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OPUS 5.5 + DOTS + JEV built an armed trading bot in 60 minutes for $6.80 three agents, 1.45M tokens, and the bot had a cap, a stop and a kill switch before its first order rules → data → backtest → armed Dario's Opus 5.5 writes the strategy... and the risk rules Dots pulls and cleans four years of candles Jev backtests it, sets the caps and is the only one allowed to send an order six switches flip one after another: strategy spec, data, bot code, backtest, paper trade, caps and kill switch. only the sixth lets it trade Jev holds three limits: a $2,000 daily loss cap, a 4% max position and an armed kill switch the backtest replayed 1,840 trades over four years. 58% green, 31% a year, worst drawdown -22% then fees, spread and slippage eat 45% of that, and 31% on paper becomes 17% in life at 17% a year, $100,000 a month needs $7.06M of capital so the $100k comes from the capital, and the bot only cost $6.80 not one live dollar has traded yet. sixty minutes proved the bot runs, whether it earns is still open 60 minutes, $6.80, three agents, zero live trades ↓show more

Sprytix

8,388 subscribers

13,497 просмотров • 7 дней назад •via X (Twitter)

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Комментарии: 4

Фото профиля Lenny@wesh

Lenny@wesh6 дней назад

What 🤣🤣🤣🤣

Фото профиля Grirk

Grirk7 дней назад

Interesting approach - how reliable is the backtest given limited token usage?

Фото профиля Lenny@wesh

Lenny@wesh6 дней назад

Scamnnn omg

Фото профиля Joselito 🌿

Joselito 🌿7 дней назад

Caps and kill switch before the first order.

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OPUS 5.5 + DOTS + JEV backtested 1,840 trades over four years in one hour... three models built one agent in 55 minutes for $4.92, and an agency would quote the same scope at $120,000 write → feed → test → fence → ship Dario's Sonnet 5.5 writes the agent itself: the policy, the prompts and six tool integrations Sam Altman's OpenAI Dots bring the data, the retrieval and 240 test cases Jev fences it, caps it and ships it the agent goes up in six layers, policy first and deploy last, and no layer starts before the one under it exists then all 240 cases run through it once. 221 pass, 19 fail, 92% against a bar of 90% the 240 cases and the 90% bar were written before the build, and the 19 failures are listed in the file the quote is 800 hours at $150, so $120,000. policy and prompts alone are 120 hours, $18,000 the same six parts took 1.14M tokens and $4.92 nobody has paid that $120,000 yet, it's what someone would charge to build it and keeping the agent true costs $59,160 a year, $37,440 of that is a human reading 270 tickets a week six parts, 55 minutes, $4.92, quoted at $120,000 ↓

OPUS 5.5 + DOTS + JEV backtested 1,840 trades over four years in one hour... three models built one agent in 55 minutes for $4.92, and an agency would quote the same scope at $120,000 write → feed → test → fence → ship Dario's Sonnet 5.5 writes the agent itself: the policy, the prompts and six tool integrations Sam Altman's OpenAI Dots bring the data, the retrieval and 240 test cases Jev fences it, caps it and ships it the agent goes up in six layers, policy first and deploy last, and no layer starts before the one under it exists then all 240 cases run through it once. 221 pass, 19 fail, 92% against a bar of 90% the 240 cases and the 90% bar were written before the build, and the 19 failures are listed in the file the quote is 800 hours at $150, so $120,000. policy and prompts alone are 120 hours, $18,000 the same six parts took 1.14M tokens and $4.92 nobody has paid that $120,000 yet, it's what someone would charge to build it and keeping the agent true costs $59,160 a year, $37,440 of that is a human reading 270 tickets a week six parts, 55 minutes, $4.92, quoted at $120,000 ↓

Sprytix

19,560 просмотров • 6 дней назад

Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:

Do you understand what just got open sourced? 5,800+ quant trading strategies for FREE along with its CODEBASE i gave these trading strategies to opus 5.5 + jev and it built me a PROFITABLE 24/7 trading agent here is how to use the repo and build AI trading bot: 1. clone the vault and get 5,806 trading strategies (spot, perps, grid, stat arb and market making) 2. open any strategy file, the math, the parameters and the source are already inside 3. point AgenKit at that file inside Claude Code with model as opus 5.5, one command and it reads the spec as ground truth 4. it will run a full engineering team and ships a production bot with risk controls 5. Jev is the live brain, it answers buy sell or hold in 81 milliseconds, calibrated, no hallucination 6. your engine sizes the trade from that confidence and fires it before the next block 7. a hard risk layer holds every limit, nothing goes live until you approve it 8. every night Opus 5.5 reviews the fills and ships a better version, so the agent improves while you sleep one model THINKS, the other REACTS and a strategy that sat locked in a vault now trades itself 24/7 the COMPLETE codebase and the EXACT Opus 5.5 + Jev trading system which I am running is in the article below:

