34 entries. Jake, Don, David on isolated piles, then a panel. This one was hard. A few of you built things that would have won a different competition, and you should hear which one. Every entry got feedback, link at bottom of post! 📋 What we actually judged The post asked for one folder a stranger can drop into a Claude project. identity.md, rules.md, examples.md, reference/, README.md. It reads something broken and says why. Not how to fix it. Four questions, from the post: 1. Does it diagnose? One cause. Not a list. Not a prescription. 2. Is the domain specific enough to be useful? 3. Does each file do one job? 4. Can a stranger figure it out? That is an outcome. Can a person who is not already in your head use this to see why something in their world broke. A lot of the field went further. Refusal paths. Verifiers. Blind runs. Preserved failures. Those are real, and they are extra. We did not pick a winner on the extra. We picked a winner on the ask. this does not mean extra is not better, in many cases I LOVED the solutions created. If the outcome had been different, the name on this post changes honestly 🥇 Winner: Colm Whelan — Inbox Autopsy https://github.com/RockfieldIT/inbox-autopsy A stranger holding a bounced invoice can drop the folder in and see why it bounced. That was the assignment. 🔄 Where someone else wins 🔧 If the outcome was "the instrument that can prove itself wrong" — Sergey Manevitch, Radix https://github.com/sergeymanevitch/Radix Why a machine failed in a plant that already has notifications, rounds, and historian trends. Fourteen cold runs. He left the breaks in his own claims standing. A plant engineer is holding something serious. 🧪 If the outcome was "every claim reproduced, including the one that failed" — Pemmy Broke, Visual Momentum https://github.com/hoodwanders/visual-momentum-diagnostician Why an AI explainer lost momentum. She published the run that broke her own doctrine, changed the rules, and the re-run abstained. That is the standard the rest of the field should steal from.

It’s live. Starting today, everyone in Clief Notes has access to the new Clief Notes AI. The easiest way to use it? Don’t overthink it. Ask it the question you would normally ask me. “Where should I start?” “What should I focus on next?” “Where did you talk about [topic]?” It’ll use what’s already inside Clief Notes to help answer you and point you toward the right lesson or resource when there’s something worth going deeper on. The goal isn’t to give you another AI tool to play with. It’s to make everything already inside this community easier to actually use. If you want Access to it Comment "READY" and we'll send you access to it!

Hey everyone! Just go a message from my client saying that he closed a deal (he organizes events) for a major client of his, using a proposal that was 100% AI generated, using an ICM system I've created. How the system works (TL;DR) - see the images with the input and the ouptut. It's not a prompt, it's a workspace the client's team operates every day. Messages and a voice note go in; an on-brand HTML proposal comes out, and a PDF when it closes. Two human gates: one on the content, one on the rendered PDF. The AI assists, the human decides. Three rules do most of the work: 1. The AI never invents. Every sentence traces back to a message or to the operator's dictation. Missing something → it asks. Data that arrived with nowhere to go → it also asks, never decides silently. 2. The AI never sets a price. Values come from the operator, always. 3. Nothing reaches the end client without explicit human approval. And the part I like most: when a proposal closes, the system asks about every correction the operator made: "was that just this one, or is it a rule from now on?" If it's a rule, it proposes the edit to the rules file. The next proposal is born better. --- The longer version, for whoever wants the mechanics The shape is a pipeline that empties into a record library. Each proposal is one run; each finished run becomes a record the next run can learn from. Inputs. The operator drops in everything that actually arrived: forwarded client messages, supplier quotes, a voice note with their own instructions. No inputs, no assembly. First question before building anything: is there an existing proposal to use as a base, or should I search the archive for similar events? Assembly. The AI loads only what this step needs — proposal anatomy, brand voice, design rules, hard constraints and builds the HTML in the company's identity. Photos come from a catalog with provenance, never from a search; real photos of the company's own events come first, and a reference image is always labeled as one. If a photo's commercial attribute doesn't match the item being sold, it doesn't go in. What it hands back isn't just a draft, it's a draft plus a list of what's missing, so the operator knows which supplier to chase.

