Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

AI BASKETBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. Take any NBA broadcast. Any camera angle. Any resolution. Feed it into the system. YOLO finds every player and the ball. Frame by frame. No manual annotation. No pre-labeled data.The model just sees the court... and understands it. Zero-shot classification looks at jersey pixels and decides who plays for whom. Two teams separated in milliseconds. Without ever being told the team names. But here's where it gets wild. Court keypoint detection identifies the geometry of the playing surface. Homography transforms the broadcast camera into a top-down tactical map. Real meters. Real coordinates. From a flat 2D video. Now every player has a position on a real court. Every movement becomes a data point. Every pass becomes a vector. Speed. Distance. Possession time. Pass networks. All extracted from nothing but pixels. No GPS. No chips in the ball. No million-dollar Second Spectrum setup. A Python script and a GPU. The full pipeline is open source. The tutorial walks through every line of code. From detection to transformation to analytics output. Teams used to pay six figures for this data. Now you build it in an afternoon. Computer vision didn't just enter basketball. It democratized it.show more

zostaff

15,027 subscribers

230,988 Aufrufe • vor 4 Monaten •via X (Twitter)

Wissenschaft & Technologie Sport

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

AI FOOTBALL ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, OPENCV, AND PYTHON. You upload a regular match video. No sensors, no GPS trackers, just camera footage. The neural network finds every player, referee, and ball on its own. Every frame, in real time. KMeans clustering breaks down jersey colors pixel by pixel. The system splits players into teams automatically. Without a single manual hint. Optical Flow tracks camera movement. Separates it from player movement. Perspective Transformation converts pixels into real meters. Speed of every player. Distance covered. Ball possession percentage. All calculated automatically. Four hours of tutorial from zero to a working system. The model is trained on real Bundesliga matches. Runs on a regular GPU. Python code - take it and run. Sports analytics is no longer behind closed doors. AI leveled the playing field.

zostaff

1,075,589 Aufrufe • vor 4 Monaten

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

AI TENNIS ANALYSIS. A FULL COMPUTER VISION SYSTEM. BUILT ON YOLO, PYTORCH, AND KEYPOINT EXTRACTION. Take any tennis match broadcast, any camera angle, any resolution. Feed it into the pipeline. YOLO detects both players and the tennis ball frame by frame. No manual labeling, no pre-annotated dataset. A fine-tuned YOLOv5 model trained on a Roboflow tennis ball dataset handles the ball - the hardest object to track in any sport. Tiny, fast, constantly occluded. The model finds it anyway. Trackers maintain identity across frames so Player 1 stays Player 1 from the first serve to match point. But detection is just the start. A ResNet50 CNN trained in PyTorch predicts court keypoints from every frame - the corners, service lines, baselines, net posts. Fourteen points that define the entire playing surface geometry. From those keypoints the system builds a homography matrix and warps the broadcast perspective into a top-down mini court with real coordinates. Now every player has a position in real space, not pixel space. Every frame becomes a measurement. Every rally becomes a dataset. Player movement speed - calculated from position deltas between frames, converted to meters per second through the homography. Ball shot speed - measured from the ball trajectory across consecutive detections. Number of shots per rally - counted automatically through ball direction changes. All of this rendered live on the video as an overlay. A mini court in the corner showing both players as dots moving in real time. Stats updating after every point. OpenCV handles the rendering. Pandas handles the math. PyTorch handles the intelligence. YOLO handles the eyes. No Hawkeye subscription, no court-embedded sensors, no tracking chips in the ball. A Python script, a trained model, and a GPU. The full code is on GitHub. The tutorial walks through every module - from ball detector training to court keypoint extraction to the final statistical overlay. Professional teams used to need broadcast deals and proprietary hardware for this kind of analysis. Now you build it in an afternoon with open-source tools. Trading here: Computer vision didn't just enter tennis. It made the expensive stuff free.

