Message App – Built with Next.js, Server Actions, and Cosmic CMS

Hacker News

Hi HN, I'm Tony with Cosmic. I built this Cosmic Message App ( https://github.com/cosmicjs/cosmic-message-app ) to show how to use Next.js, React Server Actions, and Cosmic CMS to build a simple app to add text and upload media to a message feed. All without exposing any API keys to the client. I found the development process pretty straightforward using Next.js, Cosmic JavaScript SDK, File Upload Block, and React Server Actions. Overall I'm optimistic about this stack to make building user generated content apps like this, much easier. Let me know what you think in the comments. Tony

Visit product View on Hacker NewsJun 26, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, using · Missing: mac, agents, macos

84%84% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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

28%28% 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

20%20% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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