Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
The LLM stack is a maze of APIs, GPU clouds, gateways, and serving tools. This is the map to what each layer is for and how to keep the bill sane.
Once you call more than one LLM provider, a gateway saves you from reinventing routing, fallback, caching, and spend limits in every service.
A practitioner comparison of the RAG frameworks worth using in 2026, from LlamaIndex and LangChain to Haystack, DSPy, and raw code.
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.
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 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.
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.
