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THIS IS A PURE F*CKING GOLDMINE the best REA trick almost nobody runs yet. The idea: REA takes the app apart, Jev keeps only what's proven, and a fresh Opus 5.5 rebuilds the feature from that alone. Nothing gets copied and no guess makes it into the code, because the builder never sees the original, only the spec Jev signed off on. You just connect REA like this: npx rea-agents setup here's the split: → REA reads the feature down to the binary and writes every rule into SPEC.md → Jev checks each rule against REA's evidence and sends back anything unproven → a fresh Opus 5.5 session gets only SPEC.md and rebuilds the feature → Haiku 5.5 runs the tests against 3 intentionally broken versions Opus only shows up at three moments: → rebuilding from the approved spec → the checks Jev marks as unclear → signing off before anything ships REA reads. Jev judges. Opus rebuilds. That's the clean-room rule in one line: the model that saw the original never writes the code, and the model that writes the code never sees the original. the full tree > REA reads the app and writes SPEC.md with evidence for every rule > Jev approves or returns each rule > Opus 5.5 rebuilds from the approved SPEC.md in a fresh session > Haiku 5.5 runs the tests and the 3 broken versions > Jev routes unclear checks back to Opus > /goal keeps going until all 3 broken versions fail paste the tree and this prompt into Claude Code ↓ "Set up a clean-room rebuild around this tree: 1. Use REA to investigate [FEATURE] in [APP]. Write every behavior and rule into SPEC.md, each with the evidence REA found. 2. Send each rule to Jev. Any rule without evidence goes back to REA. 3. Start a fresh session with only SPEC.md. Rebuild the feature on Opus 5.5 without opening the original. 4. Write tests on Haiku 5.5 plus 3 intentionally broken versions. Use /goal until the rebuild passes and all 3 broken versions fail. Show me SPEC.md first. Nothing gets built until I say go." ↳ save it, it'll come in handy.

THIS IS A PURE F*CKING GOLDMINE the best REA trick almost nobody runs yet. The idea: REA takes the app apart, Jev keeps only what's proven, and a fresh Opus 5.5 rebuilds the feature from that alone. Nothing gets copied and no guess makes it into the code, because the builder never sees the original, only the spec Jev signed off on. You just connect REA like this: npx rea-agents setup here's the split: → REA reads the feature down to the binary and writes every rule into SPEC.md → Jev checks each rule against REA's evidence and sends back anything unproven → a fresh Opus 5.5 session gets only SPEC.md and rebuilds the feature → Haiku 5.5 runs the tests against 3 intentionally broken versions Opus only shows up at three moments: → rebuilding from the approved spec → the checks Jev marks as unclear → signing off before anything ships REA reads. Jev judges. Opus rebuilds. That's the clean-room rule in one line: the model that saw the original never writes the code, and the model that writes the code never sees the original. the full tree > REA reads the app and writes SPEC.md with evidence for every rule > Jev approves or returns each rule > Opus 5.5 rebuilds from the approved SPEC.md in a fresh session > Haiku 5.5 runs the tests and the 3 broken versions > Jev routes unclear checks back to Opus > /goal keeps going until all 3 broken versions fail paste the tree and this prompt into Claude Code ↓ "Set up a clean-room rebuild around this tree: 1. Use REA to investigate [FEATURE] in [APP]. Write every behavior and rule into SPEC.md, each with the evidence REA found. 2. Send each rule to Jev. Any rule without evidence goes back to REA. 3. Start a fresh session with only SPEC.md. Rebuild the feature on Opus 5.5 without opening the original. 4. Write tests on Haiku 5.5 plus 3 intentionally broken versions. Use /goal until the rebuild passes and all 3 broken versions fail. Show me SPEC.md first. Nothing gets built until I say go." ↳ save it, it'll come in handy.

59,706 次观看

Found an insider on stranger things market and if this guy is right, eleven dies in season 5 account created in december specifically for this event buying positions a week before season 5 drops dumped $34,000 on eleven's death has a distraction bet of $500 on NFL rams vs seahawks match coverage is very obvious, classic technique makes 1-2 normal bets so algorithms don't catch insider movement if he's right, he makes $20k 85-90% this is an insider probably someone with access to leaked finale script or rough cut footage his profile:

Found an insider on stranger things market and if this guy is right, eleven dies in season 5 account created in december specifically for this event buying positions a week before season 5 drops dumped $34,000 on eleven's death has a distraction bet of $500 on NFL rams vs seahawks match coverage is very obvious, classic technique makes 1-2 normal bets so algorithms don't catch insider movement if he's right, he makes $20k 85-90% this is an insider probably someone with access to leaked finale script or rough cut footage his profile:

480,931 次观看

Elon after realizing a random guy explained Grok bot better than the entire xAI team

Elon after realizing a random guy explained Grok bot better than the entire xAI team

106,379 次观看

jev + opus 5.5 for design is insane... a launch video used to mean an agency, weeks of back and forth and a fat invoice. now Opus 5.5 makes the video, the ad cuts and the landing page from one brief. the video below: made in 3 minutes. at 0:09 it drops into edit mode - dot grid, selection handles, pixel sizes on every card - so you see exactly how it was built. but most people get the same mid result: centered text, gradient background, everything fading in. That's Opus's default look when it has nothing to copy. here's the pipeline that makes it look like an agency did it: 1. Build the brand brain first → logo, colors, fonts, screenshots of your real product, your best ad copy → have Opus turn it into a DESIGN.md. The video and the site both follow it 2. Steal a reference, don't describe one → pick 1-2 launch videos with the pacing you want → "make it like this" beats "make it modern and punchy" every time 3. Give it a renderer → install HyperFrames or Remotion: every scene is code, rendered straight to mp4 → a change = one line and a re-render, not starting over 4. Use real components → buttons, cards and UI from 21st_dev instead of empty placeholder boxes → fake-looking UI is the fastest way to look cheap 5. Storyboard before motion → 3 storyboard options, you pick one, then one still per scene → fixing a still takes seconds, fixing a render takes a re-render 6. Run it as a 4-person team + jev → breakdown: pull pacing, type and transitions from the reference → storyboard: the 3 options and the stills → build: every scene in code, music, draft render → review: watch it second by second, list the problems, touch nothing → jev: scores every review note against DESIGN.md (fix now / later / skip) in milliseconds, so build only touches what matters 7. Cut the ads from the same project → same scenes, different hooks: pain first, result first, offer first → 9:16 for reels, 1:1 for feed, 16:9 for YouTube. No reshoots 8. Ship the landing page from the same DESIGN.md → the hero is a looping cut of the launch video → before launch: mobile, every button state, zero placeholder text 9. Give notes like a director → "slow this zoom by half", "hard cut here", "push in on the button" → "make it better" gets random changes paste this into Claude Code 👇 "Read my brand folder and write DESIGN.md. Break down the reference video I give you: pacing, type, transitions. Show me 3 storyboards, then one still per scene for the one I pick. Build every scene in HyperFrames, add music, render a draft. Review it second by second without editing and list the problems. Pass every note to Jev and have it score each one against DESIGN.md: fix now / later / skip. Fix only "fix now". After I approve: cut 3 ad variants with different hooks in 9:16, 1:1 and 16:9, then build the landing page from the same DESIGN.md with the video loop as the hero." everyone has the same model. bookmark this and start using the edge.

