The difference between generic AI output and genuinely useful work is context. Not documents written for the purpose of feeding AI. Context that accumulates naturally from the work itself.
Every meeting you record. Every thinking session you speak through. Every conversation where a decision was made or an idea took shape. Transcribed and summarised, these build into something more valuable than any brief or strategy deck. The brief is what you thought was important. The recordings are everything, including the tangent that turned out to be the real insight.
A Coherent Space
When recordings are organised into projects, something shifts. A project stops being a folder and becomes a body of knowledge. Each recording adds a layer. Summaries compound across sessions. Patterns emerge that no single recording could reveal.
You don’t need to re-listen to forty minutes. You search, skim a summary, and pull the three minutes that matter into whatever you’re working on now.
Markdown as Fabric
SR-7 exports recordings as Markdown files with YAML front matter. Title, summary, transcript, metadata. Plain text, portable, yours.
Drop them into Obsidian and they become part of your second brain. Commit them to a Git repository and they become part of your project history. Point an AI at the folder and they become the rich fabric it works from. No API, no lock-in. Just files that any tool can read.
The value isn’t in any single file. It’s in the aggregate. Weeks of recordings, transcribed and summarised, forming a context repository that grows with every conversation.
Direct Integration via MCP
For workflows that need more than files, SR-7 runs a local MCP server. AI tools and agents on your machine can query your library directly. Search across transcripts. Read summaries. Cross-reference what was discussed in separate sessions.
Ask an agent to draft a follow-up based on last week’s recordings. Have it identify commitments across a month of meetings. Use it to surface connections you missed. The recordings don’t change. What changes is how much value you extract from them.
And because the MCP server runs locally, your context stays on your hardware. You choose what to expose and to which tools.
Capture Is All You Do
You don’t maintain this repository. It maintains itself. Speak the thought, record the meeting, start the session. Transcription, summarisation, organisation, export. All automatic, all on-device.
The richest context you have is the one you’ve been creating all along, every time you think out loud.