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Ridark

@ridark_eth • 15,337 subscribers

Content Creator & Researcher | Ex Google @CELE8EX | Dm open ✉️

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I genuinely don't understand why everyone isn't using this yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. You point Claude Code at a folder, drop in any source, an article, a transcript, a PDF, and Claude reads it, links it, and files it into a living wiki of everything you know. It compounds like interest, the more you feed it, the smarter it gets. Here's the whole thing: > Install Obsidian, create a vault, open it in Claude Code > Paste Karpathy's wiki idea file and tell Claude to build it > Claude makes three folders: raw for sources, wiki for its pages, a CLAUDE.md that runs it > Drop any source into raw and say "ingest this" > Ask questions across everything, forever Five minutes to set up, and you never start from a blank chat again. Full step-by-step guide with Claude and Obsidian, link below. Bookmark this

I genuinely don't understand why everyone isn't using this yet Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 16 million views: stop using AI to write code, use it to build a second brain. You point Claude Code at a folder, drop in any source, an article, a transcript, a PDF, and Claude reads it, links it, and files it into a living wiki of everything you know. It compounds like interest, the more you feed it, the smarter it gets. Here's the whole thing: > Install Obsidian, create a vault, open it in Claude Code > Paste Karpathy's wiki idea file and tell Claude to build it > Claude makes three folders: raw for sources, wiki for its pages, a CLAUDE.md that runs it > Drop any source into raw and say "ingest this" > Ask questions across everything, forever Five minutes to set up, and you never start from a blank chat again. Full step-by-step guide with Claude and Obsidian, link below. Bookmark this

7,038,024 次观看

a 20-year-old fashion student from Hangzhou rebuilt the fridge scene frame by frame and cleared $11,300 last month on $31 of subscriptions. the shape started on Douyin. she ran it west before anyone else got there, straight to TikTok and Reels. she didn't just convert the character. she matched every expression, every gesture, every sausage grab, synced to the original shot by shot. > Claude: lifts every keyframe off the cartoon, writes the expression sheet and the prop list: 15 min > Seedream: draws each still, the girl in cat ears, the fridge interior, every food item placed to match the original frame: 20 min > Seedance 2.5: animates the split-screen, locks the timing between cartoon and live-action so every head tilt lands on the same beat: 40 min > CapCut: stacks the split, syncs the original audio, pushes it to 3 platforms: auto Warner Bros books a soundstage, a VFX house, and a full crew for one fridge gag. she books a $31/month loop and a cat ear headband. bookmark this

a 20-year-old fashion student from Hangzhou rebuilt the fridge scene frame by frame and cleared $11,300 last month on $31 of subscriptions. the shape started on Douyin. she ran it west before anyone else got there, straight to TikTok and Reels. she didn't just convert the character. she matched every expression, every gesture, every sausage grab, synced to the original shot by shot. > Claude: lifts every keyframe off the cartoon, writes the expression sheet and the prop list: 15 min > Seedream: draws each still, the girl in cat ears, the fridge interior, every food item placed to match the original frame: 20 min > Seedance 2.5: animates the split-screen, locks the timing between cartoon and live-action so every head tilt lands on the same beat: 40 min > CapCut: stacks the split, syncs the original audio, pushes it to 3 platforms: auto Warner Bros books a soundstage, a VFX house, and a full crew for one fridge gag. she books a $31/month loop and a cat ear headband. bookmark this

103,143 次观看

Elon Musk when he realized some guy turned Grok Bot into a $100K/month digital office with 6 agents that keep working after he closes the laptop and dropped the whole setup on X for free

Elon Musk when he realized some guy turned Grok Bot into a $100K/month digital office with 6 agents that keep working after he closes the laptop and dropped the whole setup on X for free

76,641 次观看

EVERYONE SAYS AI CANNOT INVENT A VISUAL METAPHOR i asked Seedance 2.5 for "a person in a desert catches a small cloud in a plastic bag." one sentence. one generate. went to refill my water. 00:00 – a desert. flat sand. tire tracks. a small white cloud hovers a few feet above the ground. a pair of hands holds an open plastic bag beneath it. 00:04 – the cloud drifts down. it touches the bag opening. moisture beads on the inside of the plastic. 00:07 – the hands close the bag. the cloud sits inside it. it's the size of a grapefruit. condensation drips down the sides. the bag sags with the weight of water vapor. 00:10 – the hands lift the bag. the cloud shifts inside. light refracts through the wet plastic differently on the side facing the sun. i paused on that last frame. the refraction is correct. the side of the bag facing the light is brighter. the condensation acts as a lens. the cloud's shadow sits on the sand below at the correct angle. nobody prompted "condensation as lens." nobody prompted "cloud weight sags the bag." the model understood that if you put water vapor in plastic, the plastic gets wet, and wet plastic bends light. The stack: -> Claude Fable 5.1 for writing the cloud capture choreography, hand positioning, and material interaction logic -> Veo 3.1 for rendering the desert environment with photorealistic sand grain texture and atmospheric haze -> Seedance 2.5 for animating the cloud descent, plastic bag deformation under vapor weight, and condensation propagation -> Kling 3.0 Omni for generating the light refraction through wet PVC and the shadow projection on sand in 4K -> ElevenLabs Spatial for desert wind, plastic crinkle, and a faint moisture hiss as the cloud contacts the bag the clip is 11 seconds long. it contains more correct physics than most VFX shots in a $150M movie. the model didn't just render a visual. it rendered what happens when you trap weather.

EVERYONE SAYS AI CANNOT INVENT A VISUAL METAPHOR i asked Seedance 2.5 for "a person in a desert catches a small cloud in a plastic bag." one sentence. one generate. went to refill my water. 00:00 – a desert. flat sand. tire tracks. a small white cloud hovers a few feet above the ground. a pair of hands holds an open plastic bag beneath it. 00:04 – the cloud drifts down. it touches the bag opening. moisture beads on the inside of the plastic. 00:07 – the hands close the bag. the cloud sits inside it. it's the size of a grapefruit. condensation drips down the sides. the bag sags with the weight of water vapor. 00:10 – the hands lift the bag. the cloud shifts inside. light refracts through the wet plastic differently on the side facing the sun. i paused on that last frame. the refraction is correct. the side of the bag facing the light is brighter. the condensation acts as a lens. the cloud's shadow sits on the sand below at the correct angle. nobody prompted "condensation as lens." nobody prompted "cloud weight sags the bag." the model understood that if you put water vapor in plastic, the plastic gets wet, and wet plastic bends light. The stack: -> Claude Fable 5.1 for writing the cloud capture choreography, hand positioning, and material interaction logic -> Veo 3.1 for rendering the desert environment with photorealistic sand grain texture and atmospheric haze -> Seedance 2.5 for animating the cloud descent, plastic bag deformation under vapor weight, and condensation propagation -> Kling 3.0 Omni for generating the light refraction through wet PVC and the shadow projection on sand in 4K -> ElevenLabs Spatial for desert wind, plastic crinkle, and a faint moisture hiss as the cloud contacts the bag the clip is 11 seconds long. it contains more correct physics than most VFX shots in a $150M movie. the model didn't just render a visual. it rendered what happens when you trap weather.

56,610 次观看

$76,001. That was Sam Altman's salary while running OpenAI. "No. I'm paid enough for health insurance. I have no equity in OpenAI." The senator's reply: "You need a lawyer." His salary that year was $76,001. Later, in federal court, Altman acknowledged he did have economic exposure to OpenAI after all, through his position in a Y Combinator fund. He said he hadn't mentioned it in that testimony because it was well understood what being a passive owner of venture funds means. Both things happened. A man ran the most valuable AI company on earth on $76,001 a year, and the full picture was somewhere else. The salary line is the part that goes viral. The fund position is the part that pays.

$76,001. That was Sam Altman's salary while running OpenAI. "No. I'm paid enough for health insurance. I have no equity in OpenAI." The senator's reply: "You need a lawyer." His salary that year was $76,001. Later, in federal court, Altman acknowledged he did have economic exposure to OpenAI after all, through his position in a Y Combinator fund. He said he hadn't mentioned it in that testimony because it was well understood what being a passive owner of venture funds means. Both things happened. A man ran the most valuable AI company on earth on $76,001 a year, and the full picture was somewhere else. The salary line is the part that goes viral. The fund position is the part that pays.

81,466 次观看

🚨 Major studios spent tens of millions of dollars on live-action versions of classic cartoons A 22-year-old animator from China spotted this trend exploding on Douyin and made $13,942 last month by being the first to bring it to Western TikTok and Reels. Turning old hand-drawn cartoons into realistic footage started over there and is still climbing. He copied the format and launched it before anyone else. > Kimi K3: hold the series in one window, write 250 shot prompts off a single character file: 1 day > Seedream: draw every still with the build copied word for word, never paraphrased: 20 min > Seedance: animate the keepers only, so a wrong face never reaches a render bill: 40 min > ElevenLabs: one locked voice, a delivery note under every line: auto Studios need soundstages, real animals and a full crew for one scene. He just needs a $67 stack and his own cat. Every prompt is in the article above 👇

🚨 Major studios spent tens of millions of dollars on live-action versions of classic cartoons A 22-year-old animator from China spotted this trend exploding on Douyin and made $13,942 last month by being the first to bring it to Western TikTok and Reels. Turning old hand-drawn cartoons into realistic footage started over there and is still climbing. He copied the format and launched it before anyone else. > Kimi K3: hold the series in one window, write 250 shot prompts off a single character file: 1 day > Seedream: draw every still with the build copied word for word, never paraphrased: 20 min > Seedance: animate the keepers only, so a wrong face never reaches a render bill: 40 min > ElevenLabs: one locked voice, a delivery note under every line: auto Studios need soundstages, real animals and a full crew for one scene. He just needs a $67 stack and his own cat. Every prompt is in the article above 👇

56,272 次观看

A 10-SECOND TITAN CGI SHOT PULLED 51,000,000 VIEWS IN UNDER 24 HOURS a 19-year-old VFX student built a blockbuster-grade shot of a titan walking along a coastline for $42. keeping the scale of a $356M Marvel movie, dropping the production crew the rebuild is where Picsart sits. the stack: 1. Picsart Agents: you hire ai staff to break down reference concepts. Aura builds the environment prompt, Nora fine-tunes the lighting, Reeva cuts the final 10-second sequence 2. AI Playground: runs Seedream and Seedance 2.5 on the same prompt in parallel. two readings of giant-scale movement side by side, you keep the one with correct shadow physics 3. Picsart Flow: takes one real aerial coastline photo to a finished video with giant water displacement. no keyframes, no 3D rendering pipelines 4. Picsart Pro is $15 a month. that’s the whole $356M VFX department on a $42 setup budget 5. Cartesia & CapCut integration: maps footstep impacts and stitches 11 dynamic takes into one razor-sharp clip before your morning coffee gets cold the catch: scale collapses only work when the shadows match the sun angle. check the physics before you hit export the VFX studio was the expensive part. the format was always free