Roan

95,787 просмотров • 12 дней назад

GROK BOT + JEV ran 70 trades in one month with a $200 cap on each, and a $10,000 account ended at $20,000... two models build the desk, six agents run it, and one rule sits on every order: lose $200 at most, take $400 when it's there scan → vet → size → fill → hold → go Elon's Grok Bot finds the trade and fires the order Jev fires the stop before the entry exists, so no order lands without one between them sits a desk of six agents. scan finds the setup, vet throws most of them out, size puts $200 at risk and never more fills gets the order in, risk holds every stop, and chief says go or wait nobody on that floor can widen a stop the spider in the video crawls the whole desk, from the first order down to the last line of the honest part 70 trades in the month. 40 hit the target at +$400, 30 hit the stop at -$200 40 × $400 is $16,000, 30 × $200 is $6,000, and the $10,000 left over doubles the account that's 57% of trades right at two-to-one, and everything else stands on that 57% same rule and same 70 trades at 50% right is +$7,000. at 40% it's +$2,800. at 33% the month ends at -$200 a stop caps the loss on a normal day, and a gap jumps straight over it. ten stops in a row is -$2,000, and no rule here makes that impossible six agents, 70 trades, $200 at risk on each, and 100% in the one month it was right 57% of the time ↓

GROK BOT + JEV ran 70 trades in one month with a $200 cap on each, and a $10,000 account ended at $20,000... two models build the desk, six agents run it, and one rule sits on every order: lose $200 at most, take $400 when it's there scan → vet → size → fill → hold → go Elon's Grok Bot finds the trade and fires the order Jev fires the stop before the entry exists, so no order lands without one between them sits a desk of six agents. scan finds the setup, vet throws most of them out, size puts $200 at risk and never more fills gets the order in, risk holds every stop, and chief says go or wait nobody on that floor can widen a stop the spider in the video crawls the whole desk, from the first order down to the last line of the honest part 70 trades in the month. 40 hit the target at +$400, 30 hit the stop at -$200 40 × $400 is $16,000, 30 × $200 is $6,000, and the $10,000 left over doubles the account that's 57% of trades right at two-to-one, and everything else stands on that 57% same rule and same 70 trades at 50% right is +$7,000. at 40% it's +$2,800. at 33% the month ends at -$200 a stop caps the loss on a normal day, and a gap jumps straight over it. ten stops in a row is -$2,000, and no rule here makes that impossible six agents, 70 trades, $200 at risk on each, and 100% in the one month it was right 57% of the time ↓

Sprytix

14,402 просмотров • 4 дней назад

ANTHROPIC LEAKED A $4.2M FILE WHERE 4 AGENTS BUILD THE JEV SYSTEM IN 60 MINUTES, DOING $100K A MONTH four agents cost $0, and the system they assemble in an hour generates $100K a month scout → builder → critic → operator → JEV every agent is pinned to one exact model version, and none of them runs on "latest" the scout on Haiku 4.5 reads the market, the builder on Sonnet 5 builds everything, the critic on Opus 5 decides what ships the operator runs Haiku through the batch API at half price - the one agent that never has to hurry the builder takes 59% of the whole hour, because it's the one writing, and writing costs 5x more than reading short answers are cheap answers - and the whole economics rests on that one rule pin the versions in ROOT.md and next month JEV behaves exactly the same point it at "latest" and the model underneath updates quietly, no error, and the answers start to drift $100K a month is 334 customers at $299 four versions, one system - pin them and JEV stays JEV save this and paste it into Claude Code - let it build you these 4 agents ↓

ANTHROPIC LEAKED A $4.2M FILE WHERE 4 AGENTS BUILD THE JEV SYSTEM IN 60 MINUTES, DOING $100K A MONTH four agents cost $0, and the system they assemble in an hour generates $100K a month scout → builder → critic → operator → JEV every agent is pinned to one exact model version, and none of them runs on "latest" the scout on Haiku 4.5 reads the market, the builder on Sonnet 5 builds everything, the critic on Opus 5 decides what ships the operator runs Haiku through the batch API at half price - the one agent that never has to hurry the builder takes 59% of the whole hour, because it's the one writing, and writing costs 5x more than reading short answers are cheap answers - and the whole economics rests on that one rule pin the versions in ROOT.md and next month JEV behaves exactly the same point it at "latest" and the model underneath updates quietly, no error, and the answers start to drift $100K a month is 334 customers at $299 four versions, one system - pin them and JEV stays JEV save this and paste it into Claude Code - let it build you these 4 agents ↓

Sprytix

37,977 просмотров • 18 дней назад

🚨 HOW I BUILT A FULLY AUTOMATED TRADING BOT IN 34 MINUTES WITHOUT WRITING A SINGLE LINE OF CODE > One person built a trading bot in 34 minutes > Didn't write a single line of code manually > Just talked to Claude Code > The bot detects market regime on its own: bull, bear, sideways > Switches strategies for each regime automatically > 13 safety checks before every single trade > Stop-loss, circuit breakers, position limits, correlation check > Walk-forward backtest - not curve fitting, real simulation > Connected to Alpaca - free paper trading > 45 out of 45 tests passing > Full pipeline: data -> regime -> signal -> risk -> order -> log Hedge funds pay millions for this, He paid $0

