Discover the Best AI Resources
Curated essentials, no noise — just what matters
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.
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.
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.
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.
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.
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.
Bundles AI features and coding agents into JetBrains IDEs, using IDE code intelligence for completion, refactoring, and chat. Runs on the proprietary Mellum model or your choice of OpenAI, Gemini, Anthropic, and local models via Ollama or LM Studio.
Provides a diffusion-model studio for image, video, audio-video, editing, LoRA, and full training workflows so many model families share one inference and training framework.
Builds custom AI inference servers in pure Python on top of FastAPI, keeping full control over request logic while batching, GPU autoscaling, streaming, and OpenAI-spec endpoints come built in. Claims a 2x+ throughput edge over plain FastAPI.
Estimates and tracks 6D poses of novel objects without per-object fine-tuning — supports both model-based (CAD) and model-free (few reference images) setups. Trained on large-scale synthetic data with a transformer-based architecture and contrastive learning; CVPR 2024 highlight with demos and pretrained weights.