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Source channel @githubtrending · Post #14743 · May 23

#javascript#ecmascript_proposals#es2015#es2019#es6#es7#esnext#javascript#js#polyfill#ponyfill#promise#proposal#proposals#shim#symbol#weakmap core-js is a modular JavaScript library that provides polyfills for modern ECMAScript features up to 2024, including promises, symbols, collections, iterators, typed arrays, and many web standards like URL and structuredClone. It lets you use new JavaScript features in older browsers by loading only the needed parts without polluting the global namespace. It integrates well with tools like Babel and swc for optimized polyfilling. This helps you write modern, compatible code that runs smoothly across different environments, improving development efficiency and user experience. You can customize polyfill usage and even build your own tailored version for your project. https://github.com/zloirock/core-js

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@githubtrending · Post #15295 · 11/11/2025, 05:00 PM

#python#ai#faiss#gpt_oss#langchain#llama_index#llm#localstorage#offline_first#ollama#privacy#python#rag#retrieval_augmented_generation#vector_database#vector_search#vectors LEANN is a tiny, powerful vector database that lets you turn your laptop into a personal AI assistant capable of searching millions of documents using 97% less storage than traditional systems without losing accuracy. It works by storing a compact graph and computing embeddings only when needed, saving huge space and keeping your data private on your device. You can search your files, emails, browser history, chat logs, live data from platforms like Slack and Twitter, and even codebases—all locally without cloud costs. This means fast, private, and efficient AI-powered search and retrieval on your own laptop. https://github.com/yichuan-w/LEANN

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@githubtrending · Post #15168 · 09/25/2025, 12:30 PM

#python#ai#context#embedded#faiss#knowledge_base#knowledge_graph#llm#machine_learning#memory#nlp#offline_first#opencv#python#rag#retrieval_augmented_generation#semantic_search#vector_database#video_processing Memvid lets you store millions of text pieces inside a single MP4 video file using QR codes, making your data 50-100 times smaller than usual databases. You can search this video instantly in under 100 milliseconds without needing servers or internet after setup. It works offline, is easy to use with simple Python code, and supports PDFs and chat with your data. The upcoming version 2 will add features like continuous memory updates, shareable capsules, fast local caching, and better video compression, making your AI memory smarter, faster, and more flexible. This means you get a powerful, portable, and efficient way to manage and search huge knowledge bases quickly and easily. https://github.com/Olow304/memvid