🚨 HOW I BUILT A FULLY AUTOMATED TRADING BOT IN 34 MINUTES WITHOUT WRITING A SINGLE LINE OF CODE > One person built a trading bot in 34 minutes > Didn't write a single line of code manually > Just talked to Claude Code > The bot detects market regime on its own: bull, bear, sideways > Switches strategies for each regime automatically > 13 safety checks before every single trade > Stop-loss, circuit breakers, position limits, correlation check > Walk-forward backtest - not curve fitting, real simulation > Connected to Alpaca - free paper trading > 45 out of 45 tests passing > Full pipeline: data -> regime -> signal -> risk -> order -> log Hedge funds pay millions for this, He paid $0

zostaff

364,565 просмотров • 6 месяцев назад

CLAUDE CODE + GROK + JEV - THE FILE WHERE THREE HEADS CLOSE A TICKET IN 9 MINUTES FOR $1.74 a month of this costs $208.80, the same 120 merges by hand are 300 hours and $28,500 ticket → plan → patch → tests → review → merge Claude Code on Opus 5.5 writes the patch, Grok reads it back and argues, Jev holds the gate and merges only on green tests three heads, and not one of them is allowed to push code to prod on its own 38 files read, 3 touched, +412 lines, 48 tests, and the first four failed, so the patch went back two minutes twenty later it was 48 of 48, and only then did Jev open the gate out of 120 patches Claude Code and Grok signed off on 78 straight away and had a fight about the other 22 those 22 arguments are the product, because a reviewer that never objects is just decoration the meter clocked $208.80 for the month, and to catch the human bill it would have to keep running for another 136 months and here is the brutal part of this file: 6 of the 94 merged tickets came back with bugs every one of them sat in the part of the code the test suite has never read, because coverage there is 61% two models in the same blind spot is not two opinions, it is one mistake signed twice save this and paste it into Claude Code - let it run your ticket through these three heads ↓

CLAUDE CODE + GROK + JEV - THE FILE WHERE THREE HEADS CLOSE A TICKET IN 9 MINUTES FOR $1.74 a month of this costs $208.80, the same 120 merges by hand are 300 hours and $28,500 ticket → plan → patch → tests → review → merge Claude Code on Opus 5.5 writes the patch, Grok reads it back and argues, Jev holds the gate and merges only on green tests three heads, and not one of them is allowed to push code to prod on its own 38 files read, 3 touched, +412 lines, 48 tests, and the first four failed, so the patch went back two minutes twenty later it was 48 of 48, and only then did Jev open the gate out of 120 patches Claude Code and Grok signed off on 78 straight away and had a fight about the other 22 those 22 arguments are the product, because a reviewer that never objects is just decoration the meter clocked $208.80 for the month, and to catch the human bill it would have to keep running for another 136 months and here is the brutal part of this file: 6 of the 94 merged tickets came back with bugs every one of them sat in the part of the code the test suite has never read, because coverage there is 61% two models in the same blind spot is not two opinions, it is one mistake signed twice save this and paste it into Claude Code - let it run your ticket through these three heads ↓

Sprytix

12,400 просмотров • 8 дней назад

+$18,000 PER MONTH. 🤯 THIS FUCKING GENIUS BUILT HIS OWN TRADING BOT WITH AI. Instead of spending months learning Python or building everything from scratch, he used an AI tutorial to create a bot that analyzes the market, finds setups, and executes trades based on his rules. Here’s how his approach works ↓ → AI helps build and refine the bot → The bot scans the market for specific setups → It filters trades using predefined conditions → Entry + stop-loss + take-profit are automated → He lets the system handle the execution The crazy part? He didn’t need to become a professional programmer first. He used AI as the developer and turned his trading strategy into an automated system. Obviously, $18K/month isn’t guaranteed — markets can move against you and bots can lose money. But the idea is fucking powerful: → Build the strategy → Give it to AI → Automate the rules → Backtest everything → Let the system handle the repetitive work $18K A MONTH FROM A BOT HE BUILT WITH AI. Comment « crypto » and I'll send you the bot

+$18,000 PER MONTH. 🤯 THIS FUCKING GENIUS BUILT HIS OWN TRADING BOT WITH AI. Instead of spending months learning Python or building everything from scratch, he used an AI tutorial to create a bot that analyzes the market, finds setups, and executes trades based on his rules. Here’s how his approach works ↓ → AI helps build and refine the bot → The bot scans the market for specific setups → It filters trades using predefined conditions → Entry + stop-loss + take-profit are automated → He lets the system handle the execution The crazy part? He didn’t need to become a professional programmer first. He used AI as the developer and turned his trading strategy into an automated system. Obviously, $18K/month isn’t guaranteed — markets can move against you and bots can lose money. But the idea is fucking powerful: → Build the strategy → Give it to AI → Automate the rules → Backtest everything → Let the system handle the repetitive work $18K A MONTH FROM A BOT HE BUILT WITH AI. Comment « crypto » and I'll send you the bot