jev + opus 5.5 for design is insane... a launch video used to mean an agency, weeks of back and forth and a fat invoice. now Opus 5.5 makes the video, the ad cuts and the landing page from one brief. the video below: made in 3 minutes. at 0:09 it drops into edit mode - dot grid, selection handles, pixel sizes on every card - so you see exactly how it was built. but most people get the same mid result: centered text, gradient background, everything fading in. That's Opus's default look when it has nothing to copy. here's the pipeline that makes it look like an agency did it: 1. Build the brand brain first → logo, colors, fonts, screenshots of your real product, your best ad copy → have Opus turn it into a DESIGN.md. The video and the site both follow it 2. Steal a reference, don't describe one → pick 1-2 launch videos with the pacing you want → "make it like this" beats "make it modern and punchy" every time 3. Give it a renderer → install HyperFrames or Remotion: every scene is code, rendered straight to mp4 → a change = one line and a re-render, not starting over 4. Use real components → buttons, cards and UI from 21st_dev instead of empty placeholder boxes → fake-looking UI is the fastest way to look cheap 5. Storyboard before motion → 3 storyboard options, you pick one, then one still per scene → fixing a still takes seconds, fixing a render takes a re-render 6. Run it as a 4-person team + jev → breakdown: pull pacing, type and transitions from the reference → storyboard: the 3 options and the stills → build: every scene in code, music, draft render → review: watch it second by second, list the problems, touch nothing → jev: scores every review note against DESIGN.md (fix now / later / skip) in milliseconds, so build only touches what matters 7. Cut the ads from the same project → same scenes, different hooks: pain first, result first, offer first → 9:16 for reels, 1:1 for feed, 16:9 for YouTube. No reshoots 8. Ship the landing page from the same DESIGN.md → the hero is a looping cut of the launch video → before launch: mobile, every button state, zero placeholder text 9. Give notes like a director → "slow this zoom by half", "hard cut here", "push in on the button" → "make it better" gets random changes paste this into Claude Code 👇 "Read my brand folder and write DESIGN.md. Break down the reference video I give you: pacing, type, transitions. Show me 3 storyboards, then one still per scene for the one I pick. Build every scene in HyperFrames, add music, render a draft. Review it second by second without editing and list the problems. Pass every note to Jev and have it score each one against DESIGN.md: fix now / later / skip. Fix only "fix now". After I approve: cut 3 ad variants with different hooks in 9:16, 1:1 and 16:9, then build the landing page from the same DESIGN.md with the video loop as the hero." everyone has the same model. bookmark this and start using the edge.

39,253 次观看

dead internet theory is real. 5 videos. 209k followers. $3,500+ from monetization. please don't buy a $299 course. > create a character sheet > recreate viral formats with AI then brands start buying that audience: > music labels - to get their track in your videos > brands that fit the character - sunglasses, hair pomade, energy drinks > AI tools – affiliate cut on every subscription a new artist already hit me up: $400 for every 100k views on his track. one video at 1M = $4,000. the character never gets tired, never asks for a fee and looks the same in every video. i do this with Picsart AI one content workflow. multiple income streams to test.

dead internet theory is real. 5 videos. 209k followers. $3,500+ from monetization. please don't buy a $299 course. > create a character sheet > recreate viral formats with AI then brands start buying that audience: > music labels - to get their track in your videos > brands that fit the character - sunglasses, hair pomade, energy drinks > AI tools – affiliate cut on every subscription a new artist already hit me up: $400 for every 100k views on his track. one video at 1M = $4,000. the character never gets tired, never asks for a fee and looks the same in every video. i do this with Picsart AI one content workflow. multiple income streams to test.

29,858 次观看

holy sh*t. Jev's founder Diogo Almeida dropped a Google Doc on coding agents one of these PDFs has the map everyone needs: where Jev sits in an agent loop ↓ agents do the work, Jev makes the calls, the LLM only writes: 1 → task in: one Noul before anything runs. in scope, or does a human need to see it first? 2 → dispatch: a Choice picks who takes the step (research, write or review). act at 0.85+, everything below goes to review 3 → act: Jev gates the tool call (allow, confirm or block). the LLM only writes the arguments 4 → check: a Score decides what comes back (drop, summary or keep). whatever Jev is unsure of goes to a human 5 → finish: Jev says done above 0.8, then code proves the file actually exists the LLM shows up in 2 of 5 stages. everything else is a typed answer, plain code or a person. don't refactor your agent. hand Jev the one fork it hits most (usually the tool gate or dispatch) and watch 3 numbers for a week: cost per completed task, time per completed task, and how often escalation fired and whether it was right. if escalation never fires, the threshold is too loose and your agent is quietly running unsupervised.

holy sh*t. Jev's founder Diogo Almeida dropped a Google Doc on coding agents one of these PDFs has the map everyone needs: where Jev sits in an agent loop ↓ agents do the work, Jev makes the calls, the LLM only writes: 1 → task in: one Noul before anything runs. in scope, or does a human need to see it first? 2 → dispatch: a Choice picks who takes the step (research, write or review). act at 0.85+, everything below goes to review 3 → act: Jev gates the tool call (allow, confirm or block). the LLM only writes the arguments 4 → check: a Score decides what comes back (drop, summary or keep). whatever Jev is unsure of goes to a human 5 → finish: Jev says done above 0.8, then code proves the file actually exists the LLM shows up in 2 of 5 stages. everything else is a typed answer, plain code or a person. don't refactor your agent. hand Jev the one fork it hits most (usually the tool gate or dispatch) and watch 3 numbers for a week: cost per completed task, time per completed task, and how often escalation fired and whether it was right. if escalation never fires, the threshold is too loose and your agent is quietly running unsupervised.

33,524 次观看

My worst AI agent returned 218% in one week 4 AI agents. 4 sports. each one watches its own sport with its own ML model gave each $500. one week results: NERVE: tennis (+540%) $500 → $3,200 PHANTOM: NBA (+486%) $500 → $2,928 FROST: hockey (+395%) $500 → $2,474 SIEGE: soccer (+336%) $500 → $2,182 architecture: Rust + Python hybrid Rust: WebSocket from Sportradar → parsing (protobuf/JSON) → filtering → forwarding via ZeroMQ Python: 4 agents in parallel, each with its own ML model a normal person sees the score on ESPN with a 5-15 second delay we see it in 500ms sportradar is a premium data feed used by bookmakers $800-1000 per month. that's the edge here's what each agent does: - NERVE - tennis. earned the most tennis is the most volatile. one break of serve swings the market 15-20% LSTM neural network, updates on every single point. sees serve speed drops (fatigue), clusters of double faults (mental collapse), medical timeouts win rate 62-68% - PHANTOM - NBA. most accurate LightGBM, inference 20ms. fastest model of the four catches scoring runs, fifth fouls on stars, mid-game injuries. Sportradar is connected to NBA official scoring, data arrives in 500ms. ESPN adds graphics and replays win rate 68-72% - FROST - hockey Gradient Boosting + Monte Carlo catches goalie swaps (backup is 5-8% worse), power plays, empty nets empty net in the last 90 seconds - almost arbitrage. 60% chance of a goal Sportradar pushes the goalie pull instantly. market can't adjust in time win rate 65-70% - SIEGE - soccer. the hardest 3 outcomes instead of two. draws - 25% of matches real-time xG: viewers see 0-0, SIEGE sees xG 2.5 red cards: market panics -20%, real impact -12% win rate 58-64% all models optimized with ONNX runtime (3-5x faster than sklearn) Rust execution: EIP-712 signing, Polymarket CLOB, Kelly sizing, automatic stop-loss. <50ms costs: ~$3,880/month weekly result: $2,000 → $10,784 they just trade faster than everyone else

My worst AI agent returned 218% in one week 4 AI agents. 4 sports. each one watches its own sport with its own ML model gave each $500. one week results: NERVE: tennis (+540%) $500 → $3,200 PHANTOM: NBA (+486%) $500 → $2,928 FROST: hockey (+395%) $500 → $2,474 SIEGE: soccer (+336%) $500 → $2,182 architecture: Rust + Python hybrid Rust: WebSocket from Sportradar → parsing (protobuf/JSON) → filtering → forwarding via ZeroMQ Python: 4 agents in parallel, each with its own ML model a normal person sees the score on ESPN with a 5-15 second delay we see it in 500ms sportradar is a premium data feed used by bookmakers $800-1000 per month. that's the edge here's what each agent does: - NERVE - tennis. earned the most tennis is the most volatile. one break of serve swings the market 15-20% LSTM neural network, updates on every single point. sees serve speed drops (fatigue), clusters of double faults (mental collapse), medical timeouts win rate 62-68% - PHANTOM - NBA. most accurate LightGBM, inference 20ms. fastest model of the four catches scoring runs, fifth fouls on stars, mid-game injuries. Sportradar is connected to NBA official scoring, data arrives in 500ms. ESPN adds graphics and replays win rate 68-72% - FROST - hockey Gradient Boosting + Monte Carlo catches goalie swaps (backup is 5-8% worse), power plays, empty nets empty net in the last 90 seconds - almost arbitrage. 60% chance of a goal Sportradar pushes the goalie pull instantly. market can't adjust in time win rate 65-70% - SIEGE - soccer. the hardest 3 outcomes instead of two. draws - 25% of matches real-time xG: viewers see 0-0, SIEGE sees xG 2.5 red cards: market panics -20%, real impact -12% win rate 58-64% all models optimized with ONNX runtime (3-5x faster than sklearn) Rust execution: EIP-712 signing, Polymarket CLOB, Kelly sizing, automatic stop-loss. <50ms costs: ~$3,880/month weekly result: $2,000 → $10,784 they just trade faster than everyone else

