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Owned by Bill

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17 contributions to Clief Notes
The Stranger tests the Unrelenting Director
THE STRANGER TEST: When is AI work actually verified? Two principles I’ve been working with: 1. Self-attestation ≠ verification. An AI saying “tests pass” is a claim, not evidence. Independent verification isn’t about switching models. It’s about independently executing a test against evidence, without being told the original conclusion. 2. Copies aren’t corroboration. Three AI models agreeing doesn’t mean three independent confirmations. If they inherit the same assumptions, they may simply reproduce the same mistake. Count independent evidence paths, not votes. The Stranger Test: Can someone who wasn’t in the room reproduce your conclusion from the evidence alone? Give them the source material, acceptance criteria, and test instructions. Withhold your conclusion. Let them determine the result. I recently applied this to my own AI-assisted engineering work. A prototype had been reported as built and tested. But independent verification hadn’t been established. That distinction matters. BUILT / TESTED claimed. INDEPENDENTLY VERIFIED not established. This isn’t about distrusting AI. It’s about building systems where trust doesn’t depend on who tells the story. If the evidence can’t survive without the storyteller, the conclusion isn’t verified. That’s the standard I’m working toward.
0 likes • 10h
@Aurel Babiš Aurel, that’s a really important distinction. I’ve been running into the same problem building Atlas. A system reporting that approval happened isn’t the same as having evidence that it actually did. I’m curious, how are you preserving those original records without adding so much complexity that the verification becomes its own problem?
0 likes • 9h
@Grant Kirkwood Hey Grant, sorry for the delayed reply! I’ve been a bit out of pocket lately. With Atlas, I’m trying to keep evaluations pretty straightforward. First, define what success actually looks like. Then test whether the system did what it was supposed to do, rather than just trusting its own report. I also like introducing known failures to see whether the checks actually catch them. One thing I’ve learned is that adding more reviewers doesn’t necessarily make something more reliable, especially if they’re all making the same assumptions. I’m curious, what’s the biggest point of disagreement on your team? What to measure, or how to prove the results?
Stopping Those Thieving %$^&#*s
Hi all, got a question about security. In particular, existing content and stopping it from being stolen. Some of my clients live in the "paid course/paid monthly report" teaching/info space. Lately, nearly all the measures we've had in place for stopping duplication of product and reselling at ridiculously low prices has become ineffective. Cease and desist is ignored (not that it was ever really listened too) and of course its expensive and time consuming. Water-marking has become a joke with AI being able to remove them. Placing product behind any type of existing protection also seems to be easily subverted. We've even found out about a $10,000 product being bought collectively by 10 people, copied by hand/AI and re-packaged across the globe at a fraction of the cost (making them a fortune by the way). It's a racket I tells ya. So I thought I'd come to you, the brains trust, to see if anyone has some up-to-date sneaky-ass (maybe even AI driven) methods to stop these thieving $%&#*%s. Thank you in advance.
1 like • 13d
@Aaron Parker That sounds like exactly the right approach, Aaron. No sense reinventing something they’re already paying for. Once you’ve got a clearer picture of one client’s setup, I’d be happy to help think through where the actual gaps are and whether there’s a practical way to close them. I like the idea of starting with one real case rather than trying to solve the entire piracy problem at once. Looking forward to hearing what you find!
0 likes • 10h
@Aaron Parker Aaron, sorry for the late reply! I’ve been a bit out of pocket lately. I appreciate you circling back with what your clients are actually saying. I think shifting the value toward access to the creator rather than just protecting the content makes a lot of sense. It’s unfortunate that so many owners feel overwhelmed by all this. Sometimes the best solution isn’t more technology, but changing how we approach the problem. Keep being that flea in their ear! Haha.
I updated the icm-architect skill. Go grab the new version.
RinDig/icm-architect It runs in pretty much any agent now: Codex, Gemini CLI, Hermes, Claude Code, Cursor. Or hand the folder to any model and tell it to use the skill. Workspaces now start from an AGENTS .md file, so they aren’t tied to one company’s tools. It would work with others before just not as reliably, should be better now. I also cleaned it up a lot. The whole method comes down to three building blocks now: a sequence of steps, a shelf of same-shaped files, and a small map that points to everything. Every step ends at a check, whether that’s you looking at it or a small model doing it, so a mistake gets caught before the next step builds on it. There are also some small scripts that check your links and draw your whole workspace as a graph you can click through (very basic graph, obsidian is better for viz ill make a nicer one later) github.com/RinDig/icm-architect Install steps for each agent are in the README. Try it on something you do every week and tell me where it breaks.
