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Student built an AI that proved it mathematically Three lines towering above every player in 40 years of ATP data. The Big Three isn't a narrative - it's visible in the ELO curves. He tested 4 algorithms on 43 years of tennis data: → Decision Tree: 74% → Random... show more

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Novak Djokovic says Carlos Alcaraz is already a legendary tennis player and he deserves every bit of praise he gets: “The results are a testament to his already stellar career. Can’t think of any superlatives about him. He deserves every bit of praise he gets. He’s a very nice young man. Good values. Nice family. Already a legendary tennis player that made a huge mark in the history books of tennis, with only 22 years of age.” “The first time I played him he was 11 or 12 years old 😂. No, he was 18 or 19… you could already see he was destined for great things.” (via Australian Open Press)

Novak Djokovic says Carlos Alcaraz is already a legendary tennis player and he deserves every bit of praise he gets: “The results are a testament to his already stellar career. Can’t think of any superlatives about him. He deserves every bit of praise he gets. He’s a very nice young man. Good values. Nice family. Already a legendary tennis player that made a huge mark in the history books of tennis, with only 22 years of age.” “The first time I played him he was 11 or 12 years old 😂. No, he was 18 or 19… you could already see he was destined for great things.” (via Australian Open Press)

The Tennis Letter

560,014 Aufrufe • vor 6 Monaten

🚨 The future of sports betting is here - and it's on the blockchain > Sharp bettors are already leaving bookmakers for prediction markets. > 15-35% of handle is migrating within 18 months. > The reason - taxes, limits, 12% margin. > Polymarket: ~1% margin, no limits reaction in seconds. > But most people still use it like a regular sportsbook. > What if you compare Polymarket with a bookmaker and an ML model? > Three sources disagree - someone knows more. > That divergence is the edge. > Built a full pipeline: Python + Claude API + XGBoost + ELO.

🚨 The future of sports betting is here - and it's on the blockchain > Sharp bettors are already leaving bookmakers for prediction markets. > 15-35% of handle is migrating within 18 months. > The reason - taxes, limits, 12% margin. > Polymarket: ~1% margin, no limits reaction in seconds. > But most people still use it like a regular sportsbook. > What if you compare Polymarket with a bookmaker and an ML model? > Three sources disagree - someone knows more. > That divergence is the edge. > Built a full pipeline: Python + Claude API + XGBoost + ELO.

zostaff

34,030 Aufrufe • vor 4 Monaten

We all know AI is the future of technology. But almost nobody will OWN the technology shaping that future. The AI race is already underway. Billions are being invested. Companies are moving fast. AI is already reshaping entire industries. But when all this value gets created…Who actually benefits? Right now, mostly a handful of billionaires and Big Tech companies. We don’t think that’s fair. If AI is being built on the data, behavior, and contributions of ordinary people… Then ordinary people should share in the value it creates. That’s exactly what we’re building at Action Model: A future where AI is owned by the people, not just the few at the top.

We all know AI is the future of technology. But almost nobody will OWN the technology shaping that future. The AI race is already underway. Billions are being invested. Companies are moving fast. AI is already reshaping entire industries. But when all this value gets created…Who actually benefits? Right now, mostly a handful of billionaires and Big Tech companies. We don’t think that’s fair. If AI is being built on the data, behavior, and contributions of ordinary people… Then ordinary people should share in the value it creates. That’s exactly what we’re building at Action Model: A future where AI is owned by the people, not just the few at the top.

Action Model

21,280 Aufrufe • vor 3 Monaten

Former #Ravens kicker Justin Tucker, an All-Pro reinstated by #NFL after serving personal conduct policy suspension who has worked out for #Saints #Colts, displays his old form in July workout analytics Simon Mathiesen, head of Trackman Football TrackMan Football 'Justin Tucker’s Trackman data from his workouts are comparable to the data that was gathered on him from 2019-2022. These data points include his ball speed, ball height at the LOS (trajectory), and accuracy. Tucker’s three workouts the last week are some of the best sessions ever recorded on Trackman. Very rare that any kicker charts an accuracy score above 95, let alone three sessions in a row within a week.' Tucker’s agent Rob Roche 1OF1AGENCY 'Whoever signs Justin is going to be getting the best version of him that has ever played.'