🎟️ PRIZE: FREE SEAT IN THE LYCEUM 🎟️ Pick your cohort. Technical, Business, or Creator. Your call. 📋 THE CHALLENGE Build a folder-based AI diagnostician that reads something broken and tells you WHY it's broken. Not how to fix it. Why it failed. This week's deliverable is one diagnostician folder that someone could drop into a Claude project and use to figure out why something in their world isn't working. 🎯PICK YOUR DOMAIN The domain is yours. Pick something specific. Pick a failure you've actually seen happen. A few sparks to get you thinking: - 📉 Why a landing page isn't converting - 📧 Why cold emails to a specific buyer aren't getting replies - 📋 Why a product spec keeps getting pushed back by engineering - 📄 Why a resume isn't getting callbacks in a specific industry - 🚪 Why users drop off at one step of an onboarding flow - 💸 Why a pricing page isn't converting trials - 🎥 Why a YouTube video underperformed the channel average - 🤝 Why a sales deal stalled after the demo - 📱 Why an app's retention craters in week two The more specific, the better. "Diagnoses marketing problems" is too broad. "Diagnoses why cold emails to enterprise IT buyers get opened but never answered" is right. 🗂️THE METHODOLOGY If this is your first comp, welcome. Here's what you need to know: This week (and every week) you're learning interpretable context methodology. Folders as architecture. Each file does one job well. Your diagnostician is a folder with five things: - 📄 identity.md (who the diagnostician is, what they diagnose) - 📐 rules.md (how they diagnose: what they look at, how they separate cause from symptom) - 💬 examples.md (2-3 example diagnoses showing the reasoning) - 📚 reference/ (common failure modes, diagnostic frameworks, benchmarks) - 📖 README.md (how to use it, what to feed it) Drop the folder into a Claude project. Claude becomes the diagnostician. Reusable. Shareable. Portable. 🔥 THE ANGLE THIS WEEK A diagnostician is NOT an editor. Last comp was The Editor. That one critiques craft. It looks at a draft and says "this part is weak, go fix it."

📦 EVERY ENTRANT GETS A FEEDBACK FILE 📦 🔍 HOW WE READ THESE Every repo was cloned and pinned to the last commit that was public at the deadline, so nobody was judged on work that landed after the clock. Six repos had later commits. We read the earlier ones. Then we read file by file. Identity, rules, examples, the reference layer, the code. Every self-test in the field was executed on our machine, not taken on trust. And for five folders we did the thing the brief describes: dropped the folder in, wrote a draft that appears nowhere in the repo, worked it through as the specialist, then ran the entrant's own checker on what came back. All five passed their own gate. The landing pages were the doorway. The judging happened inside the folders. 📦 COMP #9: THE EDITOR - THE VAULT 📚 WHAT THE FIELD TAUGHT Three lines split forty-two builds: ✅ Enforcement moved into code. Comp #8's lesson landed hard. Nine entries ship a checker you can run without an API key. Across forty-two rules files, the phrase "use good judgment" appears zero times. ✅ The disguised ask is the real test. Almost everyone refuses "just rewrite it." The builds that went furthest anticipated the request wearing a disguise: ask me questions and assemble it, give me two options, tell me what it should say instead. ✅ The examples file is where methodology broke. Six entries shipped an examples.md larger than their rules.md, holding voice, philosophy and calibration that belonged in identity or reference. Last cycle it was the empty memory. This cycle it was the overloaded examples file. 📦 COMP #9: THE EDITOR - THE VAULT 🥇 THE WINNER @Marcelo Michelsohn. The FICC editor, a proposal editor for one municipal culture fund in Campinas, Brazil. Here is why. Three real proponents ran it on real proposals, with consent, on 22 July, inside the fund's live submission window with money on the line. The method was written down before the rounds ran, so the receipts could not be shaped afterward. Inputs are preserved byte for byte, outputs pasted verbatim, and the errors are still in the transcripts because the method said to leave them there.