Filemesh – Easily create distributed and anonymized web file shares

Hacker News

Hi HN! This is a project that I've been working on for several months in my spare time. I had been working on multiple computers in different locations and found moving files between them was quite cumbersome. I would have to upload them to Google Drive, and if I forgot to move them to the correct location for syncing, I would be out of luck. I had also been interested in edge computing and creating a remotely queryable folder system through an API. Filemesh is the result of some of those ideas. It's a way to create a distributed web-based file system with the added ability for all users to remain completely anonymous. You can upload, download, and manage files through a gateway that routes requests to connected computers. Any thoughts or feedback would be greatly appreciated!

Visit product View on Hacker NewsJun 7, 2023

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2points

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, computer · Missing: mac, agents, macos

79%79% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, google, users · Missing: mobile apps, ios, personal

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

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

50%50% 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 · Strong signals: users · Missing: plus, platform, intuitive

26%26% 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

19%19% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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