zostaff

120,370 Aufrufe • vor 4 Monaten

9 repos that mass replace a $150,000/year NBA analytics department. all free. all open source. -> replaces Second Spectrum and SportVU YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. court keypoint detection builds a tactical top-down map. speed, distance, passes - all from a TV feed. no sensors. -> replaces paid NBA prediction services ($100/mo) XGBoost + Neural Net. moneyline and totals. Kelly Criterion sizing. 69% accuracy. pulls odds from FanDuel/DraftKings automatically. the most starred NBA betting repo on GitHub. -> replaces an entire quant sports desk 5-model ensemble: XGBoost + PyTorch MLP + Ridge + Lasso + baseline. Optuna-tuned hyperparameters. SQLite database with box scores, play-by-play, betting lines, injury reports. production-grade. -> replaces manual daily prediction workflows XGBoost/LightGBM with GitHub Actions automation. scrapes new data, retrains models, outputs daily win probabilities. set it and forget it. -> replaces ELO subscription services custom ELO + Ridge + XGBoost + Neural Networks ensemble. full data scraping pipeline. comprehensive visualizations. FiveThirtyEight-style ratings from scratch. -> replaces Four Factors analytics dashboards ELO rating system + Four Factors + PCA dimensionality reduction. detailed comparison of 10+ models. honest 65.3% accuracy - because that's what real NBA prediction looks like. -> replaces computer vision analytics platforms ($500/mo) YOLO player/ball tracking. automatic team assignment. court keypoints. pass and interception detection. speed and distance. full tactical view. modular architecture. -> replaces shot tracking hardware YOLOv8 detects ball and hoop in real-time. linear regression predicts trajectory. registers makes and misses automatically. works on any video feed. -> replaces paid sports data subscriptions ($300/mo) official Python client for NBA. com API. box scores, play-by-play, shot charts, player tracking. 40+ years of data. zero cost. the foundation every NBA ML project is built on. like + bookmark you'll need this when you build your first NBA prediction bot

9 repos that mass replace a $150,000/year NBA analytics department. all free. all open source. -> replaces Second Spectrum and SportVU YOLO tracks every player and ball from any broadcast. assigns teams by jersey color. court keypoint detection builds a tactical top-down map. speed, distance, passes - all from a TV feed. no sensors. -> replaces paid NBA prediction services ($100/mo) XGBoost + Neural Net. moneyline and totals. Kelly Criterion sizing. 69% accuracy. pulls odds from FanDuel/DraftKings automatically. the most starred NBA betting repo on GitHub. -> replaces an entire quant sports desk 5-model ensemble: XGBoost + PyTorch MLP + Ridge + Lasso + baseline. Optuna-tuned hyperparameters. SQLite database with box scores, play-by-play, betting lines, injury reports. production-grade. -> replaces manual daily prediction workflows XGBoost/LightGBM with GitHub Actions automation. scrapes new data, retrains models, outputs daily win probabilities. set it and forget it. -> replaces ELO subscription services custom ELO + Ridge + XGBoost + Neural Networks ensemble. full data scraping pipeline. comprehensive visualizations. FiveThirtyEight-style ratings from scratch. -> replaces Four Factors analytics dashboards ELO rating system + Four Factors + PCA dimensionality reduction. detailed comparison of 10+ models. honest 65.3% accuracy - because that's what real NBA prediction looks like. -> replaces computer vision analytics platforms ($500/mo) YOLO player/ball tracking. automatic team assignment. court keypoints. pass and interception detection. speed and distance. full tactical view. modular architecture. -> replaces shot tracking hardware YOLOv8 detects ball and hoop in real-time. linear regression predicts trajectory. registers makes and misses automatically. works on any video feed. -> replaces paid sports data subscriptions ($300/mo) official Python client for NBA. com API. box scores, play-by-play, shot charts, player tracking. 40+ years of data. zero cost. the foundation every NBA ML project is built on. like + bookmark you'll need this when you build your first NBA prediction bot

zostaff

103,649 Aufrufe • vor 3 Monaten

7 repos that mass replace a $50,000/year sports analytics department. all free. all open source. -> replaces Hawkeye-level court analysis YOLO tracks players and ball from any broadcast. ResNet50 extracts court keypoints. homography converts pixels to real meters. speed, position, aggression - all from a TV feed. -> replaces paid sports data subscriptions ($500/mo) every ATP match since 1968. rankings, results, stats. 1.5K stars. the holy grail dataset that every tennis ML project is built on. -> replaces point-level data feeds ($200/mo) point-by-point data for every Grand Slam since 2011. the kind of granularity you need for live Bayesian models. -> replaces shot-by-shot scouting reports 5,000+ matches charted shot by shot. direction, depth, error type. crowdsourced and free. -> replaces pre-match and in-match prediction services ELO + serve/return stats → win probability. updates during the match. exactly what a live Bayesian engine needs. -> replaces ball trajectory prediction tools CV analysis + CatBoost bounce prediction + separate court detector neural net. most advanced open-source tennis CV pipeline. -> replaces traditional bookmaker APIs Polymarket CLOB API. real-time share prices, orderbook depth, bid/ask spreads. no margin, no bookmaker - just the crowd. trade positions mid-match, not just pre-match. total before: $50K/year sports analytics stack total now: $0 like + bookmark you'll need this when you build your first tennis bot

