Cheetah

Flexible, vendor-agnostic, and scaling from a single process to a fully distributed, multi-user, multi-tenant deployment — components that absorb the problems every distributed product keeps re-fighting. The notoriously unreliable Chrome extension lifecycle is one of them: workers killed mid-operation, restrictions, disruption arriving at the worst possible moment. A laptop that sleeps halfway through a job and a second server process that never held the live connection produce the same shape of failure.

Where the defaults fit, a working ecosystem comes together in a few lines of code. Where they do not, the components stay understandable and replaceable — across environments, scales, client types and tasks.

Holding those fundamental problems inside the substrate is the point: they cannot leak upward into the interchangeable parts. What remains above the boundary is product code and boilerplate — the kind that can already be delegated to an AI assistant to implement and maintain, without anyone re-deriving distributed identity, retry semantics or stale-result handling each time.

The communication and execution protocol is deliberately extensible, so it carries the other efforts too: Imprint's distilled, declarative site knowledge can be delegated out to the perimeter, on demand and dynamically.

Working at this design level is solid material for raising builders to the architectural level — the elevation pillar, and Arboretum as our answer to it. And the complexity underneath, which belongs to the problems rather than to the code, is exactly what demands multi-angle analysis instead of operational debugging alone: elevated verification, and Paladin.

Where it fits. Cheetah is for products that issue named operations to identified clients whose runtimes may disappear, restart or move, where the result still needs policy, correlation and a durable record. It is not an event stream, not an ordinary service-to-service RPC layer, and not worth the weight for a single-process application that will stay one.

A product rarely arrives as a distributed system. It begins as a concept study or a sensible single-process application. Then another client appears, another machine, a browser extension, a user whose laptop sleeps halfway through an operation, and a second server process that does not hold the live connection.

A WebSocket delivering JSON takes an afternoon. Stable identity, late and duplicate results, restart safety, routing, durable history, approval, telemetry and failure handling are the actual work. Rebuilding that connective layer for each product consumes schedules and reproduces the same subtle failures.

Separately developed products, in different domains and at different scales, kept arriving at the same boundary: either the limits of an existing library, or a greenfield design repeating troubleshooting rounds someone else had already finished. Cheetah is that cross-project experience made reusable — and later products exercise it in new settings and feed the lessons back into the shared base.

The goal is larger than a connection library. Someone building an application should spend attention on its meaningful commands, behaviours and decisions — not on recreating a distributed skeleton before the first product idea can run reliably.

The application boundary stays recognizable while the deployment grows. The same named operation runs through one in-memory process on a laptop, or across separate Redis-backed roles on several machines, without the product's command boundary changing.