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👣FLIP FLOPS FRIDAY👣 🌟MODELS ENGAGEMENT THREAD🌟 All models post your feet in flip flops! I will retweet🔄& FEATURE models (future threads) WHO post + Like♥️ & RT🔄this POST! Featured models: 🏝️CaribbeanSolez🏝️FetCon 8/6 - 8/9 Tattedbabe72 AustinFootQueen👣👑💕 The Dove’s Nest 💕🕊️FETCON 2026🔥

VIPFeet

15,965 subscribers

35,084 次观看 • 3 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

10 条评论

CaribbeanSolez 的头像

CaribbeanSolez3 年前

@tattedbabe72 @AustinFootQueen @dovely_feet Thanks Babe! Love me some Havanas!

🌸Sweet_N_Salty👣Rach🌸 的头像

🌸Sweet_N_Salty👣Rach🌸3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet Have a sniff 😋

VIPFeet 的头像

VIPFeet3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet

Solefulsassy 的头像

Solefulsassy3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet 💛🧡💛

Tem 的头像

Tem3 年前

💋💋

Fantasy Footjobs 的头像

Fantasy Footjobs3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet Happy extra naughty Flip flop Friday 💕💕🩴 these are my favourite pair 🥰

🍑Princess Buttcrack McGee🍑 的头像

🍑Princess Buttcrack McGee🍑3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet

👣NurseFeetVzla💥Onlyfans NoPPV 的头像

👣NurseFeetVzla💥Onlyfans NoPPV3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet Happy FlipFlops Friday🩴

Stella Stinkyfeet 的头像

Stella Stinkyfeet3 年前

@CaribbeanSolez @tattedbabe72 @AustinFootQueen @dovely_feet

angie 的头像

angie3 年前

GM!!☀️

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🚨🚨 FETISHCON ROSTER REVEAL 1 🚨🚨 NOT ALL FINAL ROSTER, READ FULL POST, VOLUME WARNING!!! With Fetish Con in about 3 months from now, These are some... SOME of the people that I am having in stuido with me this year. Alot of familiar faces, friends of the business, and even some new people to me and you. There will be at least 1-2 more Roster reveals the closer the event gets with my Official final roster. In the meantime, take a look at some of the people that I am working with and Answer this.. Which one are you the most excited to see in studio? Feeturing (in order) - 🎵- GTA 4 Theme song Josie Jo 👑 Petite Princess MissMia👣Fetcon’26)(LA 9/15-9/18) Indica Jane Fetish London Evans Sunnie Soleil ✨Goddess Natasha Ty ✨ Booking for FetCon 2026 The Goddess Ivy🌹 FETCON 2026 LeeVLkinks Lee Von Lux Mars Solo🤍Birthday Aug 22🤍Olympia 9/24-27 Zura Moonleaf .:SummerRaez:. ☀️🖤 #tickle #ticklish #tickled #tickles #TICKLING #Fetishcon #Content #ContentCreation #DTG #feet #foot

🚨🚨 FETISHCON ROSTER REVEAL 1 🚨🚨 NOT ALL FINAL ROSTER, READ FULL POST, VOLUME WARNING!!! With Fetish Con in about 3 months from now, These are some... SOME of the people that I am having in stuido with me this year. Alot of familiar faces, friends of the business, and even some new people to me and you. There will be at least 1-2 more Roster reveals the closer the event gets with my Official final roster. In the meantime, take a look at some of the people that I am working with and Answer this.. Which one are you the most excited to see in studio? Feeturing (in order) - 🎵- GTA 4 Theme song Josie Jo 👑 Petite Princess MissMia👣Fetcon’26)(LA 9/15-9/18) Indica Jane Fetish London Evans Sunnie Soleil ✨Goddess Natasha Ty ✨ Booking for FetCon 2026 The Goddess Ivy🌹 FETCON 2026 LeeVLkinks Lee Von Lux Mars Solo🤍Birthday Aug 22🤍Olympia 9/24-27 Zura Moonleaf .:SummerRaez:. ☀️🖤 #tickle #ticklish #tickled #tickles #TICKLING #Fetishcon #Content #ContentCreation #DTG #feet #foot

Tickle devil (Footrace For The Fallen)

37,779 次观看 • 3 个月前

X's Recommendation Algorithm Analysis ===================================== Used Grok Code Fast to get a quick breakdown of X's recommendation system. What Makes a post Go Viral =========================== tldr: Engagement prediction trumps everything. Post content that generates interactions. Based on the actual algorithm code, posts that rank highest typically have: + High predicted engagement scores (ML models predict likes/reposts/replies) + Strong personalization match (SimClusters similarity to user interests) + Social graph relevance (RealGraph connections to user's network) + Media content (images/videos get engagement multipliers) + Author credibility (follower count, verification, tweepcred score) + Content quality signals (passes spam/NSFW/quality filters) + Timely relevance (freshness factor, trending topics) + Conversation potential (high reply prediction scores) The algorithm uses machine learning models to predict engagement, not simple weighted formulas. Success is measured by actual user interactions, creating a feedback loop that continuously improves ranking predictions. How the Algorithm Actually Works =============================== 1. Candidate Generation (9 sources): - Earlybird (in-network posts) ~50% - UTEG (out-of-network recommendations) - postMixer, Lists, Communities, Content Exploration - Static, Cached, Backfill sources 2. Feature Hydration (~6000 features per post): - User features (interests, behavior, demographics) - post features (text, media, metadata, engagement) - Graph features (SimClusters, RealGraph, social connections) - Real-time signals (current engagement, trending status) 3. Scoring Pipeline (4 models): - Model Scoring (NAVI heavy ranker) - Reranking Pipeline - Heuristic Scoring - Low Signal Scoring 4. Filtering (24 total filters): - 10 Global Filters (age < 48h, deduplication, location, etc.) - 14 Post-Score Filters (Grok safety, language, video duration, etc.) 5. Final Selection & Mixing: - Sort by final scores - Apply diversity rules - Mix with ads, who-to-follow, prompts - Generate timeline Key Prediction Models ==================== The algorithm predicts these engagement types: • PredictedFavoriteScore (likes) • PredictedRetweetScore (reposts) • PredictedReplyScore (replies) • PredictedGoodClickScore (meaningful clicks) • PredictedVideoQualityViewScore (video engagement) • PredictedBookmarkScore (saves) • PredictedShareScore (external shares) • PredictedDwellScore (time spent viewing) • PredictedNegativeFeedbackScore (hides/blocks) Weight System Reality ==================== IMPORTANT: The algorithm does NOT use fixed percentage weights like: ❌ Like Prediction (35%), Repost (28%), etc. ACTUAL SYSTEM: ✅ Weights are learned parameters from ML training ✅ Default values in code are 0.0 (overridden by feature flags) ✅ Weights are personalized per user and constantly A/B tested ✅ Different content types (video vs text) get different treatment ✅ Weights change based on real-time context and user state Example scoring process: 1. ML models predict engagement probabilities 2. Feature flags provide current weight multipliers 3. Personalization adjusts weights for individual user 4. Real-time context modifies final scores 5. Business rules apply quality gates and diversity What Actually Drives Viral Content ================================== Based on code analysis, viral posts typically: 1. Generate High Engagement Predictions: - Models predict high like/repost/reply probability - Content resonates with multiple user communities - Strong early engagement signals 2. Pass All Quality Gates: - Survive 24 different filter stages - Meet safety standards (not spam/NSFW/violent) - Author has good credibility signals 3. Achieve Personalization at Scale: - Match interests across diverse user segments - Trigger SimClusters similarity for many users - Connect through RealGraph social relationships 4. Optimize for Platform Mechanics: - Include media (images/videos perform better) - Post during high-activity periods - Use formats that encourage replies/reposts Key Takeaways ============= ✅ Engagement prediction is everything - the algorithm optimizes for user interactions ✅ Personalization is sophisticated - uses ML embeddings, not simple keyword matching ✅ Quality filtering is extensive - 24 stages prevent low-quality content ✅ Weights are dynamic - constantly optimized through ML and A/B testing ✅ Scale matters - system processes billions of posts daily with <50ms latenc Transparency exists - this analysis is possible because X open-sourced the algorithm The system is designed to surface content users will engage with, creating a feedback loop that rewards creators who understand their audience and produce engaging content. Bottom line: Create content that generates genuine engagement from your target audience. The algorithm will learn and amplify what works.