A 10-SECOND TITAN CGI SHOT PULLED 51,000,000 VIEWS IN UNDER 24 HOURS a 19-year-old VFX student built a blockbuster-grade shot of a titan walking along a coastline for $42. keeping the scale of a $356M Marvel movie, dropping the production crew the rebuild is where Picsart sits. the stack: 1. Picsart Agents: you hire ai staff to break down reference concepts. Aura builds the environment prompt, Nora fine-tunes the lighting, Reeva cuts the final 10-second sequence 2. AI Playground: runs Seedream and Seedance 2.5 on the same prompt in parallel. two readings of giant-scale movement side by side, you keep the one with correct shadow physics 3. Picsart Flow: takes one real aerial coastline photo to a finished video with giant water displacement. no keyframes, no 3D rendering pipelines 4. Picsart Pro is $15 a month. that’s the whole $356M VFX department on a $42 setup budget 5. Cartesia & CapCut integration: maps footstep impacts and stitches 11 dynamic takes into one razor-sharp clip before your morning coffee gets cold the catch: scale collapses only work when the shadows match the sun angle. check the physics before you hit export the VFX studio was the expensive part. the format was always free

28,448 次观看

A 22-year-old photographer from Tromsø borrowed her roommate's mason jar and disappeared into the snow for an hour. She wouldn't say why. By the next morning, her 12-second clip had reached 3 million views. Her plan was simple: capture the northern lights in a glass jar. not a photo of them. the actual aurora. in a jar. held in her hands. glowing. The clip: a snowy forest road at night. tire tracks. footprints. aurora borealis rippling green and purple across the sky. a small flame of aurora light hovers above the path. hands reach out with a mason jar and scoop it up. the aurora ribbon sits inside the glass, swirling. green light reflects off the snow and off her fingers. She didn't invent a new trend. She borrowed the "catch impossible nature in a container" format that was already working and applied it to the one natural phenomenon every traveler wants to hold. The $29 went to a Seedance 2.5 subscription. She typed one sentence, hit generate, and walked outside to watch the real aurora while the fake one rendered. Then she ran the system again: different location, different aurora color, same jar. One character she can keep building around. One format she can reuse every clear night for the rest of winter. Find out how to do it in the article below

A 22-year-old photographer from Tromsø borrowed her roommate's mason jar and disappeared into the snow for an hour. She wouldn't say why. By the next morning, her 12-second clip had reached 3 million views. Her plan was simple: capture the northern lights in a glass jar. not a photo of them. the actual aurora. in a jar. held in her hands. glowing. The clip: a snowy forest road at night. tire tracks. footprints. aurora borealis rippling green and purple across the sky. a small flame of aurora light hovers above the path. hands reach out with a mason jar and scoop it up. the aurora ribbon sits inside the glass, swirling. green light reflects off the snow and off her fingers. She didn't invent a new trend. She borrowed the "catch impossible nature in a container" format that was already working and applied it to the one natural phenomenon every traveler wants to hold. The $29 went to a Seedance 2.5 subscription. She typed one sentence, hit generate, and walked outside to watch the real aurora while the fake one rendered. Then she ran the system again: different location, different aurora color, same jar. One character she can keep building around. One format she can reuse every clear night for the rest of winter. Find out how to do it in the article below

27,226 次观看

A 27-year-old guy from China made $150,000 using AI simply by automating content creation for models It all started with a common issue agencies face: models can't shoot enough content to scale. The strategy turned out to be ridiculously simple: 1) Upload a single photo of a girl. 2) Pick a motion or dance template from the library. 3) The AI generates a realistic video for Reels, TikTok, and Shorts in seconds. By leveraging this setup, he automated mass posting, publishing hundreds of videos daily without needing real models. The system pulls in millions of free organic views, driving traffic directly through profile links to paid subscriptions, OnlyFans, and affiliate offers. AI tools are completely solving the content shortage problem, allowing solopreneurs to run multi-million-dollar virtual agencies on their own. Bookmark this

A 27-year-old guy from China made $150,000 using AI simply by automating content creation for models It all started with a common issue agencies face: models can't shoot enough content to scale. The strategy turned out to be ridiculously simple: 1) Upload a single photo of a girl. 2) Pick a motion or dance template from the library. 3) The AI generates a realistic video for Reels, TikTok, and Shorts in seconds. By leveraging this setup, he automated mass posting, publishing hundreds of videos daily without needing real models. The system pulls in millions of free organic views, driving traffic directly through profile links to paid subscriptions, OnlyFans, and affiliate offers. AI tools are completely solving the content shortage problem, allowing solopreneurs to run multi-million-dollar virtual agencies on their own. Bookmark this

41,712 次观看

this guy showed how he makes $11,000 a month filming behind-the-scenes footage of animated movies that were never made. the non-obvious part: the characters are animated. the set is real. the crew is real. but none of it exists. he generates the "making of" as if Kung Fu Panda was shot on a practical set with physical characters. Master Shifu prepares for the battle with Tai Lung. The scene where Tai Lung escapes from prison. Shifu training the Panda. - Claude writes the production moment: which animated character, what scene, what set dressing, where the crew stands - Midjourney builds the "on-set" shot with the animated character composited into a real soundstage - Seedance 2.5 gives it that handheld, behind-the-shoulder documentary feel - CapCut posts it as "behind the scenes of ___" everyone makes the clip. he makes the myth around the clip. the full method is in the article.

this guy showed how he makes $11,000 a month filming behind-the-scenes footage of animated movies that were never made. the non-obvious part: the characters are animated. the set is real. the crew is real. but none of it exists. he generates the "making of" as if Kung Fu Panda was shot on a practical set with physical characters. Master Shifu prepares for the battle with Tai Lung. The scene where Tai Lung escapes from prison. Shifu training the Panda. - Claude writes the production moment: which animated character, what scene, what set dressing, where the crew stands - Midjourney builds the "on-set" shot with the animated character composited into a real soundstage - Seedance 2.5 gives it that handheld, behind-the-shoulder documentary feel - CapCut posts it as "behind the scenes of ___" everyone makes the clip. he makes the myth around the clip. the full method is in the article.

23,087 次观看

I genuinely don't understand what the TikTok CEO is even looking at or how the algorithms let this slide. Those faceless accounts spamming reposts with a betting link in the bio aren’t even real people. It’s a phone farm: one computer, dozens of gutted smartphones on a rack, and scripts that play the moderation system like a fiddle. Here is how this scheme works, and how it’s absolutely breaking the platform's algorithms right now: - the hardware: the boards of used phones, taken apart, sharing one power supply and one Ethernet cable, every screen mirrored to a single monitor - the content: often someone else's video, run through a uniquifier so TikTok won't flag the repost, then blasted across hundreds of accounts at once - the funnel: whatever clip catches the algorithm carries a link to a betting site, crypto exchange or dating app, and the owner takes a cut of every signup - the account flip: the farm warms profiles until they look "aged" and trusted, then sells them wholesale for ads or spam - the fake metrics: views, likes, followers and comments sold on demand to anyone who wants to look bigger than they are - the ban dodge: real phones carry real hardware IDs, so TikTok reads each account as a separate human, that's the whole reason it isn't emulators - the core idea: a lot of what looks viral was never an audience, it's one machine wearing a hundred masks how do you feel about this kind of earnings?

I genuinely don't understand what the TikTok CEO is even looking at or how the algorithms let this slide. Those faceless accounts spamming reposts with a betting link in the bio aren’t even real people. It’s a phone farm: one computer, dozens of gutted smartphones on a rack, and scripts that play the moderation system like a fiddle. Here is how this scheme works, and how it’s absolutely breaking the platform's algorithms right now: - the hardware: the boards of used phones, taken apart, sharing one power supply and one Ethernet cable, every screen mirrored to a single monitor - the content: often someone else's video, run through a uniquifier so TikTok won't flag the repost, then blasted across hundreds of accounts at once - the funnel: whatever clip catches the algorithm carries a link to a betting site, crypto exchange or dating app, and the owner takes a cut of every signup - the account flip: the farm warms profiles until they look "aged" and trusted, then sells them wholesale for ads or spam - the fake metrics: views, likes, followers and comments sold on demand to anyone who wants to look bigger than they are - the ban dodge: real phones carry real hardware IDs, so TikTok reads each account as a separate human, that's the whole reason it isn't emulators - the core idea: a lot of what looks viral was never an audience, it's one machine wearing a hundred masks how do you feel about this kind of earnings?

38,318 次观看

A 23-year-old girl built a desktop pet and ran a local LLM on a stock 1998 iMac G3 with 32 MB of RAM and Mac OS 8. She didn’t buy any modern upgrades. She used vintage hardware, a cross-compiler, and Claude to bring AI to a 28-year-old machine. Then she ported Karpathy’s llama2.c to classic Mac OS, compiled it with Retro68, used a Python script to swap the model’s data to big-endian for the PowerPC chip, and successfully generated local text via a 260K parameter model. One vintage Mac. 32 megabytes of RAM. Local inference. A free cross-compiler. Most people see old computers as electronic waste. She treated a 233 MHz processor like a live hardware challenge and forced modern AI architecture to run where it was never meant to exist.

A 23-year-old girl built a desktop pet and ran a local LLM on a stock 1998 iMac G3 with 32 MB of RAM and Mac OS 8. She didn’t buy any modern upgrades. She used vintage hardware, a cross-compiler, and Claude to bring AI to a 28-year-old machine. Then she ported Karpathy’s llama2.c to classic Mac OS, compiled it with Retro68, used a Python script to swap the model’s data to big-endian for the PowerPC chip, and successfully generated local text via a 260K parameter model. One vintage Mac. 32 megabytes of RAM. Local inference. A free cross-compiler. Most people see old computers as electronic waste. She treated a 233 MHz processor like a live hardware challenge and forced modern AI architecture to run where it was never meant to exist.

