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This guy built a visual scanner that reads 468 points on his face and 42 points on his hands from a regular webcam and turns them into a cloud of thousands of particles right between his palms. Inside, MediaPipe and TouchDesigner are linked: the first captures hands and face... from the webcam with high accuracy, the second turns those coordinates into a live plane and feeds it into a POP system that instantly generates a swarm of particles in the shape of a head. No studio, no render farmer, no VR headset. Just a laptop, a webcam, and 1 TouchDesigner session. And traditional VJ studios keep teams of 5 people on a setup with lighting, custom hardware, and commercial plugins, while his expenses are only a TouchDesigner subscription and a regular USB camera. One laptop runs MediaPipe and TouchDesigner simultaneously, holds the camera stream at 60 FPS without drops, and in parallel processes 468 face points + 21 points on each hand. The camera captures frame after frame, MediaPipe in real time sends TouchDesigner the finger coordinates and face geometry, and the POP operator inside the engine translates those numbers into thousands of particle points with colors from bright pink to gold. This setup immediately defines the role of the tool and the limits of its autonomy. It knows where the fingertips are at every moment of the frame. It knows how to read the face geometry at any angle to the camera. It knows how to draw a swarm of particles between them with the right color and contour. → MediaPipe pulls 468 points from the face and 21 points from each hand, 60 times per second → TouchDesigner receives those coordinates, builds a virtual rectangle between the fingertips, and feeds it into the POP system → POP generates thousands of particle points in the shape of a head, coloring them in a gradient from bright pink to gold → The HUD layer adds green corners and a blue neon frame, styling the image like an AR interface → All layers assemble into 1 real-time frame that projects back onto the video in the camera window → The final image is recorded to a file or broadcast to a projector for a live installation And only when the guy spreads his hands wider does the plane between the palms stretch; brings them together, it narrows. Otherwise the system runs on its own. And when he moves from his home room to a concert hall, the same laptop with the same webcam launches the same TouchDesigner session in just 5 minutes, without reconfiguration, without a new team, and without a single line of new code. In his work setup there is no studio of his own and no team for assembly. On the desk sits a laptop with a webcam, on top run MediaPipe and TouchDesigner with POP operators, and the same setup through a USB camera moves to any concert without a new configuration. Out of everything I have seen this year, this is the cleanest Creative Coding setup on 1 laptop: 0 render farms, 0 studio lighting, and between them 3 libraries, thousands of particle points, and 1 webcam.show more

Blaze

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38,242 просмотров • 6 месяцев назад •via X (Twitter)

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A guy from China just built a system that reshapes thousands of glowing particles into a jellyfish, a dragon, a statue, and a samurai. Just by moving his hands in front of a camera. Just a webcam reading his hand position. Every particle recalculates in real time. 60 frames per second. Not pre-rendered. Not a recording. Live. Clench both fists and the entire thing collapses into nothing. The whole setup runs on a regular camera and a GPU particle system. No special hardware. This is where hand tracking, computer vision, and real-time graphics have gotten. And most people haven't noticed.

A guy from China just built a system that reshapes thousands of glowing particles into a jellyfish, a dragon, a statue, and a samurai. Just by moving his hands in front of a camera. Just a webcam reading his hand position. Every particle recalculates in real time. 60 frames per second. Not pre-rendered. Not a recording. Live. Clench both fists and the entire thing collapses into nothing. The whole setup runs on a regular camera and a GPU particle system. No special hardware. This is where hand tracking, computer vision, and real-time graphics have gotten. And most people haven't noticed.

Vaibhav Sisinty

18,334 просмотров • 3 месяцев назад

A guy from China is reshaping glowing particles into a jellyfish, a statue, a dragon, and a samurai just by moving his hands in front of a camera No controller, no mouse, no keyboard Just a camera reading his hand position and a particle system recalculating thousands of points in real time, morphing one shape straight into the next Clench both fists at the end, and everything collapses into nothing This is running live at 60fps, not pre-rendered The entire scene sits inside a wireframe box tracking every motion as raw geometry reshapes into whatever his hands tell it to become

A guy from China is reshaping glowing particles into a jellyfish, a statue, a dragon, and a samurai just by moving his hands in front of a camera No controller, no mouse, no keyboard Just a camera reading his hand position and a particle system recalculating thousands of points in real time, morphing one shape straight into the next Clench both fists at the end, and everything collapses into nothing This is running live at 60fps, not pre-rendered The entire scene sits inside a wireframe box tracking every motion as raw geometry reshapes into whatever his hands tell it to become

pulmencr

50,834 просмотров • 3 месяцев назад

Apple spent 7 years and $3,500 per unit building Vision Pro, this guy did something better with a webcam in his bedroom. And no, this isn't a render, it's running live on his laptop right now with a webcam pointed at his hands. The webcam tracks 21 points on each of his hands in real time, every fingertip, every knuckle, every joint, mapped to a 3D skeleton at 60 frames per second. A piece of digital silk is rigged to that skeleton, so when he opens his palm the fabric drapes across his fingers, when he closes his hand it crumples in his fist, when he tilts his wrist the cloth slides off and folds with real physics, light reflecting off it like actual material. He can grab it, stretch it, throw it, catch it, all with his bare hands and zero hardware on his body. No headset, no gloves, no $3,500 face computer, no 600 gram brick on his skull, no 2 hour battery, no Apple ID, no App Store. Just a webcam, TouchDesigner and a kid who saw the Vision Pro keynote and thought, I can do that for free. Apple has 3,000 engineers, $200 billion in cash and 7 years of development time, they built a face computer most people will never own. This guy has a laptop, a webcam and a weekend, he built a future most people can actually use. The mixed reality industry just got embarrassed by a guy in a white t-shirt.