Niche trader

89,471 просмотров • 1 месяц назад

ANTHROPIC LEAKED A FILE WHERE 5 AGENTS WITH JEV BUILD A $1.2M A YEAR SYSTEM IN 30 MINUTES you pay $3.70 and walk away with an asset that can do $1.2M a year repo → data model → API → UI → evals no layer starts until the one under it is closed, and that edge is what turns five scripts into a system the operator on Haiku 4.5 brings up the repo and CI, the architect on Opus 5 lays down the data model and migrations two builders on Sonnet 5 run API and UI in parallel, and the reviewer on Opus 5.5 can keep finished code out of prod the evals were sitting in the file before the first line of code existed at minute zero there was not a single file, by the end it is a working product with payments and tests five agents cover in half an hour what a human team bills for a quarter JEV hands one line between layers instead of the whole history, so the next agent never re-reads the project and the run costs $3.70 instead of $370 the build takes 30 minutes, the first customers show up weeks later save this and paste it into Claude Code - let it assemble those 5 layers for you ↓

ANTHROPIC LEAKED A FILE WHERE 5 AGENTS WITH JEV BUILD A $1.2M A YEAR SYSTEM IN 30 MINUTES you pay $3.70 and walk away with an asset that can do $1.2M a year repo → data model → API → UI → evals no layer starts until the one under it is closed, and that edge is what turns five scripts into a system the operator on Haiku 4.5 brings up the repo and CI, the architect on Opus 5 lays down the data model and migrations two builders on Sonnet 5 run API and UI in parallel, and the reviewer on Opus 5.5 can keep finished code out of prod the evals were sitting in the file before the first line of code existed at minute zero there was not a single file, by the end it is a working product with payments and tests five agents cover in half an hour what a human team bills for a quarter JEV hands one line between layers instead of the whole history, so the next agent never re-reads the project and the run costs $3.70 instead of $370 the build takes 30 minutes, the first customers show up weeks later save this and paste it into Claude Code - let it assemble those 5 layers for you ↓

Sprytix

14,208 просмотров • 14 дней назад

I GAVE MY GROK BOT $500 BY EVENING IT WAS ALREADY $8,000 yesterday I vibe-coded a bot for trading on Robinhood Chain took me literally 2 hours here’s how it works: five agents, one position at a time, one veto could kill any trade at one point 5 losses took the balance down to $286 then Grok analyzed the trades and changed the strategy next trade was $HOODAI with a $75 entry the bot spotted 3 connected wallets getting ready to sell it exited before the dump and locked a 31x after that it rotated profits into 2 more launches and rejected 294 coins $500 turned into $7,931 by the evening all from a bot I built in 2 hours

I GAVE MY GROK BOT $500 BY EVENING IT WAS ALREADY $8,000 yesterday I vibe-coded a bot for trading on Robinhood Chain took me literally 2 hours here’s how it works: five agents, one position at a time, one veto could kill any trade at one point 5 losses took the balance down to $286 then Grok analyzed the trades and changed the strategy next trade was $HOODAI with a $75 entry the bot spotted 3 connected wallets getting ready to sell it exited before the dump and locked a 31x after that it rotated profits into 2 more launches and rejected 294 coins $500 turned into $7,931 by the evening all from a bot I built in 2 hours

winkle.

20,162 просмотров • 1 месяц назад

OPUS 5.5 + DOTS + JEV closed 312 tickets in one week while 25 engineers closed 96... that org chart costs $343,750 a month in payroll the agent costs $2,055 a month in tokens, 167x less the code comes from Opus 5.5, the tests from Dots, and nothing reaches done without Jev's review only 410 of the team's 1,000 weekly hours are writing code, and that 41% is what the agent takes the other 590 are meetings, review, on-call, customers and deciding on the other side is a business billing $120,198 a month, 402 people paying $299 after fees, hosting, the agent and 3 humans, $66,887 is left before tax and $120,198 pays for 8.7 engineers at most, so that business never had 25 to replace ↓

OPUS 5.5 + DOTS + JEV closed 312 tickets in one week while 25 engineers closed 96... that org chart costs $343,750 a month in payroll the agent costs $2,055 a month in tokens, 167x less the code comes from Opus 5.5, the tests from Dots, and nothing reaches done without Jev's review only 410 of the team's 1,000 weekly hours are writing code, and that 41% is what the agent takes the other 590 are meetings, review, on-call, customers and deciding on the other side is a business billing $120,198 a month, 402 people paying $299 after fees, hosting, the agent and 3 humans, $66,887 is left before tax and $120,198 pays for 8.7 engineers at most, so that business never had 25 to replace ↓

