A deep dive into the architectural patterns behind local-first autonomous development pipelines, examining how offline AI agents can orchestrate end-to-end software creation without cloud dependencies.
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.
Calling an LLM API is the easy part. Shipping reliable AI features in production demands evaluation pipelines, retrieval layers, observability, cost controls, and guardrails. Here is what real AI engineering looks like in 2026.
Forward Deployed Engineers embed inside customer teams to turn messy enterprise data into shipped AI systems. Here’s how the role emerged, what FDEs actually do, and why it matters now.
Skip the tutorials and learn AI engineering the way it actually ships in production: pick a stack, build small, evaluate honestly, and deploy behind an API. Here’s the roadmap.