Apple spent 7 years and $3,500 per unit building Vision Pro, this guy did something better with a webcam in his bedroom. And no, this isn't a render, it's running live on his laptop right now with a webcam pointed at his hands. The webcam tracks 21 points on each of his hands in real time, every fingertip, every knuckle, every joint, mapped to a 3D skeleton at 60 frames per second. A piece of digital silk is rigged to that skeleton, so when he opens his palm the fabric drapes across his fingers, when he closes his hand it crumples in his fist, when he tilts his wrist the cloth slides off and folds with real physics, light reflecting off it like actual material. He can grab it, stretch it, throw it, catch it, all with his bare hands and zero hardware on his body. No headset, no gloves, no $3,500 face computer, no 600 gram brick on his skull, no 2 hour battery, no Apple ID, no App Store. Just a webcam, TouchDesigner and a kid who saw the Vision Pro keynote and thought, I can do that for free. Apple has 3,000 engineers, $200 billion in cash and 7 years of development time, they built a face computer most people will never own. This guy has a laptop, a webcam and a weekend, he built a future most people can actually use. The mixed reality industry just got embarrassed by a guy in a white t-shirt.

Defileo🔮

3,018,058 просмотров • 6 месяцев назад

THIS 12-YEAR-OLD CHINESE KID JUST BUILT THE IRON MAN INTERFACE WITHOUT WRITING A LINE OF CODE He waves his hand and 6500 particles on the screen behind him morph into Saturn, then a globe, then an alien mountain range. The trick is MediaPipe tracking his fingers through a laptop webcam, mapped to a particle physics engine running in the browser. He didn't write the code. He told Gemini what he wanted to see, copy-pasted the output, and shipped it to GitHub the same night. The ceiling for what a kid in his bedroom can build has quietly become absurd. Last year this was a Stanford grad project and this year it is a Tuesday... Save this time capsule from 2026 ↓

THIS 12-YEAR-OLD CHINESE KID JUST BUILT THE IRON MAN INTERFACE WITHOUT WRITING A LINE OF CODE He waves his hand and 6500 particles on the screen behind him morph into Saturn, then a globe, then an alien mountain range. The trick is MediaPipe tracking his fingers through a laptop webcam, mapped to a particle physics engine running in the browser. He didn't write the code. He told Gemini what he wanted to see, copy-pasted the output, and shipped it to GitHub the same night. The ceiling for what a kid in his bedroom can build has quietly become absurd. Last year this was a Stanford grad project and this year it is a Tuesday... Save this time capsule from 2026 ↓

slash1s

20,061 просмотров • 4 месяцев назад

THIS CHINESE GIRL CREATING INSANE AI VISUALS WITH HER FINGERS IN REAL TIME. THE TOOLS COST $0. While most visual artists are paying $150+/month for tools that rate limit mid-performance and send every prompt to a third-party server – this runs entirely offline on a normal laptop. –> TouchDesigner: free for non-commercial use. –> Ollama: open source since 2023. –> Total monthly cost: $0. Here's what $0 actually builds: –> Hand tracking with AI logic: MediaPipe tracks your hands, AI reads coordinates and triggers visual states. No hardcoded rules. –> Audio-reactive visuals: Audio feeds into Ollama every frame. AI returns parameters. TouchDesigner renders. Full loop under 50ms. –> Live performance: AI generates color palettes, particle behaviors, scene transitions from crowd audio, MIDI, microphone, camera. All real-time. –> Client installations: No API key that expires. No subscription that dies mid-show. Hand over the laptop and it runs forever. The $150/month tools go down at the worst moment. This doesn't go down. Ever. Your laptop. $0/month. A visual studio that runs forever. Bookmark this post. Full demo in the video below.

THIS CHINESE GIRL CREATING INSANE AI VISUALS WITH HER FINGERS IN REAL TIME. THE TOOLS COST $0. While most visual artists are paying $150+/month for tools that rate limit mid-performance and send every prompt to a third-party server – this runs entirely offline on a normal laptop. –> TouchDesigner: free for non-commercial use. –> Ollama: open source since 2023. –> Total monthly cost: $0. Here's what $0 actually builds: –> Hand tracking with AI logic: MediaPipe tracks your hands, AI reads coordinates and triggers visual states. No hardcoded rules. –> Audio-reactive visuals: Audio feeds into Ollama every frame. AI returns parameters. TouchDesigner renders. Full loop under 50ms. –> Live performance: AI generates color palettes, particle behaviors, scene transitions from crowd audio, MIDI, microphone, camera. All real-time. –> Client installations: No API key that expires. No subscription that dies mid-show. Hand over the laptop and it runs forever. The $150/month tools go down at the worst moment. This doesn't go down. Ever. Your laptop. $0/month. A visual studio that runs forever. Bookmark this post. Full demo in the video below.

