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Garage11 – p2p webapp

Hacker News

Garage11 – p2p webapp

I've been working on a p2p webapp called Garage11(https://github.com/garage11) for quite some time now. It is not near a stable version yet, but it already (incompletely) handles some issues like e2e encryption(webcrypto/node subtle ECDH/AES GCM), db storage/ remote db(js-data + custom js-data adapter), vdom(ractive) and a peer/transport/connector abstraction(lib11) that runs in Node and in Chrome(haven't tested yet with other browsers). The idea is that you can change the data-context of the requested webapp by selecting one of the connected nodes from an overlay network. Currently i'm trying to make it work for a simple blog and node user management. Progress is going steady but slowly, because I work alone on this project. After blog/user management and basic permissions, I would like to work on a webrtc transport, which would allow to setup webrtc connections without a central sdp message broker. Other features I'm thinking of in the long run is a slack-like messenger and delayed message delivery using in-between nodes. I would like to validate this idea, to see if I'm heading in the right direction. Also I can use some tips on how to accelerate this project. A broken preliminary version is running at https://garage11.io/ Cheers, Jeroen

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, context · Missing: mac, agents, macos
69%69% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% 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
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
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: crypto · Missing: web3, chat, cryptocurrency
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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