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Shortest, a natural language AI-powered testing framework

Hacker News

Shortest, a natural language AI-powered testing framework

Shortest is an open-source AI-powered testing framework that allows developers to write end-to-end tests in plain English, as simple as: shortest(“user can sign up and create a $5 product”) The thesis is that being able to write short tests in English → more tests → fewer regressions as AI writes and ships more code. Would love to hear what you all think! Feedback (and contributions) welcome.

Visit product View on Hacker NewsDec 23, 2024

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

81%81% 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

55%55% 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: io · Missing: https docs, excited, just released

50%50% 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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

12%12% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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