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Tool to Automate API and Database Testing – Feedback Wanted

Hacker News

Tool to Automate API and Database Testing – Feedback Wanted

Hi HN community, I’m building a tool that automates API and database testing by: * Parsing database schemas to generate valid, invalid, and edge-case datasets. * Populating test databases automatically. * Validating API behavior using OpenAPI/Swagger specs. * Providing logs and simple error reports to make debugging easier. I’m trying to solve the common frustrations developers and QA engineers face when testing APIs and databases. Would this be useful in your workflow? What’s your biggest pain point in API/database testing? I’d love any feedback to refine the idea further. If anyone’s interested, I’d be happy to share more details or offer early access.

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, apis, open · Missing: mac, agents, macos
74%74% 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
68%68% 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: ide, io · Missing: https docs, excited, just released
49%49% 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
36%36% 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
30%30% 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
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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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