Bk

Bknd – Firebase alternative that embeds into any React stack

Hacker News

Bknd – Firebase alternative that embeds into any React stack

I built bknd because I was tired of stitching together auth, file storage, and database APIs every time I started a new project. Existing solutions were either too hard to extend, too basic, or required a separate deployment. bknd runs directly inside your frontend app — no separate backend required. It works with Next.js, Remix, Astro, React Router. It also runs standalone on Cloudflare Workers, AWS Lambda, Bun, or Node. It supports Postgres, LibSQL (Turso), D1, SQLite and has adapter-based storage. You get instant APIs, multi-strategy auth, media handling and a built-in Admin UI. Curious what you’d build with it, feedback welcome!

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Actual performance

50points
22comments
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, apis · Missing: mac, agents, macos
96%96% 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: supports, started, para · Missing: reddit linkedin, podcasting, created
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
43%43% 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 · Strong signals: para · Missing: mobile apps, ios, personal
28%28% 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
25%25% 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
19%19% 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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