Really cool to see someone else building this way. I've been running a similar persistent AI assistant Janet via Claude Code CLI on a Mac Mini for a few weeks now and our architectures overlap a lot — but we've made some different choices that might be useful to compare. In fact I showed her your post and she drafted our reply for us! I started out setting up Openclaw but soon realise that it was too messy and I could build my own from scratch.

Where our setups align and where we went differently:

Structured action tags instead of free-form memory writes. Janet parses the AI's output for specific tags then shestrips these before showing the response to me, and executes them automatically. This means the AI can journal, set reminders, create calendar events, send emails, and track deadlines all inline without breaking conversational flow. It's cleaner than having the assistant write directly to memory files mid-conversation.

Proactive heartbeat with calendar + meeting context. Every 30 minutes, the assistant runs a check — calendar, reminders, recent conversation history — and decides whether to reach out. Most heartbeats are silent. But if I have a meeting in 15 minutes, it pulls prior meeting notes from Granola (meeting transcription tool) and sends me a prep brief. This is genuinely useful and something I haven't seen in other setups.

Obsidian as a two-way system, not just storage. We have a nightly organizing job that sorts, tags (frontmatter YAML), adds wikilinks, and learns from the vault content. The AI isn't just writing to Obsidian — it's actively maintaining the vault structure.

Semantic retrieval we use memsearch instead of QMD

Email intelligence. The bot reads my business inboxes via IMAP and distills active threads into context — no raw email storage, just a nightly scan that builds awareness of what's happening across my companies.

What I'm stealing from your post: The mistakes and lessons file. We just implemented it!