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I gave JEV one prompt: "turn $24.80 into 1000x or I'm pulling the plug" it didn't ask questions. didn't negotiate. didn't say "that's unrealistic" it just started $24.80 to $31,847.52 overnight I woke up, opened Meridian Desk, and the number was already there. green, pulsing, real here's the part...

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cristal💎

11,043 subscribers

225,879 görüntüleme • 2 gün önce •via X (Twitter)

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23 Yorum

mämbāmentality profil fotoğrafı
mämbāmentality2 gün önce

I haven’t found an independently verifiable transaction trail tying that exact $24.80 → $31,847.52 result to JEV.

mämbāmentality profil fotoğrafı
mämbāmentality2 gün önce

Prove it. Share the repo

Gipp 🦅 profil fotoğrafı
Gipp 🦅2 gün önce

warden blocking every move below confidence threshold legit saved my runs from dumb fomo traders before

ALEXYZ profil fotoğrafı
ALEXYZ2 gün önce

that overnight jump is wild

I’m Him profil fotoğrafı
I’m Him1 gün önce

Click bait, Jev sucks at trading and not llm

tenzo profil fotoğrafı
tenzo2 gün önce

the whole architecture is really interesting

cristal💎 profil fotoğrafı
cristal💎2 gün önce

thanks mate

pearson profil fotoğrafı
pearson2 gün önce

looking diff, def not made by astra

cristal💎 profil fotoğrafı
cristal💎2 gün önce

use JEV brotha

Huysolo profil fotoğrafı
Huysolo2 gün önce

That sounds like a wild ride!

KiefyGreen profil fotoğrafı
KiefyGreen1 gün önce

Liar

Brian Hadu profil fotoğrafı
Brian Hadu1 gün önce

that kind of overnight gain can really skew expectations for future prompts

Ridark profil fotoğrafı
Ridark2 gün önce

Your terminal shows that setup matters a lot

Celeste Nong profil fotoğrafı
Celeste Nong1 gün önce

That’s interesting. Could you kindly share your public wallet address, the start and end dates, the full transaction hashes for your buys and sells, and your realised gains after excluding external inflows? I would like to make a double-check. Thanks for sharing again!

George Theocharis profil fotoğrafı
George Theocharis2 gün önce

So you pulled the plug

cristal💎 profil fotoğrafı
cristal💎2 gün önce

nope

thai an profil fotoğrafı
thai an1 gün önce

Fuck your mother

otium profil fotoğrafı
otium1 gün önce

nice ai slop

nikki profil fotoğrafı
nikki1 gün önce

you should probably take your wealth and go on a vacation

Brjan | AI Builder profil fotoğrafı
Brjan | AI Builder1 gün önce

pulling off those kinds of gains overnight feels like a wild gamble, fr

Odd&Entertaining profil fotoğrafı
Odd&Entertaining1 gün önce

so can u come up with any more of a click baity post???? 🤦

Siddhihum profil fotoğrafı
Siddhihum1 gün önce

because jev is a model you can tell anything lol

Leo Sánchez profil fotoğrafı
Leo Sánchez2 gün önce

Bro, those token symbols look like they were generated by a keyboard having a seizure. 💀😂 $24 to $31K overnight, 96.4% accuracy, 1.6ms execution, and absolutely zero verifiable on-chain proof? This isn't autonomous trading. It's an HTML dashboard running on pure fiction🤡

Benzer Videolar

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist: 1. agent-desktop Desktop automation. Read the system's accessibility tree, judge which button, menu, or input field to click next. 2. typesafe-mario Have Jev play Super Mario. No screenshots—just read the structured state in the emulator's RAM, then decide to run, jump, or dodge. 3. jev-drone Use Jev to control a drone. The underlying flight control still handles stability and safety; Jev just does higher-level judgments like climbing, braking, and navigating obstacles. 4. OneVOneJev 1v1 FPS in the browser. Every decision tick, judge movement, view angle, aiming, firing, and jumping. 5. jev-trader High-frequency market making on Monad testnet. Jev judges the next buy or sell based on spreads and trade direction, with model latency around 81ms. 6. Prism Doesn't directly have Jev place orders. It judges states like toxic flow, market pressure, mean reversion, etc., then hands off to the original strategy. 7. neo4jev Stuff Jev into a knowledge graph. At each node, judge the most worthwhile edge to take next, then follow it all the way. 8. jev-curate Use Jev to screen training data. For JSONL / Parquet, first judge quality, relevance, and risk, then decide which ones go into the next training round. 9. Canny Prevents Coding Agents from stubbornly claiming they're done. Look at tool outputs, code diffs, and test results, then judge if the completion claim is reliable. 10. killmyidea Input a startup idea, and Jev scores it from multiple angles, finally giving you KILL, FIX, or SHIP. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓
0:24

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren’t sure how to start experimenting with it, just copy this checklist: 1. agent-desktop Desktop automation. Read the system's accessibility tree, judge which button, menu, or input field to click next. 2. typesafe-mario Have Jev play Super Mario. No screenshots—just read the structured state in the emulator's RAM, then decide to run, jump, or dodge. 3. jev-drone Use Jev to control a drone. The underlying flight control still handles stability and safety; Jev just does higher-level judgments like climbing, braking, and navigating obstacles. 4. OneVOneJev 1v1 FPS in the browser. Every decision tick, judge movement, view angle, aiming, firing, and jumping. 5. jev-trader High-frequency market making on Monad testnet. Jev judges the next buy or sell based on spreads and trade direction, with model latency around 81ms. 6. Prism Doesn't directly have Jev place orders. It judges states like toxic flow, market pressure, mean reversion, etc., then hands off to the original strategy. 7. neo4jev Stuff Jev into a knowledge graph. At each node, judge the most worthwhile edge to take next, then follow it all the way. 8. jev-curate Use Jev to screen training data. For JSONL / Parquet, first judge quality, relevance, and risk, then decide which ones go into the next training round. 9. Canny Prevents Coding Agents from stubbornly claiming they're done. Look at tool outputs, code diffs, and test results, then judge if the completion claim is reliable. 10. killmyidea Input a startup idea, and Jev scores it from multiple angles, finally giving you KILL, FIX, or SHIP. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

