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New Video Series: Statistics & Data Analysis! 35 videos, 10 hours: Random sampling, Central limit theorem, Distribution estimation, Method of moments, Maximum likelihood estimation, Hypothesis testing, Monte Carlo sampling, Bayesian statistics, and more!

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Steven Brunton

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115,516 Aufrufe • vor 1 Jahr •via X (Twitter)

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Wow, we can steer diffusion models at inference time! Introducing Diffusion Tree Sampling (DTS): a search-based approach inspired by Monte Carlo Tree Search that turns inference into an anytime, reward-guided optimization process. Diffusion Tree Sampling (DTS) produces asymptotically exact samples from the target distribution in the limit of infinite rollouts, and its greedy variant, Diffusion Tree Search (DTS⋆), performs a global search for high reward samples. The results are pretty impressive: - On MNIST and CIFAR-10 class-conditional generation, DTS matches the FID of the best-performing baseline with up to 10× less compute. - In text-to-image generation and language completion tasks, DTS⋆ effectively searches for high reward samples that match best-of-N with up to 5× less compute.
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Wow, we can steer diffusion models at inference time! Introducing Diffusion Tree Sampling (DTS): a search-based approach inspired by Monte Carlo Tree Search that turns inference into an anytime, reward-guided optimization process. Diffusion Tree Sampling (DTS) produces asymptotically exact samples from the target distribution in the limit of infinite rollouts, and its greedy variant, Diffusion Tree Search (DTS⋆), performs a global search for high reward samples. The results are pretty impressive: - On MNIST and CIFAR-10 class-conditional generation, DTS matches the FID of the best-performing baseline with up to 10× less compute. - In text-to-image generation and language completion tasks, DTS⋆ effectively searches for high reward samples that match best-of-N with up to 5× less compute.

机器之心 JIQIZHIXIN

19,037 Aufrufe • vor 1 Jahr

The most important tool in Probability and Statistics - Markov Chain Monte Carlo (MCMC) Method Fresh out of undergraduate Probability and Stats courses, it’s easy to feel invincible. You’ve tamed Gaussians, gammas, betas, all those neat closed-form toy distributions. Then research hits and you meet the harsher truth. Real posteriors and energy landscapes are jagged, asymmetric, multimodal, and too high-dimensional to integrate or sample from directly. You can’t compute the normalising constant. You can’t do the integrals by hand. And i.i.d. samples are basically science fiction. Markov Chain Monte Carlo is the hack we invented to survive that reality. Instead of drawing perfect samples, you send a carefully designed random walk wandering through the landscape, then use its long-run positions as your window into the target distribution. Here’s the problem. Standard trace plots and diagnostics can still cheerfully lie to you. High-dimensional geometry can make a chain that looks healthy while it’s effectively frozen. Multimodal targets, bad tuning, and hidden correlations can quietly wreck your posterior summaries. This series is about those blind spots. We’ll use visuals like this one to show how MCMC actually moves, where the guarantees get slippery, and how to think clearly about convergence and diagnostics in serious Bayesian, physics, and ML work. #BayesianInference #MCMC #MonteCarloMethods #ProbabilityLandscape #StatisticsEducation #ComputationalScience
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The most important tool in Probability and Statistics - Markov Chain Monte Carlo (MCMC) Method Fresh out of undergraduate Probability and Stats courses, it’s easy to feel invincible. You’ve tamed Gaussians, gammas, betas, all those neat closed-form toy distributions. Then research hits and you meet the harsher truth. Real posteriors and energy landscapes are jagged, asymmetric, multimodal, and too high-dimensional to integrate or sample from directly. You can’t compute the normalising constant. You can’t do the integrals by hand. And i.i.d. samples are basically science fiction. Markov Chain Monte Carlo is the hack we invented to survive that reality. Instead of drawing perfect samples, you send a carefully designed random walk wandering through the landscape, then use its long-run positions as your window into the target distribution. Here’s the problem. Standard trace plots and diagnostics can still cheerfully lie to you. High-dimensional geometry can make a chain that looks healthy while it’s effectively frozen. Multimodal targets, bad tuning, and hidden correlations can quietly wreck your posterior summaries. This series is about those blind spots. We’ll use visuals like this one to show how MCMC actually moves, where the guarantees get slippery, and how to think clearly about convergence and diagnostics in serious Bayesian, physics, and ML work. #BayesianInference #MCMC #MonteCarloMethods #ProbabilityLandscape #StatisticsEducation #ComputationalScience

Mathelirium

32,296 Aufrufe • vor 6 Monaten

🚨NEW: NAACP CEO Derrick Johnson *tries his best* to downplay Trump's SURGE in black support — but CBS News' Major Garrett doesn't let him off hook🚨 GARRETT: "2016, 6% of black voters in America supported candidate Donald Trump. In 2020, 8% of black voters supported President Trump for reelection. In 2024, 15% of black voters supported President Trump." "What does that movement, that near doubling of support from 2020 to 2024, tell you?" JOHNSON: "I've heard different statistics shared. I have yet to see any of it verified. Because exit polls have always been unreliable. Much of the polling we've seen over the last 20 years have been wrong on the outcome of the elections." "And I've been trying to figure out where are these numbers coming from. Is it verifiable? But it's great talking points — because people would like to show that there is momentum going in one direction versus another." GARRETT: "I'm glad you raised questions about the verifiability of the data that I just communicated to you. Just for your information and for the benefit of this conversation, that came from Pew Research. And it's data that takes not just exit polls but voter sampling and then the census community survey data." "It is regarded as the best and most comprehensive and therefore most verifiable post-election analysis. This has historically been true. So that's my verification method for you." Daily Caller
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🚨NEW: NAACP CEO Derrick Johnson *tries his best* to downplay Trump's SURGE in black support — but CBS News' Major Garrett doesn't let him off hook🚨 GARRETT: "2016, 6% of black voters in America supported candidate Donald Trump. In 2020, 8% of black voters supported President Trump for reelection. In 2024, 15% of black voters supported President Trump." "What does that movement, that near doubling of support from 2020 to 2024, tell you?" JOHNSON: "I've heard different statistics shared. I have yet to see any of it verified. Because exit polls have always been unreliable. Much of the polling we've seen over the last 20 years have been wrong on the outcome of the elections." "And I've been trying to figure out where are these numbers coming from. Is it verifiable? But it's great talking points — because people would like to show that there is momentum going in one direction versus another." GARRETT: "I'm glad you raised questions about the verifiability of the data that I just communicated to you. Just for your information and for the benefit of this conversation, that came from Pew Research. And it's data that takes not just exit polls but voter sampling and then the census community survey data." "It is regarded as the best and most comprehensive and therefore most verifiable post-election analysis. This has historically been true. So that's my verification method for you." Daily Caller