X's Recommendation Algorithm Analysis ===================================== Used Grok Code Fast to get a quick breakdown of X's recommendation system. What Makes a post Go Viral =========================== tldr: Engagement prediction trumps everything. Post content that generates interactions. Based on the actual algorithm code, posts that rank highest typically have: + High predicted engagement scores (ML models predict likes/reposts/replies) + Strong personalization match (SimClusters similarity to user interests) + Social graph relevance (RealGraph connections to user's network) + Media content (images/videos get engagement multipliers) + Author credibility (follower count, verification, tweepcred score) + Content quality signals (passes spam/NSFW/quality filters) + Timely relevance (freshness factor, trending topics) + Conversation potential (high reply prediction scores) The algorithm uses machine learning models to predict engagement, not simple weighted formulas. Success is measured by actual user interactions, creating a feedback loop that continuously improves ranking predictions. How the Algorithm Actually Works =============================== 1. Candidate Generation (9 sources): - Earlybird (in-network posts) ~50% - UTEG (out-of-network recommendations) - postMixer, Lists, Communities, Content Exploration - Static, Cached, Backfill sources 2. Feature Hydration (~6000 features per post): - User features (interests, behavior, demographics) - post features (text, media, metadata, engagement) - Graph features (SimClusters, RealGraph, social connections) - Real-time signals (current engagement, trending status) 3. Scoring Pipeline (4 models): - Model Scoring (NAVI heavy ranker) - Reranking Pipeline - Heuristic Scoring - Low Signal Scoring 4. Filtering (24 total filters): - 10 Global Filters (age < 48h, deduplication, location, etc.) - 14 Post-Score Filters (Grok safety, language, video duration, etc.) 5. Final Selection & Mixing: - Sort by final scores - Apply diversity rules - Mix with ads, who-to-follow, prompts - Generate timeline Key Prediction Models ==================== The algorithm predicts these engagement types: • PredictedFavoriteScore (likes) • PredictedRetweetScore (reposts) • PredictedReplyScore (replies) • PredictedGoodClickScore (meaningful clicks) • PredictedVideoQualityViewScore (video engagement) • PredictedBookmarkScore (saves) • PredictedShareScore (external shares) • PredictedDwellScore (time spent viewing) • PredictedNegativeFeedbackScore (hides/blocks) Weight System Reality ==================== IMPORTANT: The algorithm does NOT use fixed percentage weights like: ❌ Like Prediction (35%), Repost (28%), etc. ACTUAL SYSTEM: ✅ Weights are learned parameters from ML training ✅ Default values in code are 0.0 (overridden by feature flags) ✅ Weights are personalized per user and constantly A/B tested ✅ Different content types (video vs text) get different treatment ✅ Weights change based on real-time context and user state Example scoring process: 1. ML models predict engagement probabilities 2. Feature flags provide current weight multipliers 3. Personalization adjusts weights for individual user 4. Real-time context modifies final scores 5. Business rules apply quality gates and diversity What Actually Drives Viral Content ================================== Based on code analysis, viral posts typically: 1. Generate High Engagement Predictions: - Models predict high like/repost/reply probability - Content resonates with multiple user communities - Strong early engagement signals 2. Pass All Quality Gates: - Survive 24 different filter stages - Meet safety standards (not spam/NSFW/violent) - Author has good credibility signals 3. Achieve Personalization at Scale: - Match interests across diverse user segments - Trigger SimClusters similarity for many users - Connect through RealGraph social relationships 4. Optimize for Platform Mechanics: - Include media (images/videos perform better) - Post during high-activity periods - Use formats that encourage replies/reposts Key Takeaways ============= ✅ Engagement prediction is everything - the algorithm optimizes for user interactions ✅ Personalization is sophisticated - uses ML embeddings, not simple keyword matching ✅ Quality filtering is extensive - 24 stages prevent low-quality content ✅ Weights are dynamic - constantly optimized through ML and A/B testing ✅ Scale matters - system processes billions of posts daily with <50ms latenc Transparency exists - this analysis is possible because X open-sourced the algorithm The system is designed to surface content users will engage with, creating a feedback loop that rewards creators who understand their audience and produce engaging content. Bottom line: Create content that generates genuine engagement from your target audience. The algorithm will learn and amplify what works.

tetsuo

308,672 次观看 • 11 个月前

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Introducing Workshop: cloud + on-device agentic AI. And to celebrate, we're giving away $250k in Google Gemini AI credits. (details below). The future of AI work is neither cloud-based nor local. It's both. In Workshop Cloud, you can use agents powered by frontier models like Claude and/or open source models like Z.ai's GLM-5 to build internal tools, dashboards, and AI web apps. Or, breeze through tasks like managing your Google and Meta Ads. In Workshop Desktop, you can do all the same right on your computer, plus make desktop apps, mobile apps, and 3D creations. Our favorite part? You can power the full agent experience with local models like Qwen 3.5 family on your computer. Fully offline. 2026 is the year in which local models for agentic tasks will become viable for mainstream use. But the setup for tools like OpenClaw is like setting up Linux from scratch on your computer. Workshop Desktop is one-click to install on Windows, Mac, and Linux. It recommends which open source model you should use for your hardware and lets you download and run it right in the app. And its agent harness allows you to chat, create websites, build personal utilities, and analyze data. 100% offline. Or multitask with AI models in the cloud while running other agent threads locally. Start in Workshop Cloud when you want flexibility and speed. Download your project and continue in Workshop Desktop when you want local files, privacy, and/or better performance on large code bases. Publish from either. The agent tooling space is maturing and discerning users have come to expect a lot from their tools. We've packed Workshop with features to help you 10x your productivity. - Native support for skills - Autocompaction for seamless context management - Built-in AI for your apps - Dozens of connectors, like Google Drive, Big Query, and Supabase - dbt integration to ground your dashboards in your semantic layer - Native Github integration - Private app deployment - ... and more (+ we're shipping super fast) To access the free credit offer, RT this post and reply with "Workshop". Make sure you are following us so we can DM you the instructions to redeem. - First 100 to RT + comment get $500 in credits. - Everyone else gets up to $250 And thanks to our partners Modal, Google Gemini, and Z.ai!