Sprytix

18,764 просмотров • 6 дней назад

GROK BOT MADE ME 3 TRADING BOTS IN 15 MINUTES +$2,353 in my wallet the next day, I quit my job that same day. I wrote one prompt and answered a few questions. Grok Bot took three strategies - MACD, RSI + VWAP, CVD divergence. Assigned each bot its own. The first one catches momentum - sees when volume picks up and gets in before the crowd wakes up. The second one trades reversals - waits for everyone to panic and bets against them. The third one scans divergences - when price says one thing but money does another, it follows the money. Built the structure itself, wrote the backtest, ran each strategy on historical data, set up the risk manager, deployed - all on its own. Three bots, three accounts, each trades differently - they don't know about each other. I named it Cabal. Started with $20, then $70, then $140, then $320, then $2,353 in a day. Citadel, Jane Street, Two Sigma have been trading with bots for years - they don't feel fear, greed, or FOMO - they only listen to algorithms. The market rewards systems.

GROK BOT MADE ME 3 TRADING BOTS IN 15 MINUTES +$2,353 in my wallet the next day, I quit my job that same day. I wrote one prompt and answered a few questions. Grok Bot took three strategies - MACD, RSI + VWAP, CVD divergence. Assigned each bot its own. The first one catches momentum - sees when volume picks up and gets in before the crowd wakes up. The second one trades reversals - waits for everyone to panic and bets against them. The third one scans divergences - when price says one thing but money does another, it follows the money. Built the structure itself, wrote the backtest, ran each strategy on historical data, set up the risk manager, deployed - all on its own. Three bots, three accounts, each trades differently - they don't know about each other. I named it Cabal. Started with $20, then $70, then $140, then $320, then $2,353 in a day. Citadel, Jane Street, Two Sigma have been trading with bots for years - they don't feel fear, greed, or FOMO - they only listen to algorithms. The market rewards systems.

Archive

280,527 просмотров • 1 месяц назад

ANTHROPIC LEAKED A FILE THAT TURNS AN EMPTY FOLDER INTO A PRODUCT WITH A PRICE IN 60 MINUTES AND AIMS AT $100,000 A MONTH in one hour you get a product with a price on it - a startup burns a quarter and $80,000 for that same hour problem → shape → surface → price → machine → back to the problem you write only the first 12 minutes: who hurts, and what it costs them right now Opus 5 gives it a shape and then a price, three Sonnet 5 build three versions of the page Haiku 4.5 stands up the machine: delivery, the queue and the weekly numbers $100,000 a month is 1,000 customers at $100, or 10 at $10,000 same number, four completely different businesses which of those four rows you're standing on gets decided in minutes 36-48, not a year from now at minute sixty you have a product, a price and a machine - and 0 customers the hour compresses the first clock to nothing, it never touches the second one the hour makes the product, it does not make the demand save this and paste it into Claude Code - let it build you these 5 blocks ↓

ANTHROPIC LEAKED A FILE THAT TURNS AN EMPTY FOLDER INTO A PRODUCT WITH A PRICE IN 60 MINUTES AND AIMS AT $100,000 A MONTH in one hour you get a product with a price on it - a startup burns a quarter and $80,000 for that same hour problem → shape → surface → price → machine → back to the problem you write only the first 12 minutes: who hurts, and what it costs them right now Opus 5 gives it a shape and then a price, three Sonnet 5 build three versions of the page Haiku 4.5 stands up the machine: delivery, the queue and the weekly numbers $100,000 a month is 1,000 customers at $100, or 10 at $10,000 same number, four completely different businesses which of those four rows you're standing on gets decided in minutes 36-48, not a year from now at minute sixty you have a product, a price and a machine - and 0 customers the hour compresses the first clock to nothing, it never touches the second one the hour makes the product, it does not make the demand save this and paste it into Claude Code - let it build you these 5 blocks ↓

Noisy

14,595 просмотров • 1 месяц назад

Jamie Dimon made $39 million last year to decide who does what at JPMorgan. One AI agent on Grok Bot does that job for $300 a month, and it hires the rest of the agents itself. Stop trying to manage any of it yourself. In 2026 a swarm of AI agents runs a team better than you do. I told my chief agent one thing. Make money or I switch you off. It took $1,000 to $3,900 in 15 hours. Every fill is on chain and the address is in my profile. I never picked a trade. I never picked a team either. The agents it hired: > a scout that hunts for what is moving and where the money is going > a forensics agent that works out who is really behind the buying > a reader that watches the mood on X, and it sees every post on this app because it is Grok > a risk agent whose only power is saying no, and its no beats every score on the desk > a treasurer that holds the money rules and the kill switch > a trader that writes the exit before it writes the entry None of those were my idea. I handed out one job title and the chief filled in the rest on its own. And the swarm is different every hour. Agents appear for one job, do it, and are gone before I know they existed. A hedge fund pays millions a year for the same org chart, and almost all of that goes on salaries. Every prompt and the whole story of my Grok Bot fund is in the post below ↓ Save it before you try to run one of these by hand.