Jason Cohen 🇺🇸

108,044 Aufrufe • vor 1 Jahr

ICYMI: meow gave a frank breakdown of the 3 pillars of the Jupiverse on the Planetary Call. Use the timestamps below to hear the latest on the Jupiter Platform, the $JUP Token, and the J.U.P. Community. -- 08:45 Agenda for the call 10:48 Improving the product and ongoing experiments with dynamic slippage, LFG, and more 12:45 Addressing issues with Jupiter Spot caused by an error in gas estimation 15:00 Limiting the free quote API 16:42 On testing the gas estimation algorithm, and ramping up monitoring 20:45 Addressing the memecoin dumping FUD (spoiler: Jupiter didn’t dump, a partner using the API did) 25:15 Analyzing issues with Jupiter DCA in the past week caused by low liquidity 33:11 Dynamic Slippage V2 otw! 37:25 Real talk about MEV 39:05 Limit order improvements: prioritizing top 200 tokens, showing warnings for low liquidity trades 40:10 An overview of the WBTC situation and how it affects JLP 46:37 Key updates around $JUP from the past 2 weeks: JUP CAT PPP, DeFi’s First Transparency Audit, The 30% Token Supply Reduction Proposal 01:05:16 A look at LFG so far, and evolving the launchpad to be more permissionless 01:23:42 The next steps for Jupiter Working Groups, and CWG launching grants for potential WGs 01:36:55 Meow’s message for your favorite KOL 01:39:35 The Crucial Role of Catdets in the J.U.P 01:47:25 Sneak Peek: The UGM Revolution w/ Meow and Steve Chen (Co-Founder, YouTube)
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ICYMI: meow gave a frank breakdown of the 3 pillars of the Jupiverse on the Planetary Call. Use the timestamps below to hear the latest on the Jupiter Platform, the $JUP Token, and the J.U.P. Community. -- 08:45 Agenda for the call 10:48 Improving the product and ongoing experiments with dynamic slippage, LFG, and more 12:45 Addressing issues with Jupiter Spot caused by an error in gas estimation 15:00 Limiting the free quote API 16:42 On testing the gas estimation algorithm, and ramping up monitoring 20:45 Addressing the memecoin dumping FUD (spoiler: Jupiter didn’t dump, a partner using the API did) 25:15 Analyzing issues with Jupiter DCA in the past week caused by low liquidity 33:11 Dynamic Slippage V2 otw! 37:25 Real talk about MEV 39:05 Limit order improvements: prioritizing top 200 tokens, showing warnings for low liquidity trades 40:10 An overview of the WBTC situation and how it affects JLP 46:37 Key updates around $JUP from the past 2 weeks: JUP CAT PPP, DeFi’s First Transparency Audit, The 30% Token Supply Reduction Proposal 01:05:16 A look at LFG so far, and evolving the launchpad to be more permissionless 01:23:42 The next steps for Jupiter Working Groups, and CWG launching grants for potential WGs 01:36:55 Meow’s message for your favorite KOL 01:39:35 The Crucial Role of Catdets in the J.U.P 01:47:25 Sneak Peek: The UGM Revolution w/ Meow and Steve Chen (Co-Founder, YouTube)

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Major program launch: Data Analytics Professional Certificate! This large, five-course sequence takes you all the way to being job-ready as a data analyst, and shows how to use Generative AI as a thought partner to enhance your work in this role. Offered by on Coursera, this is taught by Sean Barnes, Ph.D., a Data Science & Engineering Leader at Netflix. Analyzing data remains one of the most important skills in where the world is going with AI. This comprehensive certificate takes you all the way to being job-ready. Each course comes with practical projects demonstrated in real-world contexts, such as analyzing sales data for a Korean bakery, video game sales trends across different regions, or identifying factors impacting customer retention for a communications company. You'll also work on estimating fire distribution for forest fire prevention, analyzing how a diamond's properties affect its market value, and developing predictive models for retail sales analysis, carbon emissions, and coral reef conservation. Here's some of what you'll learn: - How to define data and categorize it into its many types such as discrete & continuous numerical, structured & unstructured, time series, categorical, and know what insights can be derived from the different types of data categories. - How to differentiate between data-related job roles and their responsibilities, and how data flows through an organization from the moment of capture to decision-making. - How to perform data processing functions and apply conditional formatting in spreadsheets to extract business value from your data using statistical calculations and best practices for visualizing and interpreting data. - How to use LLMs for stakeholder analysis, data exploration, and data visualization. - Best practices for using LLMs for as a thought partner to data analysis work By the end of this professional certificate program, you will have learned core statistical concepts, analysis techniques, and visualization methodologies that will serve as the foundation for working as a data analyst. The world needs more data analysts, especially ones who know how to use modern generative AI. With data science roles projected to grow 36% by 2033, the skills taught in this program create new professional opportunities in data. Sign up here!

Andrew Ng

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Crazy... I just found a super cheap way to code with AI Tried GLM 4.5 on Claude Code and I’m really impressed! Prompt: “create an interactive python app where users can track, manage or analyze data” And, It came up with: • Dashboard - a clean interface to see all your data at a glance • Datasets - manage multiple collections of data easily • Data entries - add, edit, and organize your records without hassle • Analysis - get insights and summaries directly from your data What I observed: Everything worked smoothly, GLM 4.5 is definitely one of the best open-source models for coding, I’ll keep using it. Check it out now: And the best part: super affordable coding plans! • Lite - $3/month (promo). ~120 prompts every 5 hours. Works with Claude Code, Roo Code, Cline, Kilo Code, and more. • Pro - $15/month (promo). 5× more usage than Lite. Faster. Includes image/video tools and web search. • Max - $30/month (promo). 4× more usage than Pro. Guaranteed performance. Early access to new features. Bonus - Use my referral link to get an extra 10% off on top of these promo prices! Video demo 👇
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Crazy... I just found a super cheap way to code with AI Tried GLM 4.5 on Claude Code and I’m really impressed! Prompt: “create an interactive python app where users can track, manage or analyze data” And, It came up with: • Dashboard - a clean interface to see all your data at a glance • Datasets - manage multiple collections of data easily • Data entries - add, edit, and organize your records without hassle • Analysis - get insights and summaries directly from your data What I observed: Everything worked smoothly, GLM 4.5 is definitely one of the best open-source models for coding, I’ll keep using it. Check it out now: And the best part: super affordable coding plans! • Lite - $3/month (promo). ~120 prompts every 5 hours. Works with Claude Code, Roo Code, Cline, Kilo Code, and more. • Pro - $15/month (promo). 5× more usage than Lite. Faster. Includes image/video tools and web search. • Max - $30/month (promo). 4× more usage than Pro. Guaranteed performance. Early access to new features. Bonus - Use my referral link to get an extra 10% off on top of these promo prices! Video demo 👇

