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introducing vim royale - real-time multiplayer duels - Race to clean broken code - elo-based matchmaking - replay & study every match - bot mode + singleplayer - built-in vimtutor to learn vim motions

153,889 次观看 • 4 个月前 •via X (Twitter)

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THIS IS INSANE — AN ANTHROPIC ENGINEER BUILT A CLAUDE CODE BOT THAT READS INSTITUTIONAL OPTIONS FLOW IN REAL TIME AND TURNED $200 INTO $14,300 TRADING ALONGSIDE THE BIGGEST ORDERS ON THE TAPE the bot doesn't predict. it stalks it reads thousands of institutional options orders streaming through SpotGamma — scores every block by size, direction, and how it historically moves price — then trades only when the heaviest hands on the tape are moving here's what's running on screen right now: → GEX Surface Analysis — 3D gamma exposure map rotating in real time, strike $8,168, GEX at 1.36B → MotiveWave footprint — ESU6 Range(20) with cumulative delta crashing to -9,500. every bid and ask printed at every level → SpotGamma Live Flow — institutional tape streaming tick by tick: SPX, QQQ, NVDA, AAPL, META, MSTR, NDX. strike, volume, premium — all live → GEXRadar — QQQ at $720.09, Hedging Pressure 53/100, Major Walls mapped, bullish flow → MNQU6 on mobile — Nasdaq 100 M1, bid 29,589 ask 29,591, TP set, P&L at +$109.50 when an institution drops $40K on SPX 7,740 calls or $28,639 on NDX puts — Claude sees it before FinTwit even screenshots it the bot makes only 10 trades per day not 50. not 100. ten. because Claude Code learned the edge isn't frequency — it's selectivity. large institutional block orders only and it exits early. before the institutions unwind. the same flow analysis that finds the entry also maps when large players historically take profit — the bot exits ahead of the crowd $200 in. $14,300 out. built by an engineer who used Claude Code to do what no dashboard can — read every institutional options order in real time and trade only when the biggest ones align you're watching one chart this bot is reading every block order on the tape to find the 10 setups that matter today save this — full setup breakdown below ↓

INSIDER

133,515 次观看 • 11 天前

At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.

AVALON

65,243 次观看 • 7 个月前

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

zostaff

48,649 次观看 • 5 个月前