Discover the Best AI Resources
Curated essentials, no noise — just what matters
AI Infra2016
Unified Node.js library for web crawling and browser automation that fetches pages and files via headless browsers or raw HTTP. Provides persistent queues, proxy rotation, session management, storage, and human-like fingerprints to build scalable data pipelines (e.g., RAG/LLM datasets).
AI Deploy2016
Converts trained PyTorch, TensorFlow, and ONNX models into GPU-tuned inference engines via layer fusion, kernel auto-tuning, and reduced precision. Cuts latency, raises throughput on NVIDIA GPUs from Turing (INT8), with FP8 on Ada+ and FP4 on Blackwell+.
AI Infra2016
Provides a NumPy/SciPy-compatible GPU array library for Python, enabling existing NumPy/SciPy numerical code to run on NVIDIA CUDA and AMD ROCm with minimal changes. Exposes low-level CUDA features (RawKernels, Streams) and offers prebuilt binaries for multiple CUDA/ROCm versions.
Reframes the VAE's tendency to ignore its latent code as a controllable design choice: by limiting a PixelCNN decoder's receptive field and using autoregressive flow priors, the code is forced to keep only global structure and discard local texture.
Builds deep learning from the ground up, first teaching the linear algebra, probability, and numerical methods most ML texts assume you know. Three parts run from math foundations to practical networks to research topics, favoring reasoning over recipes.
AI Others2017
Interactive, community-driven learning roadmaps and guides that map skills, technologies, and curated resources for developer career paths — covering frontend, backend, DevOps, ML/AI, MLOps, prompt engineering and more. Clickable nodes link to tutorials, best practices and question banks to guide study and hiring prep.
Recasts a scatter of competing graph-network designs as one message-passing recipe — propagate, aggregate, read out — then proves it on QM9, hitting chemical accuracy on most molecular property targets without hand-built descriptors.
Isolates relational reasoning into a tiny plug-in module that scores pairwise object relations, bolting onto CNN/LSTM encoders to hit super-human 95.5% on CLEVR — and proving plain convnets lack this capacity on their own.
The 2017 paper that replaced recurrence with pure self-attention, making sequence models fully parallelizable — and, almost as a side effect, laying the architectural foundation for nearly every large language model that followed, from BERT to GPT.
MLOps2017
Tracks every ML run — hyperparameters, metrics, checkpoints, dataset versions — into one dashboard you share as a live report, with Sweeps for tuning and a model registry. Weave extends it to LLM apps: tracing, evals, and production monitoring.
AI Train2017
Trains gradient-boosted decision trees with native categorical-feature handling, GPU acceleration, and production-ready prediction APIs. A strong fit for tabular ML when preprocessing categories into numeric features would add noise or leakage.