7 repos that mass replace a $50,000/year sports analytics department. all free. all open source. -> replaces Hawkeye-level court analysis YOLO tracks players and ball from any broadcast. ResNet50 extracts court keypoints. homography converts pixels to real meters. speed, position, aggression - all from a TV feed. -> replaces paid sports data subscriptions ($500/mo) every ATP match since 1968. rankings, results, stats. 1.5K stars. the holy grail dataset that every tennis ML project is built on. -> replaces point-level data feeds ($200/mo) point-by-point data for every Grand Slam since 2011. the kind of granularity you need for live Bayesian models. -> replaces shot-by-shot scouting reports 5,000+ matches charted shot by shot. direction, depth, error type. crowdsourced and free. -> replaces pre-match and in-match prediction services ELO + serve/return stats → win probability. updates during the match. exactly what a live Bayesian engine needs. -> replaces ball trajectory prediction tools CV analysis + CatBoost bounce prediction + separate court detector neural net. most advanced open-source tennis CV pipeline. -> replaces traditional bookmaker APIs Polymarket CLOB API. real-time share prices, orderbook depth, bid/ask spreads. no margin, no bookmaker - just the crowd. trade positions mid-match, not just pre-match. total before: $50K/year sports analytics stack total now: $0 like + bookmark you'll need this when you build your first tennis bot

zostaff

35,722 Aufrufe • vor 4 Monaten

21 years old. Alone. In just 6 weeks, he built what NBA clubs pay $120,000 a year for. No team. No lab. Right from his apartment. He took what used to require an entire room of analysts and handed it over to a neural network. The camera watches the court and the machine does the rest: → Recognizes all 10 players simultaneously in real time, tagging names and numbers on the fly without losing track of them in motion. → Boosted jersey number accuracy from 56% to 93% after fine-tuning. → Calculates the exact distance to the nearest defender at the release point (4 feet, 16, 30) and instantly gauges how open the player is. → Logs shot type, court position, and outcome (make or miss) completely automatically. → Identifies a player even when they are fully occluded. Tracks the ball even when it's in the referee's hand. → Zero manual labeling. Zero human verification. 5 models in the stack, trained on 2,400 playoff clips. Something professional clubs pay a full specialist's annual salary for. I put this out for free. The bottleneck in sports analytics is no longer data or speed. It’s the human. And he removed it. Save this.

21 years old. Alone. In just 6 weeks, he built what NBA clubs pay $120,000 a year for. No team. No lab. Right from his apartment. He took what used to require an entire room of analysts and handed it over to a neural network. The camera watches the court and the machine does the rest: → Recognizes all 10 players simultaneously in real time, tagging names and numbers on the fly without losing track of them in motion. → Boosted jersey number accuracy from 56% to 93% after fine-tuning. → Calculates the exact distance to the nearest defender at the release point (4 feet, 16, 30) and instantly gauges how open the player is. → Logs shot type, court position, and outcome (make or miss) completely automatically. → Identifies a player even when they are fully occluded. Tracks the ball even when it's in the referee's hand. → Zero manual labeling. Zero human verification. 5 models in the stack, trained on 2,400 playoff clips. Something professional clubs pay a full specialist's annual salary for. I put this out for free. The bottleneck in sports analytics is no longer data or speed. It’s the human. And he removed it. Save this.

Ridark

16,251 Aufrufe • vor 2 Monaten

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.

THIS GUY JUST BUILT A DRONE THAT TRACKS TARGETS WITH A LASER USING CLAUDE > No robotics team > No engineering degree > No budget He used Claude Code to write the entire system in Python - a drone that locks onto a target, follows it with a laser and corrects its own aim automatically after every attempt. The AI gets more accurate the longer it runs because it learns from every single shot. He's still improving it and this is only the beginning. You have the same tool he used. The article below is about how to actually use it to its full potential.

Anatoli Kopadze

275,518 Aufrufe • vor 3 Monaten

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.

ard

224,247 Aufrufe • vor 1 Monat

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

The biggest marketplace on Earth won't be the assets humans own. It'll be the data AI needs. Everything that can be digitised is getting pulled in to train the next models. Every scientific dataset, every piece of research sitting in a university, every sensor reading off every John Deere tractor in a field. All of it feeds the machine, and almost none of it is visible to us. What comes out is an agentic economy running at machine speed. You plug your data into a vault, and agents monetise it on your behalf, trading with other agents in real time, in a marketplace we never see. The company sitting on proprietary data becomes part of it without even knowing. This is what the Economic Singularity actually looks like from the inside. The real economy stops being the one we can see and price, and moves somewhere we can't follow. You own a piece of it, or you get priced out of it. Jordi Visser.

Raoul Pal

79,047 Aufrufe • vor 1 Monat

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

one of the biggest hurdles in computer vision is that the front end actually matters. you can’t just build models and call it a product. that’s why we’re building Manako. we see it as a “control plane” for visual intelligence. where data, policies, and operators meet. where vision stops being research and starts having real impact in the physical world. this is built for people running real systems. operators who can do 1,000x more when vision is packaged as a real ai product from a dev perspective, we’re already 2 months ahead of plan. now the focus is polish, usability, and connecting it directly into the subnet. build with vision.