SCOTTY BEAM

19,390 просмотров • 5 месяцев назад

This guy pulls $72,000 a month off a network of 12 influencers who do not exist and not one of them lives past his webcam. He got tired of watching brands burn $8,000 a month on castings and influencer contracts and a film crew for a couple of clips. So he built a generative studio and raised a whole roster from scratch not just one face. One of his clips costs $0.40 in compute. One post from a real influencer runs $1,500. He never shows up on camera himself. On camera there is only his motion under someone else's face. No model. No film crew. No casting. Here is the exact breakdown: → First he builds not a face but an operator core: one set of his expressions and one rig and a library of personas that snap onto him → Then he locks each face and only once it holds 98% across 1,000 frames even on a sharp head turn does he push the persona into the feed. Today a blonde from the US tomorrow an Asian girl but the operator behind them all stays one → A real-time rig lays a face over his webcam. Flux and LoRA hold each persona. ElevenLabs gives each one her own voice and accent. Higgsfield and Kling build the clips → TikTok and Reels push the clips into the tags. One clip pulls 200,000 to 1,200,000 views and the face is matched to the market. It reads as a live person not a render The key move that 96% skip: they invest in the face not in the operator behind it. Generate another pretty face and join a line of 800 just like it this week. Put a live operator behind the face with timing and pauses and reactions and the persona lands in 2 seconds. Pull the operator and the face glazes over and the watch time collapses. Here is where the money actually gets pulled: → Affiliate where each persona drives offers matched to her audience at 8-15% on every sale → His own stores where the top personas front his own product at 65% margin on a $52 average order → Face licensing where a brand keeps a persona on retainer at $2,500 a month so that is $7,500 a quarter not one check → Managed network as a service where he runs other people's personas at $1,800 a month each → A network of faces 12 personas across 12 markets off one operator The numbers are silly. A clip costs $0.40 in compute. One clip drives up to 1,200,000 views straight to the offers. At a 0.7% click and a $52 order that is hundreds of sales off one video. And the whole network runs on $120 a month in software and one webcam. The first night a new persona went up her clip pulled 640,000 views in 9 hours. Before an agency would have answered one email the offers cleared 280 sales for $13,700. 3,900 comments under a girl who 72 hours earlier was a prompt and one reference frame. The comments fill with "wait is this all one guy" and that argument is the fuel the algorithm runs on. Real agencies are still casting influencers and clearing contracts for weeks. And he ships a new face before they answer one email. Not a single casting. Not a single shoot. Not a dollar on ads. Just one operator. Twelve faces over him. And the discipline to sell the brain behind the persona not the face itself. Half of you are already typing that this is too much. The other half are already building their first persona. Which half are you in?

Shade

37,300 просмотров • 4 месяцев назад

I FOUND A COUPLE FORMAT THAT GETS 7M+ VIEWS WITH ONE HAND AND ONE PAIR OF GLASSES. 12 SECONDS. NO KISS. NO CAPTION. JUST A CAMERA ANGLE THAT MAKES YOUR BRAIN LIE TO ITSELF. Thread. First frame. Girl in black, camera below her chin. A man's hand enters from above and grabs her hair. Pulls. You already know what you think is about to happen and that's why you're still here. 0:01. Hand grips the hair, pulls her head back. Angle is from below looking up. This is the exact POV of every clip on sites you don't talk about. The framing is doing all the work and the video hasn't even started. 0:03. She's wearing glasses. His hand comes down and takes them off. That's it. He removed her glasses. But the way the hand moves from her hair to her face, the way she looks up with her lips parted, none of it reads as "taking off glasses." It reads as what you decided in the first second. 0:07. Hand grabs her chin, tilts her face up. She's looking into the camera from below with his fingers under her jaw. On paper a guy is holding his girlfriend's face. On paper it's nothing. On screen it's everything. The video loops and you're watching the hair grab again, already knowing it's just glasses, and it looks exactly the same. Knowing the punchline doesn't undo the setup. Your brain sees it the same way every time. The format: hand from above, camera from below, one accessory. Glasses, hair tie, necklace, doesn't matter. That geometry is the entire trick. Black top, pearl necklace, white room. If she was in sweats on a couch this would be a vlog. In this outfit with this lighting it's a scene. If you see a hand from above and a camera from below, your brain already finished the video for them. She didn't do anything. He just took off her glasses.

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I FOUND A COUPLE FORMAT THAT GETS 7M+ VIEWS WITH ONE HAND AND ONE PAIR OF GLASSES. 12 SECONDS. NO KISS. NO CAPTION. JUST A CAMERA ANGLE THAT MAKES YOUR BRAIN LIE TO ITSELF. Thread. First frame. Girl in black, camera below her chin. A man's hand enters from above and grabs her hair. Pulls. You already know what you think is about to happen and that's why you're still here. 0:01. Hand grips the hair, pulls her head back. Angle is from below looking up. This is the exact POV of every clip on sites you don't talk about. The framing is doing all the work and the video hasn't even started. 0:03. She's wearing glasses. His hand comes down and takes them off. That's it. He removed her glasses. But the way the hand moves from her hair to her face, the way she looks up with her lips parted, none of it reads as "taking off glasses." It reads as what you decided in the first second. 0:07. Hand grabs her chin, tilts her face up. She's looking into the camera from below with his fingers under her jaw. On paper a guy is holding his girlfriend's face. On paper it's nothing. On screen it's everything. The video loops and you're watching the hair grab again, already knowing it's just glasses, and it looks exactly the same. Knowing the punchline doesn't undo the setup. Your brain sees it the same way every time. The format: hand from above, camera from below, one accessory. Glasses, hair tie, necklace, doesn't matter. That geometry is the entire trick. Black top, pearl necklace, white room. If she was in sweats on a couch this would be a vlog. In this outfit with this lighting it's a scene. If you see a hand from above and a camera from below, your brain already finished the video for them. She didn't do anything. He just took off her glasses.

