aren't building trading desks like this yet in my setup jev makes the call and opus does the heavy lifting, reading the market and doing the research → real time market data → research before every decision → jev picks the trade → computer use sends the order → every action gets checked nobody stares at candles here, the desk does the work and you just watch it

jev + opus 5.5... honestly no idea why more people aren't building trading desks like this yet in my setup jev makes the call and opus does the heavy lifting, reading the market and doing the research → real time market data → research before every decision → jev picks the trade → computer use sends the order → every action gets checked nobody stares at candles here, the desk does the work and you just watch it

here is how you can turn Jev into an AI market analyst and change your life... the model shouldn't be guessing prices, it's much better at telling you what changed and whether that's real plain code screens 10,000 tickers, Jev picks who reads what, and GPT-6 Astra only argues bull vs bear on the last 40 they calculate → cut → pass → hold that keeps the bill around $400 a day instead of $60,000, and the report never tells you to buy bookmark this before your next API bill does the explaining for you ↓

here is how you can turn Jev into an AI market analyst and change your life... the model shouldn't be guessing prices, it's much better at telling you what changed and whether that's real plain code screens 10,000 tickers, Jev picks who reads what, and GPT-6 Astra only argues bull vs bear on the last 40 they calculate → cut → pass → hold that keeps the bill around $400 a day instead of $60,000, and the report never tells you to buy bookmark this before your next API bill does the explaining for you ↓

I built a tool that shows you who's about to dump on you before you ape in FLETCH reads every meme launch on Robinhood Chain as it happens and tracks wallets, holders, liquidity and whale moves it's live and fully open source, check your bags at FLETCH flags snipers buying in the same block and linked wallets sitting on most of the supply the moment they show up every token gets a FLETCH score from 0 to 100 backed by on chain evidence, so you see why the score moved the AI explains each move from what's already on chain and doesn't make predictions or shill anything stop being exit liquidity and start reading the chain

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I built a tool that shows you who's about to dump on you before you ape in FLETCH reads every meme launch on Robinhood Chain as it happens and tracks wallets, holders, liquidity and whale moves it's live and fully open source, check your bags at FLETCH flags snipers buying in the same block and linked wallets sitting on most of the supply the moment they show up every token gets a FLETCH score from 0 to 100 backed by on chain evidence, so you see why the score moved the AI explains each move from what's already on chain and doesn't make predictions or shill anything stop being exit liquidity and start reading the chain

send this Jev prompt to any LLM or AI agent it turns your content into thousands of cheap judgments → scores SEO, AIO, GEO & virality → kills weak headlines before you publish → $5 gets you ~39,000 evaluations... copy this now and thank me later:

send this Jev prompt to any LLM or AI agent it turns your content into thousands of cheap judgments → scores SEO, AIO, GEO & virality → kills weak headlines before you publish → $5 gets you ~39,000 evaluations... copy this now and thank me later:

Jev never touches your code, it only picks one option from a list the host wrote host writes the list → jev picks one id → host rechecks it → run or fall back the list is inspect, implement, verify or answer, and answer comes with zero tools a pick never grants permission, and the amber dots are stale picks falling back before they run the self improving part is receipts you replay, and a human approves every change steal the architecture before you build your next harness, article and repo below ↓

Jev + GPT-6 Astra just built the most TERRIFYING AI trading setup on the internet... [this article covers 90% of what is required to build quant-level systems] /1 GPT-6 Astra reads the order book, the tape and 3 correlated futures /2 Jev turns the signal into a trade and checks the risk limit /3 computer use clicks the order screen, no broker API needed /4 the full loop runs in 6 ms, signal to fill steal this setup in the article below👇

Jev never touches your code, it only picks one option from a list the host wrote host writes the list → jev picks one id → host rechecks it → run or fall back the list is inspect, implement, verify or answer, and answer comes with zero tools a pick never grants permission, and the amber dots are stale picks falling back before they run the self improving part is receipts you replay, and a human approves every change steal the architecture before you build your next harness, article and repo below ↓

your Claude Code agent is burning cash like an idiot and you're the one paying for it one refactor session had 31 yes or no questions, you paid full essay price for every single one Claude writes → Jev decides → low confidence goes back to Claude three decision steps in one PR loop went from 13 seconds to 0.83 everyone screaming 200x never read past the headline, you only see that on boring high volume checks full routing setup and the fallback code are in the article below ↓