Most ‘AI engineering’ tutorials stop at a notebook. This post walks through the full production stack — data pipelines, training loops, evaluation harnesses, serving, and observability — so you can build a system that actually ships.
Coding agents spend most of their budget on file and symbol discovery, not on actual reasoning. Here is how precomputed context graphs change that, and why it matters for every team shipping agentic dev tools.
Most ‘AI agents’ demos hide a complex runtime underneath. Here is what actually runs in production — the planner, the tools, the memory layer, the guardrails, and the observability stack that holds it all together.
Build a streaming token sampler from scratch in PyTorch covering temperature, top-k, top-p, min-p, and contrastive search — a CV-worthy systems project for ML engineers.
Build a working LoRA fine-tuning loop in pure NumPy, computing every gradient by hand through low-rank adapters. A practical side project that demonstrates real systems understanding to hiring managers.