SlippageKills | Edge dies in execution

PolyLayer doesn’t predict BTC. It turns public micro-imbalances on Polymarket into executable trades — before the interface, the crowd, or standard copy bots can react. Built for short-window trading (5–15 min). Why most traders lose: ⚡️ Execution is too slow You click → edge is gone Signal looks profitable. Trade isn’t Fees + slippage → negative EV Liquidity is misleading Thin book = bad fills You trade price. Contract settles differently Copy trading breaks Your entry ≠ original entry No risk control = fast losses ❗️PolyLayer solves this: Net EV calculation (after fees & slippage) Liquidity filtering before entry ⚡️ Execution without UI lag Trade selection based on real edge It doesn’t copy trades It copies the edge 🛡 Same signal Same wallet Same market Different result → Get free access to PolyLayer @poly_layerbot 🌐 Learn more: polylayer.pro

I see these wallets making ~$600k, $1.3M on Polymarket - you want to copy it, it feels easy - just copytrade same trades, same second but most of the time - you lose why? slippage you think you’re entering “at the same price” in reality - slightly worse on fast markets, that “slightly” kills the entire profit they caught the move you got the leftovers This is precisely the goal of the PolyLayer AI-powered agent office. She made a fortune in markets with 5-15 minute intervals. #polymarket

American finance bro tried to screenshot his TD Ameritrade gains for a LinkedIn post. Accidentally grabbed the wrong tab. Polymarket was open behind it. Post went up. 2,400 impressions before he noticed. Someone zoomed in. Wallet address visible. Account88888. $645K profit. 11,603 predictions. All BTC windows. His LinkedIn comments exploded. Bro you work at Morgan Stanley and this is what you actually trade? He deleted the post. Too late. The wallet was already being passed around fintwit. $35K bet → $98K payout. $57K bet → $100K payout. $70K bet → $106K payout. The strategy his compliance team would hate: buys BOTH sides when panic breaks the math. Up at 48 cents. Down at 46 cents. Total 94 cents for a guaranteed dollar. Repeat 11,000 times. His HR scheduled a meeting. He didn't show up. Resignation email went out that afternoon. 155K watching now. $0 in current positions. He withdrew everything and went dark. The LinkedIn stays deleted. The wallet stays public. Someone tried to flex traditional finance. Accidentally exposed that the real money was somewhere else. → Up - XRP Up or Down (February 19, 6AM ET) polymarket.com/event/xrp-up-o…

Here's my conversation with Jeff Kaplan, a legendary Blizzard game designer of World of Warcraft and Overwatch, which are two of the biggest, most influential games ever made. Jeff is one of the most genuine & awesome human beings I've ever met: kind, thoughtful, hilarious, and still & forever a gamer through and through. This was a truly fun & inspiring conversation. We talk about it all: the lows, the highs, the memes, the details of the game design process, and the new game he's been secretely working on: The Legend of California. I got a chance to play the game with Jeff, and it's incredibly beautiful (and fun). You can wishlist it on Steam now. I can't wait to play it with all of you! Conversation is here on X in full and is up everywhere else (see comment). Timestamps: 0:00 - Episode highlight 1:27 - Introduction 4:07 - Early games: Pac-Man, Zork, Doom, Quake 18:33 - Writing career - 170 rejection letters 34:06 - EverQuest obsession 47:04 - Getting hired at Blizzard 1:02:32 - Lowest point in Jeff's life 1:08:37 - One of Us 1:12:54 - Early Blizzard culture 1:32:36 - Building World of Warcraft 1:50:20 - How WoW changed video games 2:07:42 - Single-player vs Multi-player 2:28:35 - How Blizzard made great video games 2:54:25 - Online toxicity 3:01:59 - Why Titan failed 3:19:09 - Overwatch in six weeks 3:46:07 - Best Overwatch heroes 3:54:37 - The challenge of matchmaking 3:58:01 - Rust 4:08:22 - Why Jeff left Blizzard 4:30:35 - Diablo IV 4:32:03 - Getting back to making video games 4:40:59 - The Legend of California 4:54:44 - Greatest video game of all time 5:02:51 - AI and future of video games

Artificial Intelligence for Cybersecurity: amzn.to/4gOK3rD v/ @PacktDataML —— #AI #MachineLearning —— 𝒦𝑒𝓎 𝐹𝑒𝒶𝓉𝓊𝓇𝑒𝓈: 🟠Learn AI methods and see how they fit into cybersecurity 🔵Learn how to design solutions in cybersecurity that include AI as a key feature 🔴Acquire practical AI skills using step-by-step exercises and code examples 🟢Purchase of the print or Kindle book includes a free PDF eBook

I have loved being on the Helion board; I continue to be extremely excited about a future with abundant energy and Helion in particular. As Helion and OpenAI start to explore working together at significant scale, it is difficult for me to be on both boards. (I will have a financial interest in Helion so still be recused from negotiations, but from a governance perspective this will make things easier for both companies.) David and Chris are exceptional founders and I think people will be quite impressed by the company's progress.

