So, we tried feeding the logs back to the LLM, and it mostly produced slop. Lots of decisions nobody cared about. The biggest things that moved the needle were:

- Baseline it. We mine previous logs, github comments, etc. for "what you care about." That helps pull out decisions that you actually care to read.

- Anchor to code. "The code enshrines this decision" is more interesting than "the agent self-talked this." Agents don't always self-talk decisions, and the thing that ultimately matters is the behavior in code.

to your edit (and all totally fair):

- Yes, closed source and signup required. A lot of what we're driving towards is easy team sharing, so we're taking the bath early instead of building an OSS thing and rug-pulling later.

- Code doesn't leave your machine. There's an agent that runs locally. I know "trust me" isn't the strongest stance, but this comes from the multi-player future.

- Honestly, Goose AI is a remnant of a previous product. It's inert and we'll clean it up once we've gotten the last couple folks off the previous iteration.

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