@TheStalwart @Havelock_AI **Feature request: versioned share links for a specific Havelock query** Joe — Request for make a specific analysis addressable by URL, and put the model/API version in the path so a link is stable. **Problem** Beta is paste-only (` There is no way to send someone “this text, on this version.” To reproduce a result you have to re-paste the prompt and hope the backend has not moved. **Proposal** 1. Version in the path Treat the interface as versioned pages, not one floating beta: - ` - ` - ` Changing `v1` → `v2` / `v2.1` loads that API/model. Keep `/beta/` as an alias for current if you want. Pin each path to a specific analyzer so old links do not silently score against a new model. 2. Query parameter for the prompt Encode the submitted text (or a link to it) in the query string: ``` ``` or, when the source is a page rather than pasted body: ``` ``` On load: read `text` or `url`, fill the input, run Analyze on that version. Percent-encode the value so `?`, `&`, and `#` in the source do not break the outer URL. Prefer `url=` for long documents; keep `text=` for short passages that fit a query string. Honor the existing 30–3,000 word / 18,000 character cap and reject or truncate with a clear message. 3. “Copy link to this prompt” in the UI Next to Analyze (and again on the results view), add a button that copies the canonical link for the current version + current prompt, e.g. ` After a successful run, that same link should reopen the text and, if cheap enough, the saved result. If results are too large to put in the URL, store a short job id: ` and still expose Copy link. **Why this helps** Researchers can cite a score against a known version, compare v1 vs v2.1 on the same passage, and send a teammate the exact query instead of a screenshot and a paste dump. **Suggested first cut** Path versioning + `?text=` + Copy link. Add `?url=` and `?id=` after that if you want remote sources and durable result pages. Happy to test. Thanks Tripp

Making some progress on getting the model to dispatch sub-agents to compete on generating new ideas and then having GPT6 judge the idea. No idea if it's working, but it's kind of interesting to try observe.

Where’s a guide to getting a model to just keep trying various techniques to achieve some goal. I have two experiments I’m working on that are slightly more complex than @Havelock_AI / Fedlock. And I want the AI to just autonomously cycle through new approaches until one works.

New BPEA paper by @SVNieuwerburgh argues that AI buildout will cost $10.3T over next 8 years. Furthermore, to get a 10% return on this, those investing will need annual revenues of $3.7T by 2032! (Approx 9% of GDP!)