I built a tool to get instant test results

Hacker News

I got sick of the old software development loop: Change code -> Run tests -> wait -> wait some more -> look at failures. I decided to build a tool that will enable you to: Change code -> look at failures. No wait time, no explicit test running. Under the hood: - Runs the whole test suite and collects code coverage per test. - For each auto file save, analyzes the changes on the tests. - Runs changed tests in the background. - Display results, the loop time from change to test results is approx 250ms. Instead of: code -> alt+tab -> arrow up -> rerun all the tests -> wait ... -> test results code -> alt+tab -> test results! Check it out! < https://github.com/nabaz-io/nabaz >

Visit product View on Hacker NewsNov 16, 2022

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

67%67% 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

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: ide, io · Missing: https docs, excited, just released

47%47% 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

32%32% 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 · Strong signals: arr · Missing: mrr, revenue, profit

16%16% 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.

Incorrect prediction on native model

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