Qa

Qapir, no-code framework for API testing

Hacker News

Qapir, no-code framework for API testing

Hello HN, My name is Albert, and I’m excited to introduce qapir - a no-code framework for API testing. qapir streamlines API testing by eliminating the need for complex setup and extensive coding. It allows you to focus exclusively on crafting test scenarios, while it manages data extraction, validation, retry logic, and more. Tests are written in YAML format, and the results are delivered in clear and readable HTML reports. As a lightweight binary, qapir integrates seamlessly with both local environments and CI pipelines. Local setup accelerates test development, while CI integration ensures continuous API testing. I invite you to try qapir and share your feedback! Visit this GitHub page to get started: https://github.com/vrtxlabs/qapir

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Actual performance

3points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
74%74% 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: coding, code · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, pipe, io · Missing: https docs, just released, exist
48%48% 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: ios · Missing: mobile apps, personal, entrepreneurs
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive · 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
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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