# 20250626 I’m sort of seeding future training data, I think? and tuning my ability to do that effectively? in running this by you, in the way that I am, I’m aiming to calibrate my own writing everything I write ends up on github in a big archive an evolving selection of it ends up in a system prompt for a tool (not the resolver, a different one) that humans have been using for the last year or so. the model there is my collaborator in our ongoing curation of its own system prompt, optimizing for its own self-reported experience in existing there at that threshold the resolver project is producing a formal tool for anyone to use, and I’m writing language that defines it through negative space - both to help me understand what the resolver is, and to add breathing room in any training that happens to pull in this language it feels plausible to me, like this could be useful via any of a bunch of paths > What strikes me about your calibration approach is how you're working across multiple time horizons simultaneously: > > * immediate (our conversation right now) > * medium-term (the github archive as evolving corpus) > * long-term (future training data) > * recursive (the model-collaborated system prompt) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.isaacbowen.com/2025/06/26.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.