A speculative roadmap for forking an open-core AI meeting assistant, with a worked example of how to use AI coding tools to implement it.

Most companies are playing a game they don’t understand the rules to. They’re standing in a room full of competitors, all of them staring at the same two doors. One door says OpenAI. The other says Anthropic. They believe the strategic decision is which door to walk through. They believe this so deeply that they’re paying enormous premiums for inference that’s past the point of diminishing returns, and getting nothing their competitors aren’t also getting.

Anthropic released an open-source Claude Code skill that takes a founder from idea to a live managed agent in minutes. Hermes Agent already has most of the same primitives. Here’s the mapping, the gaps, and the blueprint.

A self-hosted, MIT-licensed research tool with an agent skill that teaches any AI how to drive it. No rate limits, no credit counters.

The Product Manager wanted to ship an MVP. Three phases. Skip the reproducible build. Skip the caching subsystem. Skip the Mermaid diagrams. Ship fast, iterate later.

The Debugger said that path would destroy maintainer trust.

“Non-Deterministic Bug Blindness,” the Debugger said. “Skip the reproduction baseline and the ‘fails on main’ gate? The agent writes a fix, runs the test, it passes, but it was passing BEFORE the fix too because the test is tautological. The trust curve is steep to climb and vertical to fall off.”

AI-generated PRs are overwhelming open source maintainers. A solution already exists in the AGENTS.md convention. So why won’t they use it?

A practical guide to installing and configuring hermes-cashew, the thought-graph memory provider for Hermes Agent. Five minutes from zero to a working semantic brain with recursive graph retrieval, think cycles, and sleep consolidation.

How I built an AI research tool for n8n workflows without knowing TypeScript, using engineering discipline to solve the vibe coding comprehension paradox.