Former #Ravens kicker Justin Tucker, an All-Pro reinstated by #NFL after serving personal conduct policy suspension who has worked out for #Saints #Colts, displays his old form in July workout analytics Simon Mathiesen, head of Trackman Football TrackMan Football 'Justin Tucker’s Trackman data from his workouts are comparable to the data that was gathered on him from 2019-2022. These data points include his ball speed, ball height at the LOS (trajectory), and accuracy. Tucker’s three workouts the last week are some of the best sessions ever recorded on Trackman. Very rare that any kicker charts an accuracy score above 95, let alone three sessions in a row within a week.' Tucker’s agent Rob Roche 1OF1AGENCY 'Whoever signs Justin is going to be getting the best version of him that has ever played.'

Aaron Wilson

26,450 Aufrufe • vor 21 Tagen

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 Aufrufe • vor 1 Monat

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

a $40/month server beat a room full of analysts to the same trade by five and a half hours market opens at 9:30. his position was already in at 4am the system is a neural net trained on 11 years of tick data. it flagged the setup before the candle that "confirmed" it had even started forming this is the part retail misunderstands about ML in markets it isn't prediction in the mystical sense. it's pattern classification at a speed and scale human eyes physically cannot match the mechanics: 847,000 labeled historical setups as training data 4,200 data points per second ingested live each new state scored against every pattern the net has ever seen, in milliseconds the model isn't asking "where is price going" it's asking "how closely does the current microstructure match the conditions that preceded a move in my training set" that's a classification problem, and classification is what neural nets do better than anything else output: 3-4 candidate trades a day. he takes the top 2 by confidence score last 90 days: 71% win rate at 2.3 average risk-reward the edge isn't the architecture. the architecture is public pytorch is free, the papers are on arxiv, the network is a few hundred lines the edge is the labeling. what you feed it and how you tag the setups is the entire game retail feeds a model price and time and gets noise a desk feeds it order flow, volatility state, cross-asset context, each example hand-labeled by outcome same network. different training data. that's the whole difference retail watches the news at the open and reacts this system scored every pattern before sunrise and already decided you're not losing because your analysis is wrong you're losing to something that doesn't sleep, doesn't panic, and doesn't second-guess a probability it already computed the dataset was free. the framework was free. the compute was $40 a month the edge was never behind a paywall. it was sitting in a format almost nobody bothered to train on full breakdown in the article below

delost

20,942 Aufrufe • vor 1 Monat

Steve Kerr was asked what makes Steph Curry different. His answer wasn't about shooting. It was about how Steph treats people. Teammates, reporters, fans, kids in the front row. Every one of them walks away feeling like the most important person in the room. The shot to be impressed by isn't the three. It's what happens before and after it. Culture isn't built in offsites. It's built in 4-second moments nobody is scoring. Charisma gets the credit. Attention is the rare skill.

Steve Kerr was asked what makes Steph Curry different. His answer wasn't about shooting. It was about how Steph treats people. Teammates, reporters, fans, kids in the front row. Every one of them walks away feeling like the most important person in the room. The shot to be impressed by isn't the three. It's what happens before and after it. Culture isn't built in offsites. It's built in 4-second moments nobody is scoring. Charisma gets the credit. Attention is the rare skill.

James Purchin

28,557 Aufrufe • vor 3 Monaten

Sergey Brin said compute is dessert. The companies winning the AI race right now are not the ones with the most chips. They are the ones with the best algorithms. Every headline you read about AI is about data centers, Megawatts, and Nvidia orders. Billions in infrastructure and more. The entire investment thesis of the last three years has been built on compute scaling as the primary moat. Sergey thinks that framing is wrong. He pulled out an example from physics. The N-body problem. Scientists have been running those simulations since the fifties. Over the decades, raw compute improved on Moore's law. But the algorithms to solve the problem improved faster. Not slightly faster. Far faster. The algorithmic gains made the compute gains look small. He says the same thing has happened in AI over the last decade. Compute is not the meal. It is the dessert. You still want it. Nobody is turning down frontier compute. But the companies that figured out the algorithms first are the ones actually ahead. The market is pricing AI winners by who has the most chips. Sergey Brin just said that is the wrong scorecard. The ones who win this are not building the biggest data center. They are solving the harder math problem.