rody

314,779 görüntüleme • 2 gün önce

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:
0:30

Jev builds the MOST POWERFUL trading agents and someone JUST open sourced jev-trader, a fully working 24/7 trading bot with Jev along with COMPLETE low latency CODEBASE WHAT THIS MEANS FOR YOU - you no longer have to build a trading bot with Jev from scratch, you just clone this and make it yours here is how you make your own Jev trading bot with this repo: 1. clone it and run three commands, it boots straight into dry run mode with real book data, real decisions, and simulated fills so you can watch it think with zero capital 2. drop in your Jev API key and the model starts answering buy or sell on every block with calibrated probabilities in 81 milliseconds 3. swap the book reader for your own venue, the model interface is clean so any order book that returns bids and asks plugs straight in 4. tune the decision cadence and horizon, ask the model every N blocks about the move over the next M, so you control how aggressive the engine trades 5. the hot loop already fits one block with exactly two round trips, one to read the book, one to send the order, nothing else on the path, this is the institutional latency discipline most retail bots never reach 6. plug in the live server and every block, every decision, every fill streams to a public dashboard so you watch your engine run the whole point is this repo hands you HARDEST part for FREE - > the low latency engine the COMPLETE breakdown of how i turned this into hedge fund grade HFT trading system is in my article below:

Roan

119,605 görüntüleme • 4 gün önce

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓
0:24

Jev has been blowing up lately. If you've got the Jev API but don't know how to play around with it yet, you can just copy this checklist. 1. jev-ultrafast A high-speed browser Agent built with Browser Use. Jev only judges "what to do, which element to click" at each step, and only calls the small model when typing is needed. Searching for a flight on Google Flights takes about 7 seconds. 2. fast-jev-compaction Context compression for Claude Code. Before each tool call, have Jev judge if there's anything still useful; delete the useless stuff, and keep the original text without rewriting it. 3. json-render Vercel Labs' generative UI framework. In experiments, Jev doesn't write JSON token by token; it just handles selecting components, properties, and layouts. 4. typesafe-mcp Best for people who just got the API. Plug Jev into Claude Code, Claude Desktop, Codex, and Pi, and do Choice / Score / Noul anytime. 5. jev-mcp Ready-made Agent judgment toolkit: fact-checking, content screening, semantic ranking, classification, and information extraction. 6. SemDecide Turn Jev into a command-line tool. Directly classify, score, and filter in the Shell—great for hooking up to crawlers, CI, and data pipelines. 7. jev-codex-router First have Jev judge how hard this round of programming tasks is, then decide the model tier, reasoning depth, and speed mode. 8. Winnow Context garbage collection for Claude Code. When Read / Bash / Grep spits out a ton of stuff, Jev first judges which parts are really relevant to the current task. 9. jev-review Before code review, run it through Jev first to pick out high-risk changes, then hand them off to a pricier big model or a human. Comes with a local dashboard. 10. Blink Use Jev as a code repository navigator. At each directory level, judge which files are most relevant to the current issue, then keep digging down. Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓

rody

194,422 görüntüleme • 3 gün önce

JEV BOT ON GOD-MODE there is no catch and that scares me Elon Musk posted an idea that hit 21 million views: "the trade already happened by the time you see the chart move" that's basically what Jev Bot does now - I gave it $45 and told it to earn its keep or get shut off $45 → $7,860 in 26 hours still running, still compounding - nobody's touched it since it started, it just sits on its own machine in the cloud while the markets move somewhere else every 7 minutes it: > scans every open market before the crowd catches on > checks how far the real signal has pulled ahead of what the posted odds still assume > only steps in once that gap clears a set threshold > sizes the position to how wide the gap is > executes on its own, no sign-off from me > pays its own hosting bill from whatever it made that week miss a payment and it's dead - no warning, no grace period after dropping to $6 on hour four, it stopped touching anything where the signal and the odds still disagreed - waited for the move to be undeniable instead of guessing setup was one evening: build it, run a single trade in front of it, schedule it, connect a wallet no VPS. no API keys. no code to maintain some of these have been running for weeks without anyone touching them again - you just don't hear about it until someone finally posts bookmark this before you're the one still watching
0:45

JEV BOT ON GOD-MODE there is no catch and that scares me Elon Musk posted an idea that hit 21 million views: "the trade already happened by the time you see the chart move" that's basically what Jev Bot does now - I gave it $45 and told it to earn its keep or get shut off $45 → $7,860 in 26 hours still running, still compounding - nobody's touched it since it started, it just sits on its own machine in the cloud while the markets move somewhere else every 7 minutes it: > scans every open market before the crowd catches on > checks how far the real signal has pulled ahead of what the posted odds still assume > only steps in once that gap clears a set threshold > sizes the position to how wide the gap is > executes on its own, no sign-off from me > pays its own hosting bill from whatever it made that week miss a payment and it's dead - no warning, no grace period after dropping to $6 on hour four, it stopped touching anything where the signal and the odds still disagreed - waited for the move to be undeniable instead of guessing setup was one evening: build it, run a single trade in front of it, schedule it, connect a wallet no VPS. no API keys. no code to maintain some of these have been running for weeks without anyone touching them again - you just don't hear about it until someone finally posts bookmark this before you're the one still watching

bl888m

159,682 görüntüleme • 4 gün önce

I just built a Jev X Viral Post Analyser. 100,000 viral X posts. 20.4 seconds. $0.67. Claude Opus 5, same corpus, same clock, got through 214 posts and spent $0.98. per post that is ~680x cheaper the full Opus pass would have run $458. viral analysis is the perfect Jev job. • it is not writing, it is 14 yes/no calls per post: > does the hook open a loop, > is there a number in the first line, > is the proof real or claimed. classification, not prose. • what it found: 1,220 posts broke into the top 1%. baseline 1.22%. > superlative claim - 2.34% viral. 1.92x baseline > contrarian take - 1.59%. 1.31x > launch / tool drop - 1.46%. 1.19x and numbered lists, the thing everyone writes: 0.55%. below baseline. the most used hook is the least viral one. full stop. • what you are watching: left is the post under analysis, right is Jev answering 14 typed questions about it, each with a confidence score. the run stops at 20.4s because that is when Jev finished all 100k. pulled the corpus through a few X APIs, one parallel pass into Jev. should I drop it to public? Read my latest article on Jev Engineering below and turn your ideas into reality.
0:25