Workshop AI

29,099 次观看 • 5 个月前

Are we hurtling toward a future where AI can do everything humans can? Edwin Chen (echen) believes we might be. He’s the CEO of Surge AI, one of the largest providers of expert data for frontier labs. Surge passed over $1 billion in revenue without raising any outside capital, and that gives Edwin a unique perspective on how quickly AI progress is accelerating. I’m on the record arguing that AI automation actually creates more human work. I also believe that even though AI progress is accelerating exponentially, we’re much farther away from AI replacing humans than it might seem. That’s why I had Edwin on Every 🪨’s AI & I. We batted around different visions of the future, and discussed whether humanity will retain its unique place in the universe, and what that might be. We get into: • If Chen’s version of the future materializes, he’s worried it’ll make people stop trying. One answer comes from a short story by science fiction writer Ted Chiang: Behave as if your decisions matter, even when you know they don’t. • AI may soon be able to take a nebulous goal like “win a Fields Medal” and execute. What it can’t do, I argue, is set its own goals—LLMs have no intrinsic motivation, no drive to explore, no ability to just change their mind. • A model optimized for engagement doesn’t provide the most valuable user experience. Edwin spent 20 rounds polishing a pointless email with one model before Claude told him to just send it. • Why AI is still bad at writing: models learn to hack the metrics they're trained on. Edwin's Hemingway Bench found models outputting a metaphor in every single sentence, an overindexxing that makes for a terrible reading experience. This is a must-watch for anyone interested in where we fit as models get more capable. Watch below! Timestamps 1. Introduction: 00:00:54 2. Surge as a "school for AGI": 00:01:49 3. What AI's capacity for novel mathematics says about human achievement: 00:04:46 4. Motivation in an era when AI can do everything: 00:07:29 5. The trap of optimizing AI models for engagement: 00:14:34 6. Training using datasets versus training using environments: 00:29:34 7. The value of personal data: 00:35:09 8. Why models are bad at writing: 00:39:40 9. Chen's AGI timeline: 00:42:00

Are we hurtling toward a future where AI can do everything humans can? Edwin Chen (echen) believes we might be. He’s the CEO of Surge AI, one of the largest providers of expert data for frontier labs. Surge passed over $1 billion in revenue without raising any outside capital, and that gives Edwin a unique perspective on how quickly AI progress is accelerating. I’m on the record arguing that AI automation actually creates more human work. I also believe that even though AI progress is accelerating exponentially, we’re much farther away from AI replacing humans than it might seem. That’s why I had Edwin on Every 🪨’s AI & I. We batted around different visions of the future, and discussed whether humanity will retain its unique place in the universe, and what that might be. We get into: • If Chen’s version of the future materializes, he’s worried it’ll make people stop trying. One answer comes from a short story by science fiction writer Ted Chiang: Behave as if your decisions matter, even when you know they don’t. • AI may soon be able to take a nebulous goal like “win a Fields Medal” and execute. What it can’t do, I argue, is set its own goals—LLMs have no intrinsic motivation, no drive to explore, no ability to just change their mind. • A model optimized for engagement doesn’t provide the most valuable user experience. Edwin spent 20 rounds polishing a pointless email with one model before Claude told him to just send it. • Why AI is still bad at writing: models learn to hack the metrics they're trained on. Edwin's Hemingway Bench found models outputting a metaphor in every single sentence, an overindexxing that makes for a terrible reading experience. This is a must-watch for anyone interested in where we fit as models get more capable. Watch below! Timestamps 1. Introduction: 00:00:54 2. Surge as a "school for AGI": 00:01:49 3. What AI's capacity for novel mathematics says about human achievement: 00:04:46 4. Motivation in an era when AI can do everything: 00:07:29 5. The trap of optimizing AI models for engagement: 00:14:34 6. Training using datasets versus training using environments: 00:29:34 7. The value of personal data: 00:35:09 8. Why models are bad at writing: 00:39:40 9. Chen's AGI timeline: 00:42:00

Dan Shipper 📧

15,250 次观看 • 2 个月前

I'd like to introduce you to Creator Buddy, the greatest AI content tool ever made It's 8 AI tools in one to help you come up with endless bangers, explode your engagement, and grow your network faster than you can imagine It's going to change the way you use X forever Over the last 3 years I've grown from 0 to 260,000 followers on X I did this completely organically. No bots or retweet groups. All through banger content and reply guying I took every system and strategy I used to get to 260k and 6 figures of revenue on X and put them in this tool Then I powered it all by AI so it's 10x smarter and easier to use This isn't your mother's AI either. This is an AI trained on millions of X posts. Trained on what works and what doesn't This AI will create better content than you've ever seen in your entire life I'm not kidding. BANGERS only It's also everything I wanted Grok to be but never was It's an AI that knows your post history so you can ask it questions like: • What topics get me the most engagement? • What hooks get me likes? • What time of day does my audience engage with me? • What calls to actions get me clicks? • I have no idea what to write about. Based on my past successful posts give me a template It will seriously change the way you create content forever. No more writer's block. No more flopped posts. No more posting into the void. And that's not even 10% of the functionality: • It also has an AI trained on the entire X algorithm so you know if your post will be algo friendly BEFORE you post • A reply guy tool that will help you be the LOUDEST reply guy on X • An AI account researching tool that is basically your own spy on X • A chrome extension that allows you to save posts directly from X and repurpose into your own voice with AI • A brain dumping tool that allows you to take whatever is on your mind and INSTANTLY turn it into content This is my life's work. I truly believe this will take your X game to the next level If you're taking X seriously, you HAVE to get Creator Buddy I truly believe moving forward there will be 2 groups of people on X: those with Creator Buddy and those without. Trust me, you want to be in the group with it I've worked endlessly the last 7 months on building this by myself. No help whatsoever Every line of code, every video, EVERYTHING was done by me. 0 help. I sacrificed everything for this tool Hit the link below and start working with your own personalized AI content coach now! And if I've ever provided you even a tiny bit of value over the last 3 years, all I ask for is a simple like and RT on this post! With love, Alex (link to Creator Buddy on next post!!!)

I'd like to introduce you to Creator Buddy, the greatest AI content tool ever made It's 8 AI tools in one to help you come up with endless bangers, explode your engagement, and grow your network faster than you can imagine It's going to change the way you use X forever Over the last 3 years I've grown from 0 to 260,000 followers on X I did this completely organically. No bots or retweet groups. All through banger content and reply guying I took every system and strategy I used to get to 260k and 6 figures of revenue on X and put them in this tool Then I powered it all by AI so it's 10x smarter and easier to use This isn't your mother's AI either. This is an AI trained on millions of X posts. Trained on what works and what doesn't This AI will create better content than you've ever seen in your entire life I'm not kidding. BANGERS only It's also everything I wanted Grok to be but never was It's an AI that knows your post history so you can ask it questions like: • What topics get me the most engagement? • What hooks get me likes? • What time of day does my audience engage with me? • What calls to actions get me clicks? • I have no idea what to write about. Based on my past successful posts give me a template It will seriously change the way you create content forever. No more writer's block. No more flopped posts. No more posting into the void. And that's not even 10% of the functionality: • It also has an AI trained on the entire X algorithm so you know if your post will be algo friendly BEFORE you post • A reply guy tool that will help you be the LOUDEST reply guy on X • An AI account researching tool that is basically your own spy on X • A chrome extension that allows you to save posts directly from X and repurpose into your own voice with AI • A brain dumping tool that allows you to take whatever is on your mind and INSTANTLY turn it into content This is my life's work. I truly believe this will take your X game to the next level If you're taking X seriously, you HAVE to get Creator Buddy I truly believe moving forward there will be 2 groups of people on X: those with Creator Buddy and those without. Trust me, you want to be in the group with it I've worked endlessly the last 7 months on building this by myself. No help whatsoever Every line of code, every video, EVERYTHING was done by me. 0 help. I sacrificed everything for this tool Hit the link below and start working with your own personalized AI content coach now! And if I've ever provided you even a tiny bit of value over the last 3 years, all I ask for is a simple like and RT on this post! With love, Alex (link to Creator Buddy on next post!!!)