Sergey Brin said compute is dessert. The companies winning the AI race right now are not the ones with the most chips. They are the ones with the best algorithms. Every headline you read about AI is about data centers, Megawatts, and Nvidia orders. Billions in infrastructure and more. The entire investment thesis of the last three years has been built on compute scaling as the primary moat. Sergey thinks that framing is wrong. He pulled out an example from physics. The N-body problem. Scientists have been running those simulations since the fifties. Over the decades, raw compute improved on Moore's law. But the algorithms to solve the problem improved faster. Not slightly faster. Far faster. The algorithmic gains made the compute gains look small. He says the same thing has happened in AI over the last decade. Compute is not the meal. It is the dessert. You still want it. Nobody is turning down frontier compute. But the companies that figured out the algorithms first are the ones actually ahead. The market is pricing AI winners by who has the most chips. Sergey Brin just said that is the wrong scorecard. The ones who win this are not building the biggest data center. They are solving the harder math problem.

Ihtesham Ali

130,249 Aufrufe • vor 2 Monaten

.Marc Andreessen 🇺🇸 says AI is the biggest technological revolution of his life: "This is the biggest technological revolution of my life. This is clearly bigger than the internet. The comps on this are things like the microprocessor, the steam engine, and electricity." "The neural network as an idea continued to be explored in academia for the last 80 years. And essentially it didn't work, it was decade after decade of excessive optimism, followed by disappointment." "Then basically we all saw what happened with the ChatGPT moment. All of a sudden it crystallized, and it was like, 'Oh my God, it turns out it works.' We're sort of three years into effectively an 80-year revolution."

.Marc Andreessen 🇺🇸 says AI is the biggest technological revolution of his life: "This is the biggest technological revolution of my life. This is clearly bigger than the internet. The comps on this are things like the microprocessor, the steam engine, and electricity." "The neural network as an idea continued to be explored in academia for the last 80 years. And essentially it didn't work, it was decade after decade of excessive optimism, followed by disappointment." "Then basically we all saw what happened with the ChatGPT moment. All of a sudden it crystallized, and it was like, 'Oh my God, it turns out it works.' We're sort of three years into effectively an 80-year revolution."

a16z

112,943 Aufrufe • vor 3 Monaten

The "Inverse Problem" Of Dark Matter Is Insane What if 85% of the universe's matter isn't missing — it's just that our models were never clean enough to know? Dr. Jenny Wagner proves mathematically that every dark matter map ever made is extrapolation. The data only tells you something local. Everything else is a model assumption wearing the costume of evidence. She then connects this to Einstein's own 1917 warning — that homogeneity and isotropy were always a placeholder, never a truth — and makes the case that cosmology is not in crisis. It's finally ready for the next level of detail.

The "Inverse Problem" Of Dark Matter Is Insane What if 85% of the universe's matter isn't missing — it's just that our models were never clean enough to know? Dr. Jenny Wagner proves mathematically that every dark matter map ever made is extrapolation. The data only tells you something local. Everything else is a model assumption wearing the costume of evidence. She then connects this to Einstein's own 1917 warning — that homogeneity and isotropy were always a placeholder, never a truth — and makes the case that cosmology is not in crisis. It's finally ready for the next level of detail.

Curt Jaimungal

38,332 Aufrufe • vor 4 Monaten

Almost $1 TRILLION has already gone into building AI infrastructure. Let that sink in for a moment. In just two years, we’ve poured the equivalent of an entire country’s GDP into chips, data centers, and compute power. That’s not hype that’s commitment. And the wild part? This is still early innings. The foundation is being built right now. What gets built on top of it is going to change everything. 🔥🚀

Almost $1 TRILLION has already gone into building AI infrastructure. Let that sink in for a moment. In just two years, we’ve poured the equivalent of an entire country’s GDP into chips, data centers, and compute power. That’s not hype that’s commitment. And the wild part? This is still early innings. The foundation is being built right now. What gets built on top of it is going to change everything. 🔥🚀

Junaid Dar

140,194 Aufrufe • vor 8 Monaten

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

The All-In Podcast

304,297 Aufrufe • vor 9 Monaten

You've been watching football wrong your entire life. This 40-minute Databricks talk on how LaLiga uses data is the proof. The goals and assists you obsess over are the least interesting numbers on the pitch. By fusing tracking data (every player's position, many times a second) with eventing data (every action on the ball), LaLiga measures the invisible game space created, defensive lines pulled apart, the value of a run nobody passed to. The best player in a match is often the one who never touched the scoresheet. Pair it with article by Roan and you are good. Once you see the game as data, you can't unsee it.

