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Ridark

@ridark_eth • 10,529 subscribers

Content Creator & Researcher | Web3 & BD/growth/smm

Shorts

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

6,874,468 views

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

25,070 views

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?

36,893 views

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.

24,199 views

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 views

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 views

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 views

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

15,948 views

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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.

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.

139,963 views • 7 days ago

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 ↓

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 ↓

479,500 views • 1 month ago

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 👇

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 👇

371,888 views • 1 month ago

> 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.

> 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.

627,767 views • 3 months ago

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.

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.

57,881 views • 10 days ago

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.

99,201 views • 23 days ago

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.

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.

77,229 views • 21 days ago

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.

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.

86,238 views • 24 days ago

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.

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.

211,874 views • 2 months ago

The 5-Minute Polymarket Sniper: Making $189,861 on "Boring" Bitcoin Fluctuations.. Profile Statistics: > Total Profit: $189,861.72 > The Biggest Win: $13.3K > Total Predictions: 95,647 > Account Created: April 2026 > Name: 0x50f7 The Strategy: 5-Minute Scalping 1) Buying the Undervalued: He enters ultra-short positions (5-minute "Up or Down" BTC windows) when the market panics or misprices the odds of an outcome. Entries are caught at heavily discounted prices, ranging from 1¢ to 39¢ per share. 2) Mathematical Edge: Out of 95,000+ trades, he captures spreads and liquidity imbalances. Unlike standard traders trying to predict crypto prices a week in advance, he simply exploits real-time order book inefficiencies. 3) Zero Emotion: Pure short-term execution, buying heavily discounted probabilities just minutes before the candle closes. Top Deals from the Dashboard: > June 6 (12:45 AM - 12:50 AM ET): Bought "Down" at 1.1¢ -> Won $13,450.72 (+$13,301.35 / +8,904.95%) > July 11 (1:30 PM - 1:45 PM ET): Bought "Up" at 3¢ -> Won $9,000.00 (+$8,730.00 / +3,233.44%) > May 21 (1:45 PM - 1:50 PM ET): Ultimate sniper shot in "Up" at 1.1¢ -> Won $8,434.21 (+$8,345.23 / +9,378.67%) > April 22 (1:30 PM - 1:35 PM ET): Deep discount entry in "Down" at 1¢ ->Won $6,876.28 (+$6,807.52 / +9,900.00%) Why does this work? On ultra-short (5-minute) prediction windows, retail traders and basic algorithms constantly create order book inefficiencies by panic-selling or overreacting to minor Bitcoin ticks. The trader locks in absurd risk-to-reward ratios (1:50 to 1:100). The Secret Driver: AI Prompts for Edge Scanning Analyzing his trading style, manually tracking thousands of micro-events and instantly spotting mispriced odds is nearly impossible. It is highly likely that he was backed by specialized LLM prompts (similar to the News-to-Edge Scanner and Catalyst Chain Reactor): 1) Instant Catalyst Assessment: Prompts allow the AI to filter out real-time news noise and calculate mathematically sound probability models faster than the crowd. 2) Identifying Contrarian Angles: The algorithm flags precise moments where crowd sentiment strays too far from objective facts. When the AI flags an inefficiency, all the trader has to do is pull the trigger and walk away with up to +9,900% ROI in just 300 seconds. Do you think this is driven by custom AI scripts or pure market microstructure intuition?

17,219 views • 5 days ago

> 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

> 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

112,799 views • 1 month ago

HE TURNED A $599 MAC MINI INTO A CLAUDE SECOND BRAIN USING OBSIDIAN, PYTHON AND A FEW ALWAYS-ON LOOPS not a fancy SaaS dashboard. not another chat tab. a tiny box on a desk that can pull lectures, transcribe audio, read articles, clean notes and write everything into a local vault while he sleeps Steve Jobs wanted the Mac mini to be the small affordable Mac anyone could put on a desk. now that same tiny Apple box can run Claude loops 24/7, process 100+ saved links, turn 2-hour lectures into structured notes, and send a morning brief for a few dollars in API calls the real trick is the loop. trigger, do, verify, retry, stop. Claude does the thinking, Obsidian stores the memory, and the Mac mini keeps running even when nobody is touching the keyboard bookmark this before you think a second brain needs another $29/month app

