Klipit.in – Online clipboard, quickly share data across devices

Hacker News

Hello, wanted to share about my new project called Klipit. How do you quickly share data between devices? Here's what most people do – - Send it to someone on WhatsApp or other messaging service. - Email it to themselves. - Use a note taking application like Google Keep, Apple Notes or OneNote (my preferred way until now). All of these require you to already have those services logged into on both the devices. Logging into any service on a new device these days is a tedious process involving 2-factor authentication. What if there was a quick way to share data between devices? One that did not require any logins or installing any apps? Starting with this thought, I built Klipit.in. An online clipboard to share data quickly between devices. You get an instant online clipboard with a unique link. No need to create any account. You can paste any data to this clipboard and simply open the link on another device. Nothing to install, no logins required. Quick and easy. There is also a QR code which you can scan to open the clipboard in browser. Some features that I may add depending on how it takes off - - Password protection - Real time refresh - Sharing files - Klipit account with multiple clipboards - Custom clipboard URL Play with it and let me know what you think.

Visit product View on Hacker NewsOct 25, 2022

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

77%77% 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: apple, google, apps · Missing: mac, agents, macos

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

61%61% 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

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% 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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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