framexin

63,594 просмотров • 3 месяцев назад

A MAN SKETCHED A DART ON A STONE WALL IN SAN MARINO, AND IT FLEW OFF THE PAGE, THROUGH A CASTLE AND BACK TO HIS HAND > 512,000 likes, 3,400 comments. Try it yourself: A man in a beanie is drawing in a notebook. The camera slides into the pencil tip, and the drawn dart lifts off the paper with its feathers spread. It crosses the clouds, reaches the fortress on the hill, squeezes through an arrow slit and rushes down a corridor, ending up with the same man at the same wall. Why this clip works: -> 512,000 likes against 3,400 comments means people watch it, they don't debate it. -> It finishes on the frame it began with, so the loop restarts without anyone pressing replay. -> Pencil, dart, clouds, window, corridor: every shot flows into the next with no visible cut. -> The man and the castle are real footage. Only the dart is generated. A loop with no seam feels like one long take, but gets watched like several. Every extra pass counts as a view. The setup is short: a real location, one drawn object, and image-to-video from a single frame of the notebook. The page never ended, so the video never did. The full recipe is in the article below ↓

A MAN SKETCHED A DART ON A STONE WALL IN SAN MARINO, AND IT FLEW OFF THE PAGE, THROUGH A CASTLE AND BACK TO HIS HAND > 512,000 likes, 3,400 comments. Try it yourself: A man in a beanie is drawing in a notebook. The camera slides into the pencil tip, and the drawn dart lifts off the paper with its feathers spread. It crosses the clouds, reaches the fortress on the hill, squeezes through an arrow slit and rushes down a corridor, ending up with the same man at the same wall. Why this clip works: -> 512,000 likes against 3,400 comments means people watch it, they don't debate it. -> It finishes on the frame it began with, so the loop restarts without anyone pressing replay. -> Pencil, dart, clouds, window, corridor: every shot flows into the next with no visible cut. -> The man and the castle are real footage. Only the dart is generated. A loop with no seam feels like one long take, but gets watched like several. Every extra pass counts as a view. The setup is short: a real location, one drawn object, and image-to-video from a single frame of the notebook. The page never ended, so the video never did. The full recipe is in the article below ↓

hammertime

731,211 просмотров • 7 дней назад

BREAKING: Claude Code found 12,847 photos of my face on my iPhone that I never took. It looked like the phone was taking them every time I picked it up. From the club. From home. At a red light while I was driving. Almost every time I took the phone in my hands there was a frame. They were sitting in a system cache with timestamps and location data. None of them were in Photos. I plugged the phone into my MacBook and asked Claude Code to scan for any front-camera files that weren’t in my Photos library or Recently Deleted. It flagged Private/var/mobile/Library/Caches/com.apple.faceid. Over 12,000 files. Mix of infrared frames and regular front-camera images. Each one had a timestamp and GPS coordinates attached. I started scrolling. Most were from the last 14 months. Then I saw one from four seconds ago. I was still looking at the folder. Apple says the TrueDepth system turns the capture into a mathematical model inside the Secure Enclave and discards the image. On this phone the raw frames were still in the cache. Several of the processes that wrote to the folder belonged to system diagnostics. A couple of old apps with camera permission also had access to the same directory. I turned off diagnostics sharing and revoked camera access from every app that didn’t need it. If you want to check your own phone, plug it in, run a backup, and ask Claude Code to look for front-camera files outside the Photos library. The folder path above is where mine were.

BREAKING: Claude Code found 12,847 photos of my face on my iPhone that I never took. It looked like the phone was taking them every time I picked it up. From the club. From home. At a red light while I was driving. Almost every time I took the phone in my hands there was a frame. They were sitting in a system cache with timestamps and location data. None of them were in Photos. I plugged the phone into my MacBook and asked Claude Code to scan for any front-camera files that weren’t in my Photos library or Recently Deleted. It flagged Private/var/mobile/Library/Caches/com.apple.faceid. Over 12,000 files. Mix of infrared frames and regular front-camera images. Each one had a timestamp and GPS coordinates attached. I started scrolling. Most were from the last 14 months. Then I saw one from four seconds ago. I was still looking at the folder. Apple says the TrueDepth system turns the capture into a mathematical model inside the Secure Enclave and discards the image. On this phone the raw frames were still in the cache. Several of the processes that wrote to the folder belonged to system diagnostics. A couple of old apps with camera permission also had access to the same directory. I turned off diagnostics sharing and revoked camera access from every app that didn’t need it. If you want to check your own phone, plug it in, run a backup, and ask Claude Code to look for front-camera files outside the Photos library. The folder path above is where mine were.

