libmodulor – An opinionated TS library to build multi-platform apps

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Initially I started building this for my own projects. The main goal was to define a unified "blueprint" allowing me to develop multi-platform apps using the same code, without code generation. I wanted to be able to develop the same functionality on web, desktop, cli, server, mobile, whatever... I've been able to achieve this by relying on TypeScript, a 4-layer architecture (UseCase => App => Product => Target) and dependency injection. This mechanism allows me to use whatever tech stack I want, provided the good adapters are developed. For instance, I have pre-built ones : node express (server), next.js (server), node hono (server in alpha), node parseArgs (CLI), node stricli (CLI), react-web-pure (web with no CSS), react-native (mobile), node mcp server (anthropic MCP in alpha), etc. The same goes with data storage : Postgres, SQLite, a txt file, whatever. It also comes with auto testing and auto documentation. Did I reinvent the wheel ? Probably on some aspects. Is it too much abstraction ? Probably as well. But I like the idea of modularity and portability. That's why it's not made for everyone, nor all types of projects. If you like testing new stuff, give it a try and feel free to ping me if needed, I'd love to help. I'm aware the documentation is not state of the art yet. I wanted to focus on the "Getting Started" Guide to give a quick overview instead of going to deeply into the details. Best,

Visit product View on Hacker NewsJan 23, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · 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: mcp, apps, new · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

44%44% 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: apps · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% 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

15%15% 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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