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Worth reading
A short take on pieces worth your time — architecture, AI, system design, and engineering careers — each one linked back to its original source.
3 articles
In-House LLM Serving at Netflix
Most companies just call a hosted LLM API and move on; Netflix's AI Platform team explains why and how they run the full inference stack themselves instead, inside their existing production environment rather than spinning up a separate ML silo. What's genuinely useful here is the decision-making, not just the architecture — the tradeoffs they weighed on cost, latency, and operational ownership before choosing to build rather than buy. Good grounding if you're evaluating whether your org actually needs to run its own inference infrastructure.
What Is RAG? How Retrieval-Augmented Generation Works
A clear, current walkthrough of retrieval-augmented generation: why it exists (grounding an LLM in your own data instead of relying on what it memorized during training), how the retrieval-then-generate pipeline actually works, and where teams run into trouble in practice — the retrieval step, not the model, is almost always the real failure point. Useful if you're deciding whether RAG is the right pattern for a project, or just want a precise mental model instead of the marketing version.
Building Effective AI Agents
Anthropic's own engineers stepping back from the hype to draw a precise line between a "workflow" (LLMs and tools wired through code you control) and an "agent" (the model deciding its own next steps) — a distinction that matters the moment you're deciding how much autonomy to actually hand an LLM in production. The five patterns it lays out — prompt chaining, routing, parallelization, orchestrator-worker, evaluator-optimizer — are the ones I keep coming back to when scoping an AI feature, because they're composable primitives rather than a framework you have to buy into. Worth reading before you reach for a heavyweight agent framework: most production use cases turn out to need a workflow, not an agent.