Trending AI repositories

Aliu-AiRobot/

ESEILANE

High-performance Knowledge Graph engine for AI, LLMs, and GraphRAG — built for the next generation of intelligent applications.

FrankS-IntelLab/

agentic-kaggle-skill

🤖 AI Agent-driven Kaggle competition workflow. Battle-tested patterns for score stabilization, submission troubleshooting, kernel workflows, and spec-driven development.

RightNow-AI/

AutoMegaKernel

An agent harness that compiles a model into one provably-correct, self-retargeting CUDA megakernel and self-tunes it past cuBLAS at batch-1 LLM decode, paper: https://arxiv.org/abs/2606.09682

ather-techie/

rag-interview-system

A complete collection of RAG interview questions, answers (505 questions & 41 RAG types), system design scenarios, architecture patterns, and production-ready concepts.

llmsresearch/

llm-flashcards

Visual knowledge bank for understanding large language models, with 180 concept cards from tokenization to deployment.

nshkrdotcom/

trinity_coordinator

TRINITY in Elixir (An Evolved LLM Coordinator): route LLM calls via a small-model hidden-state router + Axon coordination head, with Thinker/Worker/Verifier orchestration and policy loop for acceptance-driven completion.

Varn1t/

EDAgent

Multi-agent exploratory data analysis system with autonomous insights, visualization, preprocessing, and reporting workflows.

MADEVAL/

FerryAI

Native AI inference for PHP 8.3+ - run ONNX, GGUF (llama.cpp) and RubixML models directly in your PHP process via FFI. Chat, streaming, embeddings, RAG and vector search. No Python, no HTTP microservices, no Docker sidecars.

deepelementlab/

jupyter-studio

The AI-native JupyterLab — open-source Cursor for notebooks. Cmd+K inline edit, multi-step agent with cell-level tools (read/edit/run), chat with @cell/@file context, ghost-text completion, one-click traceback fix. BYO model: Anthropic, OpenAI, Gemini, Ollama, vLLM. Local-first, privacy-first, fully open source.

Y0oshi/

Text-LLM-Training-from-scratch

A from-scratch implementation of the language-model training pipeline in PyTorch: tokenization, pretraining, SFT, and preference-based alignment.