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Erugo – A privacy-focused, single-binary WeTransfer alternative

Hacker News

Erugo – A privacy-focused, single-binary WeTransfer alternative

I wanted to share a tool I've been working on called erugo - it's a privacy-respecting file sharing solution that you can host yourself. *Why I built it:* I wanted to learn Go by building something practical that I'd actually use. I needed a simple way to share large files with clients and family, but didn't want to rely on services that track usage or collect data. Most importantly, I wanted something that would just work without complex setup or dependencies. *Key Features:* - Single binary deployment (backend + frontend bundled together) - No tracking, no metrics collection, completely private - Local authentication for share creation - Human-readable share URLs - SQLite for simplicity (no external database needed) - Clean, modern UI - Configurable storage paths and file size limits - MIT licensed and completely open source *Tech Stack:* Built with Go backend and Vue.js frontend, packaged into a single binary. Uses SQLite for metadata storage and local filesystem for files. *Getting Started:* 1. Download the binary 2. Run it 3. Follow the interactive first-run setup 4. Start sharing files! The project is fully open source and available at: https://github.com/DeanWard/erugo I'm actively developing erugo and would love to hear your feedback and feature requests. Currently working on adding Docker support and white-labeling options via the UI. Let me know if you have any questions! As this is my first Go project, I'm particularly interested in feedback from experienced Go developers as I expect there are many ways to improve the code.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: dock, single, code · Missing: mac, agents, macos
86%86% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, filesystem, 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.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
12%12% 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
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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