Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
AI agents for incident triage sound great in demos. We've tried it in production. The patterns that earn their keep, the ones that backfire, and where humans still beat agents.
Most LLM eval suites correlate poorly with what real users experience. The eval patterns we run that move with prod metrics — and the ones that lied to us.
Single-provider LLM apps fail when the provider does. Multi-provider routing isn't just resilience — it's also a cost lever. The patterns we run.
Pure vector search misses exact-keyword queries. Pure BM25 misses semantic ones. Combining them with reciprocal rank fusion is the simplest large win in RAG retrieval.
Embedding indexes degrade silently. The signals that catch drift, how often to re-embed, and the operational patterns we built after one quiet quality regression.
Streaming LLM responses is easy until the client disconnects, the model stalls, or the user cancels. The patterns that keep streaming responsive without leaking spend.
When LLMs can call tools that change real state, the design decisions that matter most are about what's gated, what's automatic, and what triggers a human checkpoint.
Embeddings turn text into numbers a computer can compare. Here's the working mental model, a runnable Python example, and where embeddings fit in real apps.
A hands-on intro to prompt engineering. Learn the four levers (role, format, examples, constraints) and watch a vague prompt turn into a reliable one.
A working retrieval-augmented generation app you can run today. Markdown ingestion, embeddings, semantic search, and an LLM answer — start to finish in one afternoon.
