Discover the Best AI Resources
Curated essentials, no noise — just what matters
Generates Netflix-quality single-line subtitles and optional dubbing for videos by automating download, ASR, word-level alignment, translation, terminology management and TTS integration. Emphasizes word-level alignment with WhisperX and cinematic translation/adaptation for cleaner, single-line subtitles and smoother dubbing.
Developer framework for building AI agents that autonomously trade on Polymarket prediction markets. Bundles the Polymarket and Gamma APIs, a Chroma RAG layer that pulls in news, and a CLI to query markets, reason with an LLM, and execute trades.
Structured dataset for training and evaluating LLM agentic behavior: function-calling conversations, JSON-mode structured outputs, and extraction samples for teaching models to generate tool calls and strict structured responses. Includes single-turn and multi-turn scenarios across several configs.
Runs local LLM, vision-language, ASR, OCR, and image-generation models across NPU, GPU, and CPU from one command. Differs from Ollama and llama.cpp with first-class Qualcomm Hexagon NPU support and day-0 coverage of new models like Qwen3-VL.
Runs a native, extensible AI agent on desktop, CLI, or API to automate code, workflows, research, and writing. Built in Rust, supports 15+ LLM providers and 70+ extensions via the Model Context Protocol — designed for local-first automation and developer workflows.
Trains a sub-100M-parameter LLM from scratch — pretraining, SFT, LoRA, DPO/RLHF, and distillation, sized from ~26M up to ~100M-plus dense and MoE. Headline figure: the ~64M minimind-3 variant's SFT stage runs 1 epoch in ~2h and ~3 RMB on one NVIDIA 3090.
Stores agent memory as human- and agent-readable Markdown files with wikilinks instead of an opaque vector DB. Auto Memory/Resource/Dream jobs distill conversations into long-term notes, and hybrid wikilink + BM25 + embedding search retrieves them.
Desktop finance analytics terminal that combines CFA-level models, real-time trading and 100+ data connectors with embedded Python for analytics; includes 37 AI agents and local/multi-provider LLM support for automated research and decision workflows.
Runs and optimizes ML and generative-AI models on-device across mobile, desktop, web, and IoT. Successor to TensorFlow Lite, it adds automated GPU/NPU accelerator selection and zero-copy buffer interop to cut latency without cloud round-trips.
Trains a 65M-parameter vision-language model from scratch in ~2 hours on one RTX 3090, about 3 RMB (~$0.40) of GPU rental. Connects a frozen SigLIP2 encoder to a small MiniMind LLM via a two-layer MLP projector; full PyTorch code for pretraining and SFT.
Turns PDFs and images into clean Markdown with a 7B vision-language model, keeping tables, equations, handwriting, and multi-column reading order while removing headers and footers. Runs on one 12GB+ GPU at about 1/32 the cost of GPT-4o APIs.