Generate short social videos from Reddit threads in one command — captures post content, assembles visuals and optional TTS narration, and outputs an upload-ready MP4. Runs locally with Python + Playwright; does not auto-upload for safety.
Transformer-based foundation model for tabular data that provides pre-trained checkpoints for fast classification and regression, with GPU-accelerated local inference and an optional cloud client. Best suited for small-to-medium datasets (~≤50k rows).
Framework for building multi-channel AI assistants that autonomously plan tasks, invoke tools/skills, and keep long-term memory; supports many LLM providers and channels (WeChat, Feishu, QQ, web) for local or server 24/7 deployment.
Runs a self-hosted web server and React-based UI to generate, edit, and manage images using Stable Diffusion, SDXL and other foundation models. Includes a model manager, unified canvas, node-based workflows, gallery, and support for ckpt, diffusers and some gguf checkpoints.
Hands-on lecture series that teaches neural networks from first principles up to building a GPT: each lecture pairs a YouTube video with Jupyter notebooks and exercises so you code models (micrograd → MLPs → WaveNet-like convs → GPT) while learning training and debugging.
Unifies successive YOLO generations — YOLOv8, YOLO11, YOLOv3 and newer — under one package and a single `YOLO` API spanning detection, segmentation, classification, pose, oriented boxes and tracking, plus one-line export to ONNX, TensorRT and CoreML.
Multilingual sequence-to-sequence speech model and toolkit for speech recognition, speech-to-text translation, and language identification. Offers several model sizes (tiny → large/turbo) for different speed/accuracy trade-offs and ships with a CLI and Python API for offline transcription workflows.
Rust-and-Python toolkit that serves open-source LLMs (Llama, Falcon, Mixtral, StarCoder) over HTTP/gRPC with tensor parallelism, continuous batching, Flash/Paged Attention and quantization. Now in maintenance mode, pointing users toward vLLM and SGLang.
Build, run, and monitor LLM agents across one stack: an open framework for chaining models and tools, LangGraph for stateful agent orchestration, and LangSmith for tracing, evaluation, and deployment in production.
Build LLM-powered agents and applications from modular components: provider-agnostic model abstractions, tool integrations, retrievers for RAG, and agent orchestration primitives. Suited for prototyping and production agent workflows; requires developer wiring and dependency management.
Build full‑stack web apps entirely in Python — write frontend components and backend state as Python classes with a reactive model. Provides fast refresh, deployment tooling, and AI-focused integrations such as an AI Builder and an Agent Toolkit for connecting LLMs and image models.