Awesome session with our team and Clif.

The first #Android #app I built is installed on my phone. That sentence still feels strange. I do not know how to code, but I had an idea for software that could support a remote-viewing practice I have followed for years. The prototype let me start a session, record what happened, and draw directly on the screen. When I opened it and saw my own process working on my phone, I thought: I might have an actual product here. Something I can keep developing. Then came the awkward question. Did I build it? The best answer I have is: I built it with my AI. I brought the idea, my completed session artifacts, reference videos, knowledge of the protocols, and the judgment to recognize when the first direction was wrong. The AI handled the technical implementation and helped refine the prototype. Without those practitioner artifacts, it could have built an app. It would have missed the practice I was trying to support. That experience changed what I thought I was capable of creating. AI lowered a technical barrier that had kept ideas like this in my head. But the useful design still depended on experience the AI could not supply for me. For the builders and practitioners here: what did you have to show the AI before it could build something that fit your actual work?

An #AI provenance mark can show that material may have passed through a model. It cannot settle authorship, accuracy, or responsibility, and the trail may break during editing or resharing. The Label Is a Clue: hypernovelty.institute/artic…

#California's DROP portal lets eligible residents send one deletion request to 600+ registered data brokers. Processing can take up to 90 days, with exemptions and matching limits. What the new privacy exit does: thefastnow.com/signals/your-…

I almost shipped a book with a broken cover. Everything was lined up. Approved. Ready for the final #KDP upload. Then I looked at the cover one more time and realized it had reverted to an earlier version. Multiple rounds of edits appeared to be stacked together, and it looked terrible. I stopped the upload. The #AI searched the logs, found the approved cover, and fixed it. We missed the window for that day, so the release had to wait. That context failure was frustrating in the way AI failures often are. You explain something, move forward, and somehow end up explaining it again. Goldfish energy. But telling the AI to recover the right cover was still much faster than redesigning it myself. I got the speed and the mistake in the same workflow. Welcome to this place: amazon.com/dp/B0HDJMW6KQ This is why I keep a human reviewer at the points where digital work becomes a physical product, a public page, or a real expense. Those are the moments when someone needs the authority to stop the process and look at what is actually there. The AI handled the recovery. My eyes kept the bad cover from going out. Where does your workflow become consequential, and who has the authority to stop it?

I'm looking for five people to test Signal Atlas, an early Windows forecast journal. Crew Zero includes two feedback rounds. Completed testers receive bounded Founding-tier access for the life of the product. Windows 10/11, 18+. Apply: docs.google.com/forms/d/e/1F…

Using this as inspiration to expand my one man operation.

Same pattern every time