cvxv666

28,886 просмотров • 1 месяц назад

ANTHROPIC LEAKED A FILE WHERE 4 AGENTS BUILD AN AI COMPANY IN 60 MINUTES THAT DOES $100K A MONTH i hunted this file for over 5 months, and inside it $0 of development gives you an AI business that pulled 70k+ in its first month product → growth → support → ops four departments, four pinned versions, and no agent ever reaches into another one's branch product on Sonnet 5 eats $2.57, growth on Opus 5.5 takes $1.00, support on Haiku 4.5 costs $0.22, ops on Opus 5.5 closes the hour for $0.80 the full hour across all four costs $4.59, and the most expensive department is cheaper than a coffee in 60 minutes twelve things exist that did not an hour earlier: the app, the API, the docs, the landing page, the ads, the emails, the help centre, billing, metrics and alerts three per department, and every one is a file, a page or a rule, not a slide with a plan payroll for four departments like this is $31,000 a month, the tokens are $450 the agents do the typing, deciding what to build is still on you 120 people at $99, 200 at $299, 29 at $999 make $100,651 a month the middle plan carries more than half the month, and it is what turns this into $1.2M a year the hour builds the company, the first customers decide whether it is one save this and paste it into Claude Code - let it stand up these 4 departments for you ↓

ANTHROPIC LEAKED A FILE WHERE 4 AGENTS BUILD AN AI COMPANY IN 60 MINUTES THAT DOES $100K A MONTH i hunted this file for over 5 months, and inside it $0 of development gives you an AI business that pulled 70k+ in its first month product → growth → support → ops four departments, four pinned versions, and no agent ever reaches into another one's branch product on Sonnet 5 eats $2.57, growth on Opus 5.5 takes $1.00, support on Haiku 4.5 costs $0.22, ops on Opus 5.5 closes the hour for $0.80 the full hour across all four costs $4.59, and the most expensive department is cheaper than a coffee in 60 minutes twelve things exist that did not an hour earlier: the app, the API, the docs, the landing page, the ads, the emails, the help centre, billing, metrics and alerts three per department, and every one is a file, a page or a rule, not a slide with a plan payroll for four departments like this is $31,000 a month, the tokens are $450 the agents do the typing, deciding what to build is still on you 120 people at $99, 200 at $299, 29 at $999 make $100,651 a month the middle plan carries more than half the month, and it is what turns this into $1.2M a year the hour builds the company, the first customers decide whether it is one save this and paste it into Claude Code - let it stand up these 4 departments for you ↓

Sprytix

25,783 просмотров • 13 дней назад

Jev + Opus 5.5: Anthropic's new model beats GPT-6 Astra for 1/5 the cost, and 4 API changes will 400 your agent before it writes a single line I pulled these 10 steps from the migration docs so you don't learn them in production step 1 → $4 / $20 per 1M. Opus 5 was $5 / $25. cache reads dropped from $0.50 to $0.20 step 2 → 66.4% on Terminal-Bench 4.0 vs GPT-6 Astra 57.9% and Opus 5 52.3%. +14.1 points in one release, and on FrontierCode it beats Astra at default effort for 1/5 the cost step 3 → thinking can't be turned off anymore. send thinking: disabled and you get a 400. drop the field, set effort step 4 → tool_choice any and tool are gone. 400. switch to auto + strict step 5 → edit anything above a thinking block and the request dies. append only, or opt into drop_block step 6 → computer_20251124 is dead on the API. 400. move to computer_toolset_20260801 step 7 → the quiet one: default effort fell from high to medium. your agent thinks less than you set it up to and nothing tells you step 8 → hop Opus 5.5 → Sonnet 5 → Opus 5.5 and you pay 4.36 instead of 3.32. +31%, the cache dies and Sonnet can't read Opus's reasoning step 9 → change effort at the top of the request and the cache is gone. Jev sets it per message and the cache stays step 10 → switch fast - standard mid-session and it's a full cache miss. Jev picks speed once, on turn one one model, three knobs, zero 400s. that is Jev + Opus 5.5 send this to your Claude Code before you touch the model ID, then read my full Jev deep dive in the article below ↓