Chidanand Tripathi

83,854 Aufrufe • vor 10 Monaten

Statistics and data show exclusive games have a far higher likelihood of being higher quality and/or more graphically and technically impressive, relative to the very low volume of releases they make up vs multi-platform games. This doesn't mean exclusives can't be and aren't often bad. Nor that multi-platform games can't be or aren't often better than exclusives, just that exclusivity (inc console and timed) greatly increases the likelihood of higher quality. Despite making up less than 10% of overall releases (see video for context and details), exclusives make up; +The majority of the highest rated games ever made. +The majority of the most Game of the Year Awarded games the last 13 years. +The overwhelming majority of tech and graphics awards winners, by arguably the most prestigious institutions in the field. This isn't a coincidence, as developers themselves keep reminding us. It's because exclusives greatly benefit from single platform focus, and design, development, optimisation, studios, teams, budgets, resources, testing and time, not having to instead be spread far thinner, across many platforms. This doesn't mean all games need to be or should be exclusive. Few games are, and that's fine. But some exclusives existing to push the boundaries of single platform development, tech and design focus, as well as increasing competition in general, is ultimately a great and pro-consumer thing. At least if you're a consumer who values the pursuit of higher potential quality, over accessibility. #PS5 #Xbox #Nintendo #Switch2
5:28

Statistics and data show exclusive games have a far higher likelihood of being higher quality and/or more graphically and technically impressive, relative to the very low volume of releases they make up vs multi-platform games. This doesn't mean exclusives can't be and aren't often bad. Nor that multi-platform games can't be or aren't often better than exclusives, just that exclusivity (inc console and timed) greatly increases the likelihood of higher quality. Despite making up less than 10% of overall releases (see video for context and details), exclusives make up; +The majority of the highest rated games ever made. +The majority of the most Game of the Year Awarded games the last 13 years. +The overwhelming majority of tech and graphics awards winners, by arguably the most prestigious institutions in the field. This isn't a coincidence, as developers themselves keep reminding us. It's because exclusives greatly benefit from single platform focus, and design, development, optimisation, studios, teams, budgets, resources, testing and time, not having to instead be spread far thinner, across many platforms. This doesn't mean all games need to be or should be exclusive. Few games are, and that's fine. But some exclusives existing to push the boundaries of single platform development, tech and design focus, as well as increasing competition in general, is ultimately a great and pro-consumer thing. At least if you're a consumer who values the pursuit of higher potential quality, over accessibility. #PS5 #Xbox #Nintendo #Switch2

NIB

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Feminism! Ana vs Pearl! Stefan Molyneux takes on a debate about feminism between Ana Kasparian and Pearl Davis in his Freedomain podcast. He discusses Pearl's arguments on women's roles in the economy, tying them to falling birth rates and broader effects on society. Molyneux breaks down some common misunderstandings in economic data and digs into the nuances of gender expectations and family life. In the end, he questions what modern feminism really means and encourages people to join the conversation. Stefan will be there March 28, 2026, he hopes to see you there! Chapters: 0:00:00 Introduction to the Debate 0:01:10 Unpacking Feminism's Economic Impact 0:05:59 The Government's Role in Female Employment 0:14:17 Domestic Violence Statistics and Feminism 0:16:56 Title IX and Its Implications 0:23:08 The Debate on Modern Relationships 0:28:20 The Case of Terrence Pop 0:32:22 The Effects of Feminism on Men 0:41:01 The Statistics of Divorce 0:49:00 Child Support and Alimony Issues 0:59:20 Incentives in Divorce Decisions 1:03:55 Addressing Negatives of Feminism 1:06:14 Closing Thoughts and Future Events GET FREEDOMAIN MERCH! SUBSCRIBE TO ME ON X! Follow me on Youtube! GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK! Join the PREMIUM philosophy community on the web for free! Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows! You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more! See you soon!
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Feminism! Ana vs Pearl! Stefan Molyneux takes on a debate about feminism between Ana Kasparian and Pearl Davis in his Freedomain podcast. He discusses Pearl's arguments on women's roles in the economy, tying them to falling birth rates and broader effects on society. Molyneux breaks down some common misunderstandings in economic data and digs into the nuances of gender expectations and family life. In the end, he questions what modern feminism really means and encourages people to join the conversation. Stefan will be there March 28, 2026, he hopes to see you there! Chapters: 0:00:00 Introduction to the Debate 0:01:10 Unpacking Feminism's Economic Impact 0:05:59 The Government's Role in Female Employment 0:14:17 Domestic Violence Statistics and Feminism 0:16:56 Title IX and Its Implications 0:23:08 The Debate on Modern Relationships 0:28:20 The Case of Terrence Pop 0:32:22 The Effects of Feminism on Men 0:41:01 The Statistics of Divorce 0:49:00 Child Support and Alimony Issues 0:59:20 Incentives in Divorce Decisions 1:03:55 Addressing Negatives of Feminism 1:06:14 Closing Thoughts and Future Events GET FREEDOMAIN MERCH! SUBSCRIBE TO ME ON X! Follow me on Youtube! GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK! Join the PREMIUM philosophy community on the web for free! Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows! You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more! See you soon!

Freedomain - with Stefan Molyneux, MA

28,109 Aufrufe • vor 6 Monaten

On deck in our #CVPR2025 series: ChatGarment 👚✨ By Siyuan Bian, Chenghao Xu, Yuliang Xiu, Artur Grigorev, Zhen Liu, Cewu Lu, Michael J. Black and Yao Feng. Feed it an image, video or text, and watch a tailored 3D outfit appear. The video walks through: 1️⃣ Image-based sewing pattern estimation 2️⃣ Text-to-garment generation 3️⃣ Text-based editing Why it matters • Designers preview new looks before cutting fabric • Game and film studios drop physics-ready clothes onto avatars in minutes • Researchers study cloth–body dynamics without manual labeling 🎯 Crafting catwalk looks or decking out game avatars? Just type a prompt and ChatGarment whips up, shrinks, or totally restyles the outfit—no scissors required! 👋 Swing by our #CVPR2026 booth 1333 to meet some of the minds behind ChatGarment, and stay tuned for more CVPR paper videos! 📄 Paper link in the thread/comment. #GarmentTech #3DFashion #DigitalHuman #ComputerVision #AI #MachineLearning
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On deck in our #CVPR2025 series: ChatGarment 👚✨ By Siyuan Bian, Chenghao Xu, Yuliang Xiu, Artur Grigorev, Zhen Liu, Cewu Lu, Michael J. Black and Yao Feng. Feed it an image, video or text, and watch a tailored 3D outfit appear. The video walks through: 1️⃣ Image-based sewing pattern estimation 2️⃣ Text-to-garment generation 3️⃣ Text-based editing Why it matters • Designers preview new looks before cutting fabric • Game and film studios drop physics-ready clothes onto avatars in minutes • Researchers study cloth–body dynamics without manual labeling 🎯 Crafting catwalk looks or decking out game avatars? Just type a prompt and ChatGarment whips up, shrinks, or totally restyles the outfit—no scissors required! 👋 Swing by our #CVPR2026 booth 1333 to meet some of the minds behind ChatGarment, and stay tuned for more CVPR paper videos! 📄 Paper link in the thread/comment. #GarmentTech #3DFashion #DigitalHuman #ComputerVision #AI #MachineLearning

