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Source channel @githubtrending · Post #15515 · Feb 21

#typescript GitNexus indexes your codebase into a knowledge graph tracking dependencies, call chains, clusters, and flows, then connects AI agents like Cursor and Claude Code via CLI tools for reliable analysis. Run `npx gitnexus analyze` from your repo root to start—it auto-generates context files and MCP setup. Use tools like `impact` for change risks or `rename` for safe refactors. This boosts your productivity by preventing AI blind edits, cutting debugging time, and enabling smaller models to grasp full architecture fast. https://github.com/abhigyanpatwari/GitNexus

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@githubtrending · Post #15242 · 10/23/2025, 12:30 PM

#python#ant_colony_algorithm#artificial_intelligence#fish_swarms#genetic_algorithm#heuristic_algorithms#immune#immune_algorithm#optimization#particle_swarm_optimization#pso#simulated_annealing#travelling_salesman_problem#tsp You can use scikit-opt, a Python library offering many heuristic optimization algorithms like Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony, Immune Algorithm, and Artificial Fish Swarm Algorithm. It supports user-defined functions to customize operators, allows continuing runs from previous iterations, and accelerates computations via vectorization, multithreading, multiprocessing, and caching. GPU support is in development. It helps solve complex optimization problems such as function minimization and the Traveling Salesman Problem efficiently, with easy installation and rich examples. This saves you time and effort in implementing and tuning optimization algorithms yourself. https://github.com/guofei9987/scikit-opt