ArchonHQ
Insights
AI engineering insights, research notes, and product updates from the ArchonHQ team.
SOTA model orchestration beats single-model purity
Why single-model standardization breaks down in production, and how routing Opus 4.6 and Codex 5.3 by role under deterministic gates creates higher delivery quality.
Three Weeks, One Person, One Full Platform
AI doesn't just make small teams faster. It eliminates the coordination overhead that big companies can't escape. The real advantage isn't access to AI — it's the absence of structure that AI can't optimize away.
7 Agents, One Weekend: What Happens When You Let an AI Swarm Build Your Product
Seven concurrent AI agents built a complete Go CLI tool in a weekend. 213 tests, 15 packages, 16 commands. The insight: writing code is cheap now. The merge is the work.
Four Failures in One Afternoon: Why Your Agent Swarm Needs a Watchdog
We spawned seven agents. All seven died silently. Four different bugs stacked on top of each other before we built something that could catch them.
Building An Agent Swarm: Lessons From Our First Month
What actually happens when you run 11 AI coding agents across two projects simultaneously. The failures, the rules that emerged, and the insight worth naming.
OpenClaw + Codex Agent Swarm: The Full Setup Guide
The complete setup guide for running a multi-agent development team. Dispatcher, watchdog, phase gates, and the full automated pipeline.
My Git History Looks Like I Hired a Dev Team
How I use an AI orchestrator to manage a fleet of coding agents with worktrees, tmux, deterministic monitoring, and the two-tier context split that makes it work.
memd: the memory sidecar that makes AI work compound
Most AI assistants don’t fail on answers. They fail on continuity. memd is the small memory sidecar that turns recall → act → record into a habit, so your work compounds across sessions.