You've been watching football wrong your entire life. This 40-minute Databricks talk on how LaLiga uses data is the proof. The goals and assists you obsess over are the least interesting numbers on the pitch. By fusing tracking data (every player's position, many times a second) with eventing data (every action on the ball), LaLiga measures the invisible game space created, defensive lines pulled apart, the value of a run nobody passed to. The best player in a match is often the one who never touched the scoresheet. Pair it with article by Roan and you are good. Once you see the game as data, you can't unsee it.

gemchanger

79,892 Aufrufe • vor 2 Monaten

Larry Ellison just told every AI company on Earth they’re fighting the wrong war. The entire industry is racing to build the smartest model. More parameters. Better benchmarks. Faster inference. Ellison isn’t building a model. He’s controlling what every model needs to be useful. Every frontier AI trains on the same public internet. Same scraped pages. Same recycled text. When everyone has the same data, it’s not an advantage. It’s a floor. The only data that creates separation is private. Medical records. Financial models. Defense systems. Proprietary research locked behind firewalls for decades. That data already lives inside Oracle databases. Not Google’s. Not Microsoft’s. Not Amazon’s. Ellison didn’t enter the model war. He positioned himself above it. He rebuilt the database so AI can reason on private data without ever absorbing it. Training folds your data into the model permanently. Once it’s in, it never comes back out. Reasoning thinks with your data and hands back only the answer. The data never moves. One is surrender. The other is sovereignty. Ellison: “These are remarkable electronic brains.” He didn’t build the brain. He owns what the brain needs to think. Everyone is building the most powerful mind in human history. A mind is only as valuable as what it’s allowed to know. Own the knowledge and it doesn’t matter who builds the brain. That pattern has held through every era of human civilization. AI doesn’t break it. It proves it.

Larry Ellison just told every AI company on Earth they’re fighting the wrong war. The entire industry is racing to build the smartest model. More parameters. Better benchmarks. Faster inference. Ellison isn’t building a model. He’s controlling what every model needs to be useful. Every frontier AI trains on the same public internet. Same scraped pages. Same recycled text. When everyone has the same data, it’s not an advantage. It’s a floor. The only data that creates separation is private. Medical records. Financial models. Defense systems. Proprietary research locked behind firewalls for decades. That data already lives inside Oracle databases. Not Google’s. Not Microsoft’s. Not Amazon’s. Ellison didn’t enter the model war. He positioned himself above it. He rebuilt the database so AI can reason on private data without ever absorbing it. Training folds your data into the model permanently. Once it’s in, it never comes back out. Reasoning thinks with your data and hands back only the answer. The data never moves. One is surrender. The other is sovereignty. Ellison: “These are remarkable electronic brains.” He didn’t build the brain. He owns what the brain needs to think. Everyone is building the most powerful mind in human history. A mind is only as valuable as what it’s allowed to know. Own the knowledge and it doesn’t matter who builds the brain. That pattern has held through every era of human civilization. AI doesn’t break it. It proves it.

Dustin

97,904 Aufrufe • vor 1 Monat

Oracle just told every AI company on earth the same thing. Your models are worthless. Not the technology, talent or the billions spent training them. But the data they were trained on. Larry Ellison, the man who built Oracle into the backbone of global enterprise just dropped a bombshell. He said ChatGPT, Gemini, Grok, and Llama, all of them are training on the exact same data. The entire public internet, every Wikipedia page, Reddit thread and every news article. That means they're all converging essentially becoming the same product with different logos. Ellison's word for it is commodities. But here's where it gets dangerous. He says the real gold isn't public data, It's private data. The medical records in hospital systems, the financial data in bank vaults. The supply chain secrets of every Fortune 500 and guess where most of that data already lives. Not Google, Amazon or Microsoft but inside Oracle. Oracle databases hold most of the world's high value private enterprise data. So Oracle just launched something called AI Database 26ai. It lets the top AI models, ChatGPT, Gemini, Grok, Llama reason directly over a company's private data, without that data ever leaving the vault. They're using a technique called RAG, Retrieval Augmented Generation. The AI doesn't train on your data, it searches it in real time. Think about what that means. A bank could ask AI to analyze every loan it's ever made without exposing a single customer record. A hospital could have AI diagnose patients using its full medical history without violating HIPAA. A defense contractor could let AI reason across classified operations without data leaving a secure environment. Ellison is betting this is bigger than the training market. Bigger than the GPU boom. Bigger than the data center buildout. He called it the largest and fastest growing market in history. The numbers back the ambition. Oracle's remaining performance obligations just hit $523 billion. That's contracted revenue not yet delivered and $300 billion of it comes from OpenAI alone. Cloud revenue hit $8 billion in a single quarter, OCI grew 66 percent and GPU revenue surged 177 percent. But here's the part nobody's talking about. If private data becomes the real AI moat, then whoever controls the database controls the future of AI. And that's a level of power that should make everyone uncomfortable.

