Fr

Fragment – An AI-native notebook with YAML-based Prism Protocol

Hacker News

Fragment – An AI-native notebook with YAML-based Prism Protocol

Fragment is an AI-native notebook designed for people who think in structures — researchers, engineers, educators, writers. It combines: • Markdown notes • YAML-based “Prism Protocol” for tone/persona/boundaries • AI-rendered Scene/Flow diagrams • Config blocks that define scope, audience, and language The goal is to make AI collaboration consistent, reusable, and calm — not prompt-chasing. Demo: https://fragment.place/demo/prism Docs: https://fragment.place/docs Happy to hear feedback or questions.

Visit product View on Hacker NewsNov 20, 2025

Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

86%86% 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.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

56%56% 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.

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

35%35% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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