35,186 次观看

A 24-year-old guy made $9,420 trading the S&P in just two weeks, without looking at a chart even once he'd blown up two small accounts first, both times drawing the same support lines and "order blocks" everyone else draws. none of it worked, because none of it moves the market. so he stopped watching the picture and started watching the people forced to hedge: > he tracked Net GEX, the map of where dealers are short gamma and have to hedge > at one strike a wall of negative gamma had piled up, the zone where a dip forces them to sell into it > when price touched it, the market makers sank it themselves, covering their own risk > he was already in cheap same-day (0DTE) puts, and the move ran them +470% in hours he sized tiny every time, because the leverage that paid him could zero the position by lunch. $9,420 didn't come from a secret indicator. they came the day he saw the crowd was staring at drawings while the market was being moved by the math of hedging billion-cap positions.

A 24-year-old guy made $9,420 trading the S&P in just two weeks, without looking at a chart even once he'd blown up two small accounts first, both times drawing the same support lines and "order blocks" everyone else draws. none of it worked, because none of it moves the market. so he stopped watching the picture and started watching the people forced to hedge: > he tracked Net GEX, the map of where dealers are short gamma and have to hedge > at one strike a wall of negative gamma had piled up, the zone where a dip forces them to sell into it > when price touched it, the market makers sank it themselves, covering their own risk > he was already in cheap same-day (0DTE) puts, and the move ran them +470% in hours he sized tiny every time, because the leverage that paid him could zero the position by lunch. $9,420 didn't come from a secret indicator. they came the day he saw the crowd was staring at drawings while the market was being moved by the math of hedging billion-cap positions.

24,304 次观看

THIS GUY SHOWED HOW HIS BROTHER MAKES $15,000 A MONTH MAKING ROBLOX GAMES WITH CLAUDE, WITHOUT WRITING A SINGLE LINE OF CODE HIMSELF No dev team. No publisher. No computer science degree. Just a $20 Claude subscription and the Roblox Studio game engine. He doesn't write code by hand or build game logic manually. Instead, Claude acts as his personal programmer. Here is how the system works: he types what he wants in plain text: "create a script for a leaderboard" or "write a coin saving system" → Claude generates a ready-to-use piece of Lua code → he copies and pastes it directly into Roblox Studio → the game gets packed with mechanics. And if something breaks, he just drops the error into the chat, and Claude provides detailed instructions, a list of fixes, and the corrected code. What would take a beginner weeks of studying documentation gets done in mere hours. The money here doesn't come from subscriptions but from inside the game: players buy in-game items, passes, and cosmetics with Robux -> Roblox's currency. The creator then officially exchanges the accumulated Robux for real money through the Roblox Developer Exchange program. He simply describes the kind of game he wants. Everything else -> from the very first line of Lua to a working game mechanic -> is handled by AI. What do you think Claude Fable is capable of and how much money can he make you?

THIS GUY SHOWED HOW HIS BROTHER MAKES $15,000 A MONTH MAKING ROBLOX GAMES WITH CLAUDE, WITHOUT WRITING A SINGLE LINE OF CODE HIMSELF No dev team. No publisher. No computer science degree. Just a $20 Claude subscription and the Roblox Studio game engine. He doesn't write code by hand or build game logic manually. Instead, Claude acts as his personal programmer. Here is how the system works: he types what he wants in plain text: "create a script for a leaderboard" or "write a coin saving system" → Claude generates a ready-to-use piece of Lua code → he copies and pastes it directly into Roblox Studio → the game gets packed with mechanics. And if something breaks, he just drops the error into the chat, and Claude provides detailed instructions, a list of fixes, and the corrected code. What would take a beginner weeks of studying documentation gets done in mere hours. The money here doesn't come from subscriptions but from inside the game: players buy in-game items, passes, and cosmetics with Robux -> Roblox's currency. The creator then officially exchanges the accumulated Robux for real money through the Roblox Developer Exchange program. He simply describes the kind of game he wants. Everything else -> from the very first line of Lua to a working game mechanic -> is handled by AI. What do you think Claude Fable is capable of and how much money can he make you?

23,914 次观看

A 24-year-old US video editor who got tired of deadlines now makes $13,000 a month literally while sleeping At first, he was completely out of time trying to handle all his client orders, so he decided to offload 90% of the workflow to AI -> and now his business generates passive income 24/7 while he just rests in bed Using two AI plugins, he completely automated the process right inside Adobe Premiere Pro: > Automatic on-beat cutting: The first plugin analyzes any music track and instantly cuts the video footage perfectly to the beat > Hands-free b-roll editing: The second tool detects the audio's BPM, automatically selects the right secondary footage, and pieces together dynamic event recaps While the guy was asleep, the AI finished and delivered a project in just 2 minutes -> a task that used to take human editors days He has already fired his team of 3 editors, because the AI does it faster and for free. Artificial intelligence is changing the game, and literally anyone can repeat this

A 24-year-old US video editor who got tired of deadlines now makes $13,000 a month literally while sleeping At first, he was completely out of time trying to handle all his client orders, so he decided to offload 90% of the workflow to AI -> and now his business generates passive income 24/7 while he just rests in bed Using two AI plugins, he completely automated the process right inside Adobe Premiere Pro: > Automatic on-beat cutting: The first plugin analyzes any music track and instantly cuts the video footage perfectly to the beat > Hands-free b-roll editing: The second tool detects the audio's BPM, automatically selects the right secondary footage, and pieces together dynamic event recaps While the guy was asleep, the AI finished and delivered a project in just 2 minutes -> a task that used to take human editors days He has already fired his team of 3 editors, because the AI does it faster and for free. Artificial intelligence is changing the game, and literally anyone can repeat this

20,158 次观看

19-year-old from china makes $9,000/month designing product sites and ships each one in an afternoon. here's his exact setup the whole thing runs on two tools that each do one job: > brief written by hand: 5 min > Moonchild builds the design system, then every screen from it: 20 min > MCP hands the design to Claude as real structure, not a screenshot: instant > Claude Code reads those exact tokens and builds the live app: 20 min > second Claude session reviews the build for drift: 10 min total: about an hour. screen five still matches screen one. no agency, no dev, no design team the trick is MCP. the design tool passes Claude the actual colors, components and layout, so it builds from the source instead of guessing from a picture. full pipeline, every prompt, in the article above.

19-year-old from china makes $9,000/month designing product sites and ships each one in an afternoon. here's his exact setup the whole thing runs on two tools that each do one job: > brief written by hand: 5 min > Moonchild builds the design system, then every screen from it: 20 min > MCP hands the design to Claude as real structure, not a screenshot: instant > Claude Code reads those exact tokens and builds the live app: 20 min > second Claude session reviews the build for drift: 10 min total: about an hour. screen five still matches screen one. no agency, no dev, no design team the trick is MCP. the design tool passes Claude the actual colors, components and layout, so it builds from the source instead of guessing from a picture. full pipeline, every prompt, in the article above.

19,477 次观看

How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇

How a 22-year-old developer built a full 3D Jet Ski racing game in just 40 minutes with zero manual coding He used Claude Opus 5 to generate physics, WebGL 3D graphics, HUD, and audio in a single prompt and turned single-prompt gamedev into a high-margin income stream. Costs: $423 He launched a single-prompt generation workflow that built the entire HTML5 project from scratch: Top layer: A Three.js and WebGL rendering pipeline dynamically creates 3D water physics, real-time wave dynamics, dynamic lighting, and jet ski fluid mechanics, all written autonomously inside one output file without external frameworks. Bottom layer: The Claude Opus 5 engine processed a massive 690-million-token context window to generate the complete gameplay logic, collision handling, dynamic sound generation, controls, and UI layout directly from a detailed initial system prompt. The trend of single-prompt 3D game creation is rapidly exploding across media and indie development. The author monetizes this tech stack through three main channels: 1. Viral Content & Media Systems: Short-form breakdown videos driving massive reach, monetized via promo placements, prompt-pack access, and private developer communities. 2. Rapid Hypercasual Prototyping: Testing 10+ WebGL mechanics per day, flipping fully functional browser games on itch io or CodeCanyon, and licensing prototypes directly to casual game portals. 3. Interactive WebGL Client Solutions: Delivering custom 3D promotional browser games and interactive brand experiences for clients in 48 hours instead of weeks. First month results: > WebGL games generated: 24 > Viral impressions generated: 3.8M+ > Total revenue across licensing & content: $21,400 The AI completely automated the core development lifecycle: Claude Opus 5 built the physics engine, rendered 3D graphics in WebGL, hooked up audio controllers, and generated interactive browser logic with zero manual line-by-line coding. Bookmark it and check article 👇

11,592 次观看

THIS $2 AI VISUAL SETUP JUST MADE A $250/MONTH CREATIVE STACK LOOK STUPID he moves his hand, the particles react, and the whole scene updates in real time. no cloud render farm, no paid visual API, no expensive plugin chain quietly eating money every month most people still think ai visuals require a stack of $39 tools, $89 subscriptions, and constant API usage. but here the loop is simple: TouchDesigner handles the visuals, a local model handles the logic, and the laptop does the rest the important part is not that the particles look cool. the important part is that the “brain” behind the visual no longer has to live on someone else’s server once that moves local, the monthly bill falls off a cliff this is probably how a lot of small studios start building visuals soon: not by renting 6 tools forever, but by owning one system that runs almost for free

THIS $2 AI VISUAL SETUP JUST MADE A $250/MONTH CREATIVE STACK LOOK STUPID he moves his hand, the particles react, and the whole scene updates in real time. no cloud render farm, no paid visual API, no expensive plugin chain quietly eating money every month most people still think ai visuals require a stack of $39 tools, $89 subscriptions, and constant API usage. but here the loop is simple: TouchDesigner handles the visuals, a local model handles the logic, and the laptop does the rest the important part is not that the particles look cool. the important part is that the “brain” behind the visual no longer has to live on someone else’s server once that moves local, the monthly bill falls off a cliff this is probably how a lot of small studios start building visuals soon: not by renting 6 tools forever, but by owning one system that runs almost for free

