Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Ollama gets a model running on your laptop in minutes; vLLM serves thousands of production requests. Here's when each one earns its place.
A practitioner's guide to how LLM API pricing works, how to estimate a workload's monthly bill, and the levers that actually cut it.
A practitioner's guide to tracing, cost tracking, and evaluating LLM apps in production with Langfuse, Helicone, Arize Phoenix, and LangSmith.
A demo RAG app is easy; one users trust is not. This is the map for reliable retrieval-augmented generation: grounding, evaluation, retrieval quality, guardrails, and safe rollout.
LangChain orchestrates agents and integrations, LlamaIndex owns retrieval and RAG. Here's where they overlap, where they don't, and which to reach for.
A prompt tweak or model bump can quietly wreck answers everywhere. Ship LLM changes the way you ship risky code: gate, shadow, canary, roll back.
A practitioner's guide to picking a GPU cloud for training and inference, where hourly rates for the same H100 can differ by 3x.
A practitioner's guide to picking the right LLM API for coding, long docs, cheap extraction, reasoning, vision, voice, and on-device work.
A practitioner's guide to picking between the three frontier LLM APIs based on task, price, latency, and enterprise terms.
Prompt injection is not a prompt bug, it is an architecture problem. Here is how we design LLM apps so a poisoned document cannot hijack them.
A RAG system that invents facts erodes trust fast. Here is how we ground answers, force citations, and catch the fabrications before users do.
When our single LLM provider had a 40-minute outage, every AI feature went dark. A gateway with routing and fallback fixed that, and cut spend 30% as a bonus.