HE TURNED A $599 MAC MINI INTO A CLAUDE SECOND BRAIN USING OBSIDIAN, PYTHON AND A FEW ALWAYS-ON LOOPS not a fancy SaaS dashboard. not another chat tab. a tiny box on a desk that can pull lectures, transcribe audio, read articles, clean notes and write everything into a local vault while he sleeps Steve Jobs wanted the Mac mini to be the small affordable Mac anyone could put on a desk. now that same tiny Apple box can run Claude loops 24/7, process 100+ saved links, turn 2-hour lectures into structured notes, and send a morning brief for a few dollars in API calls the real trick is the loop. trigger, do, verify, retry, stop. Claude does the thinking, Obsidian stores the memory, and the Mac mini keeps running even when nobody is touching the keyboard bookmark this before you think a second brain needs another $29/month app

65,142 views • 1 month ago

Andrej Karpathy, founding member of OpenAI and ex-Director of AI at Tesla: "creators are paying $5,000 a month to agencies to build the exact AI 'content factory' this free talk hands you in 39 minutes." this free 2025 talk holds the entire "$1M YouTube on autopilot" the 2026 threads sell you for $500. karpathy explains the whole thing on stage: the model is the new computer, your only job is to feed it memory and verify what it gives back. that's the "second brain." that's it. the gurus wrap it in obsidian folders, a /wiki command and a "secret stack." karpathy handed you the actual idea for zero dollars -> dump raw sources in one place, let the model synthesize and maintain an interlinked wiki it can actually use. raw → synthesis → reuse. the architecture's been public the whole time. and here's the part the thread skips. he calls these models "people spirits" -> fallible, confidently wrong, output you check, not trust. the moat was never the obsidian setup. the moat is what you feed it and the judgment to catch where it's lying. the folder structure is free. the taste isn't. bookmark & watch today ↓

Andrej Karpathy, founding member of OpenAI and ex-Director of AI at Tesla: "creators are paying $5,000 a month to agencies to build the exact AI 'content factory' this free talk hands you in 39 minutes." this free 2025 talk holds the entire "$1M YouTube on autopilot" the 2026 threads sell you for $500. karpathy explains the whole thing on stage: the model is the new computer, your only job is to feed it memory and verify what it gives back. that's the "second brain." that's it. the gurus wrap it in obsidian folders, a /wiki command and a "secret stack." karpathy handed you the actual idea for zero dollars -> dump raw sources in one place, let the model synthesize and maintain an interlinked wiki it can actually use. raw → synthesis → reuse. the architecture's been public the whole time. and here's the part the thread skips. he calls these models "people spirits" -> fallible, confidently wrong, output you check, not trust. the moat was never the obsidian setup. the moat is what you feed it and the judgment to catch where it's lying. the folder structure is free. the taste isn't. bookmark & watch today ↓

48,183 views • 1 month ago

The 5-Minute Polymarket Sniper: Making $52,909 on "Boring" Bitcoin Fluctuations in a Week Profile Statistics: > Total Profit: $52,909.10 > The Biggest Win: $18.5K > Total Forecasts: 32,904 The Strategy: 5-Minute Scalping > Buying the Undervalued: He enters "Down" positions when the share price is heavily discounted, usually buying between 8¢ and 56¢. Essentially, he buys probabilities that the market is mispricing. > Mathematical Edge: By repeating this across nearly 33,000 forecasts, he leverages the law of large numbers to turn small, cheap shares into a massive avalanche of profit. > Zero Emotion: Pure execution of short-term momentum. Top Deals from the Dashboard: > March 26 (2:35 AM - 2:40 AM): Bought "Down" at 49.1¢ ➔ Won $36,318.08 (+$18,478.31 / +103.58%) > March 20 (9:35 AM - 9:40 AM): The ultimate sniper shot. Bought "Down" at just 8.2¢ ➔ Won $15,011.27 (+$11,898.33 / +382.22%) > March 24 (10:20 AM - 10:25 AM): Bought "Down" at 48.1¢ ➔ Won $22,178.75 (+$11,502.39 / +107.74%) Why does this work? On 5-minute charts, bots and retail traders constantly create inefficiencies in the order book by overreacting to minor BTC ticks. This trader simply spots the mispriced odds, locks in the best risk-to-reward ratio, and walks away with $8K to $18K in net profit in just 300 seconds.

53,610 views • 1 month ago

This girl turned a simple Claude-Canva connection into a $25,000/month money machine, bypassing years of design school. The income is incredible, but what really matters is the workflow itself. Every single image in this system is 100% generated using structured AI prompts. Agency-level skills are now completely democratized. You don't need a team, expensive software, or design experience to dominate the local business market. You only need one core system. The idea is terrifyingly simple: connect Claude to your free Canva account. Feed structured briefs into the AI to reverse-engineer professional templates. Swap out branding, aesthetics, and the entire copy using a single prompt sequence. One workflow. Endless niches. Absolute content leverage. Most people just scroll past AI and treat it like a chatbot. Smart players understand that combining LLMs with design APIs just captured maximum attention and revenue for them.