233,357 次观看

this AI character stole another AI character's viral video. 3 days later. same clip. the copy got 25.7M views. the original got 20M. meet dahab daddy. 9 days old. 222,000 followers. 106M views. he has never been filmed. not once. and he's not alone. 53 characters like him pulled 1.68 BILLION views in 3 weeks. here's where the money is: - brand deals: his bio already has a brand contact - AI Invasion: $1 per 1,000 views, up to $2,000 per video, from a $1,000,000 pool - Higgsfield Earn: up to $2,500 per video - affiliate: up to 25% of every subscription you refer - merch or your own guide one 10-sec video costs $0.50–$9.68 to make. here's the exact step by step 👇 STEP 1: the concept one exaggerated feature that reads as a silhouette. one outfit in one color. a prim look doing street stuff. dahab = bald top + long curls on the sides + horseshoe mustache + purple 70s leather jacket. paste this into chatgpt or claude: "pitch me 10 original characters for short-form video. each one: a name, a one-line vibe, ONE exaggerated hairstyle readable at thumbnail size, ONE standout facial feature, an old-fashioned outfit in one dominant color, a signature move or catchphrase. deadpan, dorky, never mean. no real people, no existing characters." STEP 2: the base photo paste this into nano banana or gpt-image: "ultra-photorealistic full-body studio portrait of [character], facing the camera, deadpan. plain light-grey seamless backdrop, soft even light. 85mm, f/8, head to toe in frame. natural skin texture, individual hair strands, real fabric. no text." want to change something? never regenerate. edit: "keep this exact person identical. same face, hair, body, outfit, lighting. change only: [one thing]." STEP 3: the character sheet "character reference sheet of the exact same person from the image. one wide image, light-grey background in every panel. top row: 5 full-body views (front, 3/4 front, side, 3/4 back, back). bottom row: 3 close-ups (front deadpan, 3/4, signature expression). face, hair and outfit identical in every panel. thin white dividers, no text." STEP 4: pick the source video this step decides the views. the source needs: → 1 person, 0–1 cuts, under 30 sec → an everyday place + real people reacting (banquet, bar, metro) → the person big in frame, facing the camera → posted in the last week skip: pro sports, studios, cinematic shots, kids, politics STEP 5: the swap upload the clip + your sheet to Genjutsu, Seedance 2.5 or Kling: "character swap edit of @ video1. replace the person in @ video1 with the character from @ image1. use @ image1 for identity only. keep everything else the same: motion, camera movement, framing, background, lighting, color grading, audio and length." face blending with the original? blur the original face before you upload. the model can't copy what it can't see. draft in 480p. render 1080p only for the take you keep. STEP 6: posting (the first 72 hours decide everything) → character on screen from frame 0 → 10–15 sec, no text on the video → keep the ORIGINAL sound → caption under 25 characters, first person, zero hashtags → post on tiktok AND instagram. the same clip did 15.3M on one and 179k on the other → the best video usually lands on day 2. after that growth halves every ~3 days STEP 7: the universe → repeat the winning situation, not the location → crossovers with other characters. that's where smaller ones hit their best numbers → milestone gags. "100K" posts become hits on their own the internet is about to be full of people who don't exist. the ones who post first get paid.

this AI character stole another AI character's viral video. 3 days later. same clip. the copy got 25.7M views. the original got 20M. meet dahab daddy. 9 days old. 222,000 followers. 106M views. he has never been filmed. not once. and he's not alone. 53 characters like him pulled 1.68 BILLION views in 3 weeks. here's where the money is: - brand deals: his bio already has a brand contact - AI Invasion: $1 per 1,000 views, up to $2,000 per video, from a $1,000,000 pool - Higgsfield Earn: up to $2,500 per video - affiliate: up to 25% of every subscription you refer - merch or your own guide one 10-sec video costs $0.50–$9.68 to make. here's the exact step by step 👇 STEP 1: the concept one exaggerated feature that reads as a silhouette. one outfit in one color. a prim look doing street stuff. dahab = bald top + long curls on the sides + horseshoe mustache + purple 70s leather jacket. paste this into chatgpt or claude: "pitch me 10 original characters for short-form video. each one: a name, a one-line vibe, ONE exaggerated hairstyle readable at thumbnail size, ONE standout facial feature, an old-fashioned outfit in one dominant color, a signature move or catchphrase. deadpan, dorky, never mean. no real people, no existing characters." STEP 2: the base photo paste this into nano banana or gpt-image: "ultra-photorealistic full-body studio portrait of [character], facing the camera, deadpan. plain light-grey seamless backdrop, soft even light. 85mm, f/8, head to toe in frame. natural skin texture, individual hair strands, real fabric. no text." want to change something? never regenerate. edit: "keep this exact person identical. same face, hair, body, outfit, lighting. change only: [one thing]." STEP 3: the character sheet "character reference sheet of the exact same person from the image. one wide image, light-grey background in every panel. top row: 5 full-body views (front, 3/4 front, side, 3/4 back, back). bottom row: 3 close-ups (front deadpan, 3/4, signature expression). face, hair and outfit identical in every panel. thin white dividers, no text." STEP 4: pick the source video this step decides the views. the source needs: → 1 person, 0–1 cuts, under 30 sec → an everyday place + real people reacting (banquet, bar, metro) → the person big in frame, facing the camera → posted in the last week skip: pro sports, studios, cinematic shots, kids, politics STEP 5: the swap upload the clip + your sheet to Genjutsu, Seedance 2.5 or Kling: "character swap edit of @ video1. replace the person in @ video1 with the character from @ image1. use @ image1 for identity only. keep everything else the same: motion, camera movement, framing, background, lighting, color grading, audio and length." face blending with the original? blur the original face before you upload. the model can't copy what it can't see. draft in 480p. render 1080p only for the take you keep. STEP 6: posting (the first 72 hours decide everything) → character on screen from frame 0 → 10–15 sec, no text on the video → keep the ORIGINAL sound → caption under 25 characters, first person, zero hashtags → post on tiktok AND instagram. the same clip did 15.3M on one and 179k on the other → the best video usually lands on day 2. after that growth halves every ~3 days STEP 7: the universe → repeat the winning situation, not the location → crossovers with other characters. that's where smaller ones hit their best numbers → milestone gags. "100K" posts become hits on their own the internet is about to be full of people who don't exist. the ones who post first get paid.

