Pastehub.app – client-side "what did I just copy?" tool

Hacker News

I kept copying stuff and opening 10 different tabs to understand it, so I built pastehub.app.100 % client-side, zero backend, nothing ever leaves your machine. Current detectors (all instant): JSON → pretty + minify JWT → decode header/payload Base64 / Base64url → decode to text or file Images → QR/barcode decode + dominant color palette + OCR (Tesseract) URLs → clean tracking params + rich preview Hex, colors, unit conversions (kglb, cmft, °C°F, etc.) Math expressions → instant calculation and ~20 more small ones It’s deliberately over-engineered for the weird stuff I paste daily. Try throwing anything at it: https://pastehub.app Feedback button () goes straight to me. Curious what breaks it :)

Visit product View on Hacker NewsNov 22, 2025

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, code, open · Missing: agents, macos, agent

36%36% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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

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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