Oracle just told every AI company on earth the same thing. Your models are worthless. Not the technology, talent or the billions spent training them. But the data they were trained on. Larry Ellison, the man who built Oracle into the backbone of global enterprise just dropped a bombshell. He said ChatGPT, Gemini, Grok, and Llama, all of them are training on the exact same data. The entire public internet, every Wikipedia page, Reddit thread and every news article. That means they're all converging essentially becoming the same product with different logos. Ellison's word for it is commodities. But here's where it gets dangerous. He says the real gold isn't public data, It's private data. The medical records in hospital systems, the financial data in bank vaults. The supply chain secrets of every Fortune 500 and guess where most of that data already lives. Not Google, Amazon or Microsoft but inside Oracle. Oracle databases hold most of the world's high value private enterprise data. So Oracle just launched something called AI Database 26ai. It lets the top AI models, ChatGPT, Gemini, Grok, Llama reason directly over a company's private data, without that data ever leaving the vault. They're using a technique called RAG, Retrieval Augmented Generation. The AI doesn't train on your data, it searches it in real time. Think about what that means. A bank could ask AI to analyze every loan it's ever made without exposing a single customer record. A hospital could have AI diagnose patients using its full medical history without violating HIPAA. A defense contractor could let AI reason across classified operations without data leaving a secure environment. Ellison is betting this is bigger than the training market. Bigger than the GPU boom. Bigger than the data center buildout. He called it the largest and fastest growing market in history. The numbers back the ambition. Oracle's remaining performance obligations just hit $523 billion. That's contracted revenue not yet delivered and $300 billion of it comes from OpenAI alone. Cloud revenue hit $8 billion in a single quarter, OCI grew 66 percent and GPU revenue surged 177 percent. But here's the part nobody's talking about. If private data becomes the real AI moat, then whoever controls the database controls the future of AI. And that's a level of power that should make everyone uncomfortable.

StockMarket.News

1,696,143 Aufrufe • vor 5 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

102,941 Aufrufe • vor 3 Monaten

Marc Andreessen explains why we are only three years into what is effectively an 80-year technological revolution: He opens with a blunt assessment: "This is the biggest technological revolution of my life. This is clearly bigger than the internet. The comps on this are things like the microprocessor and the steam engine and electricity." But to understand why, you have to go back 80 years. In the 1930s, the pioneers of computing understood the theory of computation before they'd even built the machines. And they faced a fundamental choice. Build computers in the image of the adding machine — hyper-literal, mathematical, capable of billions of operations per second, but unable to understand human speech or deal with humans the way humans like to be dealt with. Or build computers modelled on the human brain. Neural networks. They chose the adding machine. And that single decision shaped everything — mainframes, PCs, smartphones, every dollar of wealth the computer industry created over the next 80 years. IBM itself is the successor company to the National Cash Register Company of America. The lineage runs that deep. But here's what makes this moment so extraordinary. They knew about the other path. The first neural network academic paper was published in 1943. Marc points to a remarkable piece of forgotten history: "There's an interview you can watch on YouTube with the authors. It's him in his beach house, not wearing a shirt, talking about this future in which computers are going to be built on the model of the human brain." That was 1946. The vision existed. The path just wasn't taken. So neural networks spent the next eight decades living in the shadows. Kept alive by a small academic movement — first called cybernetics, then artificial intelligence — that refused to let the idea die. And for most of that time, it simply didn't work. "It was basically decade after decade after decade of excessive optimism followed by disappointment." By the time Marc reached college in 1989, AI was a backwater field. Everyone assumed it was never going to happen. But the scientists kept working. Quietly building up an enormous reservoir of concepts and ideas across those decades of disappointment. And then Christmas 2022 arrived. ChatGPT. And suddenly: "All of a sudden it's like: oh my god. It turns out it works." That moment wasn't the start of something new. It was the payoff on an 80-year-old bet that almost everyone had written off. Which is exactly why Marc's framing matters so much: "We're three years into what is effectively an 80-year revolution." Most people are treating AI like another technology cycle — something to adapt to, ride, and wait out. But if Andreessen is right, we are not adapting to a new cycle. We are standing at the very beginning of the longest and most consequential technological transformation in human history. The road not taken in the 1930s is finally being built. And we have barely broken ground.