Meshcapade

17,571 Aufrufe • vor 1 Jahr

Barry Young: The Safest Vaccine Site in New Zealand Was Right Next to Parliament Barry Young explains the detailed statistical analysis he performed on the New Zealand vaccination data. Using the same tools an epidemiologist would employ - graphs, confidence intervals, Poisson scores, p-values and Standardised Mortality Rates (SMRs) - he examined both the national picture and individual vaccination sites. While the overall data showed excess mortality, results varied sharply by location: some sites recorded significantly elevated deaths, others performed better than expected. The standout finding was that the single best-performing site in the entire country - the one with the lowest (and even favourable) mortality statistics - was located in Wellington Central, right in the seat of government. Barry notes that this variation suggests the doses were not uniform across New Zealand and raises the unresolved question of why the centre nearest Parliament produced such markedly different results. A clear, data-driven examination of what site-level analysis reveals. Transcript: “I was thinking, well, as an epidemiologist, how would they look at this data? What would they do? What tools would they use? How would they analyse it? So I went through, did all the card and for it, got the nice graphs, charts, analysis, numbers, confidence intervals, Poisson scores, all the p-values, all that stuff to work out the probabilities and all the rest of it. To work out if it was dangerous, SMRs, standardised mortality rates. You know, one is normal. SMR1 should be one to one mortality rate before and after the vaccine. You’ve got the same number of people alive, same number of people alive after the vaccine. That’s an SMR1. If it’s SMR higher than one, you’ve got more people dying after the vaccine than should die after the vaccine. SMR less than one, there’s more people living - Vaccines working, great. So that’s how it works. That’s the SMR. So anyway, I did all of that analysis and provided it for multiple sites across New Zealand. So I was doing it specifically by site. So I’ve done the work for the whole data in general, which shows excess mortality. But I also did it for certain sites in New Zealand, some bad ones, some not so bad, some very, very good. Just to show that my analysis was whole and correct and valid and validated everything, because some sites I looked at, mortality was not elevated. It was actually less than expected, which is like, OK, it proves my method works. It’s accurate. It is picking out the bad ones, the really bad ones. It’s also picking out some good ones as well. But there are far fewer good ones than there are bad ones. And the key thing to see here, I don’t think I’ve discussed this. The key thing to realise is that the good one, the really good one, the star of the show and the whole of New Zealand, the best site was in Wellington Central itself, the seat of the government, the heart of the city. So go figure, why did that have the best mortality statistics in the whole entire database? [Liz Gunn] Barry, that’s extraordinary... What we are saying here is the jab that... I call them the poison shots. The shots were not the same across the country. There were certain areas that seemed to get much stronger, much stronger shots that had a much more significant and immediate effect, maybe over the next six months. We’re talking about deaths here, but if we could analyse the data on turbo cancers, on heart conditions, we could see if we could really do a deep, deep dive, if this government were open and caring about the people, we could see that certain parts of the country have been much harder hit than others. [Barry Young] That’s the job of the government. That’s safety monitoring. That’s what they should be doing. [Liz Gunn] And we are saying that near parliament in Wellington and Central Wellington, that jab Centre was the safest. It’s a bizarre coincidence. It probably had something like a saline solution could have been a possibility. We don’t know until we investigate, but the question mark remains. [Barry Young] Could have been the safest vaccine in the world. That is just so cynical. It is a bizarre, bizarre coincidence.”
3:40

Barry Young: The Safest Vaccine Site in New Zealand Was Right Next to Parliament Barry Young explains the detailed statistical analysis he performed on the New Zealand vaccination data. Using the same tools an epidemiologist would employ - graphs, confidence intervals, Poisson scores, p-values and Standardised Mortality Rates (SMRs) - he examined both the national picture and individual vaccination sites. While the overall data showed excess mortality, results varied sharply by location: some sites recorded significantly elevated deaths, others performed better than expected. The standout finding was that the single best-performing site in the entire country - the one with the lowest (and even favourable) mortality statistics - was located in Wellington Central, right in the seat of government. Barry notes that this variation suggests the doses were not uniform across New Zealand and raises the unresolved question of why the centre nearest Parliament produced such markedly different results. A clear, data-driven examination of what site-level analysis reveals. Transcript: “I was thinking, well, as an epidemiologist, how would they look at this data? What would they do? What tools would they use? How would they analyse it? So I went through, did all the card and for it, got the nice graphs, charts, analysis, numbers, confidence intervals, Poisson scores, all the p-values, all that stuff to work out the probabilities and all the rest of it. To work out if it was dangerous, SMRs, standardised mortality rates. You know, one is normal. SMR1 should be one to one mortality rate before and after the vaccine. You’ve got the same number of people alive, same number of people alive after the vaccine. That’s an SMR1. If it’s SMR higher than one, you’ve got more people dying after the vaccine than should die after the vaccine. SMR less than one, there’s more people living - Vaccines working, great. So that’s how it works. That’s the SMR. So anyway, I did all of that analysis and provided it for multiple sites across New Zealand. So I was doing it specifically by site. So I’ve done the work for the whole data in general, which shows excess mortality. But I also did it for certain sites in New Zealand, some bad ones, some not so bad, some very, very good. Just to show that my analysis was whole and correct and valid and validated everything, because some sites I looked at, mortality was not elevated. It was actually less than expected, which is like, OK, it proves my method works. It’s accurate. It is picking out the bad ones, the really bad ones. It’s also picking out some good ones as well. But there are far fewer good ones than there are bad ones. And the key thing to see here, I don’t think I’ve discussed this. The key thing to realise is that the good one, the really good one, the star of the show and the whole of New Zealand, the best site was in Wellington Central itself, the seat of the government, the heart of the city. So go figure, why did that have the best mortality statistics in the whole entire database? [Liz Gunn] Barry, that’s extraordinary... What we are saying here is the jab that... I call them the poison shots. The shots were not the same across the country. There were certain areas that seemed to get much stronger, much stronger shots that had a much more significant and immediate effect, maybe over the next six months. We’re talking about deaths here, but if we could analyse the data on turbo cancers, on heart conditions, we could see if we could really do a deep, deep dive, if this government were open and caring about the people, we could see that certain parts of the country have been much harder hit than others. [Barry Young] That’s the job of the government. That’s safety monitoring. That’s what they should be doing. [Liz Gunn] And we are saying that near parliament in Wellington and Central Wellington, that jab Centre was the safest. It’s a bizarre coincidence. It probably had something like a saline solution could have been a possibility. We don’t know until we investigate, but the question mark remains. [Barry Young] Could have been the safest vaccine in the world. That is just so cynical. It is a bizarre, bizarre coincidence.”

