Shinmaboroshi

A California student just reverse-engineered his Meta Ray-Bans 3 with Opus 5.5 and turned them into a 3,000+ Elo poker engine They’re AI poker glasses built on Opus 5.5 and a custom card detector, and the whole workflow is open source Here’s the full breakdown: step 1 → Meta gives you no API for the glasses, so he skipped it the Ray-Bans stream video straight to his laptop. the laptop sees exactly what he sees at the table step 2 → a vision model finds every card in the frame suit and rank, with a confidence score on each: “7c 0.60”, “9s 0.85” step 3 → no week of hand-labeling photos the detector is trained on synthetic data: tens of thousands of rendered cards on random tables and lighting, already labeled. step 4 → it locks in your hand and the board “YOUR HAND: 9 8” sits in the corner. the flop, turn and river get boxed in green as they land step 5 → Opus 5.5 runs the math on every new card “straight, 89.46%” → “pair, 52.23%” → “high card, 4.61%” step 6 → it gives you the move: raise, call, fold or all in step 7 → then it reads the player across the table facial expression analysis on your opponent: “detected emotion: fear”. you’re no longer just playing the odds, you’re playing the man step 8 → everything plays in your ear as a single line: “poker odds: straight 89.46%. suggested move: all in. detected emotion: fear” under the hood: → it works for blackjack too. it counts every card it sees and tells you what’s left in the shoe. that’s when you bet big → every detection shows its confidence score on the box, so you can see when a read is shaky → the overlay draws everything live on the video: board in green, your hand in the corner, the move on screen → he tested it at a home game with friends before taking it to Vegas

THIS IS A PURE F*CKING GOLDMINE A former WorldQuant director published 151 hedge fund strategies for free, and the best trick isn't any single formula. The idea: a strategy is one file with one function, and the bot only asks it for weights after risk has already said yes. You stop rewriting the whole bot for every idea, and swapping a strategy costs one file instead of a weekend. You just make one folder: strategies/hp_filter.py here's the split: → the skeleton pulls candles, checks risk, sends orders, runs 24/7 → the strategy file returns target weights and nothing else → risk is checked before the strategy is ever asked → drop a new file in the folder and the bot can run it by name three config lines do the heavy lifting: → paper=True means no order leaves the machine → max_gross_leverage=1.0 caps every position the strategy asks for → max_daily_loss=0.03 closes everything and stops the bot Candles in. Risk checked. Weights out. the full tree > bot.py, the skeleton you write once > strategies/<name>.py, one file per formula from the book > drop the unfinished candle, use closed candles only > stale data = no trade > trade only the difference between current and target position > paper first, testnet second, real money only on your go paste the tree and this prompt into Claude ↓ "Set up a trading bot from the tree above: 1. Write bot.py once: candles via ccxt, risk checks, rebalance, 24/7 loop. 2. Put each formula from 151 Trading Strategies into its own file in strategies/ with target_weights(data). 3. Hard-code paper=True, leverage 1x and a 3% daily stop. No model can override them. 4. Backtest every strategy with real fees on data it never saw. Show me every file first. No live orders until I say go." save it, it'll come in handy.

I turned my DJI Mavic into a home security drone with Claude Opus 5.5. Honestly, this is insane. No cloud. No GPS on the target. Everything runs on the phone, iPhone or Android. Here’s the build, step by step: Step 1 → Setup. Plug a spare phone into the Mavic remote and paste the prompt below into Claude Code on Opus 5.5. Step 2 → Compatibility check. Opus checks your drone against the official DJI Mobile SDK v5 supported list. Not on it? Stop there. No firmware hacks. Step 3 → The app. It builds a small app on the official DJI SDK: Swift for iPhone, Kotlin for Android. Step 4 → Alarm link. Home Assistant detects the trigger and sends a push to your phone. One tap on “Launch.” Step 5 → Eyes. A tiny YOLO model runs on the phone’s chip: Core ML on iPhone, LiteRT on Android. Step 6 → Memory. A Kalman filter keeps the track alive. He ducks behind a fence: LOCKED → COASTING → re-locks on the same ID. Step 7 → Hands. Virtual Stick commands at 10 Hz. Box center controls yaw, box size controls distance, and the drone never gets closer than 8 m. Step 8 → Boundary behavior. At the property line it stops, climbs to 30 m, and keeps the camera on him until he’s out of sight. Step 9 → Return. It lands back on the pad. The clip, with track, distance and timestamps, is already on your phone. The alarm wakes you up, and the drone already knows where he went. Full prompt below. Then read the article below on how to reverse-engineer anything with Opus 5.5.