Jev + Opus 5.5: Anthropic's new model beats GPT-6 Astra for 1/5 the cost, and 4 API changes will 400 your agent before it writes a single line I pulled these 10 steps from the migration docs so you don't learn them in production step 1 → $4 / $20 per 1M. Opus 5 was $5 / $25. cache reads dropped from $0.50 to $0.20 step 2 → 66.4% on Terminal-Bench 4.0 vs GPT-6 Astra 57.9% and Opus 5 52.3%. +14.1 points in one release, and on FrontierCode it beats Astra at default effort for 1/5 the cost step 3 → thinking can't be turned off anymore. send thinking: disabled and you get a 400. drop the field, set effort step 4 → tool_choice any and tool are gone. 400. switch to auto + strict step 5 → edit anything above a thinking block and the request dies. append only, or opt into drop_block step 6 → computer_20251124 is dead on the API. 400. move to computer_toolset_20260801 step 7 → the quiet one: default effort fell from high to medium. your agent thinks less than you set it up to and nothing tells you step 8 → hop Opus 5.5 → Sonnet 5 → Opus 5.5 and you pay 4.36 instead of 3.32. +31%, the cache dies and Sonnet can't read Opus's reasoning step 9 → change effort at the top of the request and the cache is gone. Jev sets it per message and the cache stays step 10 → switch fast - standard mid-session and it's a full cache miss. Jev picks speed once, on turn one one model, three knobs, zero 400s. that is Jev + Opus 5.5 send this to your Claude Code before you touch the model ID, then read my full Jev deep dive in the article below ↓

Carnage

16,674 просмотров • 16 дней назад

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

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

kiosa

90,737 просмотров • 2 дней назад

whoever leaked this has bigger balls than sense someone in the Grok Bot beta posted one night of receipts and deleted them an hour later: 6 agents, 1,284 analysis jobs between midnight and 6am, total bill $4.98. same account, a second bot sent 100 outreach messages on X and came back with 41 signups, and a third closed 3 deals and now pays for its own upgrades out of what it earns i gave one of mine a research brief first and watched it hire four agents for the job and fire three of them by lunch, already past the point where i'd have hired a human this is the one-employee company, the layer where the human owns and the agents do everything else, and it installs into the Grok Bot sub you already pay for: - give one bot the hiring seat and nothing else: it spins up agents per task and kills them when the task closes, so the roster is never bigger than tonight's work - price the night before you scale it: 1,284 jobs for $4.98 is the number to beat, and a routine that can't come in under a junior's hourly rate does not get a schedule - put outreach on its own agent with a daily cap: 100 messages and 41 signups is what a clean list does, a dirty one gets the account banned by message 30 - let a bot spend only through a one-time card with a hard limit, the official client already supports it, so the worst night costs the card's ceiling and nothing else - keep one closer bot funding its own upgrades: if it can't pay for its next tool out of what it closed, it isn't a business, it's a demo turns out the roster does not travel: the biggest win came off a research desk where the brain fired agents daily, and the worst blowup off a sales bot that kept every hire on forever my position, and it is the arguable one: this is a beta, and a beta is already running companies with one human inside. the finished product doesn't make that more true, it makes it cheaper bookmark this, the three moves that set up the hiring seat, the card limit and the first paying agent are in the post below ↓

whoever leaked this has bigger balls than sense someone in the Grok Bot beta posted one night of receipts and deleted them an hour later: 6 agents, 1,284 analysis jobs between midnight and 6am, total bill $4.98. same account, a second bot sent 100 outreach messages on X and came back with 41 signups, and a third closed 3 deals and now pays for its own upgrades out of what it earns i gave one of mine a research brief first and watched it hire four agents for the job and fire three of them by lunch, already past the point where i'd have hired a human this is the one-employee company, the layer where the human owns and the agents do everything else, and it installs into the Grok Bot sub you already pay for: - give one bot the hiring seat and nothing else: it spins up agents per task and kills them when the task closes, so the roster is never bigger than tonight's work - price the night before you scale it: 1,284 jobs for $4.98 is the number to beat, and a routine that can't come in under a junior's hourly rate does not get a schedule - put outreach on its own agent with a daily cap: 100 messages and 41 signups is what a clean list does, a dirty one gets the account banned by message 30 - let a bot spend only through a one-time card with a hard limit, the official client already supports it, so the worst night costs the card's ceiling and nothing else - keep one closer bot funding its own upgrades: if it can't pay for its next tool out of what it closed, it isn't a business, it's a demo turns out the roster does not travel: the biggest win came off a research desk where the brain fired agents daily, and the worst blowup off a sales bot that kept every hire on forever my position, and it is the arguable one: this is a beta, and a beta is already running companies with one human inside. the finished product doesn't make that more true, it makes it cheaper bookmark this, the three moves that set up the hiring seat, the card limit and the first paying agent are in the post below ↓

Argona

43,764 просмотров • 1 месяц назад

OPUS 5.5 + SONNET 5.5 + JEV + OPENAI DOTS: one spider pulled 40 AI engineers out of them and put a business on autopilot 40 seats, 4 teams of 10, and every seat is filled by a model a human team of 40 covers 1,600 hours a week. these seats stay on for all 168, which is 6,720 hours, 4.2x more from the same headcount at $13,750 a seat, 40 human engineers cost $550,000 a month in payroll before anyone writes a line of code the web under them has 16 threads: sales, leads, ads, CRM, support, billing, finance, legal, hiring and 7 more a task lands on any thread, and 10 routing engineers on Jev decide which agent gets it 10 planners on Opus 5.5 keep the roadmap and review the work in plan.md, while 10 builders on Sonnet 5.5 ship code every day from build py 10 more on OpenAI Dots run ops and never switch off, 24/7 the counter stops at 40 of 40, the status flips to live, and autopilot turns on ↓

