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Source channel @githubtrending · Post #15006 · Jul 29

#go#cli#event_driven#event_driven_architecture#queues#serverless#serverless_functions#workflow_engine#workflows Inngest lets you write reliable, long-running background functions called durable workflows that automatically handle retries, scheduling, and state management without needing to manage infrastructure like queues or servers. You write functions in your preferred language using their SDKs, run and test them locally with the Inngest Dev Server, then deploy them on your own infrastructure or Inngest’s platform. It supports complex workflows with steps that retry on failure, concurrency control, and event triggers. This saves you time and effort by simplifying event-driven app development, improving reliability, and scaling automatically without extra setup. It also offers tools for monitoring and managing workflows easily. https://github.com/inngest/inngest

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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