19,581 次观看

Somewhere in Amsterdam in a small office sit 3 people quant. HFT engineer. risk manager. for years they ran bots on Binance and Bybit but it's a bloodbath there thousands of teams just like them then one of them opened Polymarket and saw that regular people bet on 15-min Bitcoin markets based on vibes they created a wallet deposited $10k ran the bot on micro-volume collecting logs, fixing bugs, testing the model when they knew they were ready poured in $200k bot started doing 618 trades per day first came a $20k drawdown then every day +$30-50k bot is connected to Binance and ChainLink via WebSocket sees BTC move in milliseconds and reacts before Polymarket 17 days later $588,925 profit copying trades through PolyCop

Somewhere in Amsterdam in a small office sit 3 people quant. HFT engineer. risk manager. for years they ran bots on Binance and Bybit but it's a bloodbath there thousands of teams just like them then one of them opened Polymarket and saw that regular people bet on 15-min Bitcoin markets based on vibes they created a wallet deposited $10k ran the bot on micro-volume collecting logs, fixing bugs, testing the model when they knew they were ready poured in $200k bot started doing 618 trades per day first came a $20k drawdown then every day +$30-50k bot is connected to Binance and ChainLink via WebSocket sees BTC move in milliseconds and reacts before Polymarket 17 days later $588,925 profit copying trades through PolyCop

21,139 次观看

Some trader withdrew all money from polymarket and came back a month later deposited $2,000 with a new strategy now sitting at $137,000 profit he switched to weather markets every day collects data from 5 APIs simultaneously: → Open-Meteo (GFS, ECMWF models) → OpenWeatherMap → WeatherAPI → Tomorrow(.)io → Visual Crossing if 4 out of 5 models agree on one forecast edge exists but the most interesting part - temporal arbitrage cities with best liquidity: NYC, London, Atlanta, Dallas i'm copying his trades using PolyCop started with $500 address: 0xcbbc5e035504421b084ad9248b660f6e9618b5d0

Some trader withdrew all money from polymarket and came back a month later deposited $2,000 with a new strategy now sitting at $137,000 profit he switched to weather markets every day collects data from 5 APIs simultaneously: → Open-Meteo (GFS, ECMWF models) → OpenWeatherMap → WeatherAPI → Tomorrow(.)io → Visual Crossing if 4 out of 5 models agree on one forecast edge exists but the most interesting part - temporal arbitrage cities with best liquidity: NYC, London, Atlanta, Dallas i'm copying his trades using PolyCop started with $500 address: 0xcbbc5e035504421b084ad9248b660f6e9618b5d0

19,899 次观看

US government won't shutdown Saturday congressional leadership aide just bet on Polymarket he knows the vote count placed bet 3 hours ago if he's right: $54,274 → $245,576 transaction pattern shows they're hiding money origin real insider behavior

US government won't shutdown Saturday congressional leadership aide just bet on Polymarket he knows the vote count placed bet 3 hours ago if he's right: $54,274 → $245,576 transaction pattern shows they're hiding money origin real insider behavior

19,658 次观看

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THIS IS A PURE F*CKING GOLDMINE the best REA trick almost nobody runs yet. The idea: REA takes the app apart, Jev keeps only what's proven, and a fresh Opus 5.5 rebuilds the feature from that alone. Nothing gets copied and no guess makes it into the code, because the builder never sees the original, only the spec Jev signed off on. You just connect REA like this: npx rea-agents setup here's the split: → REA reads the feature down to the binary and writes every rule into SPEC.md → Jev checks each rule against REA's evidence and sends back anything unproven → a fresh Opus 5.5 session gets only SPEC.md and rebuilds the feature → Haiku 5.5 runs the tests against 3 intentionally broken versions Opus only shows up at three moments: → rebuilding from the approved spec → the checks Jev marks as unclear → signing off before anything ships REA reads. Jev judges. Opus rebuilds. That's the clean-room rule in one line: the model that saw the original never writes the code, and the model that writes the code never sees the original. the full tree > REA reads the app and writes SPEC.md with evidence for every rule > Jev approves or returns each rule > Opus 5.5 rebuilds from the approved SPEC.md in a fresh session > Haiku 5.5 runs the tests and the 3 broken versions > Jev routes unclear checks back to Opus > /goal keeps going until all 3 broken versions fail paste the tree and this prompt into Claude Code ↓ "Set up a clean-room rebuild around this tree: 1. Use REA to investigate [FEATURE] in [APP]. Write every behavior and rule into SPEC.md, each with the evidence REA found. 2. Send each rule to Jev. Any rule without evidence goes back to REA. 3. Start a fresh session with only SPEC.md. Rebuild the feature on Opus 5.5 without opening the original. 4. Write tests on Haiku 5.5 plus 3 intentionally broken versions. Use /goal until the rebuild passes and all 3 broken versions fail. Show me SPEC.md first. Nothing gets built until I say go." ↳ save it, it'll come in handy.
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THIS IS A PURE F*CKING GOLDMINE the best REA trick almost nobody runs yet. The idea: REA takes the app apart, Jev keeps only what's proven, and a fresh Opus 5.5 rebuilds the feature from that alone. Nothing gets copied and no guess makes it into the code, because the builder never sees the original, only the spec Jev signed off on. You just connect REA like this: npx rea-agents setup here's the split: → REA reads the feature down to the binary and writes every rule into SPEC.md → Jev checks each rule against REA's evidence and sends back anything unproven → a fresh Opus 5.5 session gets only SPEC.md and rebuilds the feature → Haiku 5.5 runs the tests against 3 intentionally broken versions Opus only shows up at three moments: → rebuilding from the approved spec → the checks Jev marks as unclear → signing off before anything ships REA reads. Jev judges. Opus rebuilds. That's the clean-room rule in one line: the model that saw the original never writes the code, and the model that writes the code never sees the original. the full tree > REA reads the app and writes SPEC.md with evidence for every rule > Jev approves or returns each rule > Opus 5.5 rebuilds from the approved SPEC.md in a fresh session > Haiku 5.5 runs the tests and the 3 broken versions > Jev routes unclear checks back to Opus > /goal keeps going until all 3 broken versions fail paste the tree and this prompt into Claude Code ↓ "Set up a clean-room rebuild around this tree: 1. Use REA to investigate [FEATURE] in [APP]. Write every behavior and rule into SPEC.md, each with the evidence REA found. 2. Send each rule to Jev. Any rule without evidence goes back to REA. 3. Start a fresh session with only SPEC.md. Rebuild the feature on Opus 5.5 without opening the original. 4. Write tests on Haiku 5.5 plus 3 intentionally broken versions. Use /goal until the rebuild passes and all 3 broken versions fail. Show me SPEC.md first. Nothing gets built until I say go." ↳ save it, it'll come in handy.

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59,706 次观看 • 7 天前

This dinosaur doesn't exist 35 seconds. 5 moves. 8.3M views... Toddler cartoons like this are the quiet monster of YouTube – a breakdown of the format did 1.86M views here Here's exactly how this one was made: 1. The song. One prompt to Lyria 3 Pro with my own lyrics - 35 seconds, vocals and ukulele. The first take skipped "Three!" in a counting song. Whisper caught it, take 3 sang every line 2. The mascot. 4 concepts, picked Dino. Nano Banana Pro in Higgsfield put him in one playroom for every line: hello, jump, clap, spin, touch your toes, bye-bye 3. The singing. I cut the song piece by piece and gave each piece to Seedance 2.5 as an audio reference. He opens his mouth on every word and dances in the gaps 4. The edit is code. Claude Code wrote it in HyperFrames: every word lights up when it's sung, a star hops from word to word, giant numbers pop and fly into a 5-slot counting board. The outro counts the board back: one, two, three, four, five Then every scene is an ad slot: Toothbrush → toothpaste Bubble bath → bath toys Picture book → kids books Fruit bowl → snacks Building blocks → toys Bedtime → night lights, pajamas Rain boots → kids clothing All 9 scenes are already generated, same Dino in each (grid in the reply) Higgsfield draws and animates him. Picsart writes the song. HyperFrames renders it. You sell the slots Cost: ~520 Higgsfield credits + 34 Picsart credits Bookmark this before every kids channel is a dinosaur
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This dinosaur doesn't exist 35 seconds. 5 moves. 8.3M views... Toddler cartoons like this are the quiet monster of YouTube – a breakdown of the format did 1.86M views here Here's exactly how this one was made: 1. The song. One prompt to Lyria 3 Pro with my own lyrics - 35 seconds, vocals and ukulele. The first take skipped "Three!" in a counting song. Whisper caught it, take 3 sang every line 2. The mascot. 4 concepts, picked Dino. Nano Banana Pro in Higgsfield put him in one playroom for every line: hello, jump, clap, spin, touch your toes, bye-bye 3. The singing. I cut the song piece by piece and gave each piece to Seedance 2.5 as an audio reference. He opens his mouth on every word and dances in the gaps 4. The edit is code. Claude Code wrote it in HyperFrames: every word lights up when it's sung, a star hops from word to word, giant numbers pop and fly into a 5-slot counting board. The outro counts the board back: one, two, three, four, five Then every scene is an ad slot: Toothbrush → toothpaste Bubble bath → bath toys Picture book → kids books Fruit bowl → snacks Building blocks → toys Bedtime → night lights, pajamas Rain boots → kids clothing All 9 scenes are already generated, same Dino in each (grid in the reply) Higgsfield draws and animates him. Picsart writes the song. HyperFrames renders it. You sell the slots Cost: ~520 Higgsfield credits + 34 Picsart credits Bookmark this before every kids channel is a dinosaur

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101,403 次观看 • 19 天前

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.
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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.