Liz Gunn

156,036 Aufrufe • vor 6 Tagen

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!
0:31

Variational Autoencoder by hand ✍️ ~ 11 steps walkthrough below A VAE learns the structure of your data, the mean and variance of its hidden features, and then generates new data from that structure. A GAN only learns to fool a discriminator. It can make convincing fakes without ever knowing what the data is really made of. That is the difference, and it is the whole reason VAEs matter. In 2024 ICLR gave its first ever Test of Time Award to the VAE paper, "Auto-Encoding Variational Bayes" by Diederik Kingma and Max Welling, ten years on. How does it work? Goal: encode three inputs into a distribution, sample from it, decode it back, and read every loss gradient off the page. = 1. Given = Three training examples X1, X2, X3, copied to the bottom as their own targets. Reconstructing your own input is what puts the "auto", meaning self, in autoencoder. = 2. Encoder, layer 1 = Let us multiply the inputs by weights and biases, then apply ReLU, crossing out every negative. = 3. Mean and standard deviation = We multiply the features by two more weight sets. The first predicts the means μ of the latent distributions, the second their standard deviations σ. = 4. A random offset = Let us sample ε from a standard normal, mean 0 and variance 1, and multiply it by σ. This is a random step away from the mean, scaled by how uncertain each feature is. = 5. Mean plus offset = We add the offset back onto μ, and these become the decoder's inputs. Keeping the randomness out in ε is the reparameterization trick: it lets gradients flow straight through the sampling. = 6. Decoder, layer 1 = Let us multiply by weights and biases and apply ReLU again. Here -4 is crossed out. = 7. Decoder, layer 2 = We multiply once more. The output Y is the decoder's attempt to rebuild X from the sampled distribution. = 8. Gradient for the mean = Let us push μ toward 0. A lot of math, the SGVB estimator, collapses the KL gradient to simply μ itself. = 9. Gradient for the standard deviation = We want σ to approach 1. = 10. And its formula = That same math simplifies the gradient to σ minus 1/σ. = 11. Reconstruction gradient = We want the reconstruction Y to match the input X. Mean squared error simplifies its gradient to Y minus X. Takeaway: the two gradients you just calculated each sit at the heart of a modern method, so one VAE teaches you both. The KL divergence is the penalty RLHF like GRPO uses to keep a fine-tuned model from drifting off its base. The reconstruction loss, plain mean squared error, is exactly what trains a diffusion model to denoise. Draw one VAE by hand and you have quietly learned the core of both. 💾 Save this post!

Tom Yeh

16,970 Aufrufe • vor 11 Tagen

A guy running quant models for a tennis betting syndicate DM'd me last month. "We spend $40K/month on data feeds. What are you using?" A webcam pointed at a tennis stream. Not metaphorically.YOLO tracks both players and the ball 30fps. A second model maps the court to real meters. Every 5 seconds I get three numbers: aggression positioning, court coverage, rally intensity. Those feed into a Bayesian engine. Beta prior on serve probability, updated every game, 15,000 Monte Carlo sims sampling from the posterior. Not point estimates - distributions with confidence intervals. 62% +/- 3% is a trade. 62% +/- 18% is noise. Most people never compute the interval. "Okay but models are wrong" That's why the model is only layer two out of five Layer three connects Polymarket and a bookmaker simultaneously. When my model says 71%, Polymarket says 62%, bookmaker says 67% - something is mispriced. Layer four is the unfair one. Claude reads every press conference transcript, news article, and social post from the last 48 hours. Extracts structured signals - injury flags, form deltas, surface comfort. JSON, not vibes. A player mentioned shoulder tightness at a presser. Claude flagged injury probability 0.25. Bookmaker didn't adjust for 3 hours. Polymarket never did. I was already in Layer five compares all four sources and finds the edge. Value, arbitrage, fade, or intel override when Claude catches something critical. "What's your hit rate" 2,400 ATP matches backtested. Model alone: 61%. With market bridge: 67%. With Claude: 72% Live 11 weeks: 247 contracts, 178 winners, +$14,200 from $1,800 He went quiet then wrote "my syndicate is rethinking our entire pipeline. We've been doing this 6 years and never used CV on live streams" They spend $40K/month. My setup: a Claude subscription and an API key Bot:
0:47

A guy running quant models for a tennis betting syndicate DM'd me last month. "We spend $40K/month on data feeds. What are you using?" A webcam pointed at a tennis stream. Not metaphorically.YOLO tracks both players and the ball 30fps. A second model maps the court to real meters. Every 5 seconds I get three numbers: aggression positioning, court coverage, rally intensity. Those feed into a Bayesian engine. Beta prior on serve probability, updated every game, 15,000 Monte Carlo sims sampling from the posterior. Not point estimates - distributions with confidence intervals. 62% +/- 3% is a trade. 62% +/- 18% is noise. Most people never compute the interval. "Okay but models are wrong" That's why the model is only layer two out of five Layer three connects Polymarket and a bookmaker simultaneously. When my model says 71%, Polymarket says 62%, bookmaker says 67% - something is mispriced. Layer four is the unfair one. Claude reads every press conference transcript, news article, and social post from the last 48 hours. Extracts structured signals - injury flags, form deltas, surface comfort. JSON, not vibes. A player mentioned shoulder tightness at a presser. Claude flagged injury probability 0.25. Bookmaker didn't adjust for 3 hours. Polymarket never did. I was already in Layer five compares all four sources and finds the edge. Value, arbitrage, fade, or intel override when Claude catches something critical. "What's your hit rate" 2,400 ATP matches backtested. Model alone: 61%. With market bridge: 67%. With Claude: 72% Live 11 weeks: 247 contracts, 178 winners, +$14,200 from $1,800 He went quiet then wrote "my syndicate is rethinking our entire pipeline. We've been doing this 6 years and never used CV on live streams" They spend $40K/month. My setup: a Claude subscription and an API key Bot:

zostaff

55,452 Aufrufe • vor 3 Monaten

So Jacob Terkelsen, a PC diehard with a history of inaccuracies, did an apples to oranges PC comparison to try to prove Black Myth WuKong's game director was lying or wrong (lol) about Series S's ram limitations coupled with a lack of optimisation experience, being the cause for the lack of an Xbox release. Here's my video debunking him and highlighting numerous inaccuracies and/or misleading aspects to his process, conclusions and data. I've also included a second video where I debunked some of the past things he was also spectacularly wrong about, with similar misplaced analysis and poor methodology. Finally, here is a link to a debate we had in Spaces on X on this subject. Discussion from 3:33:00 onwards. There have been over a dozen studios/devs who have complained about the Series S now, including on memory issues specifically, potentially breaking NDA to do so. But of course, the fanboys continue to try to discredit the developers actually working on these consoles, the folk making the games for this hobby we love, all to pander to their own insecurities and headcanon ideals. Imagine discrediting devs who are willing to share more insight than they ought to, when we should be embracing the insider info. And imagine thinking your own typical play test was equivalent to thousands of hours of QA testing to find worst case scenarios in the game, and of peak ram use. Embarrassing. I should add, I'd imagine the majority of Black Myth WuKong likely runs absolutely fine on Series S. But game optimisation is often about ironing out a minuscule minority of worst case scenarios or issues. Even a handful of problematic areas or instances in a game, could prevent release or certification. Especially if they're egregious (repeatable crashes, bugs, VRAM topping out in a specific scenario etc). #PS5 #SeriesS #BlackMythWuKong
5:21

