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Rails-like CLI for building full-stack Nextjs apps faster

Hacker News

Rails-like CLI for building full-stack Nextjs apps faster

Hello, HN! Excited to share Kirimase, a CLI tool I built to scratch a recurring itch: is there a better way to set up my nextjs projects with my essentials packages? If you’ve grappled with the repetitive chore of configuring orm (drizzle orm or prisma), auth (next-auth, clerk or lucia-auth), trpc, shadcn-ui and resend (email), I hope this can help! Features Quick init: Dive straight into development without the mundane initial setup for core packages. Scaffolding: Inspired by the Rails CLI's scaffold feature, Kirimase helps you quickly set up new DB models + controllers (api and trpc routers) + views (using shadcn ui). Quick demo: https://twitter.com/nicoalbanese10/status/170484892457682160... Deep dive: https://www.loom.com/share/cb329939c83b4c9eb6a56abfd2638bd4?... Check out the GitHub repo ( https://github.com/nicoalbanese/kirimase ) and share your thoughts, pull requests, or even just a star if you find it handy!

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, apps · Missing: mac, agents, macos
88%88% 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: excited · Missing: https docs, just released, exist
63%63% 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
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal
34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · 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.

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