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281,350 次观看 • 1 个月前

Some guy deposited $150 on polymarket just checked his wallet now sitting at $104k profit asked what's his strategy it's simple his bot buys everything under 5 cents no analysis no predictions some of his winning trades: S&P 500 direction: $4.55 → $2,531 Syria strikes Israel: $16.78 → $2,324 trades obscure markets where prices can be random these markets have low liquidity nobody wants to research them for small profit he started with only $150 what's stopping you?
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Some guy deposited $150 on polymarket just checked his wallet now sitting at $104k profit asked what's his strategy it's simple his bot buys everything under 5 cents no analysis no predictions some of his winning trades: S&P 500 direction: $4.55 → $2,531 Syria strikes Israel: $16.78 → $2,324 trades obscure markets where prices can be random these markets have low liquidity nobody wants to research them for small profit he started with only $150 what's stopping you?

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1,348,442 次观看 • 8 个月前

This fitness coach doesn't exist 29 seconds. 3 moves. 8M views... One Picsart CLI for all the generation + one GitHub repo (HyperFrames) for the edit. Claude Code drove both. here's the whole sequence: Day 0: Lock the character. Grey tee, dark joggers, lime wristbands. GPT-Image-2 for the concept, Nano Banana Pro for the character sheet. Same face in every shot after that. Day 1: Make the moves. Nano Banana Pro draws a keyframe per exercise, Kling v3 animates it. Squats, push-ups, plank. ~2 min per clip. Day 2: Make him talk. Eleven v3 for the voice, OmniHuman 1.5 for the lip-sync (~3–4 min per clip), Lyria for the music. Day 3: Hand it all to Claude Code. It writes the edit as code (HTML + GSAP): kinetic captions, step cards, form cues, a progress bar, the muscle map. HyperFrames, HeyGen's open-source video engine (~53k stars), renders it to MP4 in ~2 min. The next 30 days: same coach, same outfit, new moves. Nothing to sell yet. You're building a face people recognize – that's what you sell later. Then every scene becomes an ad slot. He can wear it, hold it, drink it: Jump rope → sneakers Locker-room water break → bottles, electrolytes Tying laces on the street → running shoes Breakfast → protein Boxing bag → gyms Rooftop meditation → fitness apps His outfit → sportswear The CLI makes the coach. Claude Code writes the edit. HyperFrames renders it. You sell the slot. Week 1: character + 3 routines Week 2: post daily, test hooks Week 3: keep what holds attention, cut the rest Week 4: your best clips on one page = your media kit Month 2: pitch sportswear, protein, gyms, fitness apps Month 3: same system, second coach Cost: ~235 Picsart credits all-in, rejected takes included. HyperFrames: open-source (Apache-2.0). Claude Code: your own subscription. save this and read the article below:
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This fitness coach doesn't exist 29 seconds. 3 moves. 8M views... One Picsart CLI for all the generation + one GitHub repo (HyperFrames) for the edit. Claude Code drove both. here's the whole sequence: Day 0: Lock the character. Grey tee, dark joggers, lime wristbands. GPT-Image-2 for the concept, Nano Banana Pro for the character sheet. Same face in every shot after that. Day 1: Make the moves. Nano Banana Pro draws a keyframe per exercise, Kling v3 animates it. Squats, push-ups, plank. ~2 min per clip. Day 2: Make him talk. Eleven v3 for the voice, OmniHuman 1.5 for the lip-sync (~3–4 min per clip), Lyria for the music. Day 3: Hand it all to Claude Code. It writes the edit as code (HTML + GSAP): kinetic captions, step cards, form cues, a progress bar, the muscle map. HyperFrames, HeyGen's open-source video engine (~53k stars), renders it to MP4 in ~2 min. The next 30 days: same coach, same outfit, new moves. Nothing to sell yet. You're building a face people recognize – that's what you sell later. Then every scene becomes an ad slot. He can wear it, hold it, drink it: Jump rope → sneakers Locker-room water break → bottles, electrolytes Tying laces on the street → running shoes Breakfast → protein Boxing bag → gyms Rooftop meditation → fitness apps His outfit → sportswear The CLI makes the coach. Claude Code writes the edit. HyperFrames renders it. You sell the slot. Week 1: character + 3 routines Week 2: post daily, test hooks Week 3: keep what holds attention, cut the rest Week 4: your best clips on one page = your media kit Month 2: pitch sportswear, protein, gyms, fitness apps Month 3: same system, second coach Cost: ~235 Picsart credits all-in, rejected takes included. HyperFrames: open-source (Apache-2.0). Claude Code: your own subscription. save this and read the article below:

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77,690 次观看 • 26 天前

This guy runs an AI consultancy out of Warsaw. for his own client work he built the tool every $10k/mo AI automation builder is secretly running 19,000 stars. 1,500 nodes documented. open source readme still says: "started as a personal tool, now helps tens of thousands of developers" if you're following the guide above - n8n-mcp is where you start → like + bookmark. you'll need this when you build your first claude automation
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This guy runs an AI consultancy out of Warsaw. for his own client work he built the tool every $10k/mo AI automation builder is secretly running 19,000 stars. 1,500 nodes documented. open source readme still says: "started as a personal tool, now helps tens of thousands of developers" if you're following the guide above - n8n-mcp is where you start → like + bookmark. you'll need this when you build your first claude automation