CITADEL QUANTS HIDE THE GAME BEHIND A FORMULA WITH TEN TERMS, PREDICTION MARKETS RUN ON EXACTLY ONE The secret is one line. Edge = P(model) − P(market) That Z-field monster on screen is a stack of simple signals wearing a costume. A prediction market strips all of it down to one number, the crowd's guess at a probability. In plain words. If you can turn fresh news into a probability before the crowd reprices, the gap between your number and their price is the whole edge. That is why the game is not IQ, it is speed to a number. An AI that reads live news does in ninety seconds what the tab takes hours to price in. I broke down the four exact prompts that turn Grok into a probability desk, the sizing rules and the ten minute daily routine in the article. Still bets, not a salary, so size like it & the full playbook is in the article. Read it ↓

This is f*cking insane. SpaceXAI senior engineer (ex-Cursor): "99% of people open one GrokBot, ask it a question and close the tab I run 50+ GrokBot agents like a company. A Chief of Staff runs my day, 3 PMs hand out the work, 40+ workers ship it overnight, and I just check the results over coffee. That team does the job of 20 engineers" In this 1-hour workshop, SpaceXAI builds that GrokBot team from scratch, step by step GrokBot → Chief of Staff → PMs → 40+ Workers → Autonomous Company At SpaceXAI, 97% of engineers already run a team like this, and the ones who do are getting $750K+ offers. The rest are about to compete with them Worth more than any $5,000 agentic engineering course Bookmark it and watch today Then read how to build your own team of GrokBot agents in the harness engineering guide below ↓

This Claude Code prompt builds you a personal skill library. No coding, one paste, about 2 minutes. Most people burn hours re-explaining the same things to Claude: the tone, the steps, the formatting rules. Next morning, it’s all forgotten and you start from zero. This ~100-line XML file fixes that by turning your routine into reusable skills: → Scans your work for tasks you repeat every week → Ranks them and picks the 5 that cost you the most time → Builds a separate skill for each, with examples and templates → Tests every skill on a real case against your manual version → Takes your corrections and patches the weak spots How it stays fast: Claude only keeps short skill descriptions in memory and loads the full file when the task calls for it. Context stays clean, and each skill does one job well. Safety: it never touches passwords or API keys, and nothing is sent, published, or paid for without your OK. You get: a table of your routine, ready-to-use SKILL.md files, test results, and a roadmap of what to automate next. The best prompt is the one you never have to write twice. Save this for later ↓

6 formulas is not a playbook. It's half a playbook But here's what's missing Four models any serious quant stack should also account for: 1. Random Walk Paths dS_t = μS_tdt + σS_tdW_t Geometric Brownian Motion - the backbone of price simulation Without modeling hundreds of possible scenarios building up over time, you're flying blind on tail risk 2. Monte Carlo Simulation P(A) ≈ N_hit / N_total Thousands of random samples to solve problems that closed-form math can't This is how you stress-test your Kelly sizing before real capital hits the market 3. Probability Distribution f(x) = (1/σ√2π) · e^(-½((x-μ)/σ)²) Results converge into a clear distribution of potential risk If you're not mapping your P&L outcomes to a distribution, you're guessing not trading 4. Quantitative Analysis / VaR VaR_α = inf{l ∈ ℝ : P(L > l) ≤ 1 − α} Predicting the future by simulating uncertainty and volatility Value at Risk shows you the worst-case loss at a given confidence level, the one number risk desks actually care