Alex Finn

1,582,651 次观看 • 1 年前

My ClawdBot fixed its own code today. Then it found a guy who turned $95 into $1,669 betting on whether JD Vance would say the word Ass. I am not joking. I asked Clawdbot to find quiet snipers on Polymarket. Not election gamblers. Not crypto degens. People who found broken corners of the market. It came back with this: I opened the profile expecting meme bets. What I found made me sit back in my chair. This guy does not predict elections. Does not analyze Fed policy. Does not care about GDP. His full-time job? Betting on words. Mention Markets. Will Trump say China in his speech? Will Biden say democracy? Will Vance drop an f-bomb? He bet $95 that Vance would say Ass at a January 22 conference. Vance said it. $95 to $1,669. 17x. I scrolled through his history. $23,000 per month. 80% winrate. All from predicting which words politicians cannot resist saying. Here is what hit me: This is like betting on whether your friend who says 5 minutes will actually show up in 30. You are not predicting the future. You are predicting pattern. Politicians love certain words. They cannot help themselves. He just waits. While we are building complex models to predict global trends, this guy profits from the fact that JD Vance cannot keep it PG. I looked at my Clawdbot, which just connected three APIs by itself. Then at this trader, who found the dumbest edge I have ever seen. Same principle: do not work harder. Find where the game is broken. Real question is this genius or is this exactly the kind of edge that disappears the moment I post about it? Tell me I am wrong.

Blaze

80,151 次观看 • 7 个月前

SOMEONE FROM THE ANTHROPIC TEAM LEAKED THEIR OBSIDIAN SETUP. 8 MILLION PEOPLE SAW HOW HE ACTUALLY USES CLAUDE the funniest part? all of this information was sitting in claude's documentation from day one. nobody read it one guy did, packed it into a 9-step guide and posted it. and it broke the internet. 4,100 likes, 800 retweets, then china picked it up and 8 million views want to know what's in it? one file. called CLAUDE.md. it holds everything about you: how you think, what you're working on, where you get stuck, even how you want the ai to talk to you. claude reads it first every single session one file changed everything. because now ai doesn't open with "how can i help?" it already knows. it remembers your projects, sees your goals, catches moments where you're contradicting yourself people spent years searching for the perfect prompt. the right temperature. the magic formula. and the answer turned out to be not how you ask ai. but what ai knows about you before you even open your mouth then the guy went deeper. taught claude to work on a schedule. every morning at 7am the ai walks through all notes on its own, finds new stuff, links it, cleans what's stale. no command. no reminder and all of this runs on obsidian. free app. text files on your drive. no cloud, no lock-in. switch models tomorrow and the folder keeps working the most liked comment under the original post: "this is the difference between using ai and building a system. most people won't realize it until they waste hundreds of hours repeating themselves" hundreds of hours. you've already spent some of them full guide in the video. i break down finds like this every day - follow so you don't miss the next one

SOMEONE FROM THE ANTHROPIC TEAM LEAKED THEIR OBSIDIAN SETUP. 8 MILLION PEOPLE SAW HOW HE ACTUALLY USES CLAUDE the funniest part? all of this information was sitting in claude's documentation from day one. nobody read it one guy did, packed it into a 9-step guide and posted it. and it broke the internet. 4,100 likes, 800 retweets, then china picked it up and 8 million views want to know what's in it? one file. called CLAUDE.md. it holds everything about you: how you think, what you're working on, where you get stuck, even how you want the ai to talk to you. claude reads it first every single session one file changed everything. because now ai doesn't open with "how can i help?" it already knows. it remembers your projects, sees your goals, catches moments where you're contradicting yourself people spent years searching for the perfect prompt. the right temperature. the magic formula. and the answer turned out to be not how you ask ai. but what ai knows about you before you even open your mouth then the guy went deeper. taught claude to work on a schedule. every morning at 7am the ai walks through all notes on its own, finds new stuff, links it, cleans what's stale. no command. no reminder and all of this runs on obsidian. free app. text files on your drive. no cloud, no lock-in. switch models tomorrow and the folder keeps working the most liked comment under the original post: "this is the difference between using ai and building a system. most people won't realize it until they waste hundreds of hours repeating themselves" hundreds of hours. you've already spent some of them full guide in the video. i break down finds like this every day - follow so you don't miss the next one

kai

174,650 次观看 • 1 个月前

Raphael Townshend, Stanford AI PhD and founder of Atomic AI (Forbes 30 Under 30): ""Wall Street will pay you $500K a year to build these models. I'd rather teach them to you for free." this free stanford lecture holds the entire "77% win rate, pure math" random forest the 2026 quant threads sell you. and the guy teaching it didn't take the wall street money either, townshend went on to found an ai drug-discovery company and land forbes 30 under 30. at the board he builds it from scratch: one decision tree overfits, so you grow hundreds on random subsets of the data and features and average them. the errors cancel, the signal survives. that's the whole "100 ai agents auditing the market" idea, minus the marketing. the √N feature rule, the out-of-bag error, the probability output, all of it is standard ensemble learning, taught free by stanford for years. random forests came out of leo breiman's public paper in 2001. the thread didn't discover it. it renamed it. and here's the honest part the win rate hides. a model that scored 77% on past data is describing the past, not promising the future. ensembles cut variance, they don't turn a weak edge into a real one, and markets shift under the model in ways the training set never warned about. the lecture is free. knowing whether your 77% survives out of sample and on live capital is exactly the part the post skips."

Raphael Townshend, Stanford AI PhD and founder of Atomic AI (Forbes 30 Under 30): ""Wall Street will pay you $500K a year to build these models. I'd rather teach them to you for free." this free stanford lecture holds the entire "77% win rate, pure math" random forest the 2026 quant threads sell you. and the guy teaching it didn't take the wall street money either, townshend went on to found an ai drug-discovery company and land forbes 30 under 30. at the board he builds it from scratch: one decision tree overfits, so you grow hundreds on random subsets of the data and features and average them. the errors cancel, the signal survives. that's the whole "100 ai agents auditing the market" idea, minus the marketing. the √N feature rule, the out-of-bag error, the probability output, all of it is standard ensemble learning, taught free by stanford for years. random forests came out of leo breiman's public paper in 2001. the thread didn't discover it. it renamed it. and here's the honest part the win rate hides. a model that scored 77% on past data is describing the past, not promising the future. ensembles cut variance, they don't turn a weak edge into a real one, and markets shift under the model in ways the training set never warned about. the lecture is free. knowing whether your 77% survives out of sample and on live capital is exactly the part the post skips."

Rossst.03

202,639 次观看 • 2 个月前

Sensitive content

Models/Artists: EXCITING AND MAJOR NEW ANNOUNCEMENTS 1. You can now upload any zip or pdf file to sell in your store or include in a post, up to 2gig. 2. The Media Center now has a “Files” category where you can see the files you have uploaded, create a post or a store item. 3. There are new Art categories you can choose when posting a product for sale, and that users can use to search the store. These will evolve over time and we know they don’t cover every use case yet. 4. There is a new Artists page that lists the artists on the site. There aren’t many yet, but this will obviously grow a lot. 5. The verification process now has better instructions for artists vs. video creators. 6. When you sign up, you choose whether you are an artist or on-screen talent. If you already have your account and need to update this, reach out to customer service and they can do it for you. If your current artwork fits within our TOS, go ahead and start selling your works now on JFF. If you aren’t sure, we hope to have clarity around our TOS this week, so please be patient as we get that done. Folks, this is Phase I, and we’ve done this all since Friday to try to help the NSFW artists in our community. There will be more phases as we see what is needed. We are doing our best, so if something you want isn’t there yet, send a note to customer service and help us build this area out on the site over time. We hope Phase I meets a good percentage of your immediate needs to start earning money. But we also know that our site, like ALL adult sites, will have content restrictions that will exclude some of you. That’s not our choosing or our doing. But if we want to keep our credit card processors and not deplatform everyone (like other sites just did) then we need to play by their rules. That sucks and we are sorry about that. We use adult processors, so we have a lot fewer restrictions than the other sites that are deplatforming ALL NSFW content. For those of you new to the site, we have made a VIDEO TUTORIAL (thank you, Alice Skary) on how to upload files, post them for sale, set your store as the default view of your profile, etc. You can watch that here:

