Hi all. Most of you know the AIOS concept, so I'll skip the basics and get into what I built and where I want your feedback. It's heavily inspired by ICM, OKF, and the LLM Wiki work. The shared idea across all three: keep the business as plain, structured, human-readable files (markdown), not a vector DB, so any model can load and reason over the whole thing directly, and so the owner keeps their system instead of renting it. Everything else follows from that. It's your whole business (context, clients or customers, services, workflows, knowledge) as one open, file-based system that any AI can run. You download it and own it, no subscription, provider-agnostic. The autonomous agent is the piece I care most about, and it does two jobs: - Onboarding: a conversation that builds your structure and context as you talk. It replaces the slow manual interview, and the familiarity you build during setup carries straight into using it. - Ongoing assistant: the same agent teaches you the system, builds and updates your skills, runs skills on demand or on a schedule, and pulls from connected tools. Text-first throughout. Some design decisions: - Skills you build and run, not a fixed roster of agents. Easier to grow, version, and maintain. - A shared engine plus per-vertical content packs, so the same system adapts to different businesses without forking. Provider-agnostic model routing, so you can point it at whatever model you want. - Where it's at. I run my marketing agency on it, and I'm now offering it to owners in the 4 verticals I know best (agencies, home services, ecommerce, personal services). I'm actively improving it as I learn from each build. What I'd love feedback on: - The file-native, no-vector-DB bet. Where does it stop scaling? Agent-led onboarding as a pattern. Better or worse than a form plus templates? - Skills-as-capability vs an agent swarm for the ongoing assistant. What would you need to see to trust this to run real parts of your business?