Cooler

1,481,064 просмотров • 20 дней назад

This Chinese guy built a Second Brain in Obsidian and every morning gets 3 trading ideas that brought him $180,000 in 6 months. Inside he runs a pipeline of 6 workflows on N8N that automatically pulls every read article, listened podcast, and voice note into a shared Obsidian vault, and a neural network analyst every morning at 6:00 finds connections between the fresh and the old and puts the 3 strongest trading ideas for the day into the inbox. No analytics desk, no Bloomberg terminal, no Telegram chats with traders. Just a Mac Mini by the wall, an iPhone in the pocket, and 1 local Obsidian vault. And traditional quant funds keep entire teams of 8 people on salary for the same flow of insights, while his expenses are only subscriptions to Readwise, Whisper API, and N8N hosting. 6 pipelines process about 200 sources a day and close the monthly API bill at about $120. The Mac Mini itself stores the entire vault and keeps the neural network analyst running 24/7, and from the iPhone the owner drops any idea he hears on the go into a Telegram bot, and it lands in the vault inbox in just 30 seconds. The starting instruction that sits in the VAULT.md file at the root of his vault looks like this: "you are the AI analyst of a solo trader. you read his vault every morning at 6:00, find connections between fresh and old notes, and deliver 3 trading ideas he can verify in the hour before the market opens. pipelines: // Reader (pulls every article and highlight from Readwise, Twitter bookmarks, and Kindle into /notes) // Listener (transcribes podcasts through Airr and voice notes through Whisper, puts them in /notes) // Catcher (accepts any message from the Telegram bot and writes it to /inbox with a timestamp) // Connector (every night reads across the entire vault and updates the connection graph between 4,000 notes) // Briefer (at 6:00 AM writes a brief: 3 trading ideas for today plus the emerging thesis of the week, puts it in /inbox) // Mobile (lives in the iPhone, answers any question about the vault by voice, and confirms alerts while the owner is on the go). you wake the owner with a push notification only when a fresh note contradicts his active thesis or when 1 of the 3 morning ideas has a confidence score above 90%." This instruction immediately sets the role for the system and the limits of its autonomy. It knows it is supposed to connect new with old on its own. It knows it is supposed to prepare 3 trading ideas every morning on its own. It knows it connects the live trader only when a thesis is contradicted or an ultra-confident idea appears. → Reader pulls about 80 articles and highlights a day from Readwise, Twitter, and Kindle → Listener transcribes 4 to 6 podcasts a week through Airr and Whisper → Catcher intercepts all voice and text ideas through the Telegram bot, averaging 15 to 20 a day → Connector updates the connection graph between 4,000 notes every night, adding 25 to 30 new edges → Briefer puts a fresh brief with 3 trading ideas and the emerging thesis into the inbox at exactly 6:00 → Mobile answers any question about the vault by voice and confirms alerts right from the iPhone And only when a new note contradicts his active thesis or 1 of the ideas breaks 90% confidence does the orchestrator raise the owner with a push notification. And when the trader at that moment is driving to the gym or eating breakfast, the Mobile agent in his iPhone answers any quick question about the vault by voice: what he wrote about this ticker last week, which 3 sources support the idea of long NVDA, and what counter-thesis already sits in his notes. The trader makes the decision and sends the order before New York opens. The fresh brief from last Monday looks like this: "reader: 78 materials added over the weekend, 11 of them about semiconductors, 4 about energy, 3 about biotech. passing to connector." "connector: 27 new connections found between fresh materials and the vault, the strongest one is that the Goldman report from Wednesday matches the NVDA thesis you wrote 3 weeks ago." "briefer: 3 trading ideas for today: long NVDA (confidence 0.84), short Tesla at the close of the quarterly report (0.71), watch URI (0.62). emerging thesis of the week: the market is underpricing capex on data centers." "alert: your fresh note about long-term risk in semis contradicts the NVDA thesis. sending for review." In his work setup there is no cloud server, no team of analysts, and not even a Bloomberg subscription. At home sits a Mac Mini with a local Obsidian vault, on top run 6 N8N pipelines and a neural network analyst, and the same vault mirrors to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo trading setup on a second brain: $120 a month on the API, about $30,000 a month into the account, and between them 6 pipelines, 4,000 connected notes, and 1 iPhone in the pocket.

This Chinese guy built a Second Brain in Obsidian and every morning gets 3 trading ideas that brought him $180,000 in 6 months. Inside he runs a pipeline of 6 workflows on N8N that automatically pulls every read article, listened podcast, and voice note into a shared Obsidian vault, and a neural network analyst every morning at 6:00 finds connections between the fresh and the old and puts the 3 strongest trading ideas for the day into the inbox. No analytics desk, no Bloomberg terminal, no Telegram chats with traders. Just a Mac Mini by the wall, an iPhone in the pocket, and 1 local Obsidian vault. And traditional quant funds keep entire teams of 8 people on salary for the same flow of insights, while his expenses are only subscriptions to Readwise, Whisper API, and N8N hosting. 6 pipelines process about 200 sources a day and close the monthly API bill at about $120. The Mac Mini itself stores the entire vault and keeps the neural network analyst running 24/7, and from the iPhone the owner drops any idea he hears on the go into a Telegram bot, and it lands in the vault inbox in just 30 seconds. The starting instruction that sits in the VAULT.md file at the root of his vault looks like this: "you are the AI analyst of a solo trader. you read his vault every morning at 6:00, find connections between fresh and old notes, and deliver 3 trading ideas he can verify in the hour before the market opens. pipelines: // Reader (pulls every article and highlight from Readwise, Twitter bookmarks, and Kindle into /notes) // Listener (transcribes podcasts through Airr and voice notes through Whisper, puts them in /notes) // Catcher (accepts any message from the Telegram bot and writes it to /inbox with a timestamp) // Connector (every night reads across the entire vault and updates the connection graph between 4,000 notes) // Briefer (at 6:00 AM writes a brief: 3 trading ideas for today plus the emerging thesis of the week, puts it in /inbox) // Mobile (lives in the iPhone, answers any question about the vault by voice, and confirms alerts while the owner is on the go). you wake the owner with a push notification only when a fresh note contradicts his active thesis or when 1 of the 3 morning ideas has a confidence score above 90%." This instruction immediately sets the role for the system and the limits of its autonomy. It knows it is supposed to connect new with old on its own. It knows it is supposed to prepare 3 trading ideas every morning on its own. It knows it connects the live trader only when a thesis is contradicted or an ultra-confident idea appears. → Reader pulls about 80 articles and highlights a day from Readwise, Twitter, and Kindle → Listener transcribes 4 to 6 podcasts a week through Airr and Whisper → Catcher intercepts all voice and text ideas through the Telegram bot, averaging 15 to 20 a day → Connector updates the connection graph between 4,000 notes every night, adding 25 to 30 new edges → Briefer puts a fresh brief with 3 trading ideas and the emerging thesis into the inbox at exactly 6:00 → Mobile answers any question about the vault by voice and confirms alerts right from the iPhone And only when a new note contradicts his active thesis or 1 of the ideas breaks 90% confidence does the orchestrator raise the owner with a push notification. And when the trader at that moment is driving to the gym or eating breakfast, the Mobile agent in his iPhone answers any quick question about the vault by voice: what he wrote about this ticker last week, which 3 sources support the idea of long NVDA, and what counter-thesis already sits in his notes. The trader makes the decision and sends the order before New York opens. The fresh brief from last Monday looks like this: "reader: 78 materials added over the weekend, 11 of them about semiconductors, 4 about energy, 3 about biotech. passing to connector." "connector: 27 new connections found between fresh materials and the vault, the strongest one is that the Goldman report from Wednesday matches the NVDA thesis you wrote 3 weeks ago." "briefer: 3 trading ideas for today: long NVDA (confidence 0.84), short Tesla at the close of the quarterly report (0.71), watch URI (0.62). emerging thesis of the week: the market is underpricing capex on data centers." "alert: your fresh note about long-term risk in semis contradicts the NVDA thesis. sending for review." In his work setup there is no cloud server, no team of analysts, and not even a Bloomberg subscription. At home sits a Mac Mini with a local Obsidian vault, on top run 6 N8N pipelines and a neural network analyst, and the same vault mirrors to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo trading setup on a second brain: $120 a month on the API, about $30,000 a month into the account, and between them 6 pipelines, 4,000 connected notes, and 1 iPhone in the pocket.

