A web service for E2E P2P file transfers with chat

Hacker News

Hey everyone, I've submitted this 3 years ago but since then a lot has changed in the app - I've added better mobile (and PWA) support and been working on getting rid of the bugs. Direct messages have been added to the chat and the app now handles disconnections more gracefully (although it's still not possible to resume the transfer after a disconnection - this is being worked on). The app is fully open source: https://github.com/mat-sz/filedrop This repository also includes installation instructions, and it's very easy to deploy the app with Docker. Hopefully you will find this as useful as I do (I wouldn't lie by saying that I need to use this at least a few times a week thanks to being an owner of an iPhone).

Visit product View on Hacker NewsAug 27, 2023

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4points

1comments

Did not reach leaderboard

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

71%71% 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 NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released

56%56% 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 HuntOn track for Day 1 leaderboard · Strong signals: dock, open · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

52%52% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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