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PlayWord – Turn test cases into automated, executable tests

Hacker News

PlayWord – Turn test cases into automated, executable tests

Hi HN, Steve here! I'd love to share my recent side project: PlayWord – a tool that makes web automation testing simpler and more intuitive. It converts your natural language test steps into actual, executable actions on the web page. For example, here's a test case I've written: go to {TARGET_URL} input {USERNAME} in the username field input the password {PASSWORD} log in verify if the URL matches {TARGET_URL}/inventory.html Readable, right? Now imagine it running as an automated test--that's exactly what PlayWord makes possible! Features: 1. Describe test steps in natural language and say goodbye to annoying element locating. 2. Support recording: record test steps to create reusable automated tests. 3. Support screenshot reference: enhance AI understanding to better meet your needs. 4. Provide CLI and modular options: run PlayWord via CLI or integrate it into your codebase. Check it out: https://github.com/Foreverskyin0216/playword I'd love to hear your thoughts and feedback. Thanks for reading!

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, code · Missing: mac, agents, macos
80%80% 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
70%70% 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
38%38% 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
31%31% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
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
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.

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