Discover the Best AI Resources

Amphion

Unifies text-to-speech, singing voice synthesis, voice conversion, and text-to-audio/music in one PyTorch framework with shared vocoders and a common evaluation pipeline. Ships recipes, pretrained checkpoints, and visualizations of classic models.

pyannote/speaker-diarization-3.1

Performs speaker diarization (who spoke when) with pyannote-audio: combines voice-activity detection, speaker-change and overlapped-speech detection to produce time-stamped speaker segments; compatible with Hugging Face Endpoints and ASR pipelines.

The Data Engineering Handbook

Curated learning hub that aggregates roadmaps, tutorials, bootcamps, books, projects, and tool recommendations for learning data engineering and production data infrastructure. Focuses on practical applied learning (projects, interview prep, community links) rather than code libraries.

Open-LLM-VTuber

Hands-free voice-first companion with a Live2D avatar for real-time conversations with LLMs. Cross-platform web and desktop clients, runs locally or via cloud APIs, supports local ASR/TTS and modular customization for personas and models.

FreeCAD MCP

Exposes FreeCAD over the Model Context Protocol so AI assistants (e.g., Claude Desktop) can create/edit CAD documents, run FEM, execute Python inside FreeCAD, and capture screenshots. Provided as a Python MCP server plus a FreeCAD addon with local/remote modes and optional text-only output.

Collection of runnable model implementations — LLaMA, Mistral, Stable Diffusion, Whisper, CLIP, plus LoRA fine-tuning — ported to the MLX array framework so they run natively on Apple silicon's unified memory rather than CUDA.

MLX

Provides a NumPy-like array framework for building and training ML on Apple silicon, with Python, C/C++, and Swift APIs plus PyTorch-style higher-level modules. Features lazy evaluation, composable AD/vectorization, and a unified-memory multi-device model so arrays can be used on CPU and GPU without explicit copies.

Mamba

A selective State Space Model architecture and PyTorch implementation for linear-time sequence modeling. Hardware-aware, designed for information-dense tasks (e.g. language modeling), with pretrained weights on Hugging Face; requires CUDA-enabled PyTorch.

VLMEvalKit

Runs one-command evaluation of vision-language models across 80+ multimodal benchmarks, handling data download, inference, and metric scoring in a single pass. Supports 220+ LMMs; adding a new model means writing one generate_inner() function.

Builds realtime voice AI agents that run as server-side participants in WebRTC rooms — mix STT, LLM, and TTS providers or use one realtime model. Adds semantic turn detection, SIP telephony, multi-agent handoffs, and an LLM-judge test harness.

social-auto-upload

Automates uploading and scheduled publishing of videos to major Chinese and international social platforms (Douyin, Bilibili, Xiaohongshu, Kuaishou, WeChat Video Channel, TikTok, etc.). Offers a CLI, platform-specific uploader modules, headless/browser automation and agent-skill integration for scripted cross-posting workflows.

Teaches the math behind modern deep learning across 21 chapters, from shallow nets to transformers and diffusion models. Each idea is explained in words, then in equations, then visually. Full PDF, slides, and Python notebooks are free.