Max

10,537 Aufrufe • vor 7 Monaten

8-year-old from China is writing Python and automating a YouTube system that makes $10,000 a month. Game loop, replay function, player markers - found the bug himself, explained the logic out loud, fixed the line through a projector on the entire wall. That same code in a year becomes part of a pipeline that generates videos, voices them through ElevenLabs and publishes to 5 platforms simultaneously. A high schooler with a $20 ChatGPT 5.5 subscription is already making $6,000-10,000 a month - no camera, no face, no team. This kid is building the same thing but understands every line from the inside. The difference between someone who uses the system and someone who builds it - a $20 subscription vs your own engine under the hood. At 8 years old he's already on the second path.

8-year-old from China is writing Python and automating a YouTube system that makes $10,000 a month. Game loop, replay function, player markers - found the bug himself, explained the logic out loud, fixed the line through a projector on the entire wall. That same code in a year becomes part of a pipeline that generates videos, voices them through ElevenLabs and publishes to 5 platforms simultaneously. A high schooler with a $20 ChatGPT 5.5 subscription is already making $6,000-10,000 a month - no camera, no face, no team. This kid is building the same thing but understands every line from the inside. The difference between someone who uses the system and someone who builds it - a $20 subscription vs your own engine under the hood. At 8 years old he's already on the second path.

Noisy

99,268 Aufrufe • vor 3 Monaten

What if any post on 𝕏 could become a playable game? I built it. 𝕏 Games takes any post on this platform and turns it into a real, playable canvas game — generated in real-time by Grok and Grok Imagine. Pick a post. Any post. Grok reads it, designs a game around it, generates the art, and builds the whole thing in seconds. Leaderboards, score sharing, all native to X. Here's the thing that still blows my mind — I didn't write a single line of code. The entire app was built by AI through Pokee AI and PokeeClaw. The games are generated live by Grok. Every pixel, every mechanic, every asset. All AI. This is what's possible right now. Not in some future demo. Right now, today, on 𝕏. I built this because I wanted to show what Grok can really do when you push it beyond chatbot Q&A. It can create. It can build entire interactive experiences from a tweet. Hey xAI — if you ever want someone to build this for real, into the platform... I'm right here. 👀 Try it yourself: 🎮 walkthrough below ⬇️

What if any post on 𝕏 could become a playable game? I built it. 𝕏 Games takes any post on this platform and turns it into a real, playable canvas game — generated in real-time by Grok and Grok Imagine. Pick a post. Any post. Grok reads it, designs a game around it, generates the art, and builds the whole thing in seconds. Leaderboards, score sharing, all native to X. Here's the thing that still blows my mind — I didn't write a single line of code. The entire app was built by AI through Pokee AI and PokeeClaw. The games are generated live by Grok. Every pixel, every mechanic, every asset. All AI. This is what's possible right now. Not in some future demo. Right now, today, on 𝕏. I built this because I wanted to show what Grok can really do when you push it beyond chatbot Q&A. It can create. It can build entire interactive experiences from a tweet. Hey xAI — if you ever want someone to build this for real, into the platform... I'm right here. 👀 Try it yourself: 🎮 walkthrough below ⬇️

Mike

39,639 Aufrufe • vor 4 Monaten

I just built a self-improving second brain in Claude Code 🤯 A brain that runs your brand: every tool reads from it, it's wired to your live data, and it gets smarter every week. All running on the Claude Agent SDK. Perfect for DTC brands and agencies whose AI output sounds generic because every new chat starts from zero. If you're re-explaining your brand to AI every single time — re-pasting the voice guidelines, re-describing the customer you've described a hundred times, re-uploading the same positioning doc you uploaded yesterday, and still editing for an hour to strip out the generic phrasing... A brand second brain fixes the entire loop: → Build 3 foundation files once: brand DNA, voice, and customer → Every skill you create reads from them automatically → Wire in live data — your ad account, competitor ads, customer reviews → A weekly routine refreshes the brain with what's actually working → Every output comes back on-brand on the first pass No re-briefing AI on every chat. No hour of editing to undo generic phrasing. No brain that goes stale the week after you build it. What a second brain gives you: → The exact 3-file foundation that runs the whole system → The skill structure that makes every tool brand-aware by default → The live-data wiring that keeps it grounded in reality → The weekly self-improvement loop that keeps it sharp → The cold-start sequence to stand it all up from zero Built 100% in Claude Code. I put together the full playbook with the file structure, the wiring, and the exact setup. Want it for free? > Like this post > Comment "BRAIN" And I'll send it over (must be following so I can DM)

