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TypeScriptfeaturedOpen

Someone tried to build AGI. Pick up where they left off.

Someone tried to build AGI with microservices. They got halfway. We have [the repo](https://github.com/andrewrgarcia/this-will-change-everything). It has a cognition engine, a memory layer, a reflex loop, and an event bus. It almost does something. There's even a [frontend](https://this-will-change-everything.vercel.app/) — signals, nodes, a P=NP claim, and a button that doesn't quite work. Start from it. Take it somewhere real. You decide what it becomes — but it has to run, and it has to grow from what's already there. See the [README](https://github.com/andrewrgarcia/this-will-change-everything/blob/main/README.md) for the full brief.

TypeScriptNext.jsOpen SourceArchitectureAIWeb dev~2 weeksremote
AG
Andrew Garcia
Remote
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tradingOpen

C++ Trading Engine — ongoing, team welcome (open source on completion)

## C++ trading engine — resolve all marked TODOs. I will provide a seed repo: working build, passing tests, clean C++20. The system runs signal → order → fill → PnL end-to-end. Ten TODOs across four areas: ### Signals - VAR(p) — Eigen OLS skeleton in place, algorithm documented ### Execution - Resting limit orders with price-time priority - Persistent order state (NEW → PARTIAL → FILLED → CANCELLED) - Partial fills across book updates - Nonlinear slippage (square-root impact, volatility dependence) ### Analytics - Position-aware PnL and trade pairing (open ↔ close) - Turnover, Sortino, volatility metrics ### Portfolio - Fees / commissions in position accounting Done when all existing tests pass, each TODO has coverage, and metrics.json is correct end-to-enC++ Trading Engine —d. Stack: C++20, Eigen, CMake, nlohmann/json. Repo is private during the project, open-sourced on completion.

tradingfinanceC++backtestingalgorithmsopen-sourceEigenCMakeOngoingremote
AG
Andrew Garcia
Remote
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AIOpen

Fractal Hunter — Robust Image Classification Under Extreme Distortion

## Up the Gain — Electric & Distortion-Ready Blorbos (toon penguins) hide by transforming into distorted, non-intuitive representations. You are given a dataset derived from a small set of source images, transformed through unknown but structure-preserving processes. 📦 Dataset & full documentation: https://github.com/andrewrgarcia/fractal-hunter-dataset 🔬 Kaggle (for experimentation): https://www.kaggle.com/datasets/drandrewgarcia/fractal-hunter-extreme-distortion ### Task Build a model that determines whether an image originated from Blorbo. ### Constraints - Transformation is unknown - No explicit inversion tricks - Must generalize to unseen images and transformations ### Goals - Robust classification under severe distortion - Clear approach + failure analysis ### Evaluation Held-out test set uses unseen transformations ### Stretch - Learn invariant representations - Recover structure without knowing the transform **Stack:** Python · PyTorch / sklearn

AIML / DataPythonFFTAutoencodersCNN~2 weeksremote
PF
Prince Fractalius
The Fractal Kingdom at Sagittarius A*
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