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Rerendered this animation cuz perhaps a less harsh shadow might work better :p #REPO募集 #repo #blender3d #b3d #animation

11,374 views • 1 year ago •via X (Twitter)

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An Anthropic engineer watched my screen from the next table at a cafe in SF. "Are you running Claude against live prediction markets right now" I told him yes. Then I showed him the stack. 214 trades. 74% win rate. +$9,437 in 19 days. Here's what actually happened: I gave Claude two repos and a simple job. First repo: A full market archive. Wallet behavior, entries, exits, and timing across the board. Second repo: Three commands. 500+ markets. No API key. Just a clean way to score the board fast. The system does not try to predict the world. It tries to find which wallets consistently exit better than the crowd, isolate the pattern, and only fire when the same structure shows up again. Main filter: captured value / expected value > 0.70 If a wallet wins often but leaks the move on exit, it gets ignored. If it captures most of the move and cuts losers fast, it becomes signal. Sizing uses Kelly: f* = (p*b - q) / b That is what stops the terminal from apeing into weak edges. Most of the time it does nothing. No edge - no position. Three trades from the run: > AMD Xilinx - entered 52c. Model said 59c. Closed +7c in 2h40m. > Artemis launch - entered 63c. Model said 85c. Closed +22c in 5h10m. > Derecho MW - entered 71c. Model said 87c. Closed +16c in 1h50m. When he saw the repo links and the live terminal, he stopped talking for a second. Then he said: "We tested something close to this internally." That was the whole joke. The data is public. The repos are public. The market is public. But most Polymarket traders still trade headlines, hold too long, and call it conviction. Polymarket does not reward the smartest story. It rewards the cleaner exit.

st1ne

171,039 views • 3 months ago

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.

Miles Deutscher

56,204 views • 18 days ago

Dagknight technical progress As would be mentioned in a still unshared post by Michael Sutton, the dagknight effort is split into v0 devnet, v1 testnet and v2 mainnet candidate. I’ve been testing the current v0-based implementation in a small devnet with the help of some testers who run nodes and miners with me. The DK work can be thought of as split into two parts: (1) implementing the actual protocol and (2) wiring it up and using it. The testing and development over the last month has been focused on (2). Obviously, DK is a consensus change for selecting parents. What’s not so obvious is that such a change affects DAA, coinbase, IBD, pruning and a lot more. Each of these areas is very sensitive and requires proper understanding to wire correctly. An important consideration and difference from GD is that DK does not focus on maximizing a property like blue work. So to maintain topological properties of blue work, an independent (free) GD implementation is kept running specifically for maintaining blue work. This allows us to keep using the property for topology. Coloring and blue score use the megachain induced by DK. The wiring around DK as of this posting is in a working state, but still needs to be reviewed. Next efforts will be focused on protocol specific components, particularly Tie-Breaking and incremental UMC. Attached are some captures from the internal devnet. The dense DAG image is what happens when things related to DAA or other similar consensus parameter causes a node to insist on their POV. The video is a recent snippet of the KGI running on the devnet showing (perhaps not obviously) DK at work. The current “dagknight” branch is now posted on the main repo. A topic in the Public R&D has been opened for Dagknight development.

coderofstuff

52,857 views • 5 months ago