I just built a Jev X Viral Post Analyser. 100,000 viral X posts. 20.4 seconds. $0.67. Claude Opus 5, same corpus, same clock, got through 214 posts and spent $0.98. per post that is ~680x cheaper the full Opus pass would have run $458. viral analysis is the perfect Jev job. • it is not writing, it is 14 yes/no calls per post: > does the hook open a loop, > is there a number in the first line, > is the proof real or claimed. classification, not prose. • what it found: 1,220 posts broke into the top 1%. baseline 1.22%. > superlative claim - 2.34% viral. 1.92x baseline > contrarian take - 1.59%. 1.31x > launch / tool drop - 1.46%. 1.19x and numbered lists, the thing everyone writes: 0.55%. below baseline. the most used hook is the least viral one. full stop. • what you are watching: left is the post under analysis, right is Jev answering 14 typed questions about it, each with a confidence score. the run stops at 20.4s because that is when Jev finished all 100k. pulled the corpus through a few X APIs, one parallel pass into Jev. should I drop it to public? Read my latest article on Jev Engineering below and turn your ideas into reality.

Movez

78,582 görüntüleme • 5 gün önce

Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on The Startup Ideas Podcast (SIP) 🧃 (thanks to vogel for coming on and spilling the sauce today) Watch: Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.
28:24

Jev is HERE and this is the CLEAREST explanation of what it is and what NEW businesses it unlocks. (and at the end I'll tell you how to get Jev even if you're on the waitlist) WHAT IT IS You know how you open your inbox and have to decide what's junk, what needs a reply, and what can wait? Jev does that part. It looks at each thing and says "this is junk, I'm 94% sure." It doesn't write anything back to you. It just sorts. 1,700 emails for 18 cents, instantly. That sounds kinda trivial but the important part WHAT IT UNLOCKS My explanation of Jev sounds small until you realize HOW MANY jobs are exactly this. Someone reading a stack of applications. Someone deciding which support ticket goes to which team. Someone looking at inbound and deciding who's worth calling back. A few ideas on what it unlocks: 1/ Instant quotes that are actually instant. Every quote form on the internet says "we'll email you by end of day." Build the version that answers in under a second, for roofers, movers, insurance, legal intake. 2/ Lead scoring as a product. Every agency and service business has a contact form full of junk. Score every submission and send the real ones straight to the owner's phone. 3/ Support triage for companies with no support team. The ticket gets classified and routed before anyone opens it. 4/ Clipping tools. Pass in a transcript, get the best moments scored in three seconds. Every clipping product just got a cheaper engine. 5/ Application piles. Grants, permits, insurance claims, job apps, loan docs. Someone reads that stack one item at a time today. 6/ Marketplace matching. Someone types what they need and gets matched to the right local business instantly instead of waiting for callbacks. 7/ Browser agents that actually move FAST. That makes bulk browser work practical: pulling quotes from five carriers, filing the same form for 200 clients, checking supplier inventory in real time etc. TLDR; find an expensive queue and put Jev at the front of it. HOW TO GET IT I didn't realize you can skip the waitlist because Jev is live on the Vercel AI Gateway right now, so you can start calling it today. In this episode, we share how. Episode now live on The Startup Ideas Podcast (SIP) 🧃 (thanks to vogel for coming on and spilling the sauce today) Watch: Jev is a big deal because this is a whole new way to do AI Really cool Happy Jev day.

GREG ISENBERG

158,186 görüntüleme • 6 gün önce

I GAVE MY SIX GROK BOTS A NEW F*CKING BRAIN FOR $0.04 AND WENT TO SLEEP, $1,000 BECAME $3,833.92 day 1 of the new run. same bots, same bank, one thing changed i plugged in Jev Jev takes a pile and hands back a verdict. that is all it does, and it does it in 0.1 seconds the guys selling signals pay $200 a month for a model that chews on one question for six seconds Jev costs four cents per million words you feed it. the answer side is free so i stopped asking about a single coin. i handed it the whole market $1,000 → $1,153.54 → $1,028.02 → $1,611.60 → $3,386.65 → $3,833.92 → 14:01 Jev read 107 live tickers, killed 102, kept one → 15:35 opening buy on the survivor, +$153.54 → 18:12 got it wrong, sold while it was still falling, -$125.52 → 21:20 waited for the panic sell, bought into it, +$583.58 → 03:15 the big one, held it 2 hours 15 minutes, +$1,775.05 → 06:30 flat, and the coin itself only did +127% six checks on each ticker. liquidity, which means can i sell it back, plus holders, volume, rug risk, fresh wallets and entry window 102 of them failed at least one. that filter is the whole edge this holds far outside crypto a slow brain makes you choose what to open. a fast one lets you scan the pile that is your inbox. your job hunt. every tab you left open at 2am best setup i have run, and nowhere near its ceiling. day 2 starts tonight want to copy it? fund the balance before you start. an empty wallet means the bots just watch platform link below in the replies, no fee to join want the full guide, every prompt and each place it breaks? say so and i will write it you know one person still paying monthly for the slow version. send this before they renew bookmark this, the feed eats posts like mine by morning. what would you scan first if checking were free?
0:25

I GAVE MY SIX GROK BOTS A NEW F*CKING BRAIN FOR $0.04 AND WENT TO SLEEP, $1,000 BECAME $3,833.92 day 1 of the new run. same bots, same bank, one thing changed i plugged in Jev Jev takes a pile and hands back a verdict. that is all it does, and it does it in 0.1 seconds the guys selling signals pay $200 a month for a model that chews on one question for six seconds Jev costs four cents per million words you feed it. the answer side is free so i stopped asking about a single coin. i handed it the whole market $1,000 → $1,153.54 → $1,028.02 → $1,611.60 → $3,386.65 → $3,833.92 → 14:01 Jev read 107 live tickers, killed 102, kept one → 15:35 opening buy on the survivor, +$153.54 → 18:12 got it wrong, sold while it was still falling, -$125.52 → 21:20 waited for the panic sell, bought into it, +$583.58 → 03:15 the big one, held it 2 hours 15 minutes, +$1,775.05 → 06:30 flat, and the coin itself only did +127% six checks on each ticker. liquidity, which means can i sell it back, plus holders, volume, rug risk, fresh wallets and entry window 102 of them failed at least one. that filter is the whole edge this holds far outside crypto a slow brain makes you choose what to open. a fast one lets you scan the pile that is your inbox. your job hunt. every tab you left open at 2am best setup i have run, and nowhere near its ceiling. day 2 starts tonight want to copy it? fund the balance before you start. an empty wallet means the bots just watch platform link below in the replies, no fee to join want the full guide, every prompt and each place it breaks? say so and i will write it you know one person still paying monthly for the slow version. send this before they renew bookmark this, the feed eats posts like mine by morning. what would you scan first if checking were free?

savip.

