Co

Comm – a decentralized, censorship-resistant p2p messaging app

Hacker News

Comm – a decentralized, censorship-resistant p2p messaging app

I’ve been working on this p2p protocol for a few years now as a sort of hobby. I’ve recently wrapped it up as a GTK app. I’m still working on getting it fully documented, as the protocol mostly exists in code and floating around in my head. I spew it out every now and then verbally, but still need to capture it in writing. It lacks a lot of features that you'd expect, like encryption and group messaging, but, I wanted to share it because I’ve pushed really hard to get it to a minimally useful state to force myself to finally “finish” one of my ridiculous side projects. Check it out on GitHub: https://github.com/zacstewart/comm-gtk A pre-built MacOS app is in the releases for download. The underlying protocol repo is here: https://github.com/zacstewart/comm The network layer is based on the design of the Mainline DHT described in BEP 5[1]. Atop that is built a messaging layer which handles all the relaying, message acknowledgement, storing messages for delivery later, etc. Thanks for taking a look! [1]: http://www.bittorrent.org/beps/bep_0005.html

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
78%78% 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: mac, macos, code · Missing: agents, agent, cursor
62%62% 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, ide, 000 · Missing: https docs, excited, just released
54%54% 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 · Missing: plus, platform, intuitive
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
23%23% 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
14%14% 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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