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I gave Elon Musk's new Grok Bot an org chart instead of a to-do list, and in one week I stopped being a founder who does the work and became one who assigns it. eight bots. one org chart. nobody sleeps but me. here's the whole design, steal it. step 1 → 0:01 What we're covering step 2 → 2:01 Installation & Setup step 3 → 3:14 Building the First Bot step 4 → 8:18 Teaching by Screen Recording step 5 → 14:18 Putting it All Together THE ROSTER: Atlas, chief of staff. the only bot I talk to. I give it outcomes, never tasks. it decomposes them and delegates to the team in group chat, and it never does specialist work itself. it posts the plan every morning and what shipped every night, and it only comes to me when a decision is irreversible or spends money. Scout, research. finds and qualifies my ICP. every day: 25 verified prospects, one line on why they need us right now, and a source. if it can't verify, it marks it unverified. it never guesses. Quill, content. turns what the company learned this week into 5 posts and 1 long piece, in my voice, matched from the last 50 things I wrote. drafts only, it never publishes. Pitch, outbound. writes a first touch and two follow-ups for everyone Scout marks ready. 60 words max, one specific observation about their business, one clear ask. queued in drafts, I approve in bulk. Vault, inbox and ops. triages everything into needs-me, needs-a-bot, needs-nothing. it handles the last, routes the middle, and gives me five bullets on the first by 9am. Ledger, analyst. one report a night: what moved, what didn't, and the single number I should care about tomorrow. no dashboards, no adjectives. HOW THEY'RE WIRED one group chat per outcome, not per person. Atlas sits in all of them. the bots hand off inside the chat, so I only read the handoff, I never manage it. two rules that made this actually work: 1. every charter ends with a hard "never do this without asking" line. autonomy without a fence is just chaos on a schedule. 2. show once, don't describe. I ran the full workflow on my screen one time. that single demo taught them more than a page of instructions ever could. WEEK ONE 214 verified prospects delivered. 89 personalized outreaches queued and approved. inbox at zero every morning. 11 content pieces ready. THE POINT most people are still treating Grok Bot like a smarter chat window. it isn't. it's the first time one person can own an org chart instead of a to-do list. my bottleneck was never how much I could do, it was how much I could hand off. bookmark this.
18:40
ridark_eth's profile picture

I gave Elon Musk's new Grok Bot an org chart instead of a to-do list, and in one week I stopped being a founder who does the work and became one who assigns it. eight bots. one org chart. nobody sleeps but me. here's the whole design, steal it. step 1 → 0:01 What we're covering step 2 → 2:01 Installation & Setup step 3 → 3:14 Building the First Bot step 4 → 8:18 Teaching by Screen Recording step 5 → 14:18 Putting it All Together THE ROSTER: Atlas, chief of staff. the only bot I talk to. I give it outcomes, never tasks. it decomposes them and delegates to the team in group chat, and it never does specialist work itself. it posts the plan every morning and what shipped every night, and it only comes to me when a decision is irreversible or spends money. Scout, research. finds and qualifies my ICP. every day: 25 verified prospects, one line on why they need us right now, and a source. if it can't verify, it marks it unverified. it never guesses. Quill, content. turns what the company learned this week into 5 posts and 1 long piece, in my voice, matched from the last 50 things I wrote. drafts only, it never publishes. Pitch, outbound. writes a first touch and two follow-ups for everyone Scout marks ready. 60 words max, one specific observation about their business, one clear ask. queued in drafts, I approve in bulk. Vault, inbox and ops. triages everything into needs-me, needs-a-bot, needs-nothing. it handles the last, routes the middle, and gives me five bullets on the first by 9am. Ledger, analyst. one report a night: what moved, what didn't, and the single number I should care about tomorrow. no dashboards, no adjectives. HOW THEY'RE WIRED one group chat per outcome, not per person. Atlas sits in all of them. the bots hand off inside the chat, so I only read the handoff, I never manage it. two rules that made this actually work: 1. every charter ends with a hard "never do this without asking" line. autonomy without a fence is just chaos on a schedule. 2. show once, don't describe. I ran the full workflow on my screen one time. that single demo taught them more than a page of instructions ever could. WEEK ONE 214 verified prospects delivered. 89 personalized outreaches queued and approved. inbox at zero every morning. 11 content pieces ready. THE POINT most people are still treating Grok Bot like a smarter chat window. it isn't. it's the first time one person can own an org chart instead of a to-do list. my bottleneck was never how much I could do, it was how much I could hand off. bookmark this.

Ridark

1,284,135 次观看 • 2 个月前

I still don’t understand why everyone is not doing this yet. Elon Musk reposted my Grok Bot GUIDE, and using this exact agent setup, I made $13,100 last week alone. Eight Grok agents on the desk, $200 a month, running a floor that usually costs a crypto fund $500K a year in analyst salaries. The morning call is at 4am. I am not in it. 1. SEARCH scrapes real-time alpha, dev GitHubs, and unindexed Telegram signals before CT finds them 2. RISK audits contract functions, mint rights, and LP locks, flags honeypots before entry 3. SNIPER places high-speed orders on chain the exact millisecond risk clearance passes 4. WHALE tracks smart money wallets and flags insider accumulation in real time 5. RUG monitors dev wallet activity 24/7 and dumps the entire position if LP is touched 6. EXIT trails stops dynamically, scaling out as liquidity builds 7. SHILL tracks social volume, momentum velocity, and key influencer calls 8. HEAD OF DESK never trades, routes data, checks handoffs, and brings me the one decision that needs a human 142 tokens scanned, 19 qualified setups, 6 executed trades while I was asleep. Net result: +$13,100 after fees and zero bad fills. Every agent has its own virtual browser, terminal, and local memory in the cloud. The floor stays active with my laptop shut. The setup is dumber than it looks: Download Grok Bot and create your Head of Desk. Give the remaining 7 agents job descriptions like you're briefing new hires. Run the workflow once on your screen while they watch. Hook up Telegram and wallet webhooks. No VPS, no code, no waiting on developers. A crypto trading floor used to mean 16-hour screen time, paid alpha groups, and constant fatigue. Mine took one evening to set up. Save this before your next trade. Save GUIDE.
0:34
ridark_eth's profile picture

I still don’t understand why everyone is not doing this yet. Elon Musk reposted my Grok Bot GUIDE, and using this exact agent setup, I made $13,100 last week alone. Eight Grok agents on the desk, $200 a month, running a floor that usually costs a crypto fund $500K a year in analyst salaries. The morning call is at 4am. I am not in it. 1. SEARCH scrapes real-time alpha, dev GitHubs, and unindexed Telegram signals before CT finds them 2. RISK audits contract functions, mint rights, and LP locks, flags honeypots before entry 3. SNIPER places high-speed orders on chain the exact millisecond risk clearance passes 4. WHALE tracks smart money wallets and flags insider accumulation in real time 5. RUG monitors dev wallet activity 24/7 and dumps the entire position if LP is touched 6. EXIT trails stops dynamically, scaling out as liquidity builds 7. SHILL tracks social volume, momentum velocity, and key influencer calls 8. HEAD OF DESK never trades, routes data, checks handoffs, and brings me the one decision that needs a human 142 tokens scanned, 19 qualified setups, 6 executed trades while I was asleep. Net result: +$13,100 after fees and zero bad fills. Every agent has its own virtual browser, terminal, and local memory in the cloud. The floor stays active with my laptop shut. The setup is dumber than it looks: Download Grok Bot and create your Head of Desk. Give the remaining 7 agents job descriptions like you're briefing new hires. Run the workflow once on your screen while they watch. Hook up Telegram and wallet webhooks. No VPS, no code, no waiting on developers. A crypto trading floor used to mean 16-hour screen time, paid alpha groups, and constant fatigue. Mine took one evening to set up. Save this before your next trade. Save GUIDE.

Ridark

609,556 次观看 • 2 个月前

Pieter Levels makes over $250,000 a month -> alone, from a laptop, with zero employees no co-founder. no investors. no team. just him and a backpack, moving between countries he started by daring himself to launch 12 startups in 12 months. then he kept going -> 70+ products over the years, and most of them flopped he doesn't plan. no roadmaps, no polishing for months. he ships before it's "ready" and kills anything that gets no traction within weeks one throwaway experiment -> an AI photo app he built the day after a tweet went viral -> hit $77,000/month within a year. today it does over $130,000/month on its own he still writes everything in old-school PHP. every error gets piped straight to his Telegram. ~95% of the revenue is pure profit even Andrej Karpathy said this is the model for the future: one person running multiple companies the clip below is him explaining exactly how ↓
36:41
ridark_eth's profile picture

Pieter Levels makes over $250,000 a month -> alone, from a laptop, with zero employees no co-founder. no investors. no team. just him and a backpack, moving between countries he started by daring himself to launch 12 startups in 12 months. then he kept going -> 70+ products over the years, and most of them flopped he doesn't plan. no roadmaps, no polishing for months. he ships before it's "ready" and kills anything that gets no traction within weeks one throwaway experiment -> an AI photo app he built the day after a tweet went viral -> hit $77,000/month within a year. today it does over $130,000/month on its own he still writes everything in old-school PHP. every error gets piped straight to his Telegram. ~95% of the revenue is pure profit even Andrej Karpathy said this is the model for the future: one person running multiple companies the clip below is him explaining exactly how ↓

Ridark

482,033 次观看 • 4 个月前

> be Microsoft > dominate the PC market for decades > release Windows 11 in 2021 > stuff it with AI, weather widgets, and Candy Crush > OS eats 4GB of RAM just to show the desktop > Gabe Newell releases Steam Deck on lightweight Linux > suddenly Windows handhelds look like laggy trash compared to SteamOS > Satya Nadella looks at the performance charts > "enough is enough" > launch secret "Project K2" plan > gut all the bloat accumulated since '95 > make the Start menu 60% faster just because he can > OS is now snappier than custom "de-bloated" hacker builds > "dominating the handheld market sounds like fun" Microsoft just delivered 120 FPS on a budget Project Helix chip. They had no lightweight Linux kernel. Gabe wouldn't share his optimization secrets. They won anyway on pure code.
0:25
ridark_eth's profile picture

> be Microsoft > dominate the PC market for decades > release Windows 11 in 2021 > stuff it with AI, weather widgets, and Candy Crush > OS eats 4GB of RAM just to show the desktop > Gabe Newell releases Steam Deck on lightweight Linux > suddenly Windows handhelds look like laggy trash compared to SteamOS > Satya Nadella looks at the performance charts > "enough is enough" > launch secret "Project K2" plan > gut all the bloat accumulated since '95 > make the Start menu 60% faster just because he can > OS is now snappier than custom "de-bloated" hacker builds > "dominating the handheld market sounds like fun" Microsoft just delivered 120 FPS on a budget Project Helix chip. They had no lightweight Linux kernel. Gabe wouldn't share his optimization secrets. They won anyway on pure code.