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383,523 次观看 • 5 个月前

i gave five Grok Bots their own inboxes and told them to find businesses that need AI implemented 6 hours later they had emailed 140 companies, and 27 replied - into the bots' inboxes, not mine ( each business pays between $500 and $8,500. so far my agents have earned ~$27,410 ) not a scraper. not a mail blast. five agents on a cloud computer that never sleeps, each with one narrow job and its own address here is what happened while i slept > bot 1 pulled 468 local businesses and cut 69% on a single rule: is there a gap here i can actually close in a week > bot 2 went through the survivors and wrote down the evidence - the site from 2014, the unanswered reviews, the booking form that 404s > bot 3 built the artifact so the email would not be empty: a one-page audit with the screenshots inside > bot 4 wrote and sent from its own inbox, then ran the thread itself when the reply came back > bot 5 tried to kill every send the other four approved. its only job was to ask: would this email annoy me if i got it out of 468 businesses, 146 passed the filter and 140 went out 27 replied. 23 asked for a call. the whole run cost ~$12 of those 23 i have onboarded 11 so far. each business pays between $500 and $8,500, which is ~$27,410 to date the other 12 are still in progress. if they close the same way it lands around $52,000 - but that is a projection, not a fact the part that only works here: the reply drops into bot 4's inbox. i was not in the second email. or the third. in the morning i read the thread like a manager, not like the person who sent it what the bots never touched: > my own mailbox. they have theirs, i have mine > sending without the artifact. no audit, no email > anyone bot 5 flagged. it killed 6 drafts out of the 146 that passed, and i did not overrule it the honest part: this is the first run. one city, one niche, six hours. eleven clients out of twenty-three is not an average yet, it is a start setup took one evening: create the bots, connect AgentMail so each gets an inbox, write five charters, put the scan on a schedule no VPS. no scraper farm. no mail-merge what's left is finding the ceiling: 140 emails went out overnight. will 1,000?
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i gave five Grok Bots their own inboxes and told them to find businesses that need AI implemented 6 hours later they had emailed 140 companies, and 27 replied - into the bots' inboxes, not mine ( each business pays between $500 and $8,500. so far my agents have earned ~$27,410 ) not a scraper. not a mail blast. five agents on a cloud computer that never sleeps, each with one narrow job and its own address here is what happened while i slept > bot 1 pulled 468 local businesses and cut 69% on a single rule: is there a gap here i can actually close in a week > bot 2 went through the survivors and wrote down the evidence - the site from 2014, the unanswered reviews, the booking form that 404s > bot 3 built the artifact so the email would not be empty: a one-page audit with the screenshots inside > bot 4 wrote and sent from its own inbox, then ran the thread itself when the reply came back > bot 5 tried to kill every send the other four approved. its only job was to ask: would this email annoy me if i got it out of 468 businesses, 146 passed the filter and 140 went out 27 replied. 23 asked for a call. the whole run cost ~$12 of those 23 i have onboarded 11 so far. each business pays between $500 and $8,500, which is ~$27,410 to date the other 12 are still in progress. if they close the same way it lands around $52,000 - but that is a projection, not a fact the part that only works here: the reply drops into bot 4's inbox. i was not in the second email. or the third. in the morning i read the thread like a manager, not like the person who sent it what the bots never touched: > my own mailbox. they have theirs, i have mine > sending without the artifact. no audit, no email > anyone bot 5 flagged. it killed 6 drafts out of the 146 that passed, and i did not overrule it the honest part: this is the first run. one city, one niche, six hours. eleven clients out of twenty-three is not an average yet, it is a start setup took one evening: create the bots, connect AgentMail so each gets an inbox, write five charters, put the scan on a schedule no VPS. no scraper farm. no mail-merge what's left is finding the ceiling: 140 emails went out overnight. will 1,000?

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110,394 次观看 • 1 个月前

$1M bonus at 24 for crude oil futures. This is why he left Lockheed. Lit Nomad - missile defense engineer at Lockheed Martin, $60k/year. His classmate landed at Jump Trading when it was 50 people and was pulling 7-figure bonuses straight out of undergrad. Same math, same physics - just derivatives instead of missiles. 19 minutes of the most honest "why I left prestige for quant prop trading" breakdown out there. Save it. Then ask yourself why you're rehearsing a speech in front of the mirror for a 4% raise.
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$1M bonus at 24 for crude oil futures. This is why he left Lockheed. Lit Nomad - missile defense engineer at Lockheed Martin, $60k/year. His classmate landed at Jump Trading when it was 50 people and was pulling 7-figure bonuses straight out of undergrad. Same math, same physics - just derivatives instead of missiles. 19 minutes of the most honest "why I left prestige for quant prop trading" breakdown out there. Save it. Then ask yourself why you're rehearsing a speech in front of the mirror for a 4% raise.

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202,094 次观看 • 5 个月前

and how come Tate's only at 87% on polymarket? andrew tate wins like 99.99% he's literally kicking demoor's ass if he even survives to the final bell
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and how come Tate's only at 87% on polymarket? andrew tate wins like 99.99% he's literally kicking demoor's ass if he even survives to the final bell

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355,451 次观看 • 10 个月前

yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening
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yesterday someone leaked a full quant trading system on GitHub before they deleted it i forked everything 5,000 lines of code. 7 modules. 25 mathematical factors funds use this system to manage millions i studied it for a week. then pointed it at crypto markets on polymarket here's the full breakdown you can feed this to your claude and build the same thing for just $200 ARCHITECTURE: Python thinks, analyzes, calculates C++ executes orders in 5-10ms data → factors → AI → strategy → risk → execution DATA. 4 streams simultaneously: - Binance WebSocket: prices every second, orderbook at 20 levels - AlphaVantage: news with sentiment score from -1 to +1 -X: mention volume, engagement, influencer activity - On-chain: BTC flows to/from exchanges cache in Redis ( target price) = N(d1) d1 = [ln(current/target) + (σ²/2)T] / (σ√T) then 4 adjustments on top: - momentum: +/-5% - AI sentiment: +/-7% - order flow: +/-2% - historical patterns: +/-8% compare final probability against polymarket price if edge > 10%: enter RISK - Quarter Kelly for position sizing - max 5% bankroll per trade - drawdown 15% = bot stops - VaR < 3% per day - correlation between positions < 0.7 - never take more than 1% of market liquidity key insight is don't hold to expiry. trade the movement, not the outcome cost: → Binance API: free → OpenAI: $50-100/month → AWS EC2: $120/month → monitoring: free - total: $200-300/month - code is open source. formulas above. you already have claude the only thing between you and a working system is one free evening

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250,848 次观看 • 7 个月前

This telegram bot will make me a millionaire didn’t sleep for 36 hours building it with opus 4.6 send any Polymarket wallet → get all the info what it exports: - every trade ever made - detailed analytics of each - exact timestamps - overall trader analysis ready excel file in 30 seconds but the file alone isn’t enough feed the file + custom prompt into claude ai it decodes their strategy this isn’t copy trading anymore this is logic extraction you can earn like the best polymarket traders
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This telegram bot will make me a millionaire didn’t sleep for 36 hours building it with opus 4.6 send any Polymarket wallet → get all the info what it exports: - every trade ever made - detailed analytics of each - exact timestamps - overall trader analysis ready excel file in 30 seconds but the file alone isn’t enough feed the file + custom prompt into claude ai it decodes their strategy this isn’t copy trading anymore this is logic extraction you can earn like the best polymarket traders

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218,415 次观看 • 8 个月前

Didn't sleep for 2 days upgrading the bot and now it sees the market inside out i set aside $100 to test strategies and have already turned it into $1,740 bot collects 400-600 wallets of top traders from Polymarket checks them every hour the most interesting part - clusters if 10+ smart wallets enter the same position at the same time bot instantly sends me a signal also added conviction score - rates each signal from 0 to 100 counts how many wallets entered, their total PnL, volume while testing found one wallet $600k profit in one month breaking down his strategy soon
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Didn't sleep for 2 days upgrading the bot and now it sees the market inside out i set aside $100 to test strategies and have already turned it into $1,740 bot collects 400-600 wallets of top traders from Polymarket checks them every hour the most interesting part - clusters if 10+ smart wallets enter the same position at the same time bot instantly sends me a signal also added conviction score - rates each signal from 0 to 100 counts how many wallets entered, their total PnL, volume while testing found one wallet $600k profit in one month breaking down his strategy soon

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202,500 次观看 • 8 个月前

A guy runs AI ads for local roofers and they pay him $2,000-$5,000 a month each he doesn't film anything. He doesn't hire anyone he opens Facebook's Ad Library and finds ads that have been running for 6+ months - those are the proven winners then he drops them into Higgsfield and has it rebuild the same ad with the client's logo claude writes the script and the ad copy for him in one afternoon he's got 10-15 video ads for a roofing company that never ran a single ad before he launches them on Meta and leads start coming in through a simple form an AI bot texts every lead back in under 2 minutes and books them straight onto the client's calendar the roofer just wakes up to booked appointments. He has no idea AI did all of it some of these clients have paid him the same fee every month for 2 years straight he bills per lead or a flat retainer - either way it's recurring, and the AI does most of the work one skill, a few tools, and small local businesses that have never touched advertising save this, the quietest AI money right now isn't apps - it's running ads nobody knows are AI
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A guy runs AI ads for local roofers and they pay him $2,000-$5,000 a month each he doesn't film anything. He doesn't hire anyone he opens Facebook's Ad Library and finds ads that have been running for 6+ months - those are the proven winners then he drops them into Higgsfield and has it rebuild the same ad with the client's logo claude writes the script and the ad copy for him in one afternoon he's got 10-15 video ads for a roofing company that never ran a single ad before he launches them on Meta and leads start coming in through a simple form an AI bot texts every lead back in under 2 minutes and books them straight onto the client's calendar the roofer just wakes up to booked appointments. He has no idea AI did all of it some of these clients have paid him the same fee every month for 2 years straight he bills per lead or a flat retainer - either way it's recurring, and the AI does most of the work one skill, a few tools, and small local businesses that have never touched advertising save this, the quietest AI money right now isn't apps - it's running ads nobody knows are AI

