I made a CLI that will use GPT4 to generate unit tests

Hacker News

I always hated tests, so I've gone and just automated the task away. You'll get out complete tests that are known to pass from this tool. If you just want to try it out, here's the command to run for the entire project npx deepunit -- --a To run for just a specific file(s) npx deepunit -- --f path/to/file.ts For complete documentation: https://www.npmjs.com/package/deepunit Behinds the scenes there's a whole lot going on to get code that runs and compiles. I'm sure you've already used ChatGPT to write a simple function and found that most of the time there are small things to fix up before it's ready to run. A ton of time was spent handling each of those edge cases to ensure that the output is a test that runs and passes.

Visit product View on Hacker NewsOct 24, 2023

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

87%87% 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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

38%38% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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