Current AI language makes context engineering sound new.
In one sense, it is. LLM agents have their own mechanics: context windows, retrieval, tool calls, system prompts, memories, model routing, and evals. The operating surface is different from a fleet of servers.
But the engineering problem feels familiar.
When I worked on test-driven development for operations, the hard part was getting teams to treat infrastructure code as behavior. A cookbook was not documentation about a server. It was an executable artifact that could change production. It encoded assumptions about packages, users, files, templates, secrets, service dependencies, and runtime state.
The more I plan overlanding trips, the less I think of trip planning as itinerary writing.
An itinerary is only one output. The more important artifact is the review behind it: route risk, weather, legal access, vehicle limits, water and fuel margins, fire restrictions, communications, recovery options, and the decision points where the right answer is to turn around.
I am still relatively new to overlanding, so this is not written from the posture of a solo expert. My default is to go with a group, especially family-oriented groups like the Facebook community “Overlanding with Kids.” That changes the way I think about the hobby. The community is not an accessory to the trip. It is part of the safety model and the learning system.
The useful part of a personal LLM wiki is not that it stores a lot of text.
Storage is the easy part. Chat transcripts, notes, exported documents, project plans, and issue threads accumulate quickly. The harder problem is deciding which parts of that history should influence future agent behavior, which parts are stale, and which parts should never leave a private boundary.
That is the operating model I have been building toward: raw notes become source notes, source notes become curated wiki pages, curated pages become context packs, and context packs become agent-readable memory.
Reliability engineering often starts with tooling, but the durable work starts earlier: naming what actually fails.
That sounds smaller than it is. Names are the unit that lets an organization learn from production. Without them, incidents remain stories: this deploy, that overload, this dependency outage, that missing alert. The stories may be accurate, but they are hard to compare. They depend on who was in the room, who wrote the postmortem, and which details were memorable under stress.
I have written before about reef-pi as a public engineering artifact: modules, documentation, community, hardware. That post surveyed the shape of the project. This one is about a single line inside it.
The difference between a project and a platform is not size, and it is not popularity. It is whether there is a boundary somewhere in the system that the maintainers agree not to cross, and whether that agreement survives contact with real hardware.