We built a tool for fast-forwarding 95% of tests (MIT)

Hacker News

Hi, we are working on a tool for speeding up test runs, by skipping tests unaffected by code changes. Effectivly, Saving 80-95% of the time, by skipping 80-95% of tests. We started a few months ago, and have managed to get into a few production CI systems. All our prospects and users are on holiday right now. So we decided to repackage and open-source for local test running. available here ( https://github.com/nabaz-io/nabaz ) under MIT license. One line change: pytest -v -> nabaz test --cmdline "pytest -v" Stalk us on GitHub, or just Star us. Ask questions, we'll answer in under 30 seconds. we have auto refresh on.

Visit product View on Hacker NewsOct 12, 2022

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

85%85% 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.

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

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

32%32% 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

18%18% 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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