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Clipboards.pro – A simple clipboard manager I built out of frustration

Hacker News

Clipboards.pro – A simple clipboard manager I built out of frustration

Hey HN, I put together Clipboards.pro because I kept running into the same problem: I’d copy something important, then lose it the moment I copied the next thing. Most clipboard tools I tried felt either too heavy or outdated, so I decided to build my own. Right now it does a few simple things well: - Keeps a history of everything you copy. - Lets you pin the stuff you reuse all the time. - Has a quick search so you can find that snippet you copied yesterday without digging. - Works across devices, so you’re not emailing yourself notes anymore. I’ve mainly been using it for code snippets and terminal commands, but I’ve also found it handy for writing drafts, notes, and even copying multiple links at once. If you want to give it a try, here’s the link: https://clipboards.pro/ I’d love to hear your thoughts: - Do you already use a clipboard manager? - What do you wish yours did better? - What’s the most annoying “copy/paste” problem you run into? Lastly, I am proving a peace of mind, not just a clipboard manager. Thanks!

Visit product View on Hacker NewsSep 24, 2025

Actual performance

2points

1comments

Did not reach leaderboard

Launch Intel predictions

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

85%85% 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.

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

79%79% 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

48%48% 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

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

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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

16%16% 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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