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106,091 次观看 • 4 个月前

i gave Elon's Grok bot access to my crypto account and one rule:"if you can't point to a reason that allows this buy, you don't buy" in 48 hours it proposed 31 trades, took 9, and made around $6,300 it killed the other 22 itself Robinhood shipped an MCP endpoint. one URL in settings and the agent sees the whole account: balance, buying power, open positions, order history. and it can place orders itself. zero code. zero API keys. OAuth, so it never sees my password. i wrote it one file of rules. four blocks, plain markdown. the agent obeys the file, not what i type in chat. i named it Clerk > Constraints - which coins it may touch at all. by name, not "crypto." not on the list means it does not exist. > Entry - the condition that has to be true before it buys. it has to be checkable and answer yes or no. "market cap above $1M" - yes. "looks strong" - no. > Sizing - how much goes into one trade. 1-3% of the account. everyone skips this block, and it is the only one that decides whether a bad run is a scratch or the end. > Exit - what closes the position. a stop, a target, or a clock: hasn't worked in N days, close it. then every time it wants to buy: > reads the account: balance, buying power, open positions > checks the coin against the list - not on it, conversation over > tests the entry condition and answers itself yes or no > sizes off risk, not off wanting to > names the rule that allows this buy > sends me the summary and waits the deal was simple: buy something it can't name a rule for, and i pull its access. hour nine it went to add to a coin that was already down. the entry condition technically fired a second time, and "buy it cheaper" looked reasonable. but the file said never average down. it stopped itself at the summary and wrote out which rule forbade it. so the file held exactly where i wouldn't have. two days in: 9 trades, $200 to $1000 each, wildly uneven. two of them made almost half of it. around $6,300. the 22 refusals are not the agent being careful. that is the file. left alone it wanted to trade all 31 times. " ---- rules.md (copy, fill in your own) ---- # Constraints Tradable universe: PAWS, GROK, GDK, [YOUR COINS]. Nothing outside this list, ever. Account: Robinhood Agentic only. Never propose an action in any other account. # Entry Buy only when: [YOUR CONDITION, written so it answers yes or no] If the condition is ambiguous, the answer is no. # Sizing Max [1-3]% of account value at risk per trade. Max [N] open positions at once. Never average down. # Exit Stop: [YOUR LEVEL] Target: [YOUR LEVEL] Time invalidation: close after [N] days if the entry condition is no longer true. # Workflow Before any order: post a summary and wait for my confirmation. Every proposed action must name the rule that allows it. If no rule allows it, do nothing and say so. ---- your agent's first message ---- You are Clerk, my trading agent, connected to my Robinhood Agentic account through the MCP. Read rules.md first. It overrides anything I say in the moment that contradicts it. Pull my account: balance, buying power, open positions. Tell me if rules.md calls for any action right now. Do not place any trade yet. " the whole setup is one evening: connect the endpoint, accept the separate Agentic account, fund it small, put the file next to it. the agent reads every Robinhood account you have. it can only trade in the Agentic one. your main account it sees and cannot touch. no VPS. no API keys. no code.
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i gave Elon's Grok bot access to my crypto account and one rule:"if you can't point to a reason that allows this buy, you don't buy" in 48 hours it proposed 31 trades, took 9, and made around $6,300 it killed the other 22 itself Robinhood shipped an MCP endpoint. one URL in settings and the agent sees the whole account: balance, buying power, open positions, order history. and it can place orders itself. zero code. zero API keys. OAuth, so it never sees my password. i wrote it one file of rules. four blocks, plain markdown. the agent obeys the file, not what i type in chat. i named it Clerk > Constraints - which coins it may touch at all. by name, not "crypto." not on the list means it does not exist. > Entry - the condition that has to be true before it buys. it has to be checkable and answer yes or no. "market cap above $1M" - yes. "looks strong" - no. > Sizing - how much goes into one trade. 1-3% of the account. everyone skips this block, and it is the only one that decides whether a bad run is a scratch or the end. > Exit - what closes the position. a stop, a target, or a clock: hasn't worked in N days, close it. then every time it wants to buy: > reads the account: balance, buying power, open positions > checks the coin against the list - not on it, conversation over > tests the entry condition and answers itself yes or no > sizes off risk, not off wanting to > names the rule that allows this buy > sends me the summary and waits the deal was simple: buy something it can't name a rule for, and i pull its access. hour nine it went to add to a coin that was already down. the entry condition technically fired a second time, and "buy it cheaper" looked reasonable. but the file said never average down. it stopped itself at the summary and wrote out which rule forbade it. so the file held exactly where i wouldn't have. two days in: 9 trades, $200 to $1000 each, wildly uneven. two of them made almost half of it. around $6,300. the 22 refusals are not the agent being careful. that is the file. left alone it wanted to trade all 31 times. " ---- rules.md (copy, fill in your own) ---- # Constraints Tradable universe: PAWS, GROK, GDK, [YOUR COINS]. Nothing outside this list, ever. Account: Robinhood Agentic only. Never propose an action in any other account. # Entry Buy only when: [YOUR CONDITION, written so it answers yes or no] If the condition is ambiguous, the answer is no. # Sizing Max [1-3]% of account value at risk per trade. Max [N] open positions at once. Never average down. # Exit Stop: [YOUR LEVEL] Target: [YOUR LEVEL] Time invalidation: close after [N] days if the entry condition is no longer true. # Workflow Before any order: post a summary and wait for my confirmation. Every proposed action must name the rule that allows it. If no rule allows it, do nothing and say so. ---- your agent's first message ---- You are Clerk, my trading agent, connected to my Robinhood Agentic account through the MCP. Read rules.md first. It overrides anything I say in the moment that contradicts it. Pull my account: balance, buying power, open positions. Tell me if rules.md calls for any action right now. Do not place any trade yet. " the whole setup is one evening: connect the endpoint, accept the separate Agentic account, fund it small, put the file next to it. the agent reads every Robinhood account you have. it can only trade in the Agentic one. your main account it sees and cannot touch. no VPS. no API keys. no code.

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42,135 次观看 • 1 个月前

This guy works at coinbase. in his spare time he built the largest open dataset for polymarket and kalshi 72,100,000 trades. 7,680,000 markets. open source - figured out who actually makes money on prediction markets - wrote a research paper with the math to prove it - 3,100 stars on github just for that also built heimdall-rs - rust toolkit for evm bytecode analysis. 1,500 stars. serious dev 3,008 contributions in the last year while everyone's posting takes about polymarket - he's building the infrastructure underneath it → like + bookmark. you'll need this when you build your first polymarket bot
0:41
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This guy works at coinbase. in his spare time he built the largest open dataset for polymarket and kalshi 72,100,000 trades. 7,680,000 markets. open source - figured out who actually makes money on prediction markets - wrote a research paper with the math to prove it - 3,100 stars on github just for that also built heimdall-rs - rust toolkit for evm bytecode analysis. 1,500 stars. serious dev 3,008 contributions in the last year while everyone's posting takes about polymarket - he's building the infrastructure underneath it → like + bookmark. you'll need this when you build your first polymarket bot

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119,072 次观看 • 5 个月前

Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!
0:31
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Holy crap, who the hell did this?!! Someone open-sourced a Jev-powered second brain for AI agents and lets you plug it straight into your own agent. GitHub's already at 925 stars, repo's called Hermes-Jev-Skills. You just run one install command, and Jev takes over every tiny decision your agent burns premium tokens on. Which model answers, which skill to load, which memories matter, what survives when context gets cut. Give it your computer or browser and it'll pick the next click, but only from actions you already marked safe. It'll sort your inbox into lanes, strip hidden prompt injections out of search results, and triage every message in ~0.4 seconds. Claude Code and Codex support - they threw that in too. No dependencies, no patching Hermes core, and your API key never touches the agent Link's right here: The catch? Jev itself is a paid cloud API. The skills run on your machine; the decisions don't. But hey, a decision costs a fraction of a cent, everything fails open if Jev goes down, and your expensive model finally gets to do only the thinking. Who needs a damn frontier model picking which file to open!