1 like • 13d
@Sean Collins-Cruz Sean, this is exactly the direction I’ve been exploring. I keep coming back to the idea that the AI should be allowed to reason freely, but its conclusions shouldn’t automatically become established facts. I’ve been using a distinction between proposed, built, tested, and verified. Your point about calibrating against actual human judgment adds another important dimension. I especially like the semi-manual runs. Give the system real cases, record its decisions before showing it the human answer, and then study where they disagree. The interesting question to me is whether we can get the system to recognize when its own judgment isn’t reliable enough to proceed. I’d love to compare notes on your memory and ontology experiments. I suspect we’re approaching a similar problem from different directions.
0 likes • 10h
@Clare Martinez Exactly, Clare! A system can follow every instruction perfectly and still arrive at the wrong answer. That’s what fascinates me. I’m starting to think the real challenge isn’t making AI more consistent, but figuring out when we actually have enough evidence to trust its work. How are you approaching that distinction in your own builds?
Our software Idea made it through the first filter of A16z Speedrun
a16z speedrun is one of the biggest accelerators in the world. We submitted our application a bit back on the recommendation of one of our friends. Our EdubaWare software has been in closed testing for a bit (get ready, next week thousands of you will get access to it and see our changes) We received and Email yesterday saying they want to hear more from us and to send them a video in under three minutes that explains: - Your backgrounds and greatest accomplishments, professional or personal - What makes you exceptional, and why you're the team to win this market - What you're building and what problem it solves - Why this is a big, underserved opportunity - What progress you've made so far  Now fitting all that into one video is challenging, but we did our best. I hope this gives you a view into the other side of my life which is my company Eduba!
2 likes • 11h
Congrats, Jake! That’s a huge milestone! Really excited to see where EdubaWare goes. Curious, what’s been your biggest surprise from putting it in front of real testers?
Welcome to Clief Notes. Here's where to start.
Welcome to Clief Notes. Here’s where to start. 👋 You don’t need to watch the entire classroom before trying something. Pick a starting point, make something small, and bring your questions back here. 🧭 Find your way around Navigating the Course shows you what’s here and how to get around. If you’re new to working with AI, start with The Foundation. We work through prompting, context, and organizing your files so you can give an AI a job you can actually check. You’ll find guidance for Claude Code and Codex, plus a regular chat route where you save the files yourself. 💬 Use the check-ins At the bottom of the lessons, you’ll find polls and discussion posts. Share what you tried, what worked, and what needed correcting. If something doesn’t make sense, post the lesson and timestamp. “I got stuck right here” is a perfectly useful question. 🏆 Put something into practice Visit the Competitions Mega Thread for the current challenges, rules, and prizes. It’s a good excuse to finish something and see how other people approached the same problem. 📦 Looking for more? Premium: Explore The Vault for practical packages, templates, meeting takeaways, and worked examples. VIP: Head into The Drawing Room for VIP sessions and resources. Post your work. Ask questions. Help someone else when you can. What are you here to build—or what’s one task you’d love to make easier? Tell us below.
0 likes • 14d
@Alex Haynes Curious, what's been the biggest credit drain in your workflow so far? I'm always interested in how people are making these systems more efficient in practice!
1 like • 14d
@Heather Neistein Heather, welcome! Your comment caught my attention because I’ve been exploring something very similar—using AI to take some of the mental overhead out of everyday life so there’s more room for the things we actually enjoy. I’m an engineer by trade, but I’ve been learning a lot of this as I go. It’s been a fascinating rabbit hole! I’d love to hear what you’re experimenting with so far. And with your background in systems analysis, I bet you’ll bring some really interesting perspectives to the conversation. Glad you’re here!
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Bill Bloomenkranz
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@bill-bloomenkranz-3953
Biogas engineer in Madison. I direct AI, I don't code. Building Atlas, where nothing counts as proven without a receipt.

Active 9h ago
Joined Sep 30, 2026
ENTJ
Madison, WI
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