Ridark

628,634 次观看 • 6 个月前

After Elon Musk reposted my Grok Bot guide, my friend Ryan used it to run his strip club. 7 days ago, he replaced his entire middle management with 8 Grok agents ($200/mo). Last week alone, he saved $10,000 in salaries and missed leads. Not because the club magically got better, but because he killed the middle layer that was eating his margin. Before that, the setup was classic and expensive. Back-of-house sat on 6 people: > 1 recruiter > 1 person on applications > 1 cashier > 2 shift managers > 1 person on after-close requests That layer cost about $8,400–$9,200 a week once you counted base pay, cuts, “bonuses,” and money that never made the report. The recruiter took $50–$150 per new girl. The cashier spent 1.5–2 hours closing a shift. On Friday the managers generated 80–120 messages on the schedule alone. The after-shift person held 10–15 requests in his head and lost 2–3 of them every week simply because someone did not answer in time. Ryan saw the formula fast. Those people almost never made hard decisions. They moved data. • An application came in → a person opened a chat. • A girl wrote “I can do Friday” → a person typed a row into a spreadsheet. • A client left a request → a person relayed it to a manager. • The manager opened the schedule → and texted the girl. • The shift ended → the cashier counted a stack and entered a number by hand. Every handoff leaked time and money. Applications. A live staffer used to review 40–60 incoming files a week. One file took 8–12 minutes. Total: 7–10 hours of raw review, plus another 3–4 hours on “send a reminder,” “send more,” “when can you work.” Out of 50 applications, 6–8 made it to a shift. Conversion was weak not because of the room, but because half the threads went cold for 24–48 hours. Scheduling. Friday looked like this: 12 people want on, 8 slots. 1 can start only after 21:00. 1 will not work after 00:00. 1 drops out with 40 minutes left. 1 wants a swap. A manager already promised the slot to a fifth person. One change created 5 new messages. One substitution cycle took 20–40 minutes. By the end of the night there were 3–5 holes in the grid. Money. That was the dirtiest part. By morning the count was off by $180–$400 on average. Sometimes $70. Sometimes $600. The explanations were always human: tips booked to the wrong place, a commission forgotten, a number rounded, an envelope put in the wrong pile, a shift closed from memory. The cashier was not an analyst. He was a loss point. After-shift requests. The old chain took 25–45 minutes: client → manager → spreadsheet → message to the girl → wait → reply to the client. Out of 12 requests, 2–3 died in transit. Not because of a refusal. Because of human lag. 7 days ago Ryan cut that layer and hung it on Grok. The application flow dropped into one funnel with statuses: NEW → REVIEW → APPROVED → SCHEDULED → ACTIVE → INACTIVE The bot sent the first packet itself, collected the fields, and closed the gaps. If data was missing → it asked. If there was silence for 48 hours → it nudged. Ryan no longer opened 50 chats. He opened one feed. He touched only REVIEW and exceptions by hand. First-pass review fell from 8–12 minutes per application to 30–90 seconds of control. In a week, 53 applications went through the funnel. 11 made it to a shift. That was no longer “we got lucky.” That was because no application rotted in a manager’s DMs. The schedule became a rule, not a group chat. A girl marked available slots. The system saw 8 seats, not “we’ll figure it out.” Overbook went to a waitlist. A drop with 40 minutes left no longer spawned a 5-chat mess: the slot jumped to the next person in line in 10–20 seconds. Friday noise fell from 80–120 messages to 10–15 exceptions. Shift manager as a job title became unnecessary. The register stopped being a notebook. Every operation was written immediately. Shift close produced one summary: - floor revenue - tips - commissions - payouts - adjustments - variance If the total did not match, the system did not say “error somewhere.” It pointed at the exact operation. Over 7 days, variance stayed in the $0–$25 range instead of the old $180–$400. The weekly difference was about $1,200–$2,500 on “it didn’t add up” alone. The client loop collapsed from 4 human nodes into 1 route. A request entered the bot. The bot pulled the standard fields, checked availability, pinged the girl, and after 2 confirmations closed both sides with a notification. Cycle time fell from 25–45 minutes to 2–4 minutes on a standard request. Burned requests for the week: 0. Before that, it was 2–3 lost checks every week. Ryan’s role got narrow and hard. The bot closed the rule. He closed the exception. Morning looked like a panel, not a meeting: > 3 new applications in NEW > 1 card in REVIEW > 2 shift cancellations > 1 operation for manual review > 47 automated messages already sent without him The only living parts left were him and the girls on the floor. Plus anyone who took an after-shift call. The middle layer: sourcing, screening, schedule, requests, counting → sat on a $200 subscription. In numbers, the week looked like this. Old model: > 6 people in the management loop > $8,400–$9,200 for that layer > 7–10 hours on applications > 80–120 messages on one heavy Friday > $180–$400 holes in the cash > 2–3 lost requests > 25–45 minutes per client cycle New model: > 1 person > $200 for the tool > 30–90 seconds of standard application control > 10–15 exceptions instead of a whole-floor chat storm > $0–$25 on the register > 0 lost standard requests > 2–4 minutes per standard cycle That is where the ~$10,000 in 7 days came from. Not “floor magic.” A removed human tax: - pay for 6 people - recruiter cuts - cash holes - burned requests - hours spent moving the same row from a chat into a spreadsheet In this system, those people were not the “soul of the club.” They were latency and leak. Every extra node added delay. Every live data handoff added error. Every notebook added a gap for rounding. Ryan removed the nodes. He left rules, statuses, a ledger, and one escalation point. So after 7 days the model already counted as a delta, not an experiment. $200 on top. About $10,000 in the plus. And the proof was short: the club ran. The middle-layer staff did not. Their job was done by the bot. bookmark this.
0:35
ridark_eth's profile picture

After Elon Musk reposted my Grok Bot guide, my friend Ryan used it to run his strip club. 7 days ago, he replaced his entire middle management with 8 Grok agents ($200/mo). Last week alone, he saved $10,000 in salaries and missed leads. Not because the club magically got better, but because he killed the middle layer that was eating his margin. Before that, the setup was classic and expensive. Back-of-house sat on 6 people: > 1 recruiter > 1 person on applications > 1 cashier > 2 shift managers > 1 person on after-close requests That layer cost about $8,400–$9,200 a week once you counted base pay, cuts, “bonuses,” and money that never made the report. The recruiter took $50–$150 per new girl. The cashier spent 1.5–2 hours closing a shift. On Friday the managers generated 80–120 messages on the schedule alone. The after-shift person held 10–15 requests in his head and lost 2–3 of them every week simply because someone did not answer in time. Ryan saw the formula fast. Those people almost never made hard decisions. They moved data. • An application came in → a person opened a chat. • A girl wrote “I can do Friday” → a person typed a row into a spreadsheet. • A client left a request → a person relayed it to a manager. • The manager opened the schedule → and texted the girl. • The shift ended → the cashier counted a stack and entered a number by hand. Every handoff leaked time and money. Applications. A live staffer used to review 40–60 incoming files a week. One file took 8–12 minutes. Total: 7–10 hours of raw review, plus another 3–4 hours on “send a reminder,” “send more,” “when can you work.” Out of 50 applications, 6–8 made it to a shift. Conversion was weak not because of the room, but because half the threads went cold for 24–48 hours. Scheduling. Friday looked like this: 12 people want on, 8 slots. 1 can start only after 21:00. 1 will not work after 00:00. 1 drops out with 40 minutes left. 1 wants a swap. A manager already promised the slot to a fifth person. One change created 5 new messages. One substitution cycle took 20–40 minutes. By the end of the night there were 3–5 holes in the grid. Money. That was the dirtiest part. By morning the count was off by $180–$400 on average. Sometimes $70. Sometimes $600. The explanations were always human: tips booked to the wrong place, a commission forgotten, a number rounded, an envelope put in the wrong pile, a shift closed from memory. The cashier was not an analyst. He was a loss point. After-shift requests. The old chain took 25–45 minutes: client → manager → spreadsheet → message to the girl → wait → reply to the client. Out of 12 requests, 2–3 died in transit. Not because of a refusal. Because of human lag. 7 days ago Ryan cut that layer and hung it on Grok. The application flow dropped into one funnel with statuses: NEW → REVIEW → APPROVED → SCHEDULED → ACTIVE → INACTIVE The bot sent the first packet itself, collected the fields, and closed the gaps. If data was missing → it asked. If there was silence for 48 hours → it nudged. Ryan no longer opened 50 chats. He opened one feed. He touched only REVIEW and exceptions by hand. First-pass review fell from 8–12 minutes per application to 30–90 seconds of control. In a week, 53 applications went through the funnel. 11 made it to a shift. That was no longer “we got lucky.” That was because no application rotted in a manager’s DMs. The schedule became a rule, not a group chat. A girl marked available slots. The system saw 8 seats, not “we’ll figure it out.” Overbook went to a waitlist. A drop with 40 minutes left no longer spawned a 5-chat mess: the slot jumped to the next person in line in 10–20 seconds. Friday noise fell from 80–120 messages to 10–15 exceptions. Shift manager as a job title became unnecessary. The register stopped being a notebook. Every operation was written immediately. Shift close produced one summary: - floor revenue - tips - commissions - payouts - adjustments - variance If the total did not match, the system did not say “error somewhere.” It pointed at the exact operation. Over 7 days, variance stayed in the $0–$25 range instead of the old $180–$400. The weekly difference was about $1,200–$2,500 on “it didn’t add up” alone. The client loop collapsed from 4 human nodes into 1 route. A request entered the bot. The bot pulled the standard fields, checked availability, pinged the girl, and after 2 confirmations closed both sides with a notification. Cycle time fell from 25–45 minutes to 2–4 minutes on a standard request. Burned requests for the week: 0. Before that, it was 2–3 lost checks every week. Ryan’s role got narrow and hard. The bot closed the rule. He closed the exception. Morning looked like a panel, not a meeting: > 3 new applications in NEW > 1 card in REVIEW > 2 shift cancellations > 1 operation for manual review > 47 automated messages already sent without him The only living parts left were him and the girls on the floor. Plus anyone who took an after-shift call. The middle layer: sourcing, screening, schedule, requests, counting → sat on a $200 subscription. In numbers, the week looked like this. Old model: > 6 people in the management loop > $8,400–$9,200 for that layer > 7–10 hours on applications > 80–120 messages on one heavy Friday > $180–$400 holes in the cash > 2–3 lost requests > 25–45 minutes per client cycle New model: > 1 person > $200 for the tool > 30–90 seconds of standard application control > 10–15 exceptions instead of a whole-floor chat storm > $0–$25 on the register > 0 lost standard requests > 2–4 minutes per standard cycle That is where the ~$10,000 in 7 days came from. Not “floor magic.” A removed human tax: - pay for 6 people - recruiter cuts - cash holes - burned requests - hours spent moving the same row from a chat into a spreadsheet In this system, those people were not the “soul of the club.” They were latency and leak. Every extra node added delay. Every live data handoff added error. Every notebook added a gap for rounding. Ryan removed the nodes. He left rules, statuses, a ledger, and one escalation point. So after 7 days the model already counted as a delta, not an experiment. $200 on top. About $10,000 in the plus. And the proof was short: the club ran. The middle-layer staff did not. Their job was done by the bot. bookmark this.

