Typebase – A single-folder back end you write in TypeScript

Hacker News

Hey HN! I built Typebase, a library that gives you Convex's DX with Supabase's openness. After trying Supabase I liked how fast it is to spin up a DB and auth, but really didn't like using RLS and SQL for authorization. With Convex I loved how your server "lives" in your code, but disliked the DB model and the realtime-first defaults. With Typebase you just write TS files inside a typebase/ folder in your existing repo. You can define your DB tables inside a schema.ts file and export server functions that your frontend calls like local functions, fully typed. Auth is built in. Then one CLI command uploads your server to any of the available providers (Vercel, Cloudflare Workers or Deno Deploy for the servera and Neon for the DB), or generates the code so you can deploy it wherever you want. Built on top of oRPC, Drizzle, and better-auth. Happy to answer any questions or feedback!

Visit product View on Hacker NewsAug 26, 2026

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

95%95% 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

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

56%56% 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: calls · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

17%17% 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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