Generates HD short videos from a single topic/keyword — auto-creates script, finds/assembles footage, generates subtitles, TTS and background music. Offers web UI + API, batch mode, multiple LLM/TTS providers and common short-video aspect ratios.
Orchestrates low-code multi-agent teams that plan, research, code and deliver results to Telegram, Discord, and WhatsApp. Includes handoffs, guardrails, memory and RAG, and integrates 100+ LLM providers via MCP for production-ready agent workflows.
Continuously captures your screen and spoken conversations, transcribes them in real time, generates summaries and action items, and exposes a memory-backed chat that can retrieve what you've seen and heard. Works across desktop, mobile and wearable devices and supports local SDKs and cloud sync.
Turns any website into clean markdown, structured JSON, or screenshots through a single API — handling JavaScript rendering, rotating proxies, rate limits, and full-site crawling so LLM apps get web data without running scraping infrastructure.
Provides local inference, fine-tuning, and a server/CLI for vision–language and omni (image/audio/video) models via MLX. Supports multi-image chat, audio/video inputs, activation quantization (CUDA), TurboQuant KV cache, and LoRA/QLoRA fine-tuning for on-device workflows.
Chains pre-trained AI weather and climate models like GraphCast, Pangu, and FourCastNet into composable inference pipelines. Swap prognostic or diagnostic components, plug in reanalysis sources, and add ensemble perturbations or in-loop metrics.
Runs open LLMs entirely on your own machine — discover and download models from Hugging Face, chat in a desktop GUI, or expose an OpenAI-compatible local server. Native Apple MLX and llama.cpp backends; headless deploy via llmster.
Segments each PDF page into 11 labeled regions — titles, tables, formulas, figures, footnotes and more — and recovers reading order. Offers two engines: an accurate VGT visual model (~0.96 F1) or a faster CPU-only LightGBM ensemble.
Streamlines the full lifecycle of foundation models — data prep, fine-tuning (SFT/LoRA/QLoRA/GRPO), evaluation, and deployment — with ready-to-run recipes, multi-engine inference support, and cloud/CLI workflows for both laptop experiments and large-scale runs.
Runs reproducible evaluations of large language models through a Python API with built-in solvers, scorers, and model-graded grading. Ships 200+ ready-to-run evals spanning capability and safety testing, and connects to most major model providers.
Build AI workflows once and run them across model providers — GoogleAI, OpenAI, Claude, Ollama — through one SDK. Composable primitives for RAG, tool use, and agents, plus a local dev UI for tracing and debugging, with SDKs in JS/TS, Go, and Python.
Ingests documents, images, audio, video and web pages and converts them into structured, LLM-friendly markdown and parsed data. Runs locally (fits on a T4 GPU), supports ~20 file types, offers OCR, transcription, table extraction and a Gradio UI; deployable via Docker/Skypilot. Licensed under GPL-3.0; some model weights carry cc-by-nc-sa restrictions for commercial use.