Discover the Best AI Resources
Curated essentials, no noise — just what matters
PyTorch library for operator learning: neural networks that map between whole function spaces, not fixed grids, so a model trained at one resolution runs at any other. Bundles FNO, Tensorized FNO and related architectures, mainly for solving PDEs.
Sits between PyTorch and micrograd: eager tensors with autograd plus a small, fully hackable compiler that fuses operations into kernels. Adding a new accelerator backend takes about 25 low-level ops, so it runs on CUDA, Metal, AMD, and WebGPU.
Exposes a self-hosted WhatsApp HTTP/REST API that runs a real WhatsApp Web instance so apps and AI agents can read/send messages, manage contacts, and automate flows. Offers three engine modes (WEBJS, NOWEB, GOWS), Docker images, and MCP support; relies on WhatsApp Web so blocking risk exists.
Builds business systems like CRMs, ERPs, and internal tools without code. Data-model-driven design keeps data in standard relational databases separate from the UI, avoiding vendor lock-in. A microkernel plugin architecture makes features composable.
Typed Python client for the OpenAI REST API that offers synchronous and asynchronous clients, typed request/response models, streaming and Realtime support, webhook verification, and integrations for Azure and Amazon Bedrock—built for production integrations and automation.
Deploys trained SavedModels behind gRPC and REST endpoints, with hot-swappable versioning so new weights load without downtime. Built around servables, loaders, sources, and a manager, plus request batching to cut accelerator cost.
A 12-week, 24-lesson beginner-friendly AI curriculum with executable Jupyter notebooks, quizzes and labs that teach neural networks, computer vision, NLP, generative models and ethics using PyTorch and TensorFlow examples.
Showed that fine-tuning a GPT model on public GitHub code yields a capable program synthesizer, and introduced HumanEval — the docstring-to-function benchmark that still anchors code-generation evaluation. A production variant powers GitHub Copilot.
Unified metadata platform for data discovery, observability, and governance — central metadata repository, column-level lineage, and a pluggable ingestion framework with 84+ connectors. Suited for teams that need searchable data catalogs, automated lineage, and collaborative data governance.
Runs, manages, and scales AI workloads across 20+ clouds, Kubernetes, Slurm, and on-prem from one YAML or Python spec. Auto-provisions GPUs/TPUs, fails over across regions and providers when capacity is short, and routes jobs to the cheapest option.
Self-hostable personal “AI second brain” that turns web pages and documents into a searchable knowledge base, builds custom agents and automations, and connects to local or cloud LLMs with multi-platform access.