My OSS P2P file transfer tool for learning Next.js (as a C++ dev)

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Hey HN, I'm a C++ algorithms engineer, and today I'd like to share my first full-stack web project: PrivyDrop. This project was born from two ideas: First, I wanted to solve a daily annoyance of mine: I needed a simple, AirDrop-like way to send text, links, or screenshots between my phone and PC. I tried many tools, but they either required me to sign up, had various limitations, or uploaded my data to their servers, which I was never comfortable with. Second, this was a personal experiment. Last year, with all the talk about "AI replacing programmers," I got curious. As a developer with no web background, I wanted to see how long it would take to learn full-stack development from scratch (an area I'm really interested in) and build a complete application, using AI as my primary coding partner and mentor. PrivyDrop is the result of that learning and experiment. It's a peer-to-peer (P2P) file-sharing tool based on WebRTC. Its core principle is that *your data belongs only to you*: all files and text are transferred directly between your browser and the recipient's, fully end-to-end encrypted, without ever passing through an intermediate server. *Here are some of its key features:* * Completely free and open-source, with no ads. * No sign-up or installation required—just open your browser. * Direct P2P connection for privacy, security, and speed. * No limits on file size or type. * Support for entire folder transfers. * Built-in resumable transfers. The whole process has been a fantastic learning journey. I'm sharing it now in the hope that it can solve the same pain point for others. I'm really eager to hear any feedback, ideas, or even harsh criticism from the HN community! * *Live Demo:* https://www.privydrop.app * *GitHub Repo:* https://github.com/david-bai00/PrivyDrop Thanks, everyone!

Visit product View on Hacker NewsAug 26, 2025

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

71%71% predicted probability of success on Product Hunt, 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

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.

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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% 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

13%13% 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.

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