this is an absolute goldmine… I just launched the GrokBot Education Hub: • 137 talks & workshops in one place • 80+ hours of video, organized by speaker and topic • 65 speakers, including the team behind GrokBot • live countdown to upcoming GrokBot workshops the one place you need to master GrokBot ↓ bookmark it, then read the full article on building the GrokBot team from scratch below

this is an absolute goldmine… An Anthropic engineer just opened up their internal academy: • 543 talks, workshops & podcasts in one place • 238 hours of video, organized by speaker and topic • 105 people from the team behind Claude • 7 step-by-step tracks: Claude Code, agents, MCP, prompting the one place you need to master Claude ↓ bookmark it, then read the full breakdown on how to ship with Opus 5.5 in the article below

He cheated at chess with Ray-Ban Meta glasses, AI, and a MacBook.

Motion designers are going to hate me after this drop I just shipped a step-by-step motion design school, built with Opus 5.5: • 9 levels: taste → web → 3D → video → sound • 59 skills, engines and tools, every link verified • 9 live demos you can actually play with • copy-paste install lines + a prompt generator What's inside: > Easing playground: drag the curve, get GSAP / CSS / Motion code > Disney's 12 principles with a with / without toggle > A GSAP timeline you can scrub frame by frame > Keyframes vs real physics, side by side > "Every frame is a function of t": why AI video flickers > A cut-to-the-beat reel at 120 BPM > Live Openverse search for free SFX, licences included > FFmpeg export builder for 9:16, 1:1, 16:9 + a pixel Clawd that walks across your screen and drops tips + a lofi radio with 3 stations to learn to The stack it teaches: Opus writes the code → skills guide the motion → an engine renders it Free, no sign-up ↓

Morluto, REA founder: “Opus 5.5 is the strongest model for reverse engineering out there. I use it every single day. Take the REA GitHub repo, add the right prompt, and you can reverse engineer 90% of the apps that exist today.” In a 1-hour video, the engineer who built REA shows how to reverse engineer any app with it. This is worth more than a $500 AI engineering course. Watch it today, then read the article on reverse engineering with Opus 5.5 below.

Holy sh*t, my GrokBot now works on my TV as a Jarvis, with the help of Opus 5.5 I reverse-engineered my Samsung TV with Opus 5.5... 1 sentence out loud = Netflix opens and scrolls itself. No remote, no phone, no digging between the couch cushions. Here's the full guide, step by step: • step 1 → put your smart TV and your laptop on the same Wi-Fi • step 2 → paste the prompt below into Claude Code on Opus 5.5, along with a photo of your TV's Settings → About screen • step 3 → Opus maps how your TV takes commands and finds the loophole: almost every smart TV has a remote-control API on your home network. The same one the phone remote apps use • step 4 → it writes a tiny remote with exactly 3 tools: open Netflix, move, select. Nothing else • step 5 → it plugs that remote into Grok Bot as a plugin. Grok is the ears, the plugin is the hands • step 6 → press Allow on the TV once, the last time you touch the remote • step 7 → say "Hey Grok, open Netflix" and watch it browse for you The result: you talk, the TV listens... p.s: works on most Samsung, LG and Google TV sets from the last few years. Find the full prompt below Then read the article below on how to reverse-engineer anything

Boris Cherny, the creator of Claude Code: "Most engineers now run dozens, even hundreds of agents at once. The trick is to let one agent spawn and manage the others." In this 36-minute conversation, he shows how a single engineer can run an entire workforce of AI agents. One Engineer → Agent Teams → Subagents → Cross-Verification → Production-Ready Results Most people are still stuck in one chat window, thinking that's automation. This is the kind of knowledge people pay $444 for in agent engineering courses. Bookmark this and watch it today. Then read how to set up your Grok Bot

