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Starred repositories
ВКР: Анализ и выявление семантический дублей в торговых новостях
Hundreds of models & providers. One command to find what runs on your hardware.
Claude Code skill — autonomous ML intern (port of huggingface/ml-intern) with Telegram + Slack notifications
🪢 Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. Integrates with OpenTelemetry, LangChain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
A Russian data set for question answering over Wikidata
RAG Benchmark: comparing 8 retrieval-augmented generation architectures including SEQUOIA
Automatically find issues in image datasets and practice data-centric computer vision.
Detection of poetic meter, rhyme, and stress placement in the texts of Russian accentual-syllabic poems and songs.
Production-ready platform for agentic workflow development.
The open-source app everyone uses to manage agents at work
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
Неофициальный MCP-сервер для Checko.ru - проверка контрагентов (ЕГРЮЛ, ЕГРИП, арбитраж, контракты, финансы). 12 инструментов и 6 готовых сценариев.
Learn it. Build it. Ship it for others.
Learn LLM internals step by step - from tokenization to attention to inference optimization.
🦛 CHONK docs with Chonkie ✨ — The lightweight ingestion library for fast, efficient and robust RAG pipelines
Intuitive explanation of linear regression algorithm and optimization methods
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"
"RAG-Anything: All-in-One RAG Framework"
The best-benchmarked open-source AI memory system. And it's free.
A minimal Python logger that tracks everything you try when building AI - metrics, prompts, models, etc, so you can see what changed and why.
Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1
A unified library for building, evaluating, and storing speculative decoding algorithms for LLM inference in vLLM