Blaze

932,482 просмотров • 6 месяцев назад

SOMEONE BUILT A WAY TO SEND FILES BETWEEN TWO PHONES WITH NO WIFI, NO BLUETOOTH AND NO NETWORK AT ALL it works with just a screen flashing qr codes at a camera one device plays an endless stream of animated qr codes, the other one points its camera at the screen and rebuilds the file out of the light no pairing, no app, no shared network, nothing but the camera > its pulling about 129 kb/s, a full 2mb image transfers in seconds > theres no back channel, the receiver can never ask for a missed frame, so it uses fountain codes, every frame is a scrambled mix of the file rather than one piece of it > that means the receiver just collects any frames it can catch in any order, and once it has enough it can reconstruct the whole file > dropping frames costs you a little time but never breaks the transfer > you can point the camera at a stream thats already running and it just locks on mid flight he was building a cached mp3 player because he was sick of ads and bad streaming quality. he wanted a way to send tracks phone to phone without both phones being on the same network, and realized flashing qr codes could carry the data he built the whole working prototype in one night with claude code its open source and already at 375 stars

SOMEONE BUILT A WAY TO SEND FILES BETWEEN TWO PHONES WITH NO WIFI, NO BLUETOOTH AND NO NETWORK AT ALL it works with just a screen flashing qr codes at a camera one device plays an endless stream of animated qr codes, the other one points its camera at the screen and rebuilds the file out of the light no pairing, no app, no shared network, nothing but the camera > its pulling about 129 kb/s, a full 2mb image transfers in seconds > theres no back channel, the receiver can never ask for a missed frame, so it uses fountain codes, every frame is a scrambled mix of the file rather than one piece of it > that means the receiver just collects any frames it can catch in any order, and once it has enough it can reconstruct the whole file > dropping frames costs you a little time but never breaks the transfer > you can point the camera at a stream thats already running and it just locks on mid flight he was building a cached mp3 player because he was sick of ads and bad streaming quality. he wanted a way to send tracks phone to phone without both phones being on the same network, and realized flashing qr codes could carry the data he built the whole working prototype in one night with claude code its open source and already at 375 stars

Om Patel

951,210 просмотров • 3 месяцев назад

GEMINI AI COUPLE PROMPT: 9:16 aspect ratio. 8K resolution. Use the photo I uploaded as my reference. Keep the facial features the same to create a black-and-white, vertical portrait that captures a tender and intimate moment between a man and a woman, suggesting a romantic relationship. The man, on the left. He's looking at the camera and slightly to the right, his head resting against the woman's. His eyes are partially visible, but his gaze seems gentle and focused on her. His hair is dark and styled, and he appears to be wearing a light-colored, possibly white, shirt with some texture visible on the shoulder. The woman is on the right, nestled into the man's embrace. Her eyes are looking at the camera and a soft, content expression is on her face, suggesting she's comfortable and at peace in his arms. Her head is tilted, resting on his chest or shoulder. Her long wavy hair is dark and falls over her right shoulder. She's also wearing a light-colored shirt, possibly a blouse, which appears to be similar in color to his. The lighting is soft and directional, illuminating their faces and the tops of their shoulders, creating a gentle contrast with the dark background. The focus is sharp on both of their faces, highlighting the emotional connection between them. The image has a grainy texture, which adds to its classic and timeless feel. #Gemini #ai #aiprompt #couple #foryoupage