This girl turned a simple Claude-Canva connection into a $25,000/month money machine, bypassing years of design school. The income is incredible, but what really matters is the workflow itself. Every single image in this system is 100% generated using structured AI prompts. Agency-level skills are now completely democratized. You don't need a team, expensive software, or design experience to dominate the local business market. You only need one core system. The idea is terrifyingly simple: connect Claude to your free Canva account. Feed structured briefs into the AI to reverse-engineer professional templates. Swap out branding, aesthetics, and the entire copy using a single prompt sequence. One workflow. Endless niches. Absolute content leverage. Most people just scroll past AI and treat it like a chatbot. Smart players understand that combining LLMs with design APIs just captured maximum attention and revenue for them.

72,778 views • 2 months ago

The 5-Minute Polymarket Sniper: Making $150,995 on "Boring" Bitcoin Fluctuations in a Week Profile Statistics: > Total Profit: $150,995.38 (Over the last week) > The Biggest Win: $20.3K > Total Forecasts: 155 > Account Created: June 2026 The Strategy: 5-Minute Scalping: Buying the Undervalued: He enters precision positions (both "Up" and "Down") when the share price is heavily discounted, usually catching entries between 37¢ and 52¢. Effectively, he buys probabilities that the market is mispricing in real-time. Mathematical Edge: Unlike high-frequency bots making 30,000 micro-bets, this sniper takes only 155 highly calculated positions. By maintaining an incredibly high win rate and compounding returns, he turned a compact sample size into a massive avalanche of profit. Zero Emotion: Pure execution of short-term volatility and momentum around exact key minutes. Top Deals from the Dashboard: > June 11 (8:30 PM): Bought "Up" at 49.7¢ ➔ Won $40,262.39 (+$20,262.45 / +101.31%) > June 11 (9:05 PM): The ultimate sniper shot. Bought "Down" at 45.0¢ ➔ Won $33,298.1 (+$18,298.14 / +121.99%) > June 11 (8:10 PM): Bought "Up" at 49.7¢ ➔ Won $24,132.28 (+$12,132.31 / +101.10%) > June 11 (9:50 PM): Deep discount entry. Bought "Up" at 37.4¢ ➔ Won $7,805.52 (+$4,889.75 / +167.70%) Why does this work? On ultra-short-term (5-minute) prediction windows, retail traders and algorithms constantly create order book inefficiencies by panic-selling or overreacting to minor Bitcoin ticks. This trader doesn't try to guess where BTC will be next month. He simply spots the momentary mispriced odds, locks in a stellar risk-to-reward ratio, and walks away with $4,000 to $20,000 in net profit in just 300 seconds Do you think he can continue to earn that much? I'll post his profile below 👇

43,740 views • 1 month ago

A 3-week-old store is pulling $7,000 week, and its owner barely touches it. No team. No agency. No copywriter. -> Investment: a $20 Claude subscription and one product photo. It runs on Ambaras. Hundreds of AI agents spun up 600+ versions of the store, A/B-tested them live, and pushed the winner to the front. Claude wrote every page, upsell and bundle, and generated the product images from that one photo. Sales pour in from everywhere at once: storefront, Instagram Shop, Tik Tok Shop, email. She opened the dashboard to check his numbers. 600 experiments had already picked the store that prints. Now she just watches it run. $7,000 a week. 3 weeks old. Do the math.

A 3-week-old store is pulling $7,000 week, and its owner barely touches it. No team. No agency. No copywriter. -> Investment: a $20 Claude subscription and one product photo. It runs on Ambaras. Hundreds of AI agents spun up 600+ versions of the store, A/B-tested them live, and pushed the winner to the front. Claude wrote every page, upsell and bundle, and generated the product images from that one photo. Sales pour in from everywhere at once: storefront, Instagram Shop, Tik Tok Shop, email. She opened the dashboard to check his numbers. 600 experiments had already picked the store that prints. Now she just watches it run. $7,000 a week. 3 weeks old. Do the math.

