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Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

Translated:star:Github Ranking:star: Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically update daily. | Github repository ranking, automatically updated daily

  • ranking
  • stars
  • forks
  • github-rank

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

TranslatedOpen-source AI API relay benchmark, rankings & switcher for Claude Code / Codex: measured availability, latency and price

Command the forces of The Project in a battle to rebuild the world after mankind has been nearly destroyed by nuclear missiles. A 100% free and open source real-time strategy game for Windows, macOS, Linux, BSD+

  • game
  • c-plus-plus
  • strategy-game
  • cheats

allRank is a framework for training learning-to-rank neural models based on PyTorch.

  • learning-to-rank
  • ndcg
  • ranking
  • information-retrieval

Nornicdb is a distributed low-latency, Graph+Vector, Temporal MVCC with all sub-ms HNSW search, graph traversal, and writes. Using Neo4j Bolt/Cypher and qdrant's gRPC means you can switch with no changes while adding intelligent features like schemas, managed embeddings, reranking+llm, GPU accel, Auto-TLP, Policy-based Memory Decay, and MCP server.

MARA is a Mobile Application Reverse engineering and Analysis Framework. It is a toolkit that puts together commonly used mobile application reverse engineering and analysis tools to assist in testing mobile applications against the OWASP mobile security threats.

Port of a popular ruby faker gem written in kotlin. Generate realistically looking fake data such as names, addresses, banking details, and many more, that can be used for testing and data anonymization purposes.