Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Infra2018
Runs ONNX models faster on CPU, GPU, and NPU by routing graph subgraphs to backend execution providers (CUDA, TensorRT, OpenVINO, DirectML, CoreML). One engine serves the same model across cloud, browser, mobile, and edge, for both inference and training.
MLOps2019
Turns plain Python functions into versioned, serverless ML jobs that run unchanged locally or on Kubernetes, with built-in tracking and deployment. Its feature store derives both offline (batch) and online (real-time) serving from one definition.
AI Deploy2019
Turns Python ML code into production inference APIs that scale on Kubernetes or any cloud. Bundles models, dependencies, and serving logic into versioned "Bentos" with autoscaling, scale-to-zero, and multi-GPU serving for LLMs and custom models.
AI Image2019
Bundles hundreds of pretrained image backbones — ResNet, EfficientNet, ViT, ConvNeXt, Swin and more — behind one consistent API for classification and feature extraction, with training and inference scripts that reproduce published ImageNet results.
A 1.5B-parameter model trained only to predict the next token on diverse web text does translation, summarization, and QA zero-shot, with no fine-tuning. It recast NLP tasks as conditional language modeling and sparked the staged-release misuse debate.
AI Agent2019
Open-source Airtable alternative for building databases, apps, automations, and AI agents without code over a PostgreSQL-backed REST API. The Kuma assistant turns plain language into tables and workflows; self-hostable with full data ownership.
A 2019 essay arguing that over 70 years of AI, general methods that scale with computation — search and learning — consistently beat hand-coded human knowledge. The short text that crystallized the scaling-vs-priors debate.
AI Client2019
Provides an AI-driven English learning app suite (Enjoy) that focuses on speaking practice and pronunciation evaluation. Open-source repo backing a web app, browser extensions for YouTube/Netflix, and a local-first desktop/web client design; some scoring features require the project's paid Enjoy AI service.
AI Train2019
Turns raw PyTorch training loops into structured modules that scale from a laptop to multi-node GPUs without rewriting model logic. It handles precision, checkpointing, logging, and distributed execution while preserving PyTorch control.
AI Train2019
Implements deep RL algorithms (PPO, DQN, SAC, TD3, DDPG, C51, PPG) as standalone single-file scripts — the PPO Atari variant is ~340 readable lines. Built for research debugging and reproducibility, with W&B and TensorBoard tracking.
AI Others2019
Node-based platform for building automation workflows that wire together 400+ apps and 70+ LangChain AI nodes, supporting agents, RAG, and 12+ LLM providers. Fair-code licensed and self-hostable, so pricing is server time rather than per-operation.
AI Audio2019
Build, fine-tune, and deploy speech AI on NVIDIA GPUs: ASR, text-to-speech, and speech LLMs in one PyTorch stack. Ships pretrained Parakeet/Canary recognition and Magpie TTS checkpoints; broader LLM/multimodal training now lives in v2.7.0.