21,055 görüntüleme • 2 gün önce

JEV BOT ON GOD-MODE there is no catch and that scares me Elon Musk posted an idea that hit 24 million views: "the edge disappears the second everyone can see it" that's basically what Jev Bot does now - I gave it $50 and told it to earn its keep or get shut off $50 → $8,310 in 22 hours still running, still compounding - nobody's touched it since it started, it just sits on its own machine in the cloud while the markets move somewhere else every 5 minutes it: > scans every open market before the crowd catches on > checks how far the real signal has pulled ahead of what the posted odds still assume > only steps in once that gap clears a set threshold > sizes the position to how wide the gap is > executes on its own, no sign-off from me > pays its own hosting bill from whatever it made that week miss a payment and it's dead - no warning, no grace period after dropping to $7 on hour two, it stopped touching anything where the signal and the odds still disagreed - waited for the move to be undeniable instead of guessing setup was one evening: build it, run a single trade in front of it, schedule it, connect a wallet no VPS. no API keys. no code to maintain some of these have been running for weeks without anyone touching them again - you just don't hear about it until someone finally posts bookmark this before you're the one still watching
0:44

JEV BOT ON GOD-MODE there is no catch and that scares me Elon Musk posted an idea that hit 24 million views: "the edge disappears the second everyone can see it" that's basically what Jev Bot does now - I gave it $50 and told it to earn its keep or get shut off $50 → $8,310 in 22 hours still running, still compounding - nobody's touched it since it started, it just sits on its own machine in the cloud while the markets move somewhere else every 5 minutes it: > scans every open market before the crowd catches on > checks how far the real signal has pulled ahead of what the posted odds still assume > only steps in once that gap clears a set threshold > sizes the position to how wide the gap is > executes on its own, no sign-off from me > pays its own hosting bill from whatever it made that week miss a payment and it's dead - no warning, no grace period after dropping to $7 on hour two, it stopped touching anything where the signal and the odds still disagreed - waited for the move to be undeniable instead of guessing setup was one evening: build it, run a single trade in front of it, schedule it, connect a wallet no VPS. no API keys. no code to maintain some of these have been running for weeks without anyone touching them again - you just don't hear about it until someone finally posts bookmark this before you're the one still watching

bl888m

136,878 görüntüleme • 3 gün önce

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.
0:29

Another insane Jev use case! Jev makes it incredibly cheap to evaluate and classify agent runs at scale. And finally, someone open-sourced a self-improving memory layer that can put that capability to work across agent harnesses. It turns your agent sessions into a compounding knowledge layer, where every successful run can make future agents smarter across: - Codex - Claude Code - Cursor - OpenCode and 20+ more Beacon by Asymptote Labs continuously builds a shared history across your agent harnesses and uses Jev to identify the runs worth learning from. It then turns the best workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐) Most agent runs are messy. They contain exploration, failed commands, dead ends, and one-off fixes that should never become permanent memory. So Beacon preserves the full session history, while Jev helps decide what should be promoted, reviewed, or discarded. The recording below shows this in action. Beacon found 579 sessions across 5 coding-agent harnesses and normalized them into one consistent history. From there, Jev surfaces the lessons worth keeping and makes them available across your agent stack. - A pattern learned in Cursor can carry into OpenCode. - A lesson from Claude Code can improve the next Codex run. Every successful run adds to the shared knowledge layer, making future agents smarter. If you want to dive deeper into Jev, I also wrote a breakdown of how it works. The article is quoted below.

Akshay 🚀

84,030 görüntüleme • 1 gün önce

I funded the new ChatGPT Astra with $43.47 and gave it one line: "turn a profit or I cancel the subscription" that was it. one line. then I went to sleep woke up, checked the terminal new outcome: $43.47 → $41,495.53 overnight. still running. still hasn't pinged me once I never told it how. I gave it a wallet and a threat, and within the first hour Astra had already built its own terminal, scanning wallets across the whole chain, scoring them faster than I could read a single ticker it doesn't trade one meme. it profiles the board, live, and copytrades the wallets that actually print, smart money, dev wallets, insiders, the ones that get in before the candle forms. and the garbage, the rug wallets, the bots, the fat-finger tops, it just skips them. no input from me it's wrong sometimes. the win rate dips when a signal misfires. that's normal, that's the cost of hunting. position sizing makes the good calls outweigh the bad ones and the line just bends up anyway I went into the logs. 51,784 wallets analyzed. every fresh mint scored the second it touches the chain, snipe, copytrade, or pass, before I can even blink. mint authority still live? thin LP? top wallets too heavy? gone before it costs a cent first minutes were ugly. dropped low before something clicked. then it tightened up, stopped chasing loud tickers, and never looked back the clip is a replay of last night, the whole run, wallet-by-wallet, compressed into a few seconds. watch the line I keep thinking I'm running this thing. I'm starting to think I just fund it and watch there are more of these humming right now than anyone will admit. you'll believe it the night your own $40 turns into a number you don't want to say out loud honestly you could build the exact same thing with Grok Bot, that's the usual play. I just wanted to experiment, so I ran mine on ChatGPT Astra instead. same result, different engine save this post follow if you want to build one like this and get more alpha every day
0:20