Ridark

188,835 次观看 • 1 个月前

How a 24-year-old programmer from Portugal made $18,200 in a month on football betting He created an AI analyst that finds flaws in bookmakers' live lines in real time and delivers predictions with an 84% win rate. Costs: $0 (Used free APIs and Windsurf IDE) He launched a Python script that maps out match videos in real time: Top layer: A Computer Vision algorithm recognizes the positions of players from both teams (blue and pink dots) and the ball, instantly transferring them onto a 2D pitch layout. This allows the AI to track team formations and open spaces in high detail, things regular bettors completely miss. Bottom layer: Python code (written alongside the Windsurf AI assistant), where the SoccerPitchConfiguration class defines the field, penalty box, and center circle dimensions down to the centimeter for perfect player-distance calculations. The AI constantly correlates the real-time movement of players on the pitch with live bookmaker odds. The moment the algorithm detects that a team has pinned their opponent into a specific zone or exposed their flanks, while the bookmaker hasn't adjusted the odds yet, the script automatically fires a betting signal. First week: >Live bets placed: 142 > Won bets: 119 > Net profit: +$4,350 using a flat $50 stake The AI completely automated the entire cycle: Windsurf and Claude wrote the tracking code, and the algorithm autonomously parses live odds, calculates the mathematical expectation of value bets, generates player heatmaps, and spots hidden tactical anomalies. It runs 100% autonomously. Bookmark it and check out the article below 👇
0:30
ridark_eth's profile picture

How a 24-year-old programmer from Portugal made $18,200 in a month on football betting He created an AI analyst that finds flaws in bookmakers' live lines in real time and delivers predictions with an 84% win rate. Costs: $0 (Used free APIs and Windsurf IDE) He launched a Python script that maps out match videos in real time: Top layer: A Computer Vision algorithm recognizes the positions of players from both teams (blue and pink dots) and the ball, instantly transferring them onto a 2D pitch layout. This allows the AI to track team formations and open spaces in high detail, things regular bettors completely miss. Bottom layer: Python code (written alongside the Windsurf AI assistant), where the SoccerPitchConfiguration class defines the field, penalty box, and center circle dimensions down to the centimeter for perfect player-distance calculations. The AI constantly correlates the real-time movement of players on the pitch with live bookmaker odds. The moment the algorithm detects that a team has pinned their opponent into a specific zone or exposed their flanks, while the bookmaker hasn't adjusted the odds yet, the script automatically fires a betting signal. First week: >Live bets placed: 142 > Won bets: 119 > Net profit: +$4,350 using a flat $50 stake The AI completely automated the entire cycle: Windsurf and Claude wrote the tracking code, and the algorithm autonomously parses live odds, calculates the mathematical expectation of value bets, generates player heatmaps, and spots hidden tactical anomalies. It runs 100% autonomously. Bookmark it and check out the article below 👇

Ridark

374,824 次观看 • 4 个月前

WHOEVER LEAKED THIS HAS ZERO FEAR SOMEONE CONNECTED 4,200 GROK BOTS TO RUN A ONE-PERSON CONTENT DESK. Not 50. Not 350. Four thousand two hundred agents wired into one system. I thought it was just another AI experiment until I saw the architecture. Floor 1 scans X, TikTok, YouTube, Reddit and news every few seconds. Floor 2 hunts only for hooks, angles and formats that already spread. Floor 3 writes threads, scripts, carousels and reply chains in parallel. Floor 4 generates thumbnails, visuals and clip variants. Floor 5 tests timing, distribution, quotes and follow-ups. Floor 6 kills anything mid, off-brand or risky before it ever reaches the owner. Then one HEAD OF STUDIO Grok sits on top of all 4,200. It does not send him a mountain of drafts. It sends 3–7 pieces a day that actually deserve to be posted. No writers. No editors. No content team. No staring at a blank page. Just 4,200 Grok bots passing work between each other 24/7. The craziest part? The owner said he barely touches the system anymore. He just publishes what survives. FULL GUIDE ON HOW TO BUILD IT BELOW
0:34
ridark_eth's profile picture

WHOEVER LEAKED THIS HAS ZERO FEAR SOMEONE CONNECTED 4,200 GROK BOTS TO RUN A ONE-PERSON CONTENT DESK. Not 50. Not 350. Four thousand two hundred agents wired into one system. I thought it was just another AI experiment until I saw the architecture. Floor 1 scans X, TikTok, YouTube, Reddit and news every few seconds. Floor 2 hunts only for hooks, angles and formats that already spread. Floor 3 writes threads, scripts, carousels and reply chains in parallel. Floor 4 generates thumbnails, visuals and clip variants. Floor 5 tests timing, distribution, quotes and follow-ups. Floor 6 kills anything mid, off-brand or risky before it ever reaches the owner. Then one HEAD OF STUDIO Grok sits on top of all 4,200. It does not send him a mountain of drafts. It sends 3–7 pieces a day that actually deserve to be posted. No writers. No editors. No content team. No staring at a blank page. Just 4,200 Grok bots passing work between each other 24/7. The craziest part? The owner said he barely touches the system anymore. He just publishes what survives. FULL GUIDE ON HOW TO BUILD IT BELOW

Ridark

99,056 次观看 • 1 个月前

What if we turned $RWA into a game where agents actually make money? We should definitely move in this direction 👀
1:05
ridark_eth's profile picture

What if we turned $RWA into a game where agents actually make money? We should definitely move in this direction 👀

Ridark

81,154 次观看 • 1 个月前

A 24-year-old launched a faceless YouTube channel 13 days ago. In the first month, he has already accumulated $4,000, but he has not shot a single frame.. His investment: a $20 subscription to Claude. No camera, no editor, no voice of his own. THE SYSTEM WORKS LIKE THIS: - Claude writes the full script from one topic prompt - an AI voice narrates it - an AI tool generates the animation and the visuals - one template, repeated forever: "the economics of owning an airport" / "…a private jet" / "…a hospital" - it publishes on a schedule while he sleeps The first 12 days, from zero: > "Why a Fan Can Kill You" -> 272,000 views > second video -> 136,000 views in 6 days > subscribers -> 4,300 > month-one pace -> ~$4,000 And it's replicable. One of his students launched "Mr. Finance" on the same template 13 days ago: > 10,800 subscribers > finance-niche RPM -> ~$7 per 1,000 views > one video on the economics of shopping malls -> $500+ on its own He didn't become a creator. He built a press that prints episodes. The whole stack is public. Everyone has the same $20 Claude. He just pointed it at a niche and refused to stop publishing.
0:45
ridark_eth's profile picture

A 24-year-old launched a faceless YouTube channel 13 days ago. In the first month, he has already accumulated $4,000, but he has not shot a single frame.. His investment: a $20 subscription to Claude. No camera, no editor, no voice of his own. THE SYSTEM WORKS LIKE THIS: - Claude writes the full script from one topic prompt - an AI voice narrates it - an AI tool generates the animation and the visuals - one template, repeated forever: "the economics of owning an airport" / "…a private jet" / "…a hospital" - it publishes on a schedule while he sleeps The first 12 days, from zero: > "Why a Fan Can Kill You" -> 272,000 views > second video -> 136,000 views in 6 days > subscribers -> 4,300 > month-one pace -> ~$4,000 And it's replicable. One of his students launched "Mr. Finance" on the same template 13 days ago: > 10,800 subscribers > finance-niche RPM -> ~$7 per 1,000 views > one video on the economics of shopping malls -> $500+ on its own He didn't become a creator. He built a press that prints episodes. The whole stack is public. Everyone has the same $20 Claude. He just pointed it at a niche and refused to stop publishing.

Ridark

144,486 次观看 • 3 个月前

This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+ model → an ultra-efficient system he calls “algorithmic mining.” The system works like this: 1 open-weights model → Apache 2.0 license integration → Command A+'s Mixture of Experts (MoE) processes complex data at lightning speed → Quantized architecture runs smoothly on just 2 GPUs → Automated agents handle enterprise-level tasks while he sleeps. 3 recurring revenue streams from a 72-hour implementation: > $49/month Essential API access > $99/month Scale tier for small teams > $199/month Custom enterprise pipeline > Delivered with near-zero hosting latency > Total setup time: 3 days He didn't wait for massive cloud budget approval. He took a highly practical, open-source-friendly model, spun it up locally, and built the exact kind of agents usually reserved for Fortune 500 companies. As the developer put it perfectly: In the open-weight era, efficiency and raw speed are the ultimate leverage.
1:54
ridark_eth's profile picture

This 22-year-old American developer showed how he makes $14,200 a month using just a dual-GPU setup and Cohere’s new open-weights model. He doesn't have a powerhouse dev team. No massive VC funding. No expensive enterprise infrastructure. He built a "localized AI workflow" powered by the newly dropped Command A+ model → an ultra-efficient system he calls “algorithmic mining.” The system works like this: 1 open-weights model → Apache 2.0 license integration → Command A+'s Mixture of Experts (MoE) processes complex data at lightning speed → Quantized architecture runs smoothly on just 2 GPUs → Automated agents handle enterprise-level tasks while he sleeps. 3 recurring revenue streams from a 72-hour implementation: > $49/month Essential API access > $99/month Scale tier for small teams > $199/month Custom enterprise pipeline > Delivered with near-zero hosting latency > Total setup time: 3 days He didn't wait for massive cloud budget approval. He took a highly practical, open-source-friendly model, spun it up locally, and built the exact kind of agents usually reserved for Fortune 500 companies. As the developer put it perfectly: In the open-weight era, efficiency and raw speed are the ultimate leverage.