So Jacob Terkelsen, a PC diehard with a history of inaccuracies, did an apples to oranges PC comparison to try to prove Black Myth WuKong's game director was lying or wrong (lol) about Series S's ram limitations coupled with a lack of optimisation experience, being the cause for the lack of an Xbox release. Here's my video debunking him and highlighting numerous inaccuracies and/or misleading aspects to his process, conclusions and data. I've also included a second video where I debunked some of the past things he was also spectacularly wrong about, with similar misplaced analysis and poor methodology. Finally, here is a link to a debate we had in Spaces on X on this subject. Discussion from 3:33:00 onwards. There have been over a dozen studios/devs who have complained about the Series S now, including on memory issues specifically, potentially breaking NDA to do so. But of course, the fanboys continue to try to discredit the developers actually working on these consoles, the folk making the games for this hobby we love, all to pander to their own insecurities and headcanon ideals. Imagine discrediting devs who are willing to share more insight than they ought to, when we should be embracing the insider info. And imagine thinking your own typical play test was equivalent to thousands of hours of QA testing to find worst case scenarios in the game, and of peak ram use. Embarrassing. I should add, I'd imagine the majority of Black Myth WuKong likely runs absolutely fine on Series S. But game optimisation is often about ironing out a minuscule minority of worst case scenarios or issues. Even a handful of problematic areas or instances in a game, could prevent release or certification. Especially if they're egregious (repeatable crashes, bugs, VRAM topping out in a specific scenario etc). #PS5 #SeriesS #BlackMythWuKong

NIB

35,640 Aufrufe • vor 1 Jahr

LAUNCH ANNOUNCEMENT Finding the perfect idea, title and thumbnail concept can be time consuming and is what essentially leads to more views and growth to your channel. Now imagine saving research time by 50%, freeing hours to enhance video quality. Well we have a solution to never run out of ideas on ! Watch the video below to see the tool in action! The 1 of 10 Finder: Discover hundreds of thousands of high-performing videos to inspire your next idea, title and thumbnail. This data-backed approach makes it easier than ever to more easily find your next banger video. For every 15 Retweets, I’m giving away 1 Yearly Access + 1H Consulting Call Deep Diving Your channel ($500) The benefit of using this tool vs simply searching on Youtube: Youtube only has most viewed and relevant as good filters. In our tool, 100% of the video results are 1 of 10s, meaning that EVERY. SINGLE. RESULT. is an excellent inspiration for your next video since they have been proven to succeed regardless of the niche. How it works? Simply enter a keyword or a niche, and you'll uncover outlier videos. You can even type out prompts like Midjourney and the search will understand. You can then find similar videos to the ones that you like for even more inspiration. You can also bookmark the thumbnails on your personal vision board for constant inspiration, bounce around top outliers per niche and even play with the random outlier button for infinite inspiration. How this tool helps you to find ideas, titles and thumbnails? Say you have no idea what video to film next. You can go on the tool and either bounce around niches or click on random outliers. What this will do is inspire you with ONLY data-backed ideas meaning that any of the videos you see has a good potential to be repackaged for your own channel, even if the inspiration is in a different niche. Why pay for this? - Find ideas, titles and thumbnail concepts faster saving you hours of research - Vision Board for saved thumbnails - 1 hour free consulting call with me ($500 value, you essentially get a discounted strategy call + 1 year free of the tool 😆) - Community built around 1 of 10 and surround yourself with peer creators that have that 1 of 10 mentality - First access to upcoming tools - Infinite inspiration with our random button generator, bounce around categories or use the similar feature - 1 idea here can lead to your next 1M view - Discover videos you would never have seen prior to using this tool and find opportunities before anyone else - First week price never to be seen ever again For who is this for? If this tool allows you to find even just 1 viral idea for the whole year at 1M views: 0-100k subs: Boosted viewership opens doors to lucrative sponsorships and collaborations. 100k - 1M subs: If a data-backed idea leads to an increment of even just 5%, it makes the tool worth it for the year 1M+: If a data-backed idea leads to an increment of even just 1%, it makes the tool worth it for the year Who are we? For the past 3 years, I’ve worked hands-on with Youtubers from a few thousand subscribers to 10s of millions to 50M+. I closely work with youtube channels by optimizing all facets of content creation, from titles, thumbnails, retention, ideas, etc. I have seen all the problems that creators are facing and I have a passion to create as many tools as possible in the space that will solve these problems which in turn will lead to lower barriers to entry to content creation which will then hopefully lead to more dope content on the Internet😄 And the genius dev behind the tool? Meet Riad , ex-Microsoft and AI engineer. His expertise and love for Youtube has led to this state-of the art YT tool! You can be sure that your user experience will be smooth. Also meet cocadmin , ex-Ubisoft DevOps + 2nd biggest French Developer Youtuber with nearly 200K subs. I will choose 1 person for every 15 retweets at random to do one strategy call with + 1 year free access to the tool.
2:53

LAUNCH ANNOUNCEMENT Finding the perfect idea, title and thumbnail concept can be time consuming and is what essentially leads to more views and growth to your channel. Now imagine saving research time by 50%, freeing hours to enhance video quality. Well we have a solution to never run out of ideas on ! Watch the video below to see the tool in action! The 1 of 10 Finder: Discover hundreds of thousands of high-performing videos to inspire your next idea, title and thumbnail. This data-backed approach makes it easier than ever to more easily find your next banger video. For every 15 Retweets, I’m giving away 1 Yearly Access + 1H Consulting Call Deep Diving Your channel ($500) The benefit of using this tool vs simply searching on Youtube: Youtube only has most viewed and relevant as good filters. In our tool, 100% of the video results are 1 of 10s, meaning that EVERY. SINGLE. RESULT. is an excellent inspiration for your next video since they have been proven to succeed regardless of the niche. How it works? Simply enter a keyword or a niche, and you'll uncover outlier videos. You can even type out prompts like Midjourney and the search will understand. You can then find similar videos to the ones that you like for even more inspiration. You can also bookmark the thumbnails on your personal vision board for constant inspiration, bounce around top outliers per niche and even play with the random outlier button for infinite inspiration. How this tool helps you to find ideas, titles and thumbnails? Say you have no idea what video to film next. You can go on the tool and either bounce around niches or click on random outliers. What this will do is inspire you with ONLY data-backed ideas meaning that any of the videos you see has a good potential to be repackaged for your own channel, even if the inspiration is in a different niche. Why pay for this? - Find ideas, titles and thumbnail concepts faster saving you hours of research - Vision Board for saved thumbnails - 1 hour free consulting call with me ($500 value, you essentially get a discounted strategy call + 1 year free of the tool 😆) - Community built around 1 of 10 and surround yourself with peer creators that have that 1 of 10 mentality - First access to upcoming tools - Infinite inspiration with our random button generator, bounce around categories or use the similar feature - 1 idea here can lead to your next 1M view - Discover videos you would never have seen prior to using this tool and find opportunities before anyone else - First week price never to be seen ever again For who is this for? If this tool allows you to find even just 1 viral idea for the whole year at 1M views: 0-100k subs: Boosted viewership opens doors to lucrative sponsorships and collaborations. 100k - 1M subs: If a data-backed idea leads to an increment of even just 5%, it makes the tool worth it for the year 1M+: If a data-backed idea leads to an increment of even just 1%, it makes the tool worth it for the year Who are we? For the past 3 years, I’ve worked hands-on with Youtubers from a few thousand subscribers to 10s of millions to 50M+. I closely work with youtube channels by optimizing all facets of content creation, from titles, thumbnails, retention, ideas, etc. I have seen all the problems that creators are facing and I have a passion to create as many tools as possible in the space that will solve these problems which in turn will lead to lower barriers to entry to content creation which will then hopefully lead to more dope content on the Internet😄 And the genius dev behind the tool? Meet Riad , ex-Microsoft and AI engineer. His expertise and love for Youtube has led to this state-of the art YT tool! You can be sure that your user experience will be smooth. Also meet cocadmin , ex-Ubisoft DevOps + 2nd biggest French Developer Youtuber with nearly 200K subs. I will choose 1 person for every 15 retweets at random to do one strategy call with + 1 year free access to the tool.

