PasteDB – A Feature-Rich Pastebin

Hacker News

Hi HN! I built PasteDB, a free pastebin for sharing code snippets, notes, markdown, and images. Some of the features include: - Syntax highlighting for many languages - Public, unlisted, and private pastes - End-to-end encrypted private/unlisted pastes - Password protection - Version history - Image uploads - Custom URLs - Expiration options and burn-after-read - Markdown preview - QR code sharing - Analytics dashboard for your pastes The frontend is built with HTML/CSS/JavaScript, and the backend uses FastAPI with MongoDB. I'd love to hear your feedback, bug reports, feature suggestions, or UX improvements. Try it here: https://pastedb.netlify.app

Visit product View on Hacker NewsJul 31, 2026

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

68%68% 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 HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

60%60% 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

37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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

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

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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