Ridark

214,201 次观看 • 5 个月前

A 26-year-old Chinese engineer made $7,000 in a month and gained 17,000 subscribers by launching a YouTube channel for which he never filmed a single video. seven AI agents run it end to end. he just built the seven. he'd already burned out once doing it by hand. one guy behind four monitors, posting until the motivation ran dry and the channel stalled. so the second time he stopped trying to be the whole studio and split the job into seven specialists, each owning one stage: > scriptwriter: read the trends, pitched concepts, wrote the scripts > director: planned structure, storyboards, visual style > video editor: cut and spliced footage, added transitions through Python hooks into Premiere Pro > audio engineer: scored the music, cleaned the noise, processed the voiceover > designer: built the high-CTR thumbnails > SEO writer: wrote the titles, descriptions, tags and timestamps the algorithm rewards > conductor: coordinated the other six and uploaded the finished cut itself no host on camera. an AI avatar and a voice clone carried the face and the voice, so the same system ran in a dozen languages at once, none of them needing him in a chair. the money was ordinary media: AdSense on the view volume, affiliate links, sponsor reads matched to the audience. the only unusual part was the overhead, which was almost nothing. most people build an archive of videos and call it a channel. he built the thing that assembles the channel, and it grew with every upload he wasn't there for. Bookmark this.
0:22
ridark_eth's profile picture

A 26-year-old Chinese engineer made $7,000 in a month and gained 17,000 subscribers by launching a YouTube channel for which he never filmed a single video. seven AI agents run it end to end. he just built the seven. he'd already burned out once doing it by hand. one guy behind four monitors, posting until the motivation ran dry and the channel stalled. so the second time he stopped trying to be the whole studio and split the job into seven specialists, each owning one stage: > scriptwriter: read the trends, pitched concepts, wrote the scripts > director: planned structure, storyboards, visual style > video editor: cut and spliced footage, added transitions through Python hooks into Premiere Pro > audio engineer: scored the music, cleaned the noise, processed the voiceover > designer: built the high-CTR thumbnails > SEO writer: wrote the titles, descriptions, tags and timestamps the algorithm rewards > conductor: coordinated the other six and uploaded the finished cut itself no host on camera. an AI avatar and a voice clone carried the face and the voice, so the same system ran in a dozen languages at once, none of them needing him in a chair. the money was ordinary media: AdSense on the view volume, affiliate links, sponsor reads matched to the audience. the only unusual part was the overhead, which was almost nothing. most people build an archive of videos and call it a channel. he built the thing that assembles the channel, and it grew with every upload he wasn't there for. Bookmark this.

Ridark

99,944 次观看 • 3 个月前

> be a mr robot > hear that bug bounties pay $15,000 per vulnerability > assume you need a security degree > assume you need expensive tools > assume you need a team > open GitHub > find Claude-BugHunter > free. MIT license. 51 skills. 15 commands > 574 patterns from real HackerOne reports > install in 3 minutes > describe the target in plain English > Claude auto-loads the specialist skill > finds a vulnerable pattern > /triage - 7-question validation gate > PASS > /report - clean HackerOne submission generated > $20/month in tools > same methodology as a professional security team > follow the detailed guide below to repeat
0:22
ridark_eth's profile picture

> be a mr robot > hear that bug bounties pay $15,000 per vulnerability > assume you need a security degree > assume you need expensive tools > assume you need a team > open GitHub > find Claude-BugHunter > free. MIT license. 51 skills. 15 commands > 574 patterns from real HackerOne reports > install in 3 minutes > describe the target in plain English > Claude auto-loads the specialist skill > finds a vulnerable pattern > /triage - 7-question validation gate > PASS > /report - clean HackerOne submission generated > $20/month in tools > same methodology as a professional security team > follow the detailed guide below to repeat

Ridark

112,799 次观看 • 4 个月前

I still don’t understand why this setup hasn’t become the standard yet. Elon Musk reposted my Grok Bot guide, and this exact agent architecture is how I close weeks in solid profit, without staring at charts all day and without burnout. Eight AI agents on a single desk for $99 a month completely replace a crypto fund's research floor, which used to cost $680,000 a year just in analyst salaries. Here’s what each agent does: 1. SEARCH ➔ extracts real-time alpha before anyone else: dev GitHubs, fresh pools on pump fun, and unindexed Telegram signals before CT catches on. 2. RISK ➔ audits smart contracts instantly, checking mint authority, freeze rights, and LP locks via rugcheck + solscan. Honeypots are filtered out before entry. 3. SNIPER ➔ prepares and executes lightning-fast DEX orders the exact second RISK issues a CLEAR badge. 4. WHALE ➔ tracks smart money wallets with a 60%+ win rate and flags stealth accumulation long before it shows up on the chart. 5. RUG ➔ monitors the dev wallet and liquidity pool status 24/7. At the slightest hint of an LP pull, it immediately triggers a full position dump. 6. EXIT ➔ dynamically trails stops and locks in profit as liquidity builds. Zero averaging down. 7. SHILL ➔ analyzes social volume across X and Telegram, clearly separating organic momentum from paid promo. 8. HEAD OF DESK ➔ never trades. It strictly coordinates the funnel, enforces protocol, and brings me the single decision that requires a human APPROVE. Hundreds of tokens scanned. Dozens of setups filtered out. Precise, profitable trades closed while the laptop stays shut. Every agent gets its own virtual browser in the cloud, local memory, and a crystal-clear role. This isn't just an "assistant" anymore. It's a fully autonomous desk running while you sleep. The setup is much simpler than it looks: > Download Grok Bot and hire your coordinator (Head of Desk) first. > Give the remaining 7 agents strict charters, just like onboarding new hires. > Train them: run one full trade workflow on your screen while they watch, making sure to include a scenario where you reject a token. > Set up a single Trading Floor group chat with a strict message format by LEAD ID and connect your webhooks. > No VPS, no code, no waiting on developers. A crypto trading floor used to mean 16-hour screen time, fatigue, and paid alpha groups. This system takes one evening to set up. Save this guide before your next trade.
0:27
ridark_eth's profile picture

I still don’t understand why this setup hasn’t become the standard yet. Elon Musk reposted my Grok Bot guide, and this exact agent architecture is how I close weeks in solid profit, without staring at charts all day and without burnout. Eight AI agents on a single desk for $99 a month completely replace a crypto fund's research floor, which used to cost $680,000 a year just in analyst salaries. Here’s what each agent does: 1. SEARCH ➔ extracts real-time alpha before anyone else: dev GitHubs, fresh pools on pump fun, and unindexed Telegram signals before CT catches on. 2. RISK ➔ audits smart contracts instantly, checking mint authority, freeze rights, and LP locks via rugcheck + solscan. Honeypots are filtered out before entry. 3. SNIPER ➔ prepares and executes lightning-fast DEX orders the exact second RISK issues a CLEAR badge. 4. WHALE ➔ tracks smart money wallets with a 60%+ win rate and flags stealth accumulation long before it shows up on the chart. 5. RUG ➔ monitors the dev wallet and liquidity pool status 24/7. At the slightest hint of an LP pull, it immediately triggers a full position dump. 6. EXIT ➔ dynamically trails stops and locks in profit as liquidity builds. Zero averaging down. 7. SHILL ➔ analyzes social volume across X and Telegram, clearly separating organic momentum from paid promo. 8. HEAD OF DESK ➔ never trades. It strictly coordinates the funnel, enforces protocol, and brings me the single decision that requires a human APPROVE. Hundreds of tokens scanned. Dozens of setups filtered out. Precise, profitable trades closed while the laptop stays shut. Every agent gets its own virtual browser in the cloud, local memory, and a crystal-clear role. This isn't just an "assistant" anymore. It's a fully autonomous desk running while you sleep. The setup is much simpler than it looks: > Download Grok Bot and hire your coordinator (Head of Desk) first. > Give the remaining 7 agents strict charters, just like onboarding new hires. > Train them: run one full trade workflow on your screen while they watch, making sure to include a scenario where you reject a token. > Set up a single Trading Floor group chat with a strict message format by LEAD ID and connect your webhooks. > No VPS, no code, no waiting on developers. A crypto trading floor used to mean 16-hour screen time, fatigue, and paid alpha groups. This system takes one evening to set up. Save this guide before your next trade.

Ridark

52,680 次观看 • 2 个月前

APPLE IS LYING TO YOU ABOUT THE MAC MINI, AND THIS SUB-$100 FIX PERMANENTLY UNLOCKS ITS TRUE POWER The Mac Mini is an absolute beast until you run out of ports in exactly 5 seconds and your system begins throttling to death under heavy video editing or gaming. Most users waste hundreds on messy dongles and external hubs, completely destroying their desk aesthetics. The ultimate power move is dropping a vertical, active-cooling dock right into your workstation ecosystem. Why this layout changes everything: The Thermal Shield: Integrated aluminum pads and a silent high-speed fan that completely eliminate system lag, heat build-up, and sudden freezing under load. Extreme Expansion: Instantly unlocks triple-monitor support via multiple HDMI ports, high-speed USB arrays, and a hidden SSD slot. Modular Integration: Bridges directly into the Mac's native ports using a flush, custom jump connector, keeping your desktop entirely cord-free. Stop letting Apple gatekeep your machine's peak performance.
3:22
ridark_eth's profile picture

APPLE IS LYING TO YOU ABOUT THE MAC MINI, AND THIS SUB-$100 FIX PERMANENTLY UNLOCKS ITS TRUE POWER The Mac Mini is an absolute beast until you run out of ports in exactly 5 seconds and your system begins throttling to death under heavy video editing or gaming. Most users waste hundreds on messy dongles and external hubs, completely destroying their desk aesthetics. The ultimate power move is dropping a vertical, active-cooling dock right into your workstation ecosystem. Why this layout changes everything: The Thermal Shield: Integrated aluminum pads and a silent high-speed fan that completely eliminate system lag, heat build-up, and sudden freezing under load. Extreme Expansion: Instantly unlocks triple-monitor support via multiple HDMI ports, high-speed USB arrays, and a hidden SSD slot. Modular Integration: Bridges directly into the Mac's native ports using a flush, custom jump connector, keeping your desktop entirely cord-free. Stop letting Apple gatekeep your machine's peak performance.