Marc Andreessen explains why we are only three years into what is effectively an 80-year technological revolution: He opens with a blunt assessment: "This is the biggest technological revolution of my life. This is clearly bigger than the internet. The comps on this are things like the microprocessor and the steam engine and electricity." But to understand why, you have to go back 80 years. In the 1930s, the pioneers of computing understood the theory of computation before they'd even built the machines. And they faced a fundamental choice. Build computers in the image of the adding machine — hyper-literal, mathematical, capable of billions of operations per second, but unable to understand human speech or deal with humans the way humans like to be dealt with. Or build computers modelled on the human brain. Neural networks. They chose the adding machine. And that single decision shaped everything — mainframes, PCs, smartphones, every dollar of wealth the computer industry created over the next 80 years. IBM itself is the successor company to the National Cash Register Company of America. The lineage runs that deep. But here's what makes this moment so extraordinary. They knew about the other path. The first neural network academic paper was published in 1943. Marc points to a remarkable piece of forgotten history: "There's an interview you can watch on YouTube with the authors. It's him in his beach house, not wearing a shirt, talking about this future in which computers are going to be built on the model of the human brain." That was 1946. The vision existed. The path just wasn't taken. So neural networks spent the next eight decades living in the shadows. Kept alive by a small academic movement — first called cybernetics, then artificial intelligence — that refused to let the idea die. And for most of that time, it simply didn't work. "It was basically decade after decade after decade of excessive optimism followed by disappointment." By the time Marc reached college in 1989, AI was a backwater field. Everyone assumed it was never going to happen. But the scientists kept working. Quietly building up an enormous reservoir of concepts and ideas across those decades of disappointment. And then Christmas 2022 arrived. ChatGPT. And suddenly: "All of a sudden it's like: oh my god. It turns out it works." That moment wasn't the start of something new. It was the payoff on an 80-year-old bet that almost everyone had written off. Which is exactly why Marc's framing matters so much: "We're three years into what is effectively an 80-year revolution." Most people are treating AI like another technology cycle — something to adapt to, ride, and wait out. But if Andreessen is right, we are not adapting to a new cycle. We are standing at the very beginning of the longest and most consequential technological transformation in human history. The road not taken in the 1930s is finally being built. And we have barely broken ground.

Big Brain AI

382,046 Aufrufe • vor 4 Monaten

Bill O’Reilly just RIPPED APART the “free speech” spin surrounding Jimmy Kimmel. He turned the tables with one brutal fact that blows the left’s entire narrative to pieces: “Kimmel is the VIOLATOR of free speech. He’s not the victim. Kimmel has not put on a non-liberal voice in more than three years on his program. Three years!” O’Reilly stressed. Directing his attention to all of the public stations, O’Reilly lamented, “NO ONE who’s not a liberal gets on any of these shows.” “THAT is a violation of freedom of speech.” Find more stories like this at

Bill O’Reilly just RIPPED APART the “free speech” spin surrounding Jimmy Kimmel. He turned the tables with one brutal fact that blows the left’s entire narrative to pieces: “Kimmel is the VIOLATOR of free speech. He’s not the victim. Kimmel has not put on a non-liberal voice in more than three years on his program. Three years!” O’Reilly stressed. Directing his attention to all of the public stations, O’Reilly lamented, “NO ONE who’s not a liberal gets on any of these shows.” “THAT is a violation of freedom of speech.” Find more stories like this at

