Open-Source AI Test Conductor with Playwright/Puppeteer

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Hey HN, This weekend our team created Playwright-AI. It's a tool that hooks Anthropic's computer use model into Playwright (on top of Puppeteer) to execute browser tests from natural language test cases. ai(”Test users can create a new project.”) Why tho? Testing Sucks! We wanted to see if we could just feed test cases in natural language to AI and it would test them exactly as a human would do. Writing E2E UI tests is super brittle, you change `sign in` button to say `login` and everything breaks…! If you've written UX E2E tests in a large org I'm sure you will know this pain well. We were inspired by zerostep.com Fully Open-Source: MIT-licensed, free to use, and open for contributions. This is just the initial version, and we're eager to learn and make it even better, based on community feedback.

Visit product View on Hacker NewsNov 12, 2024

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

90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

66%66% 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 · Strong signals: users · Missing: mobile apps, ios, personal

47%47% 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

21%21% 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

15%15% 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.

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