Become a great builder
in the AI Era
In-depth writing on machine learning, data engineering, and the systems behind modern AI — for people who want to go from curious to capable.
Six tracks, one goal: build things that work
Each track goes deep — theory, intuition, and code — so you can go from reading a paper to running experiments.
ML / DL / RL
Neural networks from scratch, backprop, reinforcement learning — and the architectures that changed everything.
Backend Engineering
APIs, databases, streaming systems, and the infrastructure that feeds every production AI application.
Cloud Infrastructure
Kubernetes, serverless, VPCs, and CI/CD — deploying and scaling AI applications reliably in the cloud.
Model Inference
CUDA kernels, memory bandwidth, batching strategies — how to make training and serving fast.
Distributed Systems
Async workloads, replicas, horizontal scaling, and the architecture of highly concurrent systems.