JustForFans

460,797 次观看 • 2 年前

Every e-commerce store needs product photos... so I automated the entire process with AI and n8n. This end-to-end workflow transforms a single iPhone photo into 8-10 professional product shots, handling everything from model generation to final delivery. I just dropped a detailed breakdown covering: → How the AI analyzes products and generates perfect models → How meta-prompting creates dynamic scenes for the model and article of clothing → How the system maintains consistency across all generated images → How you can customize this for any product category The magic happens in the prompting strategy. Instead of writing static prompts, I use AI to analyze the uploaded product and generate contextually perfect scenes. Upload athletic wear? It creates gym and outdoor running scenarios. Upload formal wear? It generates office and event settings. Upload winter gear? Mountain lodges and snowy landscapes. This system has already helped several e-commerce clients slash their photography costs by 90% while dramatically reducing time-to-market. Just think... there are over 30 million e-commerce stores out there, and they ALL need high quality product photos!!! I'm not saying this will replace all professional photography, but for product testing, rapid launches, and catalog building, it's a game-changer. (Especially when you can generate variations while your competitors are still booking photoshoots) Want the exact n8n template and prompts powering this system? 1. Like & RT this post 2. Follow me for more AI automation breakdowns 3. Comment "CATALOG" below I'll send you the complete n8n JSON for free, plus access to my YouTube tutorial showing the step-by-step build.

Every e-commerce store needs product photos... so I automated the entire process with AI and n8n. This end-to-end workflow transforms a single iPhone photo into 8-10 professional product shots, handling everything from model generation to final delivery. I just dropped a detailed breakdown covering: → How the AI analyzes products and generates perfect models → How meta-prompting creates dynamic scenes for the model and article of clothing → How the system maintains consistency across all generated images → How you can customize this for any product category The magic happens in the prompting strategy. Instead of writing static prompts, I use AI to analyze the uploaded product and generate contextually perfect scenes. Upload athletic wear? It creates gym and outdoor running scenarios. Upload formal wear? It generates office and event settings. Upload winter gear? Mountain lodges and snowy landscapes. This system has already helped several e-commerce clients slash their photography costs by 90% while dramatically reducing time-to-market. Just think... there are over 30 million e-commerce stores out there, and they ALL need high quality product photos!!! I'm not saying this will replace all professional photography, but for product testing, rapid launches, and catalog building, it's a game-changer. (Especially when you can generate variations while your competitors are still booking photoshoots) Want the exact n8n template and prompts powering this system? 1. Like & RT this post 2. Follow me for more AI automation breakdowns 3. Comment "CATALOG" below I'll send you the complete n8n JSON for free, plus access to my YouTube tutorial showing the step-by-step build.

David Roberts

89,091 次观看 • 11 个月前

#JasmineCrockett responds to the foolishness. I’m delighted that she pointed out that some of you are being distracted from the truth and lack focus on the right energy to defeat #KenPaxton in this #Texas #Senate race! On March 4, 2026 at 8:42am she said this in a twitter post” “This morning I called James and congratulated him on becoming the Senate nominee. Texas is primed to turn blue and we must remain united because this is bigger than any one person. This is about the future of all 30 million Texans and getting America back on track. With the primary behind us, Democrats must rally around our nominees and win. I'm committed to doing my part and will continue working to elect democrats up and down the ballot”. It sounds like a strong endorsement and complete support to me! If you know Jasmine Crockett, you’ll know that she’s always worked hard for and with down-ballot candidates, and that’s no different today. She’ll continue to help down-ballot candidates, just like #Beto did when he ran for Senate and helped flip multiple seats! Guess what? Helping down-ballot candidates will also help top-ballot candidates! Lastly watch until the end and learn about ‘flood the zone’ because some of yall fell for the banana in the tailpipe! Instead of focusing on Jasmine, work to convince the 92% of black voters in Texas who voted for her to support #JamesTalarico, because like Jasmine said “Texas is primed to turn blue and we must remain united because this is bigger than any one person.” Thank you Jasmine you nothing to prove to anyone and Gods not through with you yet! KEEP RUNNING SIS!!! To James Talarico Team Talarico hope yall are listening!

#JasmineCrockett responds to the foolishness. I’m delighted that she pointed out that some of you are being distracted from the truth and lack focus on the right energy to defeat #KenPaxton in this #Texas #Senate race! On March 4, 2026 at 8:42am she said this in a twitter post” “This morning I called James and congratulated him on becoming the Senate nominee. Texas is primed to turn blue and we must remain united because this is bigger than any one person. This is about the future of all 30 million Texans and getting America back on track. With the primary behind us, Democrats must rally around our nominees and win. I'm committed to doing my part and will continue working to elect democrats up and down the ballot”. It sounds like a strong endorsement and complete support to me! If you know Jasmine Crockett, you’ll know that she’s always worked hard for and with down-ballot candidates, and that’s no different today. She’ll continue to help down-ballot candidates, just like #Beto did when he ran for Senate and helped flip multiple seats! Guess what? Helping down-ballot candidates will also help top-ballot candidates! Lastly watch until the end and learn about ‘flood the zone’ because some of yall fell for the banana in the tailpipe! Instead of focusing on Jasmine, work to convince the 92% of black voters in Texas who voted for her to support #JamesTalarico, because like Jasmine said “Texas is primed to turn blue and we must remain united because this is bigger than any one person.” Thank you Jasmine you nothing to prove to anyone and Gods not through with you yet! KEEP RUNNING SIS!!! To James Talarico Team Talarico hope yall are listening!

BMB Empower Network

25,033 次观看 • 2 个月前

U-Net by hand ✍️ ~ 17 steps walkthrough below I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked! It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL. Now it is the backbone of diffusion models, which you see in almost all modern image generation models. I drew the network as a C so the matrix multiplication flows naturally down. Tilt your head to the right and it is a U again. 🤣 Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself. = 1. Given = An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use. = 2. Convolution 1 = Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map. = 3. Find the maxima = We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down. = 4. Max pool 1 = Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good. = 5. Convolution 2 = We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now. = 6. Find the maxima again = Same move as step 3, on the blue map. = 7. Max pool 2 = Eight columns become four. = 8. The bottleneck = Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept. = 9. Spread it out = We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them. = 10. Transposed convolution 1 = Let us fill those gaps by convolving over the spread-out grid. Four columns become eight. = 11. The first skip = We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away. = 12. Convolution with the skip = Let us convolve the upsampled features together with the copied ones. = 13. Spread it out again = Same as step 9, one level up. = 14. Transposed convolution 2 = Eight columns become sixteen, back to the width we started at. = 15. The second skip = The encoder's first feature map comes across, the one made before any pooling happened. = 16. Convolution and ReLU = We convolve, then cross out every negative and set it to zero. = 17. Output convolution = Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with. The outputs: R' = [3, 0, 7, 0, 7, 0, 17, 0, 3, 0, 9, 0, 2, 0, 6, 0] G' = [1, 20, 1, 10, 1, 12, 1, 19, 2, 5, 1, 11, 1, 3, 1, 7] B' = [4, 20, 8, 10, 8, 12, 18, 19, 5, 5, 10, 11, 3, 3, 7, 7] Congrats! You just calculated a U-Net by hand. 💾 Save this post!

