The irony: the device promised "AI that remembers everything" but couldn't actually use most of my data. LLM context windows max out around 200k tokens.
  6 months of transcripts = millions of tokens. The "AI memory" was just summarization of recent conversations.

  So I built a local workflow with Claude Code to actually make use of the data:

  1. Parse and structure transcripts by date/topic
  2. Extract decisions, action items, and key insights
  3. Build a searchable knowledge base with cross-references
  4. Generate a CLAUDE.md file - portable context I can give any AI assistant

  The CLAUDE.md concept is the most useful part. It's a structured file describing who I am, how I work, my preferences, ongoing projects. Now any AI I use
   can read it and have context about me without needing my entire conversation history.

  I wrote up the full prompts so others can do this with their own voice data (works with Omi, Plaud, or any export). The bigger realization: these devices
   are architecturally limited until we get either infinite context or good local-first AI.

  Happy to answer questions about the workflow or the technical limitations I hit.