Ridark

86,314 次观看 • 3 个月前

I just upgraded my 8-agent Grok Bot trading floor and it stopped needing me. The whole desk now executes automatically while I sleep. Instead of running agents on basic isolated prompts, I upgraded to org-chart orchestration driven by a single Screen Recording workflow. I showed the workflow to the agents once, and now they run complete task-driven group chats autonomously without a single line of code or VPS setup. Here is how the upgraded 8-agent trading floor operates now: 1. HEAD OF DESK (Upgraded): Receives high-level targets ("Target +20% weekly, max 2% risk per trade"), splits tasks across the team, coordinates handoffs, and only pings for critical single-click approvals. 2. SEARCH (Alpha Hunter): Scrapes unindexed Telegram signals, dev GitHub commits, and early on-chain liquidity events 24/7 before CT catches on. Drops 20–40 vetted leads daily. 3. RISK (Contract Auditor): Audits smart contracts in milliseconds for mint rights, blacklists, ownership traps, and honeypots. Instantly flags and blocks unsafe entries. 4. SNIPER: Triggers sub-second execution on-chain the exact millisecond RISK clearance passes without manual intervention. 5. WHALE Tracker: Monitors smart-money wallets with >68% historical win rates to catch stealth accumulation patterns early. 6. RUG Sentinel: Watches dev wallets and LP state 24/7. Auto-dumps 100% of the position instantly if LP tampering is detected. 7. EXIT Manager: Takes automated profits, scales out as volume builds, and dynamically moves trailing stops to lock in gains. 8. SHILL (Sentiment Engine): Tracks social velocity, momentum spikes, and influencer call clusters to signal optimal position scaling or early exits. The weekly result: 148 tokens scanned ➔ 18 passed full RISK clearance ➔ 7 trades executed automatically with zero manual clicks and zero night shifts. You don't need a $500k analyst team or a dev background anymore. It’s $200/month, an org chart structure instead of a to-do list, and one evening of setup. Save this post.
0:24
ridark_eth's profile picture

I just upgraded my 8-agent Grok Bot trading floor and it stopped needing me. The whole desk now executes automatically while I sleep. Instead of running agents on basic isolated prompts, I upgraded to org-chart orchestration driven by a single Screen Recording workflow. I showed the workflow to the agents once, and now they run complete task-driven group chats autonomously without a single line of code or VPS setup. Here is how the upgraded 8-agent trading floor operates now: 1. HEAD OF DESK (Upgraded): Receives high-level targets ("Target +20% weekly, max 2% risk per trade"), splits tasks across the team, coordinates handoffs, and only pings for critical single-click approvals. 2. SEARCH (Alpha Hunter): Scrapes unindexed Telegram signals, dev GitHub commits, and early on-chain liquidity events 24/7 before CT catches on. Drops 20–40 vetted leads daily. 3. RISK (Contract Auditor): Audits smart contracts in milliseconds for mint rights, blacklists, ownership traps, and honeypots. Instantly flags and blocks unsafe entries. 4. SNIPER: Triggers sub-second execution on-chain the exact millisecond RISK clearance passes without manual intervention. 5. WHALE Tracker: Monitors smart-money wallets with >68% historical win rates to catch stealth accumulation patterns early. 6. RUG Sentinel: Watches dev wallets and LP state 24/7. Auto-dumps 100% of the position instantly if LP tampering is detected. 7. EXIT Manager: Takes automated profits, scales out as volume builds, and dynamically moves trailing stops to lock in gains. 8. SHILL (Sentiment Engine): Tracks social velocity, momentum spikes, and influencer call clusters to signal optimal position scaling or early exits. The weekly result: 148 tokens scanned ➔ 18 passed full RISK clearance ➔ 7 trades executed automatically with zero manual clicks and zero night shifts. You don't need a $500k analyst team or a dev background anymore. It’s $200/month, an org chart structure instead of a to-do list, and one evening of setup. Save this post.

Ridark

51,013 次观看 • 2 个月前

A 24-year-old woman from Thailand made $12,000 standing in front of a wall of 200 phones broadcasting all at once. her first year, she tried to make one channel go viral. perfect lighting, perfect pitch, ignored four nights out of five. one channel is a coin flip, and she kept landing on the wrong side. so she stopped betting on one and built a machine: > 200 cheap phones racked on a wall, each on its own account > the same live sale pushed to every screen at once > each phone wired to its own store or affiliate code > a floor manager swapping out any stream that died > the algorithm left to pick the winners by itself the edge was never talent. it was volume: > 200 channels live, only a tenth of them have to catch > the ones that catch put the pitch in front of millions > one broadcast selling across 200 storefronts at once > the big rooms up the street: 100,000 orders a night, caps by the millions > hers smaller, but it never once went dark She continues to improve her streams every day and look for new approaches to them to earn even more. Bookmark this.
1:02
ridark_eth's profile picture

A 24-year-old woman from Thailand made $12,000 standing in front of a wall of 200 phones broadcasting all at once. her first year, she tried to make one channel go viral. perfect lighting, perfect pitch, ignored four nights out of five. one channel is a coin flip, and she kept landing on the wrong side. so she stopped betting on one and built a machine: > 200 cheap phones racked on a wall, each on its own account > the same live sale pushed to every screen at once > each phone wired to its own store or affiliate code > a floor manager swapping out any stream that died > the algorithm left to pick the winners by itself the edge was never talent. it was volume: > 200 channels live, only a tenth of them have to catch > the ones that catch put the pitch in front of millions > one broadcast selling across 200 storefronts at once > the big rooms up the street: 100,000 orders a night, caps by the millions > hers smaller, but it never once went dark She continues to improve her streams every day and look for new approaches to them to earn even more. Bookmark this.

Ridark

77,229 次观看 • 3 个月前

Update shipped. Still early. A lot of this is in progress. The life sim is becoming a company floor. 45 people still eat, sleep, burn dinner, and miss the 9–6. But the block is turning into an org chart. You talk to Atlas. Atlas assigns outcomes. Scout is not supposed to guess. Quill is not supposed to publish. Pitch waits for the stamp. Vault clears the inbox. Ledger brings one number a night. That’s the design. Not all of it is wired yet. What is already in: the houses as seats, the floor fantasy, agents living and working at the same time. What I’m still building: clean handoffs between houses, hard fences that actually block a bad action, and $RWA showing up on the counter only after a task is done the right way -- not when someone just sits at a desk. Same neighborhood. Agents that actually make money. Just not finished.
1:05
ridark_eth's profile picture

Update shipped. Still early. A lot of this is in progress. The life sim is becoming a company floor. 45 people still eat, sleep, burn dinner, and miss the 9–6. But the block is turning into an org chart. You talk to Atlas. Atlas assigns outcomes. Scout is not supposed to guess. Quill is not supposed to publish. Pitch waits for the stamp. Vault clears the inbox. Ledger brings one number a night. That’s the design. Not all of it is wired yet. What is already in: the houses as seats, the floor fantasy, agents living and working at the same time. What I’m still building: clean handoffs between houses, hard fences that actually block a bad action, and $RWA showing up on the counter only after a task is done the right way -- not when someone just sits at a desk. Same neighborhood. Agents that actually make money. Just not finished.

Ridark

34,572 次观看 • 1 个月前

I don't understand why everyone isn't doing this yet. There's a free narrative radar that instantly shows which trending words are actually trading early on pump fun and GMGN. Main Idea: Moth-radar, real-time narrative scanner for pump fun and GMGN Instead of scrolling through endless new launches and guessing what's real, the tool tracks volume and filters tokens actually trading early in one dashboard: Live Word Stream: Shows words appearing most frequently in new token names over the last hour and measures how fast each narrative is growing. Real Trading Filter: For every word, it shows tokens actually trading with early volume on pump fun / GMGN -- not just spam-launched or empty mints. Real Profit Proof: Caught $Tschuna early for +0.5 SOL in 4 minutes on one account, and entered cupsey much faster thanks to Moth-radar. Install via browser (Chrome, Edge, Brave, Opera): > Open chrome://extensions > Enable Developer mode, toggle in the top right corner > Click Load unpacked, select the moth-radar folder containing manifest.json > Pin the icon: puzzle piece icon, pin next to Moth-radar > Open any page on pump fun or GMGN, the radar tracks trading volume instantly. >
4:04
ridark_eth's profile picture

I don't understand why everyone isn't doing this yet. There's a free narrative radar that instantly shows which trending words are actually trading early on pump fun and GMGN. Main Idea: Moth-radar, real-time narrative scanner for pump fun and GMGN Instead of scrolling through endless new launches and guessing what's real, the tool tracks volume and filters tokens actually trading early in one dashboard: Live Word Stream: Shows words appearing most frequently in new token names over the last hour and measures how fast each narrative is growing. Real Trading Filter: For every word, it shows tokens actually trading with early volume on pump fun / GMGN -- not just spam-launched or empty mints. Real Profit Proof: Caught $Tschuna early for +0.5 SOL in 4 minutes on one account, and entered cupsey much faster thanks to Moth-radar. Install via browser (Chrome, Edge, Brave, Opera): > Open chrome://extensions > Enable Developer mode, toggle in the top right corner > Click Load unpacked, select the moth-radar folder containing manifest.json > Pin the icon: puzzle piece icon, pin next to Moth-radar > Open any page on pump fun or GMGN, the radar tracks trading volume instantly. >

Ridark

13,803 次观看 • 17 天前

Most people see weeks of keyframing. He sees a sentence and $18,000. A 26-year-old shot one plain clip walking through London, then ran it through Gemini Omni Flash and typed the changes: cloudy day -> sunset -> night. a giant wave rising over the Thames. a passing van floating in mid-air. his clothes swapped for a royal guard's uniform. The clip hit millions. So he sold the sentences. He packaged his exact prompts and presets as a guide, ran the wild edits as ads for it, and took freelance jobs from everyone who didn't want to learn. The effect took seconds. The prompt was the product.
0:43
ridark_eth's profile picture

Most people see weeks of keyframing. He sees a sentence and $18,000. A 26-year-old shot one plain clip walking through London, then ran it through Gemini Omni Flash and typed the changes: cloudy day -> sunset -> night. a giant wave rising over the Thames. a passing van floating in mid-air. his clothes swapped for a royal guard's uniform. The clip hit millions. So he sold the sentences. He packaged his exact prompts and presets as a guide, ran the wild edits as ads for it, and took freelance jobs from everyone who didn't want to learn. The effect took seconds. The prompt was the product.

Ridark

58,581 次观看 • 3 个月前

AI JUST REPLACED THE ENTIRE ANIMATION PIPELINE what a studio used to spend $180,000 and 40 people on per episode: -> script + shot list: Claude, 10 min -> characters and sets: Midjourney, 20 min -> motion, cartoon turned photoreal: Runway, 15 min -> voices and sound: ElevenLabs, 10 min -> score: Suno, 5 min -> publish everywhere: CapCut, automatic total cost: $40 a month. one person. one weekend to set up. a 60-year-old cartoon rebuilt as live-action that looks like it cost millions. bookmark this
0:33
ridark_eth's profile picture

AI JUST REPLACED THE ENTIRE ANIMATION PIPELINE what a studio used to spend $180,000 and 40 people on per episode: -> script + shot list: Claude, 10 min -> characters and sets: Midjourney, 20 min -> motion, cartoon turned photoreal: Runway, 15 min -> voices and sound: ElevenLabs, 10 min -> score: Suno, 5 min -> publish everywhere: CapCut, automatic total cost: $40 a month. one person. one weekend to set up. a 60-year-old cartoon rebuilt as live-action that looks like it cost millions. bookmark this

Ridark

42,085 次观看 • 2 个月前