Frop.io – painless remote presentations

Hacker News

Hi HN! https://frop.io is a simple application that allows you to present content (PDF presentations, Images, Text files, YouTube videos) on a remote screen without installing any software. It's completely based on web standards (Websockets, JS) and runs in most modern browsers. Simply start a session by calling frop.io/[yoursessionname] I built frop.io to be able to use a RaspberryPi as a presentation endpoint but wasn't happy with the existing solutions. This is what I came up with. The next steps are adding more supported media types and clients for FireTV and Apple TV. Mobile supported is currently very limited, but basic PDF/Image sharing is possible using iPhones and Androids. Consider frop.io an early preview of what's to come, but I was eager to share this side project and I'm looking forward to your opinions and comments. Thanks!

Visit product View on Hacker NewsFeb 15, 2017

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5points

3comments

Did not reach leaderboard

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

83%83% 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, presentations, using · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% 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

17%17% 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.

Incorrect prediction on native model

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