François Chollet (@fchollet) has spent years asking a different question than most of the AI world. Instead of scaling what already works, he’s trying to understand what intelligence actually is and how to build it from first principles. In this episode of the @LightconePod, he traces that path from his early work on deep learning to the creation of the @arcprize, and the launch of ARC V3, a new benchmark designed to measure something deeper than performance: the ability to learn, adapt, and reason efficiently in entirely new environments. He explains why today’s systems may be hitting limits, what recent breakthroughs really mean, and why reaching true general intelligence may require a fundamentally different approach. 00:00 - AGI by 2030? 00:31 - Introducing Ndea: A New Path Beyond Deep Learning 01:08 - A New ML Paradigm 01:30 - Replacing neural nets with compact symbolic programs 03:04 - Why Ndea Isn’t Competing With Coding Agents 05:20 - Why Everyone Might Be Wrong About Scaling LLMs 07:22 - Why Coding Agents Suddenly Work So Well 08:50 - The Limits of LLMs in Non-Verifiable Domains 10:48 - What AGI Actually Means (And Why Most Definitions Are Wrong) 13:30 - Why Deep Learning Hits a Wall 14:00 - ARC’s Origin Story 18:20 - ARC Benchmarks Explained: From V1 to V3 22:49 - The RL Loop Powering Coding Agents Today 27:03 - ARC-AGI V3: Measuring “Agentic Intelligence” 31:14 - Inside the ARC Game Studio 35:31 - Could AGI Fit in 10,000 Lines of Code? 44:01 - Building Ndea: From Idea to Compounding Research Stack 46:46 - The Future of ARC: Benchmarks That Evolve With AI 47:21 - Why There’s Still Huge Opportunity for New AI Paradigms 53:37 - How to Build a Breakout Open Source Project - Lessons From Keras 56:39 - Advice For How To Think About AI

5 minutes ago, @karpathy just dropped karpathy/jobs! he scraped every job in the US economy (342 occupations from BLS), scored each one's AI exposure 0-10 using an LLM, and visualized it as a treemap. if your whole job happens on a screen you're cooked. average score across all jobs is 5.3/10. software devs: 8-9. roofers: 0-1. medical transcriptionists: 10/10 💀 karpathy.ai/jobs

Been experimenting with procedural locomotion on #GaussianSplat environments from @theworldlabs . Built a little multi-legged robot in Unity that raycasts against the splat data to figure out where to place its feet, no meshes or colliders involved... Pretty fun to climb surfaces, walk on walls, and set a weird number of legs on the fly :) Still rough around the edges but pretty fun to watch it figure things out. #GaussianSplatting #Unity3D #WorldLabs #ProceduralAnimation

📈 TRIBE v2 accurately predicts brain activity across cortical & subcortical regions, significantly better than standard linear models. Encoding accuracy follows a log-linear scaling law with data volume — no plateau in sight.

Today we're introducing TRIBE v2 (Trimodal Brain Encoder), a foundation model trained to predict how the human brain responds to almost any sight or sound. Building on our Algonauts 2025 award-winning architecture, TRIBE v2 draws on 500+ hours of fMRI recordings from 700+ people to create a digital twin of neural activity and enable zero-shot predictions for new subjects, languages, and tasks. Try the demo and learn more here: go.meta.me/tribe2