OPUS 5.5 + SONNET 5.5 + JEV + OPENAI DOTS: one spider pulled 40 AI engineers out of them and put a business on autopilot 40 seats, 4 teams of 10, and every seat is filled by a model a human team of 40 covers 1,600 hours a week. these seats stay on for all 168, which is 6,720 hours, 4.2x more from the same headcount at $13,750 a seat, 40 human engineers cost $550,000 a month in payroll before anyone writes a line of code the web under them has 16 threads: sales, leads, ads, CRM, support, billing, finance, legal, hiring and 7 more a task lands on any thread, and 10 routing engineers on Jev decide which agent gets it 10 planners on Opus 5.5 keep the roadmap and review the work in plan.md, while 10 builders on Sonnet 5.5 ship code every day from build py 10 more on OpenAI Dots run ops and never switch off, 24/7 the counter stops at 40 of 40, the status flips to live, and autopilot turns on ↓

Noisy

14,894 просмотров • 5 дней назад

ANTHROPIC LEAKED A $4.4M FILE WHERE 4 AGENTS BUILD A BUSINESS DOING $120K A MONTH IN 20 MINUTES four agents run for $0, and the business on top of them pulls $120,000 a month without a single salary scout → builder → seller → critic → back to the scout the scout finds who buys and at what price, the builder makes the page and the checkout the seller writes the offer and the outreach, the critic decides whether it ships at all Haiku 4.5 searches, two Sonnet 5 build and sell, Opus 5 passes the verdict no agent grades its own work - each one is checked by the next in the ring and the ring closes: the critic checks the scout one agent always gives itself an A, four in a ring never do a kickoff meeting alone takes 60 minutes, here the whole business is done in 20 $120,000 a month is 401 customers at $299 but all four come from one model family, and they share one blind spot the agents catch the errors at the edges, the one down the middle only you catch save this and paste it into Claude Code - let it build you this ring ↓

ANTHROPIC LEAKED A $4.4M FILE WHERE 4 AGENTS BUILD A BUSINESS DOING $120K A MONTH IN 20 MINUTES four agents run for $0, and the business on top of them pulls $120,000 a month without a single salary scout → builder → seller → critic → back to the scout the scout finds who buys and at what price, the builder makes the page and the checkout the seller writes the offer and the outreach, the critic decides whether it ships at all Haiku 4.5 searches, two Sonnet 5 build and sell, Opus 5 passes the verdict no agent grades its own work - each one is checked by the next in the ring and the ring closes: the critic checks the scout one agent always gives itself an A, four in a ring never do a kickoff meeting alone takes 60 minutes, here the whole business is done in 20 $120,000 a month is 401 customers at $299 but all four come from one model family, and they share one blind spot the agents catch the errors at the edges, the one down the middle only you catch save this and paste it into Claude Code - let it build you this ring ↓

Sprytix

43,407 просмотров • 19 дней назад

Jev builds the BEST trading agents and someone JUST open sourced QuantDinger, a full AI Trading OS with Jev your strategy no longer fires blind, every entry gets reviewed by Jev first and bad trades die before they cost you here is how you set it up: 1. run one install command, it asks for your admin login, generates every secret, and boots the whole stack in docker 2. start in paper mode, backtest your Python strategy on server side data before a single real dollar moves 3. drop your Jev API key into System Settings, AI / LLM, and flip on AI Decision Filter 4. now every entry gets scored across evidence quality, signal consistency, market regime, account risk, and execution quality 5. Jev returns a typed decision with full probabilities and confidence, not a paragraph of text you have to parse 6. rejected entries NEVER reach the exchange, and every decision lands in an audit timeline with the reason and latency the whole point is this repo hands you the HARDEST part for FREE > the risk gate between your strategy and your money Bookmark it, then read my step by step guide below on making money from your own agent team for free

Jev builds the BEST trading agents and someone JUST open sourced QuantDinger, a full AI Trading OS with Jev your strategy no longer fires blind, every entry gets reviewed by Jev first and bad trades die before they cost you here is how you set it up: 1. run one install command, it asks for your admin login, generates every secret, and boots the whole stack in docker 2. start in paper mode, backtest your Python strategy on server side data before a single real dollar moves 3. drop your Jev API key into System Settings, AI / LLM, and flip on AI Decision Filter 4. now every entry gets scored across evidence quality, signal consistency, market regime, account risk, and execution quality 5. Jev returns a typed decision with full probabilities and confidence, not a paragraph of text you have to parse 6. rejected entries NEVER reach the exchange, and every decision lands in an audit timeline with the reason and latency the whole point is this repo hands you the HARDEST part for FREE > the risk gate between your strategy and your money Bookmark it, then read my step by step guide below on making money from your own agent team for free

Khairallah AL-Awady

11,730 просмотров • 18 дней назад