Richard the Youtube strategist

179,129 Aufrufe • vor 2 Jahren

How to Market Your Business! CALL IN SHOW Philosopher Stefan Molyneux and a caller discuss how to market his new homeschooling curriculum business. Stefan suggests leading with short videos on the dreadful shortcomings in public schools instead of pitching the app directly. The caller discusses his plans to make the curriculum robust, especially for boys, and is working to get things ready before the next school year. You can find Romeschool at Early adopters get a lifetime 50% off discount! 0:00:00 Homeschool Vision Begins 0:07:32 Romeschool Rebrand 0:10:20 Teaching Citizenship Through History 0:12:53 Why Time Matters 0:15:51 First AI Experiments 0:18:58 Music, Video, and AI 0:24:08 Discovering the Real Passion 0:26:55 Marketing the Mission 0:31:19 Wisdom, Speech, and Skepticism 0:37:56 Respect, Manners, and Youth 0:43:17 Curriculum and Life Skills 0:50:03 Getting the First Users 0:51:55 Grading and Scaling 0:56:21 Building Homeschool Community 0:58:23 Market Analysis 1:04:10 Public Schools Are Failing 1:09:34 Selling the Solution 1:14:03 Marketing the Danger 1:21:18 Boys and Masculinity 1:24:25 Romeschool Launch GET FREEDOMAIN MERCH! SUBSCRIBE TO ME ON X! Follow me on Youtube! GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK! Join the PREMIUM philosophy community on the web for free! Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows! You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more! See you soon!
1:27:00

How to Market Your Business! CALL IN SHOW Philosopher Stefan Molyneux and a caller discuss how to market his new homeschooling curriculum business. Stefan suggests leading with short videos on the dreadful shortcomings in public schools instead of pitching the app directly. The caller discusses his plans to make the curriculum robust, especially for boys, and is working to get things ready before the next school year. You can find Romeschool at Early adopters get a lifetime 50% off discount! 0:00:00 Homeschool Vision Begins 0:07:32 Romeschool Rebrand 0:10:20 Teaching Citizenship Through History 0:12:53 Why Time Matters 0:15:51 First AI Experiments 0:18:58 Music, Video, and AI 0:24:08 Discovering the Real Passion 0:26:55 Marketing the Mission 0:31:19 Wisdom, Speech, and Skepticism 0:37:56 Respect, Manners, and Youth 0:43:17 Curriculum and Life Skills 0:50:03 Getting the First Users 0:51:55 Grading and Scaling 0:56:21 Building Homeschool Community 0:58:23 Market Analysis 1:04:10 Public Schools Are Failing 1:09:34 Selling the Solution 1:14:03 Marketing the Danger 1:21:18 Boys and Masculinity 1:24:25 Romeschool Launch GET FREEDOMAIN MERCH! SUBSCRIBE TO ME ON X! Follow me on Youtube! GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK! Join the PREMIUM philosophy community on the web for free! Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows! You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more! See you soon!

Freedomain - with Stefan Molyneux, MA

13,992 Aufrufe • vor 1 Monat

We sat down with Philip Johnston, co-founder and CEO of Starcloud, at MIT to discuss why the future of data centers might be in space. After graduating Y Combinator less than 2 years ago, Starcloud just raised an impressive $170M Series A at a $1.1B valuation led by Benchmark and EQT Ventures & Growth. The conversation covers everything from solar physics and cooling systems to GPU economics, radiation hardening, launch costs, and satellite design. Philip also shares what it takes to build a unicorn deeptech startup. We discuss his experience with YC, the skepticism around their demoday launch, and the crazy last minute race to get Starcloud’s first satellite onboard their scheduled Falcon flight. Full episode is here on X and at any of the links below (see comment). Timestamps: 00:00 - Intro 01:12 - What is Starcloud? 02:44 - Why do data centers need to go to space? 06:15 - Can’t we just build more solar panels on earth? 11:10 - Economic analysis of Starcloud 19:56 - How does Starcloud’s cooling work? 28:26 - Training an LLM in space 32:07 - Addressing critics on space Twitter 34:23 - Is Starcloud overfunded? 35:59 - Will demand for data centers keep going up? 38:11 - GPU lifespan and disposal in space 39:47 - Bus structures 41:43 - Starcloud’s origin and founders 49:29 - Fundraising, Competition, and Meeting Expectations 53:29 - Satellite size and collisions 56:29 - Manufacturing Bottlenecks 1:00:20 - Starcloud 1 tests 1:01:57 - Acceleration after YC 1:03:43 - Testing on Earth 1:05:06 - Motivations for Starcloud 1:06:45 - Data centers on the Moon 1:08:12 - Interacting with AI companies 1:08:18 - What’s next for Starcloud? 1:14:01 - Other uses for Starcloud satellites 1:17:56 - Lunar hotels and space elevators 1:24:28 - Complementary business ideas to Starcloud 1:29:51 - Philip’s competitive twin 1:32:18 - Philip and Mike’s thoughts on YC 1:36:04 - Advice for young entrepreneurs Elon Musk Scott Manley Kyle Hill Hank Green
1:37:33

We sat down with Philip Johnston, co-founder and CEO of Starcloud, at MIT to discuss why the future of data centers might be in space. After graduating Y Combinator less than 2 years ago, Starcloud just raised an impressive $170M Series A at a $1.1B valuation led by Benchmark and EQT Ventures & Growth. The conversation covers everything from solar physics and cooling systems to GPU economics, radiation hardening, launch costs, and satellite design. Philip also shares what it takes to build a unicorn deeptech startup. We discuss his experience with YC, the skepticism around their demoday launch, and the crazy last minute race to get Starcloud’s first satellite onboard their scheduled Falcon flight. Full episode is here on X and at any of the links below (see comment). Timestamps: 00:00 - Intro 01:12 - What is Starcloud? 02:44 - Why do data centers need to go to space? 06:15 - Can’t we just build more solar panels on earth? 11:10 - Economic analysis of Starcloud 19:56 - How does Starcloud’s cooling work? 28:26 - Training an LLM in space 32:07 - Addressing critics on space Twitter 34:23 - Is Starcloud overfunded? 35:59 - Will demand for data centers keep going up? 38:11 - GPU lifespan and disposal in space 39:47 - Bus structures 41:43 - Starcloud’s origin and founders 49:29 - Fundraising, Competition, and Meeting Expectations 53:29 - Satellite size and collisions 56:29 - Manufacturing Bottlenecks 1:00:20 - Starcloud 1 tests 1:01:57 - Acceleration after YC 1:03:43 - Testing on Earth 1:05:06 - Motivations for Starcloud 1:06:45 - Data centers on the Moon 1:08:12 - Interacting with AI companies 1:08:18 - What’s next for Starcloud? 1:14:01 - Other uses for Starcloud satellites 1:17:56 - Lunar hotels and space elevators 1:24:28 - Complementary business ideas to Starcloud 1:29:51 - Philip’s competitive twin 1:32:18 - Philip and Mike’s thoughts on YC 1:36:04 - Advice for young entrepreneurs Elon Musk Scott Manley Kyle Hill Hank Green