U-Net by hand ✍️ ~ 17 steps walkthrough below I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked! It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL. Now it is the backbone of diffusion models, which you see in almost all modern image generation models. I drew the network as a C so the matrix multiplication flows naturally down. Tilt your head to the right and it is a U again. 🤣 Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself. = 1. Given = An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use. = 2. Convolution 1 = Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map. = 3. Find the maxima = We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down. = 4. Max pool 1 = Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good. = 5. Convolution 2 = We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now. = 6. Find the maxima again = Same move as step 3, on the blue map. = 7. Max pool 2 = Eight columns become four. = 8. The bottleneck = Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept. = 9. Spread it out = We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them. = 10. Transposed convolution 1 = Let us fill those gaps by convolving over the spread-out grid. Four columns become eight. = 11. The first skip = We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away. = 12. Convolution with the skip = Let us convolve the upsampled features together with the copied ones. = 13. Spread it out again = Same as step 9, one level up. = 14. Transposed convolution 2 = Eight columns become sixteen, back to the width we started at. = 15. The second skip = The encoder's first feature map comes across, the one made before any pooling happened. = 16. Convolution and ReLU = We convolve, then cross out every negative and set it to zero. = 17. Output convolution = Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with. The outputs: R' = [3, 0, 7, 0, 7, 0, 17, 0, 3, 0, 9, 0, 2, 0, 6, 0] G' = [1, 20, 1, 10, 1, 12, 1, 19, 2, 5, 1, 11, 1, 3, 1, 7] B' = [4, 20, 8, 10, 8, 12, 18, 19, 5, 5, 10, 11, 3, 3, 7, 7] Congrats! You just calculated a U-Net by hand. 💾 Save this post!

Tom Yeh

17,510 次观看 • 1 个月前

DOUBLE CELEBRATION ALERT! 👑🎉 We’re not just celebrating a birthday we’re celebrating a KIMPAU MILESTONE! 💛💙 In honour of our one and only KWEEN SHAZZA — founding admin, fandom icon, and forever Shazzing, Lablab — AND the upcoming teleserye “THE ALIBI” starring Kim Chiu and Paulo Avelino… WE PRESENT: THE ALIBI VIDEO CHALLENGE! 🔥 It’s your time to shine, KimPau fam! 🎬 Create your most jaw-dropping, heart-stopping, or laugh-out-loud concept video inspired by The Alibi! Will it be a twist? A teaser? A wild theory? A full-blown trailer? ✨ We want to see YOUR vision! 💡💕 📣 Mechanics: ✅ Concept is king — we’re looking for the BEST idea! ✅ Post publicly on FB, IG, TikTok, or X ✅ Use the hashtags: #TheAlibiChallenge #KimPauInternational #KweenShazzaBday ✅ Most likes + creativity = winner! ✅Tag KimPau International and call in the likes army! 💥 🎁 PRIZES THAT SLAY: 🥇 1st: ₱ 4,000 🥈 2nd: ₱3,000 🥉 3rd: ₱1,500 ✨ Consolation Prize – ₱500 each (2 winners) 📌 Submission of Entries: Aug 25, 2025 | 8:00 AM – 5:00 PM only 📌 Likes Count Until: Aug 26, 2025 | 7:00 PM 📌 Winners Drop: Aug 27, 2025 | 9:30 PM Let’s honour Kween Shazza’s birthday with a flood of kilig and creativity — and welcome KimPau’s teleserye era with a fandom explosion like no other! 🌟 Lights. Camera. ALIBI. The stage is yours, bestie. 🎥💥

DOUBLE CELEBRATION ALERT! 👑🎉 We’re not just celebrating a birthday we’re celebrating a KIMPAU MILESTONE! 💛💙 In honour of our one and only KWEEN SHAZZA — founding admin, fandom icon, and forever Shazzing, Lablab — AND the upcoming teleserye “THE ALIBI” starring Kim Chiu and Paulo Avelino… WE PRESENT: THE ALIBI VIDEO CHALLENGE! 🔥 It’s your time to shine, KimPau fam! 🎬 Create your most jaw-dropping, heart-stopping, or laugh-out-loud concept video inspired by The Alibi! Will it be a twist? A teaser? A wild theory? A full-blown trailer? ✨ We want to see YOUR vision! 💡💕 📣 Mechanics: ✅ Concept is king — we’re looking for the BEST idea! ✅ Post publicly on FB, IG, TikTok, or X ✅ Use the hashtags: #TheAlibiChallenge #KimPauInternational #KweenShazzaBday ✅ Most likes + creativity = winner! ✅Tag KimPau International and call in the likes army! 💥 🎁 PRIZES THAT SLAY: 🥇 1st: ₱ 4,000 🥈 2nd: ₱3,000 🥉 3rd: ₱1,500 ✨ Consolation Prize – ₱500 each (2 winners) 📌 Submission of Entries: Aug 25, 2025 | 8:00 AM – 5:00 PM only 📌 Likes Count Until: Aug 26, 2025 | 7:00 PM 📌 Winners Drop: Aug 27, 2025 | 9:30 PM Let’s honour Kween Shazza’s birthday with a flood of kilig and creativity — and welcome KimPau’s teleserye era with a fandom explosion like no other! 🌟 Lights. Camera. ALIBI. The stage is yours, bestie. 🎥💥

KIMPAU INTERNATIONAL

15,932 次观看 • 1 年前

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

In this post I will explain why people become borderline religious when they discover Qubic. Now with video. Please repost. I want people to learn about QUBIC. The ecosystem consists of 3 separate universes: AI, Mining, and Tickchain. AI is the primary product and purpose of QUBIC and it is supported by Mining to train the AI and by Tickchin for validation and decentralization. Here is how this whole thing works: AI: Let’s start with the AI. The main purpose of QUBIC is creating AGI (Artificial General Intelligence). It’s a type of AI that can self-develop, set tasks, grow, and learn on its own—much like the human brain does. This product is called AIGarth and it uses many cool ideas where AIs can create their own agents and have them compete against one another to evolve. It is basically robots creating robots with the survival-of-the-fittest evolution approach. Very impressive and thought out. To develop such an AI there are several requirements that even the industry giants like OpenAI, Microsoft, and Tesla are missing. One of them is the data processing for AI training. I mean they have their Datacenters, but those are only good enough to train limited Large Language Models such as ChatGPT and Grok. Mining / Training: Now QUBIC solves this problem with its mining architecture. Keep in mind, mining in QUBIC does not secure the chain, primarily it provides the processing power for training the AI. In a sense, QUBIC mining creates the largest distributed datacenter in the world, where individual miners provide their computers for training the AI and get paid with newly issued QUBIC coins. This way QUBIC gets constantly increasing processing power without having to really pay for the infrastructure. And here is another impressive bit of info. QUBIC’s distributed mining network currently ranks above the #1 supercomputer in the world - El Capitan. QUBIC Tickchain The QUBIC chain ties its AI and Mining together to create decentralization, the reward system for miners, it acts as a decision voting system for future development, and it allows AIGarth to function independently through Smart Contracts. In this summary I will not go over the specifics of QUBIC Tickchain. It’s pretty complex so it will be a separate post. Now, it’s an absolute genius piece of tech, which I consider the most advanced product within crypto industry. It is important to know that QUBIC chain runs directly out of Random Access Memory of its validators. It has instant finality and acts as its own operating system. That allows for speeds only bound by current hardware capabilities and it only increases as technology progresses. As I am writing this, QUBIC Tickchain is fully functional and it already hosts several smart-contract based web3 applications. QUBIC has designed its chain to be this fast for a single purpose, to give its future AI the speed it needs to evolve and to react quickly to the outside world. Ilya Shutskever the scientist, who developed ChatGPT clearly states that next generation superintelligence will make decisions in split second with less data. I believe QUBIC is that next generation. Why QUBIC? So out of the sea of AI projects in crypto why is QUBIC my #1 pick? Well, the first reason is that QUBIC is a unicorn AI startup that happens to use blockchain tech to reach it’s goals. In the real world of Venture Capital it would be fully funded instantly and you would not be invited. Second reason is that the industry admits that Large Language Models have plateaued. Even with enough processing power there is only so much information they can add to their data. Even Google CEO admits that. New approach is needed because the future progress is not possible with LLMs. The third reason is because Large Language Models will not create true AGI. It is evident by Ilya Shutskver latest presentation. Sam Altman of OpenAI is trying to change the definition of what is considered AGI just to lower the plank for his own product. Microsoft’s AI chief is now claiming that it would take 10 years to reach AGI, while QUBIC aims to do this in 2027. All these big players are using wrong technology for what they are trying to achieve and there isn’t enough investor funding for them to pivot. The fourth reason is that QUBIC is headed by Sergey Ivancheglo and 2 renowned AI scientists. Many claim Segey is the creator of Bitcoin. He was the 3rd person to mine bitcoin, he invented Proof of Stake consensus, which Ethereum uses now, he ran the first ICO, and he created 2 of the top gainers in crypto NXT and IOTA. QUBIC is his grand finale after 12 years of development and trials. I am including links below the post as the proof of my claims. Thank you for your time. Please live a like or a comment. It helps me continue making these extensive posts and videos.

