Open-source Next.js 15 boilerplate – auth, DB, intl, tests, monitoring

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I started this boilerplate in July 2020 and I’ve been maintaining it for 5 years. It began on Next.js 9 and kept upgrading to Next.js 15+ (App Router), while upgrading the stack over time (Tailwind 1 → 4, ESLint 8, swapping Cypress → Playwright, etc.). The goal is simple: I kept rebuilding the same setup, so I packaged it and kept it updated. What you get (preconfigured, keep only what you need): - Next.js 15 (App Router) + TypeScript + Tailwind 4 - Auth with Clerk (magic links, MFA, social, passkeys) - I18n via next-intl - DB with Drizzle ORM (PGlite locally) - Forms with React Hook Form + Zod validation - Testing: Vitest (unit), Playwright (integration/E2E) - CI with GitHub Actions; Storybook for UI work - SEO (Open Graph, JSON-LD, sitemap, robots) - Observability: Sentry, logging with LogTape, log management & uptime/monitoring - Security: Arcjet (bot detection, rate limiting, shield rules) - DX details: ESLint/Prettier, Lefthook + lint-staged, Commitlint, absolute imports, bundle analyzer - AI code review It’s free and open source (MIT). Today the project sits around 11.8k GitHub stars and 2.2k forks. I’m still actively maintaining it and adding features. Repo: https://github.com/ixartz/Next-js-Boilerplate Why I built it Spinning up auth, a DB, i18n, tests, and lint/format/CI for each new app was repetitive. This gives me (and hopefully you) a production-ready base in minutes, with opinionated defaults you can start. I’m open to suggestions and feedback, what would you like to see next? I’ll hang around in the comments to answer questions.

Visit product View on Hacker NewsAug 28, 2025

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

78%78% 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: new, code, open · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, code review, 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.

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

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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.

Incorrect prediction on native model

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