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313–336 of 1,836 · Most stars

Succinct Compositional Program Graph (SCPG) static analysis engine & 14-diagram UML generator in Rust.

  • code-property-graph
  • compiler-theory
  • program-analysis
  • robdd

Claude skills for LinkedIn. 12 Claude Code and Codex skills that write human-sounding LinkedIn posts, craft comments that get noticed, analyze your feed, and build a publishing cadence, all from your terminal. Content engineering by Creative Content Crafts. MIT.

A professional, high-fidelity de-anonymization and vulnerability diagnostic framework. Features a zero-coupling modular architecture for browser-based intelligence gathering, hardware fingerprinting, and security research.

  • browser-security
  • cybersecurity
  • de-anonymization
  • ethical-hacking

A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

Blendshape and kinematics calculator for Mediapipe/Tensorflow.js Face, Eyes, Pose, and Finger tracking models.

  • vtuber
  • mediapipe-facemesh
  • mediapipe-pose
  • mediapipe-hands

Red Ink - A one-stop Xiaohongshu image-and-text generator based on the 🍌Nano Banana Pro🍌, "One Sentence, One Image: Generate Xiaohongshu Text and Images."

  • ai
  • aigc
  • content-generator
  • social-media

Dockerized OpenAI-compatible wrapper for Kokoro-82M text-to-speech w/multiplatform CPU, AMD, NVIDIA GPU PyTorch; multi-speaker, clone-tuning, caption timestamps, SSML, websockets, optional readalong web UI

TranslatedBased on Dineshkarthik's project. Cross-platform, supports viewing download progress on the web, supports issuing download commands via bot, supports downloading from private groups that have already been joined even when downloading of their resources is restricted. telegram media download,Download media files from a telegram conversation/chat/channel up to 2GiB per file

  • media-downloader
  • flask
  • cross-platform
  • downloader

A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.