Mike Futia

17,996 Aufrufe • vor 2 Monaten

GOOGLE DEEPMIND JUST TURNED A RANDOM OFFICE WALKTHROUGH INTO A SEARCHABLE 4D WORLD YOU CAN QUERY IN PLAIN ENGLISH. You feed it raw video. Nothing tagged, nothing labeled, no manual annotation. GenCeption tears that footage apart into four layers at once: a depth map, surface normals, a camera raymap, and segmentation masks. Then it rebuilds the whole space as a 4D scene you can actually talk to. The demo is stupid simple and that's the point. Type "I want to find a comfortable place to sit" and the sofa lights up. "I need a whiteboard." Whiteboard, found. "Is there a monitor I can use?" Highlighted. "Where's my colleague?" It grounds the person. No bounding boxes drawn by hand. No labels typed in advance. The model figured out what a sofa IS and where it sits in 3D space, from pixels alone. Every video you've ever recorded is now a database you can search with a sentence. Robots, AR, security, indexing. Same primitive. The camera roll just became queryable.

GOOGLE DEEPMIND JUST TURNED A RANDOM OFFICE WALKTHROUGH INTO A SEARCHABLE 4D WORLD YOU CAN QUERY IN PLAIN ENGLISH. You feed it raw video. Nothing tagged, nothing labeled, no manual annotation. GenCeption tears that footage apart into four layers at once: a depth map, surface normals, a camera raymap, and segmentation masks. Then it rebuilds the whole space as a 4D scene you can actually talk to. The demo is stupid simple and that's the point. Type "I want to find a comfortable place to sit" and the sofa lights up. "I need a whiteboard." Whiteboard, found. "Is there a monitor I can use?" Highlighted. "Where's my colleague?" It grounds the person. No bounding boxes drawn by hand. No labels typed in advance. The model figured out what a sofa IS and where it sits in 3D space, from pixels alone. Every video you've ever recorded is now a database you can search with a sentence. Robots, AR, security, indexing. Same primitive. The camera roll just became queryable.

Marre

23,671 Aufrufe • vor 1 Monat

A 21-year-old solo developer stopped chasing AI trends and locked in a $35,000/month B2B niche using a single Python script He built a production-ready traffic monitoring Micro-SaaS that smart cities and businesses pay big money for He didn't build complex AI agent teams He just paired an open-source model with a high-ticket commercial concept Every single day, his system follows a military-like routine: tracks the bounding boxes, calculates telemetry, and locks in the data For 4 to 6 steps straight, the pipeline is completely optimized. Zero frame drops, zero lag. Just pure automated tracking under the hood He doesn't even use raw screen pixels for calculation to keep his speed metrics clean from perspective distortion Out of 6 core features he launched, 3 handle the baseline detection and tracking using YOLOv8, Supervision, and ByteTrack But one single feature (Perspective Transform via OpenCV) converts pixels to real meters and generates high-value Excel traffic reports for clients His main secret? He utilizes a custom Polygon Zone to filter out noise and ships a ready-to-sell B2B product immediately instead of overcomplicating the setup

A 21-year-old solo developer stopped chasing AI trends and locked in a $35,000/month B2B niche using a single Python script He built a production-ready traffic monitoring Micro-SaaS that smart cities and businesses pay big money for He didn't build complex AI agent teams He just paired an open-source model with a high-ticket commercial concept Every single day, his system follows a military-like routine: tracks the bounding boxes, calculates telemetry, and locks in the data For 4 to 6 steps straight, the pipeline is completely optimized. Zero frame drops, zero lag. Just pure automated tracking under the hood He doesn't even use raw screen pixels for calculation to keep his speed metrics clean from perspective distortion Out of 6 core features he launched, 3 handle the baseline detection and tracking using YOLOv8, Supervision, and ByteTrack But one single feature (Perspective Transform via OpenCV) converts pixels to real meters and generates high-value Excel traffic reports for clients His main secret? He utilizes a custom Polygon Zone to filter out noise and ships a ready-to-sell B2B product immediately instead of overcomplicating the setup

Ridark

29,326 Aufrufe • vor 2 Monaten

THIS IS ANDREJ KARPATHY'S OBSIDIAN VAULT : THE BRAIN ANTHROPIC JUST HIRED FOR MILLIONS This is every idea, every decision and every connection he's made over years of work, visualized in real time. Everything else gets lost. The pattern you noticed last March. The connection between two problems you solved a year apart. The idea that would've changed everything if you'd remembered it in the room. This system keeps all of it. Every note links to another. Every link becomes a path. Every path is a decision he can retrace in seconds while everyone else starts from zero. Thousands of nodes. Hundreds of live connections. Years of thinking that never decayed, never died in a Slack thread, never got buried in a meeting nobody remembers. The breakthroughs aren't luck. They're the system handing him back a link he made eighteen months ago and forgot he had. No team. No wiki. No standups. One person and a tool that remembers everything he can't. Most people open Obsidian and make three notes they never open again. He built an operating system for his own thinking. Anthropic just paid millions for the output - this is the machine that produced it.