13,387 views • 10 days ago

I don’t get why people are still spending weeks building online stores manually. This girl does the exact same thing in a minute, the AI builds the site by itself. It used to take a designer, a developer, a copywriter, and a couple of weeks to launch a product landing page. Now, that entire workflow has shrunk down to a single input field, and here is how it looks in practice: → Find a trending product on TikTok Shop, one with proven demand (in the video, it's a silicone face mask strap that’s been sold over 57,000 times). → Copy the product link. → Paste it into an AI landing page builder and within a minute, it generates a complete one-page site: description, images, and reviews. → Publish it to Shopify with a single click. → The same tool automatically generates ad creatives for Meta and Google, all that's left is to launch them. You don't even need to buy the inventory upfront: the supplier ships it directly to the customer. But here is the honest truth that these kinds of videos usually skip. Building the website is no longer the bottleneck -> now, everything hinges on product selection and advertising, which are actually the hardest and most expensive parts. AI eliminated the technical hassle, but it didn't eliminate competition or the need for an ad budget. That’s why a "store in a minute" is real, but "money in a minute" is not. 57,000 sales is a verifiable demand metric from TikTok Shop, so I kept it. There are no income claims in the post and there weren't any to begin with. Bookmark this 💾

I don’t get why people are still spending weeks building online stores manually. This girl does the exact same thing in a minute, the AI builds the site by itself. It used to take a designer, a developer, a copywriter, and a couple of weeks to launch a product landing page. Now, that entire workflow has shrunk down to a single input field, and here is how it looks in practice: → Find a trending product on TikTok Shop, one with proven demand (in the video, it's a silicone face mask strap that’s been sold over 57,000 times). → Copy the product link. → Paste it into an AI landing page builder and within a minute, it generates a complete one-page site: description, images, and reviews. → Publish it to Shopify with a single click. → The same tool automatically generates ad creatives for Meta and Google, all that's left is to launch them. You don't even need to buy the inventory upfront: the supplier ships it directly to the customer. But here is the honest truth that these kinds of videos usually skip. Building the website is no longer the bottleneck -> now, everything hinges on product selection and advertising, which are actually the hardest and most expensive parts. AI eliminated the technical hassle, but it didn't eliminate competition or the need for an ad budget. That’s why a "store in a minute" is real, but "money in a minute" is not. 57,000 sales is a verifiable demand metric from TikTok Shop, so I kept it. There are no income claims in the post and there weren't any to begin with. Bookmark this 💾

30,229 views • 28 days ago

Peter Steinberger Quietly Started A Shift That Makes Claude 35x Cheaper To Run The way Claude Code talks to tools matters more than people think. Most use MCP, and it quietly eats tokens, it loads everything into context every time, even tools you never call. One benchmark: MCP burned 35x more tokens than a CLI on the same task. Peter Steinberger, the OpenClaw guy, got annoyed by this and started building lean CLIs himself. That kicked off a tool called Printing Press. You point Claude Code at any website, even ones with no API like ESPN or Craigslist, and it builds a small command tool for it in about 10 minutes. In one demo, a request pulled 132,000 tokens of raw data, but the tool processed it locally and only handed Claude a 2,000-token summary. The rest never touched the context window. It also comes with ~50 ready-made tools you can grab right away. To start, point Claude Code at the links from and ask it to set it up. Bookmark this.

Peter Steinberger Quietly Started A Shift That Makes Claude 35x Cheaper To Run The way Claude Code talks to tools matters more than people think. Most use MCP, and it quietly eats tokens, it loads everything into context every time, even tools you never call. One benchmark: MCP burned 35x more tokens than a CLI on the same task. Peter Steinberger, the OpenClaw guy, got annoyed by this and started building lean CLIs himself. That kicked off a tool called Printing Press. You point Claude Code at any website, even ones with no API like ESPN or Craigslist, and it builds a small command tool for it in about 10 minutes. In one demo, a request pulled 132,000 tokens of raw data, but the tool processed it locally and only handed Claude a 2,000-token summary. The rest never touched the context window. It also comes with ~50 ready-made tools you can grab right away. To start, point Claude Code at the links from and ask it to set it up. Bookmark this.

32,351 views • 1 month ago

🚨 Netflix spends $500,000 per minute of animation a 23-year-old spent $124 last month and made $12,345 > Claude writes every script: 10 minutes > Midjourney draws every frame: 20 minutes > Runway animates every scene: 15 minutes > ElevenLabs voices every character: 10 minutes > Suno scores every episode: 5 minutes > Make publishes automatically: 0 minutes they built an industry that took 80 years to make sense he made it make sense on a Sunday afternoon how he built it is in the article above

🚨 Netflix spends $500,000 per minute of animation a 23-year-old spent $124 last month and made $12,345 > Claude writes every script: 10 minutes > Midjourney draws every frame: 20 minutes > Runway animates every scene: 15 minutes > ElevenLabs voices every character: 10 minutes > Suno scores every episode: 5 minutes > Make publishes automatically: 0 minutes they built an industry that took 80 years to make sense he made it make sense on a Sunday afternoon how he built it is in the article above

43,561 views • 1 month ago