Supabase Testcontainers for Rust

Hacker News

I utilize Supabase as the basis for many of the applications that I create. I find it valuable to pursue a test driven development strategy for both my organic coding as well as my AI assisted coding. This makes me lean on Testcontainers often. While there are Testcontainers available for many of the foundational building block services we all use, there was not one for Supabase (at least in Rust). So I created one. Hope this helps someone out there. Thanks. Full disclosure I also use AI to assist me in doing this in a timely manner. If you find any issues or broken code, please submit an issue. I've added tests, but sometimes things fall through the cracks. Thank you.

Visit product View on Hacker NewsNov 30, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: coding, code · Missing: mac, agents, macos

73%73% 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: created · Missing: supports, reddit linkedin, podcasting

52%52% 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: lua, io · Missing: https docs, excited, just released

33%33% 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

33%33% 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

27%27% 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

20%20% 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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