THIS IS ANDREJ KARPATHY'S OBSIDIAN VAULT : THE BRAIN ANTHROPIC JUST HIRED FOR MILLIONS This is every idea, every decision and every connection he's made over years of work, visualized in real time. Everything else gets lost. The pattern you noticed last March. The connection between two problems you solved a year apart. The idea that would've changed everything if you'd remembered it in the room. This system keeps all of it. Every note links to another. Every link becomes a path. Every path is a decision he can retrace in seconds while everyone else starts from zero. Thousands of nodes. Hundreds of live connections. Years of thinking that never decayed, never died in a Slack thread, never got buried in a meeting nobody remembers. The breakthroughs aren't luck. They're the system handing him back a link he made eighteen months ago and forgot he had. No team. No wiki. No standups. One person and a tool that remembers everything he can't. Most people open Obsidian and make three notes they never open again. He built an operating system for his own thinking. Anthropic just paid millions for the output - this is the machine that produced it.

Avid

64,799 Aufrufe • vor 3 Monaten

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

A DOCTOR BUILT AN AI-QUERYABLE DATABASE OF EVERY DISEASE HE KNOWS IN OBSIDIAN. THE SAME STACK CAN RUN AN ENTIRE COMPANY ON ONE PERSON'S BRAIN Zoomed out, his vault looks like a constellation. Every disease linked to its symptoms, every symptom to the conditions it shows up in, every treatment to what it treats. Adding a new case is one note that wires itself into the rest. It's a personal database, and the structure has nothing to do with medicine. It's just nodes and links. Once you see that, the same setup runs anything: > a startup that needs to remember every customer, decision, and meeting; > a school whose curriculum links to every lecture and student question; > a business where SOPs, clients, and vendors all sit in one queryable graph. Drop Claude on top, and the vault stops being a map. It becomes something you can ask, in plain English, across everything you've ever written down. Full Claude + Obsidian build in the article below. Bookmark this

Yarchi

32,594 Aufrufe • vor 2 Monaten

Create AI ads that look like they were shot by a full production team in minutes. Try it here → Creatify turns any product page or link into a high-performing video ad. No scripts, no editing, no camera, just drop your product URL and it automatically builds the visuals, voice, and pacing. It’s built for creators and brands who want performance-ready ads without hiring teams. Product videos, avatar ads, and Aurora-powered ultra-realistic presenters, all in one place. I tested it on a real product and the result speaks for itself. From AI avatars that deliver your script naturally to full video ads made from a single URL, it’s easily one of the fastest ways to create marketing-ready content. Start creating in seconds →

Eyisha Zyer

25,432 Aufrufe • vor 10 Monaten

$Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.” The model was designed for a factory economy. Standardized inputs. Predictable outputs. That economy is gone. The assembly line is gone. But the education system still runs on its logic. A student who masters algebra in two weeks sits through eight more weeks because the calendar says so. A student who struggles gets dragged forward because the schedule doesn’t wait. Neither is being served. Both are being processed. Musk: “Allow people to progress at the fastest pace that they can or are interested in, in each subject.” AI doesn’t teach a classroom. It teaches a student. One at a time. Every time. It skips what a student already knows. It finds where they’re stuck and approaches it from a different angle. It adjusts in real time. Not at the end of a semester when the damage is already done. A student obsessed with basketball learns fractions through shooting percentages. A student who builds in Minecraft learns geometry through architecture. The subject doesn’t change. The entry point does. No teacher with thirty students can do this. Not because they lack skill. Because the math doesn’t work. AI doesn’t have that constraint. Musk: “You do not need to tell your kid to play video games. They will play video games on autopilot all day. So if you can make it interactive and engaging, then you can make education far more compelling.” The brain isn’t broken. The format is. Kids learn complex systems and strategic thinking for hours voluntarily. Then walk into a classroom and can’t focus for twenty minutes. That’s not a discipline problem. That’s a design problem. Musk: “A university education is often unnecessary. You probably learn the vast majority of what you’re going to learn there in the first two years. And most of it is from your classmates.” Four years. Six figures of debt. And the real value comes from the people sitting next to you. Not the institution charging you. The degree doesn’t certify knowledge. It certifies endurance. Musk: “If the goal is to start a company, I would say no point in finishing college.” The system was built to train employees. If you’re not trying to be one, it has nothing left to offer you. Every lecture. Every textbook. Every curriculum. Now available instantly. Personalized to any learner. Adapted to any pace. The question isn’t whether the old model survives. It’s how long we keep forcing students through it while the replacement already exists.$

Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.” The model was designed for a factory economy. Standardized inputs. Predictable outputs. That economy is gone. The assembly line is gone. But the education system still runs on its logic. A student who masters algebra in two weeks sits through eight more weeks because the calendar says so. A student who struggles gets dragged forward because the schedule doesn’t wait. Neither is being served. Both are being processed. Musk: “Allow people to progress at the fastest pace that they can or are interested in, in each subject.” AI doesn’t teach a classroom. It teaches a student. One at a time. Every time. It skips what a student already knows. It finds where they’re stuck and approaches it from a different angle. It adjusts in real time. Not at the end of a semester when the damage is already done. A student obsessed with basketball learns fractions through shooting percentages. A student who builds in Minecraft learns geometry through architecture. The subject doesn’t change. The entry point does. No teacher with thirty students can do this. Not because they lack skill. Because the math doesn’t work. AI doesn’t have that constraint. Musk: “You do not need to tell your kid to play video games. They will play video games on autopilot all day. So if you can make it interactive and engaging, then you can make education far more compelling.” The brain isn’t broken. The format is. Kids learn complex systems and strategic thinking for hours voluntarily. Then walk into a classroom and can’t focus for twenty minutes. That’s not a discipline problem. That’s a design problem. Musk: “A university education is often unnecessary. You probably learn the vast majority of what you’re going to learn there in the first two years. And most of it is from your classmates.” Four years. Six figures of debt. And the real value comes from the people sitting next to you. Not the institution charging you. The degree doesn’t certify knowledge. It certifies endurance. Musk: “If the goal is to start a company, I would say no point in finishing college.” The system was built to train employees. If you’re not trying to be one, it has nothing left to offer you. Every lecture. Every textbook. Every curriculum. Now available instantly. Personalized to any learner. Adapted to any pace. The question isn’t whether the old model survives. It’s how long we keep forcing students through it while the replacement already exists.

Teddy - PolyBackTest.com

317,912 Aufrufe • vor 1 Monat

I BUILT A BOT THAT PREDICTS NBA GAMES BEFORE TIPOFF. IT RUNS EVERY DAY AT 5:00 PM. HERE'S WHAT'S INSIDE. Three data sources. Three probability layers. One automated pipeline. No manual input. Every evening the bot wakes up. Pulls today's NBA slate. Builds features from live data.Generates predictions. Sends them to me before the first whistle. The ML layer: XGBoost on three seasons of data. Four Factors. ELO ratings. Net rating. Pace. Back-to-back fatigue. Rest days. Travel distance. Every stat computed only from data available before the game. No future leakage. Ever. The sportsbook layer: spread and moneyline converted into implied probabilities. The spread alone contains the aggregated intelligence of an entire betting market. The bot doesn't fight it. It absorbs it. The Polymarket layer: live crowd-sourced probabilities from a decentralized prediction market. No vig. Prices shift in seconds when a star sits out. The bot pulls them through the Gamma API in real time. Then all three layers talk to each other. All three agree - high confidence bet. Polymarket diverges from the books - someone knows something. My model diverges from both - either I found edge or I found noise. The bot flags every disagreement automatically. The fourth layer: Claude API reads everything. Injury reports. Schedule context. Matchup history. Generates a written explanation for every single prediction. No black box. Every call has reasoning attached. The stack: Python. nba_api. pandas. XGBoost. scikit-learn. SHAP. Polymarket Gamma API. Claude API. Flask. GitHub Actions. It took mass of work to build. It takes zero effort to run. 5:00 PM ET. Every day. Predictions land before tipoff. Trading here: I didn't build a model. I built a machine.

I BUILT A BOT THAT PREDICTS NBA GAMES BEFORE TIPOFF. IT RUNS EVERY DAY AT 5:00 PM. HERE'S WHAT'S INSIDE. Three data sources. Three probability layers. One automated pipeline. No manual input. Every evening the bot wakes up. Pulls today's NBA slate. Builds features from live data.Generates predictions. Sends them to me before the first whistle. The ML layer: XGBoost on three seasons of data. Four Factors. ELO ratings. Net rating. Pace. Back-to-back fatigue. Rest days. Travel distance. Every stat computed only from data available before the game. No future leakage. Ever. The sportsbook layer: spread and moneyline converted into implied probabilities. The spread alone contains the aggregated intelligence of an entire betting market. The bot doesn't fight it. It absorbs it. The Polymarket layer: live crowd-sourced probabilities from a decentralized prediction market. No vig. Prices shift in seconds when a star sits out. The bot pulls them through the Gamma API in real time. Then all three layers talk to each other. All three agree - high confidence bet. Polymarket diverges from the books - someone knows something. My model diverges from both - either I found edge or I found noise. The bot flags every disagreement automatically. The fourth layer: Claude API reads everything. Injury reports. Schedule context. Matchup history. Generates a written explanation for every single prediction. No black box. Every call has reasoning attached. The stack: Python. nba_api. pandas. XGBoost. scikit-learn. SHAP. Polymarket Gamma API. Claude API. Flask. GitHub Actions. It took mass of work to build. It takes zero effort to run. 5:00 PM ET. Every day. Predictions land before tipoff. Trading here: I didn't build a model. I built a machine.

