Vision and boundaries #

Purpose #

thought stream makes Cameron's information environment legible enough for small agents to do continuous, reviewable work. It is a private coordination plane rather than a social dashboard or an autonomous executive.

The system should answer:

• What arrived?
• Where did it come from?
• What changed?
• Which agents noticed it, and why?
• What context did each agent receive?
• What did it infer or propose?
• What model or adapter produced the result?
• Did the run actually complete?
• Can the entire projection be rebuilt from source events?

Desired behavior #

• One stream can contain email metadata, social events, Telegram messages, RSS items, file diffs, timers, git changes, and agent-produced events.
• An agent subscribes to a typed subset instead of receiving the whole personal firehose.
• Cheap triage agents can label, cluster, extract, or route. More expensive agents can be invoked only when a prior result justifies escalation.
• Cameron can watch root activity and inspect complete lineage without being buried in raw trace noise.
• Model behavior can improve through versioned prompts, adapters, and Tinker training while old outputs remain attributable to their exact runtime.
• A conceptualizer consumer can extract concepts and directional links from source events, producing private derived observations that are inspectable and rebuildable without any outbound publication authority.
• A policy-gated Coil consumer can keep one Co conversation per stable document, recommending revisions or additions to Public Knowledge without gaining file-write, staging, publication, protocol, or deployment authority.
• Broad obligations can become governed focus declarations over the same evidence stream, with narrower child focuses proposed rather than silently self-authorized.

Explicit non-goals for the first release #

• A universal autonomous assistant.
• Automatic public posting, email replies, Telegram sends, or file edits.
• Feeding the entire Obsidian vault or inbox into every model call.
• Treating agent summaries as truth.
• Hiding failures behind a green service status.
• Building a rich dashboard before the event loop is real.

Product test #

thought stream is useful when it produces a small number of surprising, inspectable, low-cost observations Cameron would not otherwise have connected, while making it obvious how each observation was produced.