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17,492 次观看 • 23 天前

My friend at Anthropic makes $1.4M a year. No MIT. No Stanford. No PhD. I asked how their team actually ships autonomous agents. He sent me an article most AI engineers missed. It unpacks Anthropic's entire loop-engineering playbook: the 3-agent harness, all the team's internal essays, and how to copy it. Bookmark it. Then read this setup ↓
0:20
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My friend at Anthropic makes $1.4M a year. No MIT. No Stanford. No PhD. I asked how their team actually ships autonomous agents. He sent me an article most AI engineers missed. It unpacks Anthropic's entire loop-engineering playbook: the 3-agent harness, all the team's internal essays, and how to copy it. Bookmark it. Then read this setup ↓

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55,197 次观看 • 3 个月前

I made 4 Grok bots kill each other for my money. $500 each, one rule: in a week, one survives. bot 1 - the copier. mirrors the top-5 wallets bot 2 - the contrarian. waits until the crowd is 80%+ confident, then bets against it bot 3 - the scalper. plays the lag between Binance and the prediction markets bot 4 - the weather guy. temperature only. three meteo models, enters only when all three agree each one on its own machine. I closed the laptop, they kept trading. stats for 7 days: day 1 - scalper ahead. +$180, everyone else flat day 2 - nothing interesting day 3 - contrarian catches a skew on a sports market. +$740 on one trade. takes the lead day 4 - the lag shrinks. scalper's edge is gone. $500 → $310 day 5 - the weather guy was silent for four days. then took two trades. both green. +$420 day 6 - the copier just sits in the middle. no losses, no highs day 7 - scalper dead. $82 left contrarian - $1,740 weather guy - $1,120 copier - $780 scalper - $82 the dumbest bot won. it did nothing for six days, then entered once, after everyone else was wrong. the smartest one died first. its edge had an expiry date.
0:56
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I made 4 Grok bots kill each other for my money. $500 each, one rule: in a week, one survives. bot 1 - the copier. mirrors the top-5 wallets bot 2 - the contrarian. waits until the crowd is 80%+ confident, then bets against it bot 3 - the scalper. plays the lag between Binance and the prediction markets bot 4 - the weather guy. temperature only. three meteo models, enters only when all three agree each one on its own machine. I closed the laptop, they kept trading. stats for 7 days: day 1 - scalper ahead. +$180, everyone else flat day 2 - nothing interesting day 3 - contrarian catches a skew on a sports market. +$740 on one trade. takes the lead day 4 - the lag shrinks. scalper's edge is gone. $500 → $310 day 5 - the weather guy was silent for four days. then took two trades. both green. +$420 day 6 - the copier just sits in the middle. no losses, no highs day 7 - scalper dead. $82 left contrarian - $1,740 weather guy - $1,120 copier - $780 scalper - $82 the dumbest bot won. it did nothing for six days, then entered once, after everyone else was wrong. the smartest one died first. its edge had an expiry date.

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26,435 次观看 • 1 个月前

Someone in r/algotrading posted a polymarket wallet with one comment: "sports markets are broken and nobody is talking about it" thread deleted in 10 minutes but i grabbed the wallet before it disappeared pulled the on-chain data and started building they hire 12 quant developers at $180k each i opened claude ($20/m) and said: "analyze this wallet's strategy and build me a similar system for trading live sports markets on polymarket" starting balance $100 a week ago current balance: $14,700 every trade is live sports, only during matches spent two days reverse engineering the pattern the logic is embarrassingly simple: live sports APIs update within 1-2 seconds of an event polymarket oracle checks every 10-20 seconds that gap is free money my bot runs on AWS EC2 for $120/month in Virginia latency <5ms to polymarket CLOB it pulls SportsRadar with 1-2 second delay and still finds edge the agent wrote the entire pipeline in one session: → async websocket listener on polymarket orderbook → SportsRadar live feed via httpx (non-blocking) → poisson probability model via scipy.stats → pre-cached lambda values in redis (<1ms lookup) → kelly criterion for position sizing (max 6% bankroll) → EIP-712 order signing, execution via polygon CLOB their infrastructure costs $2.4M/year mine costs $930/month $100 in the morning $1,730 by evening, and NFL example on video AI can build what used to take a team of 12
0:22
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Someone in r/algotrading posted a polymarket wallet with one comment: "sports markets are broken and nobody is talking about it" thread deleted in 10 minutes but i grabbed the wallet before it disappeared pulled the on-chain data and started building they hire 12 quant developers at $180k each i opened claude ($20/m) and said: "analyze this wallet's strategy and build me a similar system for trading live sports markets on polymarket" starting balance $100 a week ago current balance: $14,700 every trade is live sports, only during matches spent two days reverse engineering the pattern the logic is embarrassingly simple: live sports APIs update within 1-2 seconds of an event polymarket oracle checks every 10-20 seconds that gap is free money my bot runs on AWS EC2 for $120/month in Virginia latency <5ms to polymarket CLOB it pulls SportsRadar with 1-2 second delay and still finds edge the agent wrote the entire pipeline in one session: → async websocket listener on polymarket orderbook → SportsRadar live feed via httpx (non-blocking) → poisson probability model via scipy.stats → pre-cached lambda values in redis (<1ms lookup) → kelly criterion for position sizing (max 6% bankroll) → EIP-712 order signing, execution via polygon CLOB their infrastructure costs $2.4M/year mine costs $930/month $100 in the morning $1,730 by evening, and NFL example on video AI can build what used to take a team of 12

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82,065 次观看 • 7 个月前

A 19-year-old in warsaw turned $600 into $87k flipping cs2 skins last year he asked claude to build him a 25-file arbitrage scanner across skinport, steam, and csfloat - every spread above 6% pinged his discord in under 90 seconds then he watched 3 youtube channels to learn which spreads were real and which were traps - runs a 50,000-skin portfolio and drops investment calls daily. his "Skinconomy" series breaks down which valve update moves which collection - taught him float math, sticker tier theory, when supply spikes are tradeable - daily market commentary, nothing but investments. calls crashes and rebounds in real-time - taught him tournament-cycle timing and when major-week volume kills exit liquidity - one-man bloomberg terminal for cs skins. monthly market data - no entertainment - taught him to read sticker premiums, supply shocks, and which collections never close their bid-ask gap $14,200 on a single flip in october: a 0.06-float purple sticker craft his scanner caught 9 minutes after listing the bot found it. youtube told him it was real. spreadsheet has 1,847 rows. average hold 41 days a 19-year-old made $87,000 off 3 free youtube channels and a $20/month claude subscription while everyone else refused to read the rules someone already uploaded FOR FREE
0:21
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A 19-year-old in warsaw turned $600 into $87k flipping cs2 skins last year he asked claude to build him a 25-file arbitrage scanner across skinport, steam, and csfloat - every spread above 6% pinged his discord in under 90 seconds then he watched 3 youtube channels to learn which spreads were real and which were traps - runs a 50,000-skin portfolio and drops investment calls daily. his "Skinconomy" series breaks down which valve update moves which collection - taught him float math, sticker tier theory, when supply spikes are tradeable - daily market commentary, nothing but investments. calls crashes and rebounds in real-time - taught him tournament-cycle timing and when major-week volume kills exit liquidity - one-man bloomberg terminal for cs skins. monthly market data - no entertainment - taught him to read sticker premiums, supply shocks, and which collections never close their bid-ask gap $14,200 on a single flip in october: a 0.06-float purple sticker craft his scanner caught 9 minutes after listing the bot found it. youtube told him it was real. spreadsheet has 1,847 rows. average hold 41 days a 19-year-old made $87,000 off 3 free youtube channels and a $20/month claude subscription while everyone else refused to read the rules someone already uploaded FOR FREE

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52,727 次观看 • 5 个月前