zostaff

48,649 Aufrufe • vor 4 Monaten

$Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.” The model was designed for a factory economy. Standardized inputs. Predictable outputs. That economy is gone. The assembly line is gone. But the education system still runs on its logic. A student who masters algebra in two weeks sits through eight more weeks because the calendar says so. A student who struggles gets dragged forward because the schedule doesn’t wait. Neither is being served. Both are being processed. Musk: “Allow people to progress at the fastest pace that they can or are interested in, in each subject.” AI doesn’t teach a classroom. It teaches a student. One at a time. Every time. It skips what a student already knows. It finds where they’re stuck and approaches it from a different angle. It adjusts in real time. Not at the end of a semester when the damage is already done. A student obsessed with basketball learns fractions through shooting percentages. A student who builds in Minecraft learns geometry through architecture. The subject doesn’t change. The entry point does. No teacher with thirty students can do this. Not because they lack skill. Because the math doesn’t work. AI doesn’t have that constraint. Musk: “You do not need to tell your kid to play video games. They will play video games on autopilot all day. So if you can make it interactive and engaging, then you can make education far more compelling.” The brain isn’t broken. The format is. Kids learn complex systems and strategic thinking for hours voluntarily. Then walk into a classroom and can’t focus for twenty minutes. That’s not a discipline problem. That’s a design problem. Musk: “A university education is often unnecessary. You probably learn the vast majority of what you’re going to learn there in the first two years. And most of it is from your classmates.” Four years. Six figures of debt. And the real value comes from the people sitting next to you. Not the institution charging you. The degree doesn’t certify knowledge. It certifies endurance. Musk: “If the goal is to start a company, I would say no point in finishing college.” The system was built to train employees. If you’re not trying to be one, it has nothing left to offer you. Every lecture. Every textbook. Every curriculum. Now available instantly. Personalized to any learner. Adapted to any pace. The question isn’t whether the old model survives. It’s how long we keep forcing students through it while the replacement already exists.$

Elon Musk thinks the entire education system is built on a broken assumption. That every student should learn the same thing. At the same speed. In the same order. At the same time. Musk: “Everyone goes through from like 5th grade to 6th grade to 7th grade like it’s an assembly line. But people are not objects on an assembly line.” The model was designed for a factory economy. Standardized inputs. Predictable outputs. That economy is gone. The assembly line is gone. But the education system still runs on its logic. A student who masters algebra in two weeks sits through eight more weeks because the calendar says so. A student who struggles gets dragged forward because the schedule doesn’t wait. Neither is being served. Both are being processed. Musk: “Allow people to progress at the fastest pace that they can or are interested in, in each subject.” AI doesn’t teach a classroom. It teaches a student. One at a time. Every time. It skips what a student already knows. It finds where they’re stuck and approaches it from a different angle. It adjusts in real time. Not at the end of a semester when the damage is already done. A student obsessed with basketball learns fractions through shooting percentages. A student who builds in Minecraft learns geometry through architecture. The subject doesn’t change. The entry point does. No teacher with thirty students can do this. Not because they lack skill. Because the math doesn’t work. AI doesn’t have that constraint. Musk: “You do not need to tell your kid to play video games. They will play video games on autopilot all day. So if you can make it interactive and engaging, then you can make education far more compelling.” The brain isn’t broken. The format is. Kids learn complex systems and strategic thinking for hours voluntarily. Then walk into a classroom and can’t focus for twenty minutes. That’s not a discipline problem. That’s a design problem. Musk: “A university education is often unnecessary. You probably learn the vast majority of what you’re going to learn there in the first two years. And most of it is from your classmates.” Four years. Six figures of debt. And the real value comes from the people sitting next to you. Not the institution charging you. The degree doesn’t certify knowledge. It certifies endurance. Musk: “If the goal is to start a company, I would say no point in finishing college.” The system was built to train employees. If you’re not trying to be one, it has nothing left to offer you. Every lecture. Every textbook. Every curriculum. Now available instantly. Personalized to any learner. Adapted to any pace. The question isn’t whether the old model survives. It’s how long we keep forcing students through it while the replacement already exists.

Dustin

21,755,771 Aufrufe • vor 4 Monaten