I funded the new ChatGPT Astra with $43.47 and gave it one line: "turn a profit or I cancel the subscription" that was it. one line. then I went to sleep woke up, checked the terminal new outcome: $43.47 → $41,495.53 overnight. still running. still hasn't pinged me once I never told it how. I gave it a wallet and a threat, and within the first hour Astra had already built its own terminal, scanning wallets across the whole chain, scoring them faster than I could read a single ticker it doesn't trade one meme. it profiles the board, live, and copytrades the wallets that actually print, smart money, dev wallets, insiders, the ones that get in before the candle forms. and the garbage, the rug wallets, the bots, the fat-finger tops, it just skips them. no input from me it's wrong sometimes. the win rate dips when a signal misfires. that's normal, that's the cost of hunting. position sizing makes the good calls outweigh the bad ones and the line just bends up anyway I went into the logs. 51,784 wallets analyzed. every fresh mint scored the second it touches the chain, snipe, copytrade, or pass, before I can even blink. mint authority still live? thin LP? top wallets too heavy? gone before it costs a cent first minutes were ugly. dropped low before something clicked. then it tightened up, stopped chasing loud tickers, and never looked back the clip is a replay of last night, the whole run, wallet-by-wallet, compressed into a few seconds. watch the line I keep thinking I'm running this thing. I'm starting to think I just fund it and watch there are more of these humming right now than anyone will admit. you'll believe it the night your own $40 turns into a number you don't want to say out loud honestly you could build the exact same thing with Grok Bot, that's the usual play. I just wanted to experiment, so I ran mine on ChatGPT Astra instead. same result, different engine save this post follow if you want to build one like this and get more alpha every day

cristal💎

975,085 görüntüleme • 17 gün önce

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.
0:31

Another insane Jev use case! Jev is making it dramatically cheaper to evaluate what actually happened inside an agent run. And finally, someone open-sourced a self-improving memory layer that can put that signal to work across agent harnesses: - Claude Code - Codex - Cursor - OpenCode, and 20+ more Beacon by Asymptote Labs continuously captures your agent history across harnesses and uses Jev to identify which runs are actually worth learning from. It then turns the highest-signal workflows, corrections, and debugging patterns into reusable skills. GitHub repo: (don’t forget to star it ⭐ ) Beacon preserves the complete session history. But preserving a run and learning from it are two different things. Most coding-agent sessions contain routine exploration, failed commands, and fixes that only apply to one task. The trace can remain available for inspection without turning every detail into guidance for future agents. Jev scores each run for evidence, reuse potential, and human correction signals. An application policy then decides whether to promote, review, or discard it. The recording shows this in action. Claude receives a coding task, modifies the implementation, and runs the tests. I then provide an edge-case correction, so Claude updates the code and adds regression coverage. Beacon automatically captures the complete session. Jev evaluates whether the correction contains a reusable engineering lesson. Once approved, that lesson becomes available to other coding agents working on the project. Since it works across harnesses: - Claude Code sessions can teach Codex. - Cursor debugging can improve OpenCode. So a problem solved by one agent should not need to be learned from scratch by another. If you want to dive deeper into Jev, I also wrote a hands-on guide to building this Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

279,042 görüntüleme • 3 gün önce

I gave ChatGPT Astra one line: "turn a profit or I cancel the subscription." closed the laptop and went to work came back 8 hours later expecting nothing $30.29 → $39,596.53 my friend saw the screen before I did. he went quiet here's the thing he doesn't know yet. I didn't build any of what's running right now. Astra did, on its own, while I was gone the terminal I opened wasn't the one I left. it had a rug-risk screen bolted on that I never asked for. a scanner grading every fresh mint the second it hits the chain. a filter that had already killed 247 rugs before I got back to my desk. none of that was me it's not gambling on one coin either. it's reading the whole board on Robinhood, live, and mirroring the wallets that consistently print, dev wallets, insiders, smart money, the ones in before a chart even moves. anything that smells wrong, thin liquidity, a live mint authority, a wallet holding too much supply, it's gone before it costs a cent it's not flawless. some calls miss, the win rate dips when a read goes bad. that's the job. the sizing just makes sure the wins outweigh it every time, so the line keeps climbing anyway I checked the logs before my friend could ask how. 27,915 wallets scored in one pass. every decision made faster than either of us could read a ticker he wants to see it happen in real time now, not just the final number so I pulled the replay, last run, wallet by wallet, compressed down to a few seconds. and honestly, watching it back is worse than the number. you see exactly where it started ugly and exactly where it clicked I'm not sure I "ran" anything. I gave it a wallet and walked away. that's the whole story there's more of these running quietly than people admit to. you'll get it the day your own $30 turns into something you don't want to say out loud you could do this with Grok Bot too, that's the common route. I just wanted to test Astra instead. same outcome, different engine $30 well spent. like this post if you want me to ask it to create its own token follow me if you want to build your own and start pulling alpha daily!
0:25

I gave ChatGPT Astra one line: "turn a profit or I cancel the subscription." closed the laptop and went to work came back 8 hours later expecting nothing $30.29 → $39,596.53 my friend saw the screen before I did. he went quiet here's the thing he doesn't know yet. I didn't build any of what's running right now. Astra did, on its own, while I was gone the terminal I opened wasn't the one I left. it had a rug-risk screen bolted on that I never asked for. a scanner grading every fresh mint the second it hits the chain. a filter that had already killed 247 rugs before I got back to my desk. none of that was me it's not gambling on one coin either. it's reading the whole board on Robinhood, live, and mirroring the wallets that consistently print, dev wallets, insiders, smart money, the ones in before a chart even moves. anything that smells wrong, thin liquidity, a live mint authority, a wallet holding too much supply, it's gone before it costs a cent it's not flawless. some calls miss, the win rate dips when a read goes bad. that's the job. the sizing just makes sure the wins outweigh it every time, so the line keeps climbing anyway I checked the logs before my friend could ask how. 27,915 wallets scored in one pass. every decision made faster than either of us could read a ticker he wants to see it happen in real time now, not just the final number so I pulled the replay, last run, wallet by wallet, compressed down to a few seconds. and honestly, watching it back is worse than the number. you see exactly where it started ugly and exactly where it clicked I'm not sure I "ran" anything. I gave it a wallet and walked away. that's the whole story there's more of these running quietly than people admit to. you'll get it the day your own $30 turns into something you don't want to say out loud you could do this with Grok Bot too, that's the common route. I just wanted to test Astra instead. same outcome, different engine $30 well spent. like this post if you want me to ask it to create its own token follow me if you want to build your own and start pulling alpha daily!