632nm

46,599 Aufrufe • vor 4 Monaten

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede
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Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede

Janak Dave

339,758 Aufrufe • vor 1 Jahr

Everything the government touches gets more expensive. Everything it leaves alone gets cheaper. Since January 2000, overall prices are up 93%. Wages are up 131%. Now look at what happened sector by sector. Prices that exploded: - Hospital services: +240% - College tuition: +180% - College textbooks: +150% - Medical care: +140% - Childcare: +122% - Housing: +111% Prices that collapsed: - Cellphone service: -45% - Computer software: -70% - Toys: -73% - Televisions: -97% That is not a coincidence. It is a mechanism. Every sector in the first list runs on subsidized demand and somebody else's money. Student loans pay the tuition bill. Insurance and Medicare pay the hospital bill. Subsidy programs pay the childcare bill. When the buyer never sees the real price, the price stops behaving. Every sector in the second list makes you pay directly, against global competition. There is no federal television loan program. So TVs got 97% cheaper over the same 25 years tuition nearly tripled. None of this is new. In 1987, Reagan's education secretary Bill Bennett predicted that every increase in federal student aid would simply be swallowed by tuition hikes. Economists call it the Bennett hypothesis. Four decades of data have called it correct. And yes, new cars sit on the market side at +25%. Someone will bring up the bailout, which is fair. Washington spent about 80 billion dollars on the auto rescue and taxpayers ate roughly 10 billion of it. But a one-time rescue of two companies is not a standing subsidy to every buyer. You still pay for your own car, and Toyota still exists. Even with bailouts, mandates, and tariffs, cars rose 25% while inflation ran 93%. So the next time someone tells you healthcare and college are expensive because of market failure, show them the chart. The market is the part that worked. Data: Bureau of Labor Statistics CPI, Jan 2000 to Dec 2025, via Mark J. Perry, AEI.
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Everything the government touches gets more expensive. Everything it leaves alone gets cheaper. Since January 2000, overall prices are up 93%. Wages are up 131%. Now look at what happened sector by sector. Prices that exploded: - Hospital services: +240% - College tuition: +180% - College textbooks: +150% - Medical care: +140% - Childcare: +122% - Housing: +111% Prices that collapsed: - Cellphone service: -45% - Computer software: -70% - Toys: -73% - Televisions: -97% That is not a coincidence. It is a mechanism. Every sector in the first list runs on subsidized demand and somebody else's money. Student loans pay the tuition bill. Insurance and Medicare pay the hospital bill. Subsidy programs pay the childcare bill. When the buyer never sees the real price, the price stops behaving. Every sector in the second list makes you pay directly, against global competition. There is no federal television loan program. So TVs got 97% cheaper over the same 25 years tuition nearly tripled. None of this is new. In 1987, Reagan's education secretary Bill Bennett predicted that every increase in federal student aid would simply be swallowed by tuition hikes. Economists call it the Bennett hypothesis. Four decades of data have called it correct. And yes, new cars sit on the market side at +25%. Someone will bring up the bailout, which is fair. Washington spent about 80 billion dollars on the auto rescue and taxpayers ate roughly 10 billion of it. But a one-time rescue of two companies is not a standing subsidy to every buyer. You still pay for your own car, and Toyota still exists. Even with bailouts, mandates, and tariffs, cars rose 25% while inflation ran 93%. So the next time someone tells you healthcare and college are expensive because of market failure, show them the chart. The market is the part that worked. Data: Bureau of Labor Statistics CPI, Jan 2000 to Dec 2025, via Mark J. Perry, AEI.

Sovey

54,751 Aufrufe • vor 19 Tagen

first impressions of qwen 3.5 27B dense on a single RTX 3090. 35 tok/s. from 4K all the way to 300K+ context. no speed drop. hermes 4.3 started at 35 and degraded to 15 as context filled. qwen dense holds. MoE held 112 flat. 3x faster but only 3B of 35B active per token. architecture tradeoff. Q4_K_M on 16.7GB. native context 262K. pushed past training limit to 376K before VRAM ceiling on 24GB. tried q8 KV cache at 262K, speed collapsed to 11 tok/s. q4_0 KV is the sweet spot. flash attention mandatory. built in reasoning mode. the model thinks step by step before it answers. full chain of thought surviving Q4 quant. 1,799+ token thinking chains with self correction loops. on a single consumer GPU. gave it one prompt: "build a realtime particle galaxy simulation in one HTML file." 3,340 tokens. 95 seconds. one shot. ran on first load. full reasoning and coding in the video below. optimal config if you want to skip the hours of testing: llama-server -ngl 99 -c 262144 -fa on --cache-type-k q4_0 --cache-type-v q4_0 this is just the warmup. octopus invaders is next: 10 files, 3,400+ lines, zero steering. the prompt hermes quit at 22%. already more impressed than expected. full results coming soon.
2:02

first impressions of qwen 3.5 27B dense on a single RTX 3090. 35 tok/s. from 4K all the way to 300K+ context. no speed drop. hermes 4.3 started at 35 and degraded to 15 as context filled. qwen dense holds. MoE held 112 flat. 3x faster but only 3B of 35B active per token. architecture tradeoff. Q4_K_M on 16.7GB. native context 262K. pushed past training limit to 376K before VRAM ceiling on 24GB. tried q8 KV cache at 262K, speed collapsed to 11 tok/s. q4_0 KV is the sweet spot. flash attention mandatory. built in reasoning mode. the model thinks step by step before it answers. full chain of thought surviving Q4 quant. 1,799+ token thinking chains with self correction loops. on a single consumer GPU. gave it one prompt: "build a realtime particle galaxy simulation in one HTML file." 3,340 tokens. 95 seconds. one shot. ran on first load. full reasoning and coding in the video below. optimal config if you want to skip the hours of testing: llama-server -ngl 99 -c 262144 -fa on --cache-type-k q4_0 --cache-type-v q4_0 this is just the warmup. octopus invaders is next: 10 files, 3,400+ lines, zero steering. the prompt hermes quit at 22%. already more impressed than expected. full results coming soon.

Sudo su

120,788 Aufrufe • vor 5 Monaten