I dump all my private notes into an LLM and tell it to build me a site

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I’ve always found the concept of AI "hallucinations" fascinating. We constantly debate whether models are creative or just making stuff up, so I wanted to see what happens when you remove the guardrails entirely. Tresbuchet is a publishing experiment. I drop a loose prompt into the agent along with a folder full of random text files, chat logs, and thoughts I’ve accumulated over the years. No specific instructions, no schema, no guidance on how to style it. I just tell it to "Go." It reads the unstructured text and generates a completely new iteration of the site based on how it interprets the data. It's been interesting tracking how different platforms (Claude, Codex, Gemini) handle the exact same mess of text differently. I've found that giving the models less instruction actually yields more interesting UI and copy results than trying to prompt-engineer a specific outcome. I'm just building stuff to see what the models do when left to their own devices. Curious what you guys think of the approach.

Visit product View on Hacker NewsFeb 28, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, claude, model · Missing: mac, agents, macos

97%97% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersIH features products with proven revenue · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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