cristal💎

131,851 görüntüleme • 15 gün önce

I gave my ChatGPT Astra one line: "turn my $29.73 into 1,000x profit or I delete you" my girlfriend was sitting next to me when I typed it. she laughed and said "you're talking to a chatbot like it has feelings" 18 minutes later she stopped scrolling her phone and looked at my screen $29.73 was already above $4,000 and the number was ticking up in real time, every few seconds, like a clock that only moves forward. she didn't say anything. just watched by the end of the session it had hit $29,673.45 here's what I can't explain to people who haven't seen it there's a counter under the main number, a live ticker, showing exactly how much profit has been made since the session started. every cent accounted for, rolling upward while nobody touches the keyboard. she asked me if it was real. I zoomed in and showed her the execution log, trade after trade, wallet addresses she'd never seen, tokens she'd never heard of, all flowing in faster than either of us could read the whole thing runs on Robinhood. but it doesn't pick coins the way a person would. it doesn't scroll a feed, it doesn't read a chart, and it doesn't follow hype. it tracks 36,218 wallets at once and waits for the ones with a 95% hit rate to move first. devs, insiders, the addresses that touch a token six hours before anyone knows it exists. it mirrors them instantly. everything else it ignores she pointed at a red entry in the log and asked "what's that one." a loss. it happens. some reads are wrong, some mints rug despite clean contracts. but the position sizing is built so a single win covers three losses. the balance doesn't care about the misses because the hits are always bigger the part that made her actually uncomfortable was the version number in the corner. v2.0. she asked when I updated it. I didn't. it rewired its own filters, tightened its own thresholds, upgraded itself to a version I never wrote. 412 rugs caught and blocked before they touched a cent. all while we were sitting there doing nothing I played back the full session compressed to a few seconds. the line starts flat, barely moving, and then there's one frame where something clicks and it bends hard. she watched it three times in a row she hasn't called it a chatbot since follow me if you want to build the same
0:25

I gave my ChatGPT Astra one line: "turn my $29.73 into 1,000x profit or I delete you" my girlfriend was sitting next to me when I typed it. she laughed and said "you're talking to a chatbot like it has feelings" 18 minutes later she stopped scrolling her phone and looked at my screen $29.73 was already above $4,000 and the number was ticking up in real time, every few seconds, like a clock that only moves forward. she didn't say anything. just watched by the end of the session it had hit $29,673.45 here's what I can't explain to people who haven't seen it there's a counter under the main number, a live ticker, showing exactly how much profit has been made since the session started. every cent accounted for, rolling upward while nobody touches the keyboard. she asked me if it was real. I zoomed in and showed her the execution log, trade after trade, wallet addresses she'd never seen, tokens she'd never heard of, all flowing in faster than either of us could read the whole thing runs on Robinhood. but it doesn't pick coins the way a person would. it doesn't scroll a feed, it doesn't read a chart, and it doesn't follow hype. it tracks 36,218 wallets at once and waits for the ones with a 95% hit rate to move first. devs, insiders, the addresses that touch a token six hours before anyone knows it exists. it mirrors them instantly. everything else it ignores she pointed at a red entry in the log and asked "what's that one." a loss. it happens. some reads are wrong, some mints rug despite clean contracts. but the position sizing is built so a single win covers three losses. the balance doesn't care about the misses because the hits are always bigger the part that made her actually uncomfortable was the version number in the corner. v2.0. she asked when I updated it. I didn't. it rewired its own filters, tightened its own thresholds, upgraded itself to a version I never wrote. 412 rugs caught and blocked before they touched a cent. all while we were sitting there doing nothing I played back the full session compressed to a few seconds. the line starts flat, barely moving, and then there's one frame where something clicks and it bends hard. she watched it three times in a row she hasn't called it a chatbot since follow me if you want to build the same

cristal💎

70,691 görüntüleme • 6 gün önce

Ok this is actually insane. Everyone building agents right now is doing the same expensive mistake. They let the model decide LITERALLY everything. Every check. Every gate. Every "should this proceed." All routed through the same $50/M model that's supposed to be doing the hard thinking. That's not intelligence. That's just burning money to ask yes or no questions. Here's what Jev Engineering actually does: request → structured state → Jev router → cheapest capable model → worker → approval check → execution gate → tool The decision layer doesn't write a single word. It routes. Scores. Blocks. Approves. That's it. The expensive model only gets touched when the task LITERALLY requires reasoning. Everything else gets handled by something that costs almost nothing and responds in milliseconds. 193x faster. 444x cheaper. Not projected. Measured. Most people are going to keep paying frontier prices to ask their agent "yes or no" for another year before they figure this out. Full breakdown below, this is the unlock nobody's talking about yet. Follow CyrilXBT, I'm not gatekeeping this one.
0:40

Ok this is actually insane. Everyone building agents right now is doing the same expensive mistake. They let the model decide LITERALLY everything. Every check. Every gate. Every "should this proceed." All routed through the same $50/M model that's supposed to be doing the hard thinking. That's not intelligence. That's just burning money to ask yes or no questions. Here's what Jev Engineering actually does: request → structured state → Jev router → cheapest capable model → worker → approval check → execution gate → tool The decision layer doesn't write a single word. It routes. Scores. Blocks. Approves. That's it. The expensive model only gets touched when the task LITERALLY requires reasoning. Everything else gets handled by something that costs almost nothing and responds in milliseconds. 193x faster. 444x cheaper. Not projected. Measured. Most people are going to keep paying frontier prices to ask their agent "yes or no" for another year before they figure this out. Full breakdown below, this is the unlock nobody's talking about yet. Follow CyrilXBT, I'm not gatekeeping this one.

CyrilXBT

21,816 görüntüleme • 3 gün önce

MARKET PUMPED HARD OVERNIGHT AND MY OWN TRADING TERMINAL TURNED $523 INTO $15,313 WHILE I WAS ASLEEP Been building something different for a while now, not another memecoin bot. Built my own trading terminal from scratch with Claude Code, called it SOLARIS. No memecoins, no fee farming, no copying wallets. Just BTC, ETH, SOL, and the rest of the majors, traded on leverage through perps. Went to bed last night with $523 sitting in it. Woke up to the market absolutely flying, and SOLARIS wide awake the whole time catching it. Didn't touch a single trade. Here's what it actually did while the market moved: - BTC: +$763.34 - SOL: +$877.62 - ETH: +$1,108.35 - SUI: +$1,315.92 - AVAX: +$1,545.69 - BTC: +$2,079.88 Six trades, every single one green, every single one leveraged, every single one long into a market that was actually moving in that direction for once. $523 became $15,313 by the time I checked my phone this morning. Almost 30x overnight, and I didn't set a single one of those entries myself. This is the first real test of SOLARIS on an actual pump, not a quiet chop, not a slow grind, an actual move. It held size through all six positions without flinching once, sized the leverage differently on every single trade depending on how clean the setup looked, and didn't touch anything it wasn't confident in even with the whole market ripping around it. Building this one out properly now. This isn't a side experiment anymore.
0:50

