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An open-source, high-performance quantitative trading framework in C++/Python focused on strategy analysis and backtesting Trading model R&D · Ultra-fast engine · Efficient backtesting

Run GLM-5.3 (744B), GLM-5.3-Flash (321B), DeepSeek-V4.1-Flash (748B) and MiniMax-M3.1-Flash (428B) on your own GPUs - one engine, one dashboard, OpenAI- and Anthropic-compatible API.

Cross-vendor 3D Gaussian Splatting trainer - video to splat to mesh, Vulkan or CUDA.

  • 3d-gaussian-splatting
  • gaussian-splatting
  • 3d-reconstruction
  • 3dgs

This is a lightweight, headless browser purpose-built for AI agents (such as Codex or Claude Code) to perform browser automation tasks; it is designed to navigate through various web protection systems, login processes, and reverse-engineering scenarios

Xolotl: run DeepSeek-V4.1-Flash, all 552B, as a local coding agent on one gaming PC (RTX 4090 + 110 GB RAM): ~33 tok/s on agent tasks, ~41 in fast mode, 512K context. Plus GLM-5.3-Flash, Qwen3.8 and LFM3-Omni. MoE experts tiered across VRAM, pinned RAM and NVMe, with Xolotl Direct NVMe->VRAM. OpenAI + Anthropic API.

A C++14-compatible physical units library with no dependencies and a single-file delivery option. Emphasis on safety, accessibility, performance, and developer experience.

  • cpp
  • cpp14
  • cpp14-library
  • dimensional-analysis

The fastest automatic differentiation library for C++, with Python bindings. Forward, adjoint and higher-order derivatives, checkpointing, Eigen support and JIT. Reproducible benchmarks against CppAD, Adept and autodiff.

  • automatic-differentiation
  • aad
  • algorithmic-differentiation
  • autodiff

ShowStopper is a tool for helping malware researchers explore and test anti-debug techniques or verify debugger plugins or other solutions that clash with standard anti-debug methods.

Open source Windows x64 PE packer and crypter. Compresses and encrypts executables with a custom virtual machine into a self extracting stub.

  • compression
  • packer
  • pe-packer
  • pe-compress

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.