GEMINI AI COUPLE PROMPT: 9:16 aspect ratio. 8K resolution. Use the photo I uploaded as my reference. Keep the facial features the same to create a black-and-white, vertical portrait that captures a tender and intimate moment between a man and a woman, suggesting a romantic relationship. The man, on the left. He's looking at the camera and slightly to the right, his head resting against the woman's. His eyes are partially visible, but his gaze seems gentle and focused on her. His hair is dark and styled, and he appears to be wearing a light-colored, possibly white, shirt with some texture visible on the shoulder. The woman is on the right, nestled into the man's embrace. Her eyes are looking at the camera and a soft, content expression is on her face, suggesting she's comfortable and at peace in his arms. Her head is tilted, resting on his chest or shoulder. Her long wavy hair is dark and falls over her right shoulder. She's also wearing a light-colored shirt, possibly a blouse, which appears to be similar in color to his. The lighting is soft and directional, illuminating their faces and the tops of their shoulders, creating a gentle contrast with the dark background. The focus is sharp on both of their faces, highlighting the emotional connection between them. The image has a grainy texture, which adds to its classic and timeless feel. #Gemini #ai #aiprompt #couple #foryoupage

mariz 😍

1,500,000 просмотров • 1 год назад

For anyone using Grok Bot or Claude to make video edits, there are a few things nobody seems to be doing. This is a small clip from an upcoming video of mine. FIXES: graphics pinned to the screen instead of the scene, dark scenes that break into bands and blocks on X and YouTube, numbers that jiggle while they count, and soft shots that don't match the rest of the edit. 👾Graphics pinned to the screen instead of the scene The top half of this video has its graphics pinned to the screen the way most edits do, and the bottom half has them locked to the world. When the camera moves, a label that points at something stays where it was and the picture slides out from under it. NASA Ames tested this with pilots in 1995. Landing in a simulator, they took 2.5 seconds longer to react to a plane on the runway when their flight symbols were on the head-up display instead of the instrument panel. Their explanation was that symbols on the glass don't move the way the world outside does, and that difference pulls attention onto them. Their fix was to draw the symbols as objects in the world so they move with it, and pilots flew their path more accurately. In an edit, that means tracking how the background moves in each shot, ignoring the people in it, and moving every graphic that points at something along with the camera. A label on a target follows the target, and titles and counters stay fixed. 👾Dark scenes that break into bands and blocks on X and YouTube: X and YouTube compress what you upload, and the smooth gradients in dark footage turn into bands and blocks. The fix is to smooth those gradients before the grain goes on. 👾Numbers that jiggle while they count: Counters jiggle because the digits in a lot of fonts are different widths, so the number shifts sideways as it counts. Putting each digit in a fixed-width slot keeps it still. 👾Soft shots that don't match the rest of the edit: Measure the sharpness of every shot too. The softest one stands out next to the shots around it, and that's the one to regenerate.

For anyone using Grok Bot or Claude to make video edits, there are a few things nobody seems to be doing. This is a small clip from an upcoming video of mine. FIXES: graphics pinned to the screen instead of the scene, dark scenes that break into bands and blocks on X and YouTube, numbers that jiggle while they count, and soft shots that don't match the rest of the edit. 👾Graphics pinned to the screen instead of the scene The top half of this video has its graphics pinned to the screen the way most edits do, and the bottom half has them locked to the world. When the camera moves, a label that points at something stays where it was and the picture slides out from under it. NASA Ames tested this with pilots in 1995. Landing in a simulator, they took 2.5 seconds longer to react to a plane on the runway when their flight symbols were on the head-up display instead of the instrument panel. Their explanation was that symbols on the glass don't move the way the world outside does, and that difference pulls attention onto them. Their fix was to draw the symbols as objects in the world so they move with it, and pilots flew their path more accurately. In an edit, that means tracking how the background moves in each shot, ignoring the people in it, and moving every graphic that points at something along with the camera. A label on a target follows the target, and titles and counters stay fixed. 👾Dark scenes that break into bands and blocks on X and YouTube: X and YouTube compress what you upload, and the smooth gradients in dark footage turn into bands and blocks. The fix is to smooth those gradients before the grain goes on. 👾Numbers that jiggle while they count: Counters jiggle because the digits in a lot of fonts are different widths, so the number shifts sideways as it counts. Putting each digit in a fixed-width slot keeps it still. 👾Soft shots that don't match the rest of the edit: Measure the sharpness of every shot too. The softest one stands out next to the shots around it, and that's the one to regenerate.

tetsuo

49,624 просмотров • 18 дней назад

THIS ROBOT HAS A DEAD FLY'S BRAIN AND IT WALKS LIKE A FLY scientists sliced the fly into thousands of layers thinner than a virus to copy every wire in its body the fly didn't survive the map did: 166,700 neurons, from the brain all the way down to the legs so somebody built it a new body six metal legs, 18 motors, two camera eyes and a battery strapped to its back no walking code, they just wired the leg neurons into the motors and switched it on > it turned its head and looked straight at the camera > then it walked, three legs down, three legs up, the exact way a real fly does > when someone reached for it, it froze > when the hand got closer, it ran nobody taught it any of that the fly has been dead for years, and its brain still knows how to escape next on the list is a mouse

