orch-hub
A local control plane for AI coding agents — one operator, a portfolio of repos, and year-long projects kept inside window-sized tasks.
orch-hub is a control plane I design and build for my own AI-assisted development. It runs resident on my Mac, is operated from a phone over a private network, and coordinates per-repository orchestrator programs that drive autonomous dev/review loops over task files. The repository is private; this page describes what it is and why it exists.
The product in use
The dashboard keeps the portfolio legible at a glance: repository readiness, live-run state, and active-task counts share one operating surface. A waiting run is deliberately more visible than an idle one, because it is the place where human attention can unblock the system.
When a run reaches a decision boundary, the task view exposes the question in context. The operator can discuss a change or give a binding answer from the same phone-first surface; the agent continues afterward without turning the whole workflow back into a terminal session.
The unit is a product lifecycle, not a chat session
A production-grade project is rarely one or two agent sessions — from idea to launch it is months of work across hundreds of sessions. Most AI coding surfaces manage the single session well and leave the time axis to the human. orch-hub manages the layer above: which repos are ready, what each backlog holds, which runs are waiting on a human answer, what happened while you were away — the operating state of a whole portfolio, not a list of conversations.
Bounded working set over unbounded project time
The hub fronts a protocol stack — a task lifecycle plus a curated, machine-parsable project memory — built around one invariant: the working set stays bounded while the project grows without bound. Every task is sized to one context window; completed work passes an admission gate that distills durable knowledge into a timeless snapshot and archives the rest. The agent in month twelve faces the same bounded shape as the agent on day one — and so does the operator: the interface converges instead of accreting.
Human in the loop, at the right altitude
Agents run autonomously inside their task; the human is pulled in only at escalation points — a question, a plan gate, a review verdict, a new-task intake. Those arrive as push notifications and one-tap answers on a phone, so steering a fleet of long-running agents fits into the gaps of a day rather than demanding a terminal.
Stack: Python · FastAPI · file-contract IPC with detached orchestrator subprocesses · mobile-first dependency-free SPA · launchd resident service · push notifications · private-network-only, fail-closed bearer auth.