@Matt Payne thanks :)

@Ryan Nolan you're welcome, I'm still pretty new to this but had to get a local instance running the learn on.

I was thinking that 'second brains' really should be exactly that and you're onto a good thing here - after all, isn't automation all about reducing friction and toil?

I think that if you break these questions down and post them in the community, you will probably get some good feedback.

I’d keep the core architecture, but simplify it and separate concerns more cleanly. Your current direction (markdown + Obsidian + automation) is actually stronger long-term than most AI-native stacks because it’s portable, filesystem-based, and easier to control. The main issue I see, is that there are too many layers acting as sources of truth at once (Obsidian, Drive, Sheets, AI routing, etc.). For a stable, reliable system you need a single source of truth. I would: - keep Obsidian as the editing/UI layer - keep markdown files as the canonical datastore - use AI only for fuzzy tasks (classification, summarization, extraction) - make routing/storage deterministic (code or 'no-code' this up old style) - use Google Drive only for backup/sync and not as part of the active logic (that belongs in your orchestration layer) - replace Sheets mapping with metadata/frontmatter or config files (remove an external dependency and locate metadata where it can be easily processed) - add a proper indexing layer (Postgres + pgvector or Chroma, etc.) for RAG instead of querying synced files directly You mentioned a simplified vault structure - I'd consider flattening the structure because AI/RAG systems generally work better with smaller atomic notes with strong metadata than deeply nested semantic folders. Frontmatter like subject, semester, type, status, tags, etc. will scale much better than increasingly complex directory logic. The main trade-off with this is that you lose intuitive folder-based navigation in exchange for much more powerful search, filtering, and AI-friendly retrieval through metadata. Another gain is reduced cognative load in terms of removing the decision on where to place notes. I would not move fully into Notion/agent-style systems yet. A lot of “AI-native” architectures become fragile because they rely too heavily on probabilistic behavior where deterministic automation would be more reliable. The key is to really think in terms of system design: keep AI handling the fuzzy/semantic parts (classification, summarization, retrieval) while the core system itself stays deterministic and predictable (storage, routing, state management, indexing). That separation usually leads to systems that are far more reliable, scalable, and maintainable long-term.

# copy patch into repo cp /path/to/patch.patch /path/to/repo/ # go to repo cd /path/to/repo # apply patch (preserves commit metadata if it was made with git format-patch) git am patch.patch # if conflicts occur: # resolve conflicts, then: git add . git am --continue # or abort: git am --abort