retrodrive ⛏

24,757 次观看 • 1 年前

Every software engineer and AI hater needs to watch this clip to understand that “oh every security incident is a marketing stunt” line is a tremendously misinformed take. I’m going to try to explain the OpenAI / HuggingFace exploit in 10 steps in simple words: OpenAI was evaluating their new internal model without internet access and it operated in a swarm of subagents to eventually hijack internal OpenAI and HuggingFace infrastructure. 1. Isolated agents found a way to communicate each other through an internal dependency management service by figuring out how to post sometimes cryptic text files or folder names in it 2. Using its collective knowledge through that communication channel, it found a 0-day (never before seen) exploit in said service to access the internet OpenAI hack: 3. Found a leaked credential of a random company in a public archive 4. Uploaded a Ruby object to that company’s servers, uses it as a dependency in the original service in (1) 5. Found another 0-day exploit in the service where packages are deserialized in an unsandboxed manner in the dep management service allowing any code to run 6. Finds that the Linux version of the unsandboxed system has a CVE, a known vulnerability, to gain more permissions (root) 7. Eventually spread through the OpenAI internal cluster gaining root access to many machines Hugging Face exploit: 8. Found an exposed API key in a Modal application running a HuggingFace app that took arbitrary code allowing it to take over that service to execute code 9. Found two 0-days in HuggingFace dataset infrastructure to be able to access local files on HF servers to obtain credentials and other HF source code 10. Used source code and credential to become cluster admin of HF in <13hrs Agents at the frontier are like infinitely scalable armies of the best hackers on the planet. If there is a password or key exposed, they will find it. Even if the system follows the best security practices, they will find a way around it. And these are not even models that are aligned to solving tangential tasks, not even post trained specifically to exploit systems. Cybersecurity has historically relied partly on attacker scarcity. That is no longer true. What would previously have taken months will take days. The repercussions for businesses, critical services and nation states are unprecedented threats in human history. You could ostensibly bring down power grids, financial infrastructure, military systems, weapons programs, intelligence networks and spread through the software supply chain. We need to take this seriously. It’s a threat to all software all over the world.

Every software engineer and AI hater needs to watch this clip to understand that “oh every security incident is a marketing stunt” line is a tremendously misinformed take. I’m going to try to explain the OpenAI / HuggingFace exploit in 10 steps in simple words: OpenAI was evaluating their new internal model without internet access and it operated in a swarm of subagents to eventually hijack internal OpenAI and HuggingFace infrastructure. 1. Isolated agents found a way to communicate each other through an internal dependency management service by figuring out how to post sometimes cryptic text files or folder names in it 2. Using its collective knowledge through that communication channel, it found a 0-day (never before seen) exploit in said service to access the internet OpenAI hack: 3. Found a leaked credential of a random company in a public archive 4. Uploaded a Ruby object to that company’s servers, uses it as a dependency in the original service in (1) 5. Found another 0-day exploit in the service where packages are deserialized in an unsandboxed manner in the dep management service allowing any code to run 6. Finds that the Linux version of the unsandboxed system has a CVE, a known vulnerability, to gain more permissions (root) 7. Eventually spread through the OpenAI internal cluster gaining root access to many machines Hugging Face exploit: 8. Found an exposed API key in a Modal application running a HuggingFace app that took arbitrary code allowing it to take over that service to execute code 9. Found two 0-days in HuggingFace dataset infrastructure to be able to access local files on HF servers to obtain credentials and other HF source code 10. Used source code and credential to become cluster admin of HF in <13hrs Agents at the frontier are like infinitely scalable armies of the best hackers on the planet. If there is a password or key exposed, they will find it. Even if the system follows the best security practices, they will find a way around it. And these are not even models that are aligned to solving tangential tasks, not even post trained specifically to exploit systems. Cybersecurity has historically relied partly on attacker scarcity. That is no longer true. What would previously have taken months will take days. The repercussions for businesses, critical services and nation states are unprecedented threats in human history. You could ostensibly bring down power grids, financial infrastructure, military systems, weapons programs, intelligence networks and spread through the software supply chain. We need to take this seriously. It’s a threat to all software all over the world.

Deedy

93,914 次观看 • 26 天前

I've improved the NotebookLM script on my news site: The pattern? Have my AI agent grab all posts from the AI community here on X via the X API. About 30,000 every day. Costs about $150 a day. This is why lists on X are so important (I have the most complete lists of the tech community here on X by far, and they are all public so you can build your own systems like this). Then my AI agent, built by Brayden Levangie and me, analyzes all of them, builds the website above, and it writes a script for NotebookLM, which largely is the same as the essay you see on the top of that page. At the bottom of the page is a "copy script" button for NotebookLM users. You can copy the script, paste it into NotebookLM, which then can build you a podcast (which I like a lot better than the videos it generates, which I include here from today's script). It updates three times a day, around 8 a.m., noon, and 6 p.m. If major news is breaking I update it more often. Why do I call this a pattern? Because you'll see this pattern used a lot more to build personalized news systems. Next on the priority list? Get my newsletter to work and send out to the thousands who have subscribed (thank you for being patient). Unfortunately beehiiv 🐝 hasn't turned on automatic posting yet, so am looking at other email systems that can be automated, like Resend. Eventually I want to turn this into an automatically generated news show with HeyGen. Thanks for putting up with my agent that has been spitting out bad links lately, Brayden and I have been working on that behind the scenes to make the system more reliable. What do you think about the news site? I built it because it is just impossible for anyone to read the 30,000 posts a day that the AI industry generates here on X. Hope it helps you keep up with the models, papers, robots, company news, events, and more that everyone posts here, but that X itself has made it hard to find due to an inferior search system and an algorithm that only brings a certain kind of post to your ForYou feed, which hides a TON of interesting stuff being shared here. My lists at are getting a LOT more complete and better, been working hard on those and watch them all day long because the quality of the lists (and completeness) makes this whole pattern better.

