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Auto Ontology is a Python framework for building semantic, graph-based reasoning layers over data. It enables deterministic query generation, better context retrieval, and more explainable AI for LLM and agent workflows.

TakaTime is a blazingly fast, privacy-first coding activity tracker and the open-source, self-hosted alternative to WakaTime. Track your development activity across Neovim, VS Code, JetBrains IDEs, and Antigravity while generating rich analytics, beautiful dashboards, and GitHub profile statistics—all powered by your own MongoDB database.

IsoHill is a new isometric engine for Flash Player 11 [molehill] built on top of the open 2D framework Starling. Features: Plugins - engine-wide modifications, Includes a TMX [CVS export] parser, Components - dynamic logic can be added and removed from IsoSprites, Virtually unlimited layers, Anti-aliasing of isometric tiles, deterministic framework, Mipmapping, and under BSD license

TranslatedAntifouling coating evidence panel: formulation sandbox, route biofouling stress, literature bounds. Simulation ≠ measurement. Live: https://simulator.jassyhong.top

  • biofouling
  • formulation
  • antifouling
  • evidence-panel

Ragpicker is a Plugin based malware crawler with pre-analysis and reporting functionalities. Use this tool if you are testing antivirus products, collecting malware for another analyzer/zoo.

Turms is a pure python implementation of the awesome graphql-codegen library, following a simliar extensible design.

Elixir implementation of the Git object storage, but with the goal to implement the same semantic with other storage and topics

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.