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TNTStack – Monorepo template for cross-platform apps (Tauri+Next.js)

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TNTStack – Monorepo template for cross-platform apps (Tauri+Next.js)

Hi HN, I originally switched from PyQt5 to Tauri to get smaller desktop binaries and better performance. Later, I wanted to add a web version and put everything into a monorepo. That's when I hit a wall. Using a standard React app for Tauri (desktop) and Next.js for the web meant maintaining two entirely different architectures. Even though I was sharing UI components, I was essentially building and debugging two separate apps. To solve this, I replaced the standard React setup in Tauri with Next.js (using static HTML export). TNTStack is the result. It's a cross-platform monorepo template where both web and native apps run on the exact same Next.js architecture. You write the core and ui once, and it builds for all platforms. It’s a specific stack, but figuring out the configuration for this dual Next.js setup took a lot of trial and error. Hopefully, it saves someone else the setup time. I would love to hear your thoughts on this monorepo structure and the dual Next.js approach. Repo: https://github.com/odest/tntstack Live Demo: https://tntstack.odest.dev Docs: https://tntstack.odest.dev/docs

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, using · Missing: mac, agents, macos
77%77% 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: io · Missing: https docs, excited, just released
58%58% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, para · Missing: mobile apps, ios, personal
41%41% 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
16%16% 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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