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E2EE Messaging with a Decentralized Microfrontend Architecture

Hacker News

E2EE Messaging with a Decentralized Microfrontend Architecture

Decentralized Architecture: https://positive-intentions.com/blog/decentralised-architect... While my approach here could be considered overly complicated (because, well, it is), I’m trying something new, and it’s entirely possible this strategy won’t be viable long-term. My philosophy is “there’s only one way to find out.” I’m not necessarily recommending this approach, just sharing my journey and what I’m doing. Potential Benefits I’ve identified some interesting benefits to this approach: Statics as Chat App Infrastructure: https://positive-intentions.com/blog/statics-as-a-chat-app-i... While I often see module federation and microfrontends discouraged in online discussions, I believe they’re a good fit for my specific approach. I’m optimistic about the benefits and wanted to share the details. When serving the federated modules, I can also host the Storybook statics. I think this could be an excellent way to document the modules in isolation. Modules and Applications Here are some examples of the modules and how they’re being used: Cryptography Modules: https://cryptography.positive-intentions.com/?path=%2Fdocs%2... P2P Framework: https://p2p.positive-intentions.com/?path=%2Fdocs%2Fe2e-test... This setup allows me to create microfrontends that consume these modules, enabling me to share functionality between different applications. The following applications, which have distinct codebases (and a distinction between open and closed source), would be able to leverage this: P2P Chat: https://chat.positive-intentions.com/ P2P File Transfer: https://file.positive-intentions.com/ Sharing these dependencies should make it easier to roll out updates to core mechanics across these diverse applications. Furthermore, this functionality also works when I create an Android build with Tauri. This could streamline the process of creating new applications that utilize these established modules. Considerations and Future I’m sure there will be some distinct testing and maintenance overhead with this architecture. However, depending on how it’s implemented, I believe it could work and make it easier to improve upon the current functionality. It’s important to note that everything about this project is far from finished. Some might view this as an overly complicated way to achieve what npm already does. However, I think this approach offers greater flexibility by allowing for the separation of open and closed-source code for the web. Of course, being JavaScript, the “source code” will always be accessible, especially in the age of AI where reverse-engineering is more possible than ever before.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
90%90% 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: new, code, open · Missing: mac, agents, macos
61%61% 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
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, para · Missing: mobile apps, ios, personal
41%41% 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
40%40% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, crypto · Missing: web3, cryptocurrency, make money
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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