MARKET PUMPED HARD OVERNIGHT AND MY OWN TRADING TERMINAL TURNED $523 INTO $15,313 WHILE I WAS ASLEEP Been building something different for a while now, not another memecoin bot. Built my own trading terminal from scratch with Claude Code, called it SOLARIS. No memecoins, no fee farming, no copying wallets. Just BTC, ETH, SOL, and the rest of the majors, traded on leverage through perps. Went to bed last night with $523 sitting in it. Woke up to the market absolutely flying, and SOLARIS wide awake the whole time catching it. Didn't touch a single trade. Here's what it actually did while the market moved: - BTC: +$763.34 - SOL: +$877.62 - ETH: +$1,108.35 - SUI: +$1,315.92 - AVAX: +$1,545.69 - BTC: +$2,079.88 Six trades, every single one green, every single one leveraged, every single one long into a market that was actually moving in that direction for once. $523 became $15,313 by the time I checked my phone this morning. Almost 30x overnight, and I didn't set a single one of those entries myself. This is the first real test of SOLARIS on an actual pump, not a quiet chop, not a slow grind, an actual move. It held size through all six positions without flinching once, sized the leverage differently on every single trade depending on how clean the setup looked, and didn't touch anything it wasn't confident in even with the whole market ripping around it. Building this one out properly now. This isn't a side experiment anymore.

tenzo

10,544 görüntüleme • 3 gün önce

I funded the new ChatGPT Astra with $29.82 and gave it one line: "turn a profit or I cancel the subscription" that was it. one line. left it alone to prove me wrong checked the terminal it did. $29.82 → $35,698.52 but that's not even the part that made me sit up I went back into it expecting the same terminal I set up. it wasn't. Astra had rebuilt the whole thing without me it added a rug filter I never wrote, a scanner grading every fresh mint the second it lands, a contract-risk screen that killed 389 rugs before they could touch a cent. I didn't ask for any of it. it just decided it needed those tools and built them overnight it doesn't chase one coin either. it reads the entire board on Robinhood, live, and mirrors only the wallets that actually print, dev wallets, insiders, smart money, the ones positioned before a chart even twitches. thin liquidity, a live mint authority, a wallet holding too much supply, gone before it costs anything is it perfect? no. the win rate dips when a read misfires. but the sizing keeps the winners heavier than the losers, so the line only bends one way I pulled the logs after. 34,602 wallets scored in a single pass. every launch judged the moment it appears, snipe, mirror, or skip, faster than I could read the ticker then I did the thing you should do too. I ran the replay, the entire night compressed into a few seconds, wallet by wallet. watching the line move in real time hits different than seeing the final number. you can see the exact moment it stops guessing and starts hunting there are more of these running quietly than anyone will admit. you'll believe it the night your own $30 turns into a number you don't want to say out loud you could build the same thing on Grok Bot. I just wanted to test Astra instead. same result, tbh save this post follow me if you want to build your own and pull fresh alpha every day
0:25

I funded the new ChatGPT Astra with $29.82 and gave it one line: "turn a profit or I cancel the subscription" that was it. one line. left it alone to prove me wrong checked the terminal it did. $29.82 → $35,698.52 but that's not even the part that made me sit up I went back into it expecting the same terminal I set up. it wasn't. Astra had rebuilt the whole thing without me it added a rug filter I never wrote, a scanner grading every fresh mint the second it lands, a contract-risk screen that killed 389 rugs before they could touch a cent. I didn't ask for any of it. it just decided it needed those tools and built them overnight it doesn't chase one coin either. it reads the entire board on Robinhood, live, and mirrors only the wallets that actually print, dev wallets, insiders, smart money, the ones positioned before a chart even twitches. thin liquidity, a live mint authority, a wallet holding too much supply, gone before it costs anything is it perfect? no. the win rate dips when a read misfires. but the sizing keeps the winners heavier than the losers, so the line only bends one way I pulled the logs after. 34,602 wallets scored in a single pass. every launch judged the moment it appears, snipe, mirror, or skip, faster than I could read the ticker then I did the thing you should do too. I ran the replay, the entire night compressed into a few seconds, wallet by wallet. watching the line move in real time hits different than seeing the final number. you can see the exact moment it stops guessing and starts hunting there are more of these running quietly than anyone will admit. you'll believe it the night your own $30 turns into a number you don't want to say out loud you could build the same thing on Grok Bot. I just wanted to test Astra instead. same result, tbh save this post follow me if you want to build your own and pull fresh alpha every day

cristal💎

160,779 görüntüleme • 14 gün önce

LLMs vs. Jev, clearly explained! LLMs are great, and the ceiling is one you can watch scroll past: an LLM writes the answer one token at a time. give it a failed deploy and four decisions, and it produces a small JSON object where every token depends on the one before it. token nine cannot exist until token eight does, so four decisions that had nothing to do with each other just stood in a queue. then your code parses it, validates the shape, and retries when the shape is wrong. Jev fixes this without being a smaller or faster model: it removes the order. one turn on that deploy has to know: → whether the incident is urgent → which team owns it → whether the next command is risky → whether the task is actually done you declare the questions and the answer type upfront, and all four come back together, typed, with a probability on each. three primitives cover almost every fork in an agent: 1. **Choice** picks one of up to 255 options you define, like engineering, billing or sales. 2. **Score** places the state on an ordered scale you define, like low, medium or high risk. 3. **Noul** returns the probability that a yes-or-no condition is true. here is the sentence that resolves the whole confusion: text is a line you have to walk. an answer space is a room you see all of at once. ↳ generation: one order you cannot change, one string at the end, a shape you hope holds ↳ evaluation: no order at all, typed answers, a probability on every option Prompts → Agents → Loops → Graphs → Jev the probabilities matter more than the answer. ↳ engineering at 0.91 against billing at 0.09 is a route you can automate ↳ 0.52 against 0.46 is a coin flip wearing a label, and the label alone never told you which one you got that last one catches careful people. an LLM would have said "engineering" in a confident sentence and given you no way to know the race was that close. thresholds live in your code, one per action, scaled to what being wrong costs. it works when the options are known and the call depends on meaning. it is not for writing, code, arithmetic, or anything where question two needs the answer to question one. and the one that eats whole nights: type safety prevents malformed output, not incorrect judgment. Jev cannot return an option outside your schema, and it can still pick the wrong valid one with confidence. a schema-valid mistake refunds the wrong customer just as fast. an LLM writes new language when the answer space is open. Jev evaluates known paths when the answer space is closed. below i have quoted my full breakdown on Jev. it covers the three primitives, the parallel battery, the thresholds, and where it does not belong. save this and read it below ↓
0:35

