CarbonSheets – Host your own customizable cheatsheet portal In GitHub

Hacker News

Hi HN, I built CarbonSheets over the weekend.. a simple and free cheatsheet portal where you can host and share your documentation and cheatsheets directly in your own GitHub repository with minimal setup. Key Features: Self-hosted content: Keep your cheatsheets in your own Git repos. Developer UX: Built-in keyboard shortcuts, customizable view modes, and a distraction-free Zen mode. Rich Formatting & Tools: Powered by a flexible JSON schema with an in-app editor and schema validator. Support mermaid JS, Syntax highlighting, callouts, results section etc. Exporting: Export cheatsheets directly to PNG or PDF. Links: Live App: carbonsheets.vercel.app Instructions and Clone-able GitHub Repo to host your own: github.com/elnoxvie/carbonsheet (opinion) I'd love to hear your thoughts on the app!

Visit product View on Hacker NewsJul 27, 2026

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

71%71% 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.

AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive

68%68% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

55%55% 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

47%47% 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 · Missing: mobile apps, ios, personal

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

17%17% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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