THIS ROBOT HAS A DEAD FLY'S BRAIN AND IT WALKS LIKE A FLY scientists sliced the fly into thousands of layers thinner than a virus to copy every wire in its body the fly didn't survive the map did: 166,700 neurons, from the brain all the way down to the legs so somebody built it a new body six metal legs, 18 motors, two camera eyes and a battery strapped to its back no walking code, they just wired the leg neurons into the motors and switched it on > it turned its head and looked straight at the camera > then it walked, three legs down, three legs up, the exact way a real fly does > when someone reached for it, it froze > when the hand got closer, it ran nobody taught it any of that the fly has been dead for years, and its brain still knows how to escape next on the list is a mouse

explos1ve

69,198 просмотров • 1 месяц назад

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

Ridark

15,948 просмотров • 5 месяцев назад

$166,000 neurons from a dead fruit fly now hunt drones from a bedroom desk. A white drone the size of a palm lifts off the carpet, and a black 3D-printed turret on the desk swings after it, with the fly's brain working its 2 stepper motors, 1 camera and 2 barrels through a nest of rainbow wires on a breadboard. That brain sits in the top-left corner of the screen and flares every time the drone jerks. A Patriot interceptor costs about $4 million, and armies are firing them at drones worth a fraction of that. This brain is smaller than a poppy seed, and Google put it online for free 2 weeks ago. Then someone gave it a job it was built for. Most of a fly's brain exists to see motion, and this brain is male, and male flies spend their lives chasing whatever moves. The drone climbs past the ceiling fan and the crosshair climbs with it, then drops when it drops. For 18 seconds it never loses the drone. In the last shot the drone flies home to the guy holding the controller, and the crosshair follows it right onto his chest. It spent its whole life dodging swatters. Now it's the one aiming.$

166,000 neurons from a dead fruit fly now hunt drones from a bedroom desk. A white drone the size of a palm lifts off the carpet, and a black 3D-printed turret on the desk swings after it, with the fly's brain working its 2 stepper motors, 1 camera and 2 barrels through a nest of rainbow wires on a breadboard. That brain sits in the top-left corner of the screen and flares every time the drone jerks. A Patriot interceptor costs about $4 million, and armies are firing them at drones worth a fraction of that. This brain is smaller than a poppy seed, and Google put it online for free 2 weeks ago. Then someone gave it a job it was built for. Most of a fly's brain exists to see motion, and this brain is male, and male flies spend their lives chasing whatever moves. The drone climbs past the ceiling fan and the crosshair climbs with it, then drops when it drops. For 18 seconds it never loses the drone. In the last shot the drone flies home to the guy holding the controller, and the crosshair follows it right onto his chest. It spent its whole life dodging swatters. Now it's the one aiming.

Spike 1%

36,956 просмотров • 1 месяц назад

Wow! Send A File By Just The Camera! A vibe coder used AI to build a file transfer system that sends data between two phones using only a screen and a camera. One phone displays a new type of animated QR codes while the other scans them to rebuild the file, with no Wi-Fi, Bluetooth, or cables needed. It is fully optical and local. The system uses fountain codes that create each QR frame as a random mix of file data. This keeps transfers working even if some frames are missed, reaching speeds of about 129 KB/s for a 2 MB image. The entire project was built in one night and released as open source. The idea came from a music project where the developer wanted to share MP3 files without streaming or using the same network. Animated QR codes became the solution, showing a creative new way to transfer files with everyday phone hardware. GitHub link:

Wow! Send A File By Just The Camera! A vibe coder used AI to build a file transfer system that sends data between two phones using only a screen and a camera. One phone displays a new type of animated QR codes while the other scans them to rebuild the file, with no Wi-Fi, Bluetooth, or cables needed. It is fully optical and local. The system uses fountain codes that create each QR frame as a random mix of file data. This keeps transfers working even if some frames are missed, reaching speeds of about 129 KB/s for a 2 MB image. The entire project was built in one night and released as open source. The idea came from a music project where the developer wanted to share MP3 files without streaming or using the same network. Animated QR codes became the solution, showing a creative new way to transfer files with everyday phone hardware. GitHub link:

Brian Roemmele

16,847 просмотров • 3 месяцев назад

A FEW YEARS AGO THIS NEEDED A CREW. NOW IT NEEDS A PROMPT A late-night host compliments his guest. She says nothing, swings both legs up onto his desk and asks if it's only incredible or the best he's ever seen. He laughs and gives up. No host, no guest, no studio. None of it exists. - The legs landing on the desk are the hard part Two feet have to arrive on the edge of the desk with believable weight and no clipping through the wood, and his reaction has to land right on the beat after. How clips like this get made, the three-tool pipeline behind them, and how operators turn it into money is in the article below.

A FEW YEARS AGO THIS NEEDED A CREW. NOW IT NEEDS A PROMPT A late-night host compliments his guest. She says nothing, swings both legs up onto his desk and asks if it's only incredible or the best he's ever seen. He laughs and gives up. No host, no guest, no studio. None of it exists. - The legs landing on the desk are the hard part Two feet have to arrive on the edge of the desk with believable weight and no clipping through the wood, and his reaction has to land right on the beat after. How clips like this get made, the three-tool pipeline behind them, and how operators turn it into money is in the article below.

capONE 💎

1,069,617 просмотров • 1 месяц назад

$A 20-year-old engineering enthusiast from Guangdong Province has shared his new invention A display, wires, a circuit board, a circular frame detector, and a battery He places a part inside the ring, the sensors recognize the item, and it instantly appears on the display The project creator claims he used a combination of a high-frequency inductance coil, custom firmware running on a powerful microcontroller, and a matrix of field sensors According to him, the device captures the slightest changes in electromagnetic response and material density, digitizing the shape of the object in a fraction of a second$

A 20-year-old engineering enthusiast from Guangdong Province has shared his new invention A display, wires, a circuit board, a circular frame detector, and a battery He places a part inside the ring, the sensors recognize the item, and it instantly appears on the display The project creator claims he used a combination of a high-frequency inductance coil, custom firmware running on a powerful microcontroller, and a matrix of field sensors According to him, the device captures the slightest changes in electromagnetic response and material density, digitizing the shape of the object in a fraction of a second

Bober_smart

134,052 просмотров • 3 месяцев назад

China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

savip.

105,419 просмотров • 4 месяцев назад