Just read LeCun's latest paper. His team trained the first world model that can't collapse. Let me explain why this matters. It's called LeWorldModel. World models predict what happens next physically. Objects moving, falling, colliding. That's the base layer for robots that plan, cars that simulate before they steer, any AI that acts in reality instead of just talking about it. The catch is nobody could train these reliably. The models kept cheating. They'd map every input to the same output. Like a weather app stuck on "sunny" forever. Technically predicting. Completely useless. So teams piled on fixes. Frozen encoders, stop-gradient hacks, 6+ loss hyperparameters. A fragile stack too brittle for production. This team asked a different question. What if you make collapse mathematically impossible? An encoder turns each video frame into a small vector. A predictor takes that vector plus an action and guesses the next one. First loss: how wrong was the guess. Second loss: a regularizer called SIGReg that checks if vectors spread out like a bell curve. If they start looking the same, the loss spikes. The model can't cheat because the math won't let it. That simplicity is what makes the results possible. Six hyperparameters became one. 15M parameters. Trains on one GPU in hours. Plans 48x faster. Encodes with ~200x fewer tokens. Open-source. I could run this on my own hardware. Which changes who gets to build physical AI. Not just big labs anymore. Any team, any startup, any grad student. LeCun has pushed JEPA as the path forward. The criticism was always training instability. This paper removes that objection. Two directions compete in AI right now. Bigger LLMs with more compute. Or small models learning physics from raw pixels.

At 4:11 PM ET, President Trump extended his “pause” of US strikes on Iranian power plants and oil prices fell -6%. 40 minutes later, that entire decline was erased. Markets are becoming numb to the headlines.

I asked Claude to code a script to find the most profitable wallet on Polymarket He wrote it in minutes. I ran it on my Mac. It scanned thousands of wallets Filtered by recent activity Ranked by PnL And here is one of the best: - Past month profit: $787,878 - 37,195 predictions - Consistent short-term BTC “Up or Down” trade - Dozens of 5–15 minute contracts - $20K–$40K wins stacking repeatedly His wallet: @k9Q2mX4L8A7ZP3R?via=0xchainmind" target="_blank" rel="nofollow noopener">polymarket.com/@k9Q2mX4L8A7ZP… Then I asked Claude to analyze the pattern Conclusion: systematic micro-edge capture in ultra short timeframes. Small statistical advantages compounded thousands of times. Most people look for big predictions This wallet just exploits tiny inefficiencies, over and over, until they turn into seven figures

@SamsungAU again glitching android shit

Capture all the action seamlessly with the stabilisation of Super Steady mode on the new Galaxy S26 Ultra.* Available now.

Who will win the Peruvian presidential election? This candidate is the runner‑up on polls AND #polymarket , but market and big money is saying NO (68%) Wdyt ? #Polymarket #whalewatch #Prediction #Elections #Peru2026

16.7% win rate. $2.86M in profits. Wait, what? sovereign2013 proves you don't need to win often — you need to win BIG. $345M volume, sports-heavy focus, and $191K profit just this week on Polymarket. Lose small, win large. That's the game.

$1,823,451 in 30 days. $4.5M+ all-time PnL. All from sports markets on Polymarket. Wallet: @432614799197?via=polycool" target="_blank" rel="nofollow noopener">polymarket.com/@432614799197?… 4,548 bets. Win rate: 51%. Basically a coin flip… yet printing like it’s 90%. Why? Risk/reward. Big hits: > PSG NO at 36¢ → +$1.46M > Bills at 46¢ → +$1.33M > Real Madrid YES at 36.7¢ → +$1.12M

if your predictions are trash, just tail these traders with 95%+ accuracy. shining light on two beasts converting edges into big bags. first off: "0xD9E...f2" profile: @0xD9E0AACa471f48F91A26E8669A805f2?modal=signup&mt=1&via=1234" target="_blank" rel="nofollow noopener">polymarket.com/@0xD9E0AACa471… daily win rate 99.5%, total pnl $596k, biggest score $18.3k. strategy? hammers sports like nba/nhl/cbb/epl – spreads, over/unders, underdogs. hundreds of wagers/week, slim edges, ultra low drawdown (0.1%), stacking tiny victories into huge piles. /// one more interesting trader: "033033033" profile: @033033033?modal=signup&mt=1&via=1234" target="_blank" rel="nofollow noopener">polymarket.com/@033033033?mod… pnl $153k, biggest win $19.7k. strategy? dives into geopolitics and entertainment events, snags near-certain outcomes at 95-99¢ discounts, high conviction buys on yes/no bets close to resolution. sparse but near-perfect trades, exploits last-minute market lags, skips risky stuff. /// these guys sharp minds nailing inefficiencies like pros. shows you can bank on politics and events predictions without being an expert. wanna auto-copy their moves? go to @copy" target="_blank" rel="nofollow noopener">kreo.app/@copy and do it in one click.