I built a no-install, no-login, WebRTC-based text and file sharing site

Hacker News

I got tired of texting/emailing myself links or jumping through hoops just to move something between devices, so I built Clip Fish. Existing solutions (PushBullet, Discord, Telegram, Signal, etc.) often don't meet my needs. Most require logins and installations, which isn't ideal for quick, one-off transfers or sharing with someone who doesn't use the same app. Some tools come close. PairDrop is decent, but the UI can be confusing, messages don’t persist, and sharing across networks takes too many steps. Clip Fish aims to fix this: -No signups or installs -Simple QR code scanning to connect devices -Intuitive, chat-like interface for sending text and files -Session and message persistence, even after refresh -Fully peer-to-peer over WebRTC (messages are never routed through a server) -Sessions can be cleared anytime It’s open-source and easy to self-host. The code and hosting instructions are available on GitHub at: https://github.com/clip-fish/web . It’s still under active development, so if you encounter any issues or have suggestions, please let me know. I'd really appreciate any feedback.

Visit product View on Hacker NewsApr 21, 2025

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

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

best fitHighest predicted score across all platforms for this description.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

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

55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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