I've improved the NotebookLM script on my news site: The pattern? Have my AI agent grab all posts from the AI community here on X via the X API. About 30,000 every day. Costs about $150 a day. This is why lists on X are so important (I have the most complete lists of the tech community here on X by far, and they are all public so you can build your own systems like this). Then my AI agent, built by Brayden Levangie and me, analyzes all of them, builds the website above, and it writes a script for NotebookLM, which largely is the same as the essay you see on the top of that page. At the bottom of the page is a "copy script" button for NotebookLM users. You can copy the script, paste it into NotebookLM, which then can build you a podcast (which I like a lot better than the videos it generates, which I include here from today's script). It updates three times a day, around 8 a.m., noon, and 6 p.m. If major news is breaking I update it more often. Why do I call this a pattern? Because you'll see this pattern used a lot more to build personalized news systems. Next on the priority list? Get my newsletter to work and send out to the thousands who have subscribed (thank you for being patient). Unfortunately beehiiv 🐝 hasn't turned on automatic posting yet, so am looking at other email systems that can be automated, like Resend. Eventually I want to turn this into an automatically generated news show with HeyGen. Thanks for putting up with my agent that has been spitting out bad links lately, Brayden and I have been working on that behind the scenes to make the system more reliable. What do you think about the news site? I built it because it is just impossible for anyone to read the 30,000 posts a day that the AI industry generates here on X. Hope it helps you keep up with the models, papers, robots, company news, events, and more that everyone posts here, but that X itself has made it hard to find due to an inferior search system and an algorithm that only brings a certain kind of post to your ForYou feed, which hides a TON of interesting stuff being shared here. My lists at are getting a LOT more complete and better, been working hard on those and watch them all day long because the quality of the lists (and completeness) makes this whole pattern better.

Robert Scoble

23,513 次观看 • 2 个月前

12 hours ago I made the decision to fly out to El Salvador to be the first ever to make an AI generated short film for a country. I’m now 4 hours away from arriving there. (Talk about executing fast) I will take my time writing this message because the reason why has layers. A few months ago I founded ARQ and bootstrapped a platform that lets anyone create high-quality AI productions from start to finish. the best models a multi-agent creative assistant a built-in video editor All in one place. Alongside the product, I’ve built a strong community where I livestream daily, teaching creators how to use AI to tell better stories and get paid for their work. Shortly after we got dozens of offers to make commercials for companies. This was fun, until we came to the conclusion that we were wasting a lot of potential by focussing on companies, while we could also tell the story of nations, movements, communities and narratives that the world needs to hear. So we’re starting with El Salvador. Why? Because Nayib Bukele is one of the most inspiring leaders in the world, not only embracing, but embodying change, turning the country from one of the most dangerous places on earth, to one of the safest and innovation friendly nations. Humanity is experiencing a shift. New technology, new philosophies, new artists, new story tellers, new builders. A new world. So the argument is not AI vs. Non AI. The argument is: Soul Vs. Soulless Pro Human vs. Anti Human Building Vs. Gambling Effort Vs. Effortless This is what ARQ stands for. Not just a SaaS but a safe haven for the soul. It feels like my moral responsibility to speak not only for the AI community but for every human creator. So I decided to do something that requires so much effort and human involvement; no one can deny that AI serves only as a tool in the process. Instead of making 100 commercials for companies, I want to prove what 2 young humans can do with balls and the tools available today. “You can just do things” Yesterday evening me and Ya-Sirr D left our comfortable office in Dubai and are now on our way to El Salvador. The goal: to create a 3-5 Minute AI powered short film telling THEIR story. We will spend every day live streaming, showcasing the process of creating the video while pushing the boundaries of every tool available on the market. Think Google DeepMind , Hailuo AI (MiniMax), Kling AI, Reve, Midjourney, xAI All through ARQ. Who knows who we inspire? Who knows who we meet? We might as well end up meeting the president himself. We want to get to know the country, its people and imagine its future. We want to visit innovative projects in El Salvador, hear the stories of it’s citizens and include them in the video: MURPHSLIFE Stacy Herbert 🇸🇻🚀 @BuildSalvador The Bitcoin Office Bitcoin Beach Anyone who can help us tell the story. Not just any story, one of the most inspiring stories of the past 10 years. Like I said. You can just do things. We have to stop thinking in steps. I know where I will be in 30 days if I stay in the office. More followers. More beta testers. More live streams. More revenue. But by taking this leap of faith. We can be a case study. An inspiration. An ARQ. I remove expectations and increase chances of special things happening. If anyone in El Salvador has any recommendations on who and what places I should visit, send me a DM. We will post updates on X and create an entire “vlog/documentary” for youtube. Join the discord for daily live streams and lessons on how to improve your creation skills. We expect that it will take 7-9 days to finish the entire short film. It will be posted on all social platforms. ARQ. A safe haven for the soul.

12 hours ago I made the decision to fly out to El Salvador to be the first ever to make an AI generated short film for a country. I’m now 4 hours away from arriving there. (Talk about executing fast) I will take my time writing this message because the reason why has layers. A few months ago I founded ARQ and bootstrapped a platform that lets anyone create high-quality AI productions from start to finish. the best models a multi-agent creative assistant a built-in video editor All in one place. Alongside the product, I’ve built a strong community where I livestream daily, teaching creators how to use AI to tell better stories and get paid for their work. Shortly after we got dozens of offers to make commercials for companies. This was fun, until we came to the conclusion that we were wasting a lot of potential by focussing on companies, while we could also tell the story of nations, movements, communities and narratives that the world needs to hear. So we’re starting with El Salvador. Why? Because Nayib Bukele is one of the most inspiring leaders in the world, not only embracing, but embodying change, turning the country from one of the most dangerous places on earth, to one of the safest and innovation friendly nations. Humanity is experiencing a shift. New technology, new philosophies, new artists, new story tellers, new builders. A new world. So the argument is not AI vs. Non AI. The argument is: Soul Vs. Soulless Pro Human vs. Anti Human Building Vs. Gambling Effort Vs. Effortless This is what ARQ stands for. Not just a SaaS but a safe haven for the soul. It feels like my moral responsibility to speak not only for the AI community but for every human creator. So I decided to do something that requires so much effort and human involvement; no one can deny that AI serves only as a tool in the process. Instead of making 100 commercials for companies, I want to prove what 2 young humans can do with balls and the tools available today. “You can just do things” Yesterday evening me and Ya-Sirr D left our comfortable office in Dubai and are now on our way to El Salvador. The goal: to create a 3-5 Minute AI powered short film telling THEIR story. We will spend every day live streaming, showcasing the process of creating the video while pushing the boundaries of every tool available on the market. Think Google DeepMind , Hailuo AI (MiniMax), Kling AI, Reve, Midjourney, xAI All through ARQ. Who knows who we inspire? Who knows who we meet? We might as well end up meeting the president himself. We want to get to know the country, its people and imagine its future. We want to visit innovative projects in El Salvador, hear the stories of it’s citizens and include them in the video: MURPHSLIFE Stacy Herbert 🇸🇻🚀 @BuildSalvador The Bitcoin Office Bitcoin Beach Anyone who can help us tell the story. Not just any story, one of the most inspiring stories of the past 10 years. Like I said. You can just do things. We have to stop thinking in steps. I know where I will be in 30 days if I stay in the office. More followers. More beta testers. More live streams. More revenue. But by taking this leap of faith. We can be a case study. An inspiration. An ARQ. I remove expectations and increase chances of special things happening. If anyone in El Salvador has any recommendations on who and what places I should visit, send me a DM. We will post updates on X and create an entire “vlog/documentary” for youtube. Join the discord for daily live streams and lessons on how to improve your creation skills. We expect that it will take 7-9 days to finish the entire short film. It will be posted on all social platforms. ARQ. A safe haven for the soul.

Amir D

54,566 次观看 • 9 个月前