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Open-source Next.js dashboard starter with auth, RBAC and Storybook

Hacker News

Open-source Next.js dashboard starter with auth, RBAC and Storybook

Hey HN, I'm Matt. Recently I've been working on an open-source Next.js dashboard starter - the goal was to have things like auth, RBAC, theming, i18n and accessibility ready out of the box so you don't have to build them from scratch with every new project. It comes with 90+ reusable components built on top of Shadcn UI plus 60+ chart variations from Recharts, all documented in the included Storybook. There is also an optional Node.js backend which enables the full Better-Auth flow, and a separate, lightweight version of the starter with just the layout and the core setup. Live version: https://demo.nellavio.com/ Storybook: https://storybook.nellavio.com/ All links: https://nellavio.com/ Tech stack: Next.js 16, React 19, Tailwind 4, Shadcn UI, Recharts, TanStack Table, Zustand, Better-Auth, next-intl. License: MIT Happy to hear any feedback

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos
69%69% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
51%51% 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 · Strong signals: plus · Missing: platform, intuitive, reviews
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
36%36% 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
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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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