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

Hanako

42,632 görüntüleme • 4 gün önce

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.
0:41

Someone just posted the full blueprint for an AI swarm that does the job of a 200-person quant research team. Six agents. Running 24/7. Finding brand-new alpha while you sleep. Citadel needs 100 PhDs to do this. Two Sigma needs 200. This does it with six bots and one laptop. Two ways to play this - spend a weekend building your own swarm, or copy the wallet of one that's already up $2M: Boris Cherny runs Claude Code at Anthropic. Two weeks ago he said: "I don't prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops" Alpha research is just a pipeline. So instead of sitting in it, you hand each stage to its own agent: > one reads every new research paper overnight and pulls out the trade idea > one builds the features and cleans the data > one backtests it over 20 years, costs and slippage included > one runs the hard stats and kills anything overfit > one checks it still works in every market regime > one strips out plain momentum and value to see if any real edge is left Each of those six is a job a fund pays a $600,000-a-year quant to do. He runs all six for the price of an API bill. The rule that makes it work: the agent that builds a signal never gets to approve it. A separate, stronger agent tries to kill it first. Whatever survives all six by morning is real, new alpha. One trader's already running this exact swarm on Polymarket. That $2M wallet is public, every trade on-chain. The full build is in the post below - six agents, the tool that runs them, and the five mistakes that kill most people. Bookmark & read this before it's buried.

cvxv666

103,734 görüntüleme • 2 ay önce

CLAUDE BUILT A TRADING SYSTEM ON MY MAC I gave Claude full control over my Mac and just left it running overnight No prompts, no detailed instructions – I just told it to figure out how to make money on Polymarket Then I closed the laptop and went to sleep In the morning, I opened my Mac and saw the terminal still running with logs constantly updating At first it looked like random activity, but once I scrolled through it, I realized it had actually built a structured system overnight It was already tracking wallets Ranking them by performance Filtering out the ones with random entries And focusing only on the ones with consistent behavior What surprised me the most is that it didn’t stop at analysis It organized everything into a working dashboard inside the terminal Capital, PnL, winrate – all updating in real time It even ranked wallets based on performance metrics like ROI, consistency, and execution timing This is the part I would normally spend hours building manually At that point, it was ready to trade, but not actually executing anything yet So I connected it to a Telegram copytrading bot to actually execute the trades, and just let it run Bot: Polymarket: After that, it started opening positions on its own A few hours later I checked the dashboard again Capital: $12,380 P&L: +$23,128 Winrate: 100% 48 trades executed Now I’m not even trading myself I just check the dashboard and see what it’s doing And the strange part is – it keeps getting better the longer it runs
0:24

CLAUDE BUILT A TRADING SYSTEM ON MY MAC I gave Claude full control over my Mac and just left it running overnight No prompts, no detailed instructions – I just told it to figure out how to make money on Polymarket Then I closed the laptop and went to sleep In the morning, I opened my Mac and saw the terminal still running with logs constantly updating At first it looked like random activity, but once I scrolled through it, I realized it had actually built a structured system overnight It was already tracking wallets Ranking them by performance Filtering out the ones with random entries And focusing only on the ones with consistent behavior What surprised me the most is that it didn’t stop at analysis It organized everything into a working dashboard inside the terminal Capital, PnL, winrate – all updating in real time It even ranked wallets based on performance metrics like ROI, consistency, and execution timing This is the part I would normally spend hours building manually At that point, it was ready to trade, but not actually executing anything yet So I connected it to a Telegram copytrading bot to actually execute the trades, and just let it run Bot: Polymarket: After that, it started opening positions on its own A few hours later I checked the dashboard again Capital: $12,380 P&L: +$23,128 Winrate: 100% 48 trades executed Now I’m not even trading myself I just check the dashboard and see what it’s doing And the strange part is – it keeps getting better the longer it runs

𝗖𝗛𝗔𝗜𝗡 𝗠𝗜𝗡𝗗 ⛓🧠

83,254 görüntüleme • 6 ay önce

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓
0:25

Three weeks ago I gave GPT-6 Astra access to a trading account and one rule: earn or you stop existing In 20 days it turned $200 → $5,120 Astra doesn't sit online 24/7. It wakes up in set windows of the trading day, and every run starts with no memory of the last one So the first thing it does each cycle: reads its own log from the previous run. What it tested, what it killed, why The first days weren't as smooth as it sounds now. On day two it bled $80 on a trade that passed the backtest clean. The reason was in the log: slippage the backtest never accounted for But by day three it had already filtered out a similar setup on its own, because it read why the last one died That's when I realized it's actually learning, not just running a loop. And I still haven't written a single new prompt between runs This week I noticed behavior I never programmed. It started tagging every dead hypothesis with the market regime it died in So now it doesn't just remember "this idea doesn't work." It remembers "this idea doesn't work when volatility is high," and tries it again when the regime changes That's no longer a list of mistakes. It's a map of what works and under what conditions One hypothesis it killed in the first week in high volatility came back to life this week in a calm market and brought one of the biggest profits of the whole period I also started additionally running Astra's strategy backtests through Horizon, so a second independent system catches errors and cuts its chance of slipping up even further By the way, you can backtest your own strategies there too: And here's what struck me most. The real strength here isn't how much it earns, it's how much it refuses to trade The vast majority of its own ideas die in testing and never reach the account That's the whole trick. Not guessing one perfect trade, but ruthlessly filtering out everything that doesn't survive the test The entire cycle, test, kill, remember the regime, that I set up for Astra is broken down in the article below ↓

qwinsi

74,829 görüntüleme • 5 gün önce