The Vigilant Fox 🦊

406,952 Aufrufe • vor 10 Monaten

Walter Isaacson reveals how Elon Musk fired 85% of Twitter's employees in three rounds and why "He brought in the engineers and figured the amount of people doing Tesla full self-driving and autopilot software was about one-tenth of what was doing software for Twitter. He said this can't be the case" "He fired 85% in three different rounds. First was firing people because they had a team from Tesla's autopilot team grading the code written in the past year. Then he fired people who didn't seem totally all in or loyal. Then another round of layoffs" "At each step, almost everybody says that's enough, it's going to destroy things. They were partly right. There was degradation of the service, but not as much as half the services I use" "He has an algorithm. Question every requirement, then delete delete delete. And the corollary, if you don't end up adding back 20% of what you deleted, you didn't delete enough in the first round" "You asked me if he overdid it. He probably overdid it by 20%, which is his formula"

Walter Isaacson reveals how Elon Musk fired 85% of Twitter's employees in three rounds and why "He brought in the engineers and figured the amount of people doing Tesla full self-driving and autopilot software was about one-tenth of what was doing software for Twitter. He said this can't be the case" "He fired 85% in three different rounds. First was firing people because they had a team from Tesla's autopilot team grading the code written in the past year. Then he fired people who didn't seem totally all in or loyal. Then another round of layoffs" "At each step, almost everybody says that's enough, it's going to destroy things. They were partly right. There was degradation of the service, but not as much as half the services I use" "He has an algorithm. Question every requirement, then delete delete delete. And the corollary, if you don't end up adding back 20% of what you deleted, you didn't delete enough in the first round" "You asked me if he overdid it. He probably overdid it by 20%, which is his formula"

Jaynit

748,307 Aufrufe • vor 2 Monaten

Someone trained a swarm model on 3 years of NBA data and let it loose on Polymarket The result: $1.49M. He didn't build a better model. He built a better crowd. Here's how the system actually works. He takes MiroFish - an open-source engine that simulates thousands of AI agents and feeds it raw sports data. The actual building blocks: > Player stat vectors: points, rebounds, assists, eFG%, usage rate across 3 full seasons > Team form tensors: last 10 games, home/away splits, pace-of-play, defensive rating > Matchup history: head-to-head records, positional mismatches, referee tendencies > Injury probability models: medical staff reports weighted by recovery timelines > Line movement tracking: where the sharp money flows before tip-off Check how this wallet works: MiroFish generates 4,096 agents - analysts, bettors, insiders, oddsmakers - each with different reasoning. They argue, form clusters, shift opinions. Consensus emerges. He pipes that consensus into a 12-layer transformer trained on the full history of his 16,695 predictions. The model compares MiroFish output against live Polymarket odds. When the gap exceeds his Kelly criterion threshold, it enters. Lakers at 40 cents. MiroFish said 62%. One position: $190,823. The edge isn't data. Every sharp has data. The edge is simulating how 4,000 fake humans process it - before the real crowd does.

Someone trained a swarm model on 3 years of NBA data and let it loose on Polymarket The result: $1.49M. He didn't build a better model. He built a better crowd. Here's how the system actually works. He takes MiroFish - an open-source engine that simulates thousands of AI agents and feeds it raw sports data. The actual building blocks: > Player stat vectors: points, rebounds, assists, eFG%, usage rate across 3 full seasons > Team form tensors: last 10 games, home/away splits, pace-of-play, defensive rating > Matchup history: head-to-head records, positional mismatches, referee tendencies > Injury probability models: medical staff reports weighted by recovery timelines > Line movement tracking: where the sharp money flows before tip-off Check how this wallet works: MiroFish generates 4,096 agents - analysts, bettors, insiders, oddsmakers - each with different reasoning. They argue, form clusters, shift opinions. Consensus emerges. He pipes that consensus into a 12-layer transformer trained on the full history of his 16,695 predictions. The model compares MiroFish output against live Polymarket odds. When the gap exceeds his Kelly criterion threshold, it enters. Lakers at 40 cents. MiroFish said 62%. One position: $190,823. The edge isn't data. Every sharp has data. The edge is simulating how 4,000 fake humans process it - before the real crowd does.

BuBBliK

504,562 Aufrufe • vor 5 Monaten