The conversation history with your AI tools is a piece of infrastructure now. Treating it like ephemeral chat is fine for the casual case; treating it that way for daily work means you lose context, repeat yourself, and slowly poison your own retrieval.
Halfway through the year. The frontier moved, the workhorse tier got crowded, the price floor dropped. The honest leaderboard isn't a single ranking, it's a routing decision per workload, and the menu in July looks meaningfully different from the menu in May.
Two years ago, every other LinkedIn post promised six-figure salaries for prompt engineers. Today the title's mostly gone from job boards. The work didn't go away. The framing did, and it's worth being clear about why.
Twelve months in, the home setup has changed shape: a Mac mini for the always-on lighter services, a Synology underneath for storage and platform services, fast portable SSDs at the working ends, and since March a Mac Studio for inference.
An M4 Max with 64 GB of unified memory comfortably runs open-weights models that needed a serious GPU rig a year ago. The price-per-capability curve crossed something quietly, and the consequence for who can run frontier-adjacent inference at home is bigger than it looks.
The infrastructure-as-code review pipeline is one of the highest-leverage places to deploy AI in an engineering org, and almost nobody is doing it well. The reasons it's underused are mostly accidental rather than principled.
Every platform team buries the business in an eighty-nine-row YAML and calls it self-service. That isn't self-service, it's a configuration handoff. Decisions as Code is the discipline of extracting the five decisions that actually matter and letting the platform absorb the rest.
Twelve months of running agentic coding tools daily, and the failure mode that keeps repeating isn't the dramatic one. It's the quiet one, the agent doing the thing it was asked, exactly, to a result nobody wanted.
GPT-5 dropped this week. The benchmark deltas are real, the marketing layer is loud, and the question for shops with already-working AI stacks is which routing decisions actually need to change. Honest answer: fewer than the launch suggests.
Eleven seconds, very thorough, immaculate audit trail. A walk through what one Tuesday afternoon in my homelab teaches you about working with agentic coding tools, and the workflow changes that follow.
AWS shipped its own agent SDK two weeks ago, joining LangGraph, Semantic Kernel, the OpenAI Agents SDK, the Anthropic Agents SDK, and a half-dozen others. The question isn't which one's best. It's whether the framework layer is still where the differentiation happens at all.
It isn't a cluster in the data-center sense. It's a small, deliberate setup, a Mac Studio for inference, a Mac mini for the always-on services, fast portable SSDs at the working ends, and a real NAS underneath. Worth being honest about what it is and what it isn't.