SpaceXAI engineer Lauren Tan just released the GrokBot Agent Team Harness, built on top of her /pstack. This is wild... Hand it to your GrokBot agent and it will build a properly structured team of 20 self-learning agents from zero. Then check out the full GrokBot agent team roadmap below ↓

Holy sh*t, I just turned my TV into Jarvis with Opus 5.5 + ChatGPT voice mode I reverse-engineered my Samsung TV using Opus 5.5... Say one sentence out loud, and 6 Claude Code agents light up on my living room TV. Here’s the full guide, step by step: step 1 → connect your Samsung Smart TV and your laptop to the same Wi-Fi step 2 → drop the prompt below into Claude Code running Opus 5.5, along with a photo of Settings → Support → About This TV step 3 → Opus figures out how the TV accepts commands and spots the loophole: Samsung’s built-in remote-control API on your home network can load any web page in the TV’s browser step 4 → it launches 6 Claude Code agents in a tmux grid on your laptop and serves them as a private page, visible only on your Wi-Fi step 5 → it then builds a tiny “Jarvis” connector with just one tool: open the agent wall. ChatGPT voice mode is the ears, the connector is the hands step 6 → tap Allow on the TV once. That’s the last time you’ll touch the remote step 7 → say “Open Claude Code CLI on my TV” and watch the windows appear one by one The result: the laptop does the heavy lifting, the TV becomes your control room... p.s. it should work on most Samsung Smart TVs running Tizen. Mine (in the video) runs Smart Hub. The remote now lives in a drawer Full prompt below Then read the article below to learn how to reverse-engineer anything

An animated launch video that takes a pro motion designer a week? Claude Opus 5.5 built it in 3 hours, and every detail is still editable. 99% of people use AI to generate random clips. The real shift is using Opus 5.5 to build complete motion graphics, 3D product ads, and animated films, with editable layers, custom branding, and full creative control. Claude Motion → Brand References → Editable Graphics → 3D Scenes → Complete Videos The 12-minute video shows how to recreate viral launch videos, build 3D product ads, and make animated explainers using Claude Motion. Generate → Customize → Animate → Export Worth more than a $500 AI video course. Skip the scrolling, watch the video today, then read the article below ↓

It's getting f*cking dangerous A 23-year-old quant dev from China ran 300 Claude Code agents in parallel as his personal trading desk. Then he plugged them into a verification layer so none of them could feed him made-up numbers. 300 AI quants. Trading research on autopilot. His live dashboard shows: > 300 agents scanning 100 companies at once: earnings, news, filings > Every claim checked against the source in ~100 ms > Three passes before anything reaches his trading signals, with zero errors left > Everything lands in an Obsidian vault, ready before the market opens The trick is the split. One model does the quant research, and a cheaper one only judges whether each claim is true. The loop runs until everything checks out. In trading, one hallucinated number can wipe out a whole position. A hedge fund quant team would bill weeks for this. He got it in a single run. Bookmark this. You'll want the full breakdown ↓

Anthropic engineer: “Stop prompting Claude. Build a system that prompts itself.” In 55 minutes, she walks through exactly how Anthropic builds agents that remember, correct their own mistakes, and improve with every run, all using graphs. None of the paid agent courses I’ve taken goes into this much detail. Watch the video first, then read the guide below to build the same system with Graphs.

Google Brain co-founder Andrew Ng: “In the future, 100% of tasks will be done by AI agents. 80% of my tasks are already handled by Claude & GrokBot agents. Prompting is dead.” In a 25-minute talk, he breaks down what the future of AI Engineering looks like and how to get ready for it. Worth more than a $500 AI engineering course. Watch it today, then read the full breakdown on building agentic teams with GrokBot in the article below.