Autotab – An AI-powered Chrome extension to create Selenium scripts

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Autotab is a Chrome extension that writes Selenium code to mirror your actions as you navigate the browser. See it in action: https://youtu.be/UypAcozIaoo Autotab lets you create browser automations that actually work. We designed it around two principles: 1. Show, don’t tell: In a domain like web automation, it's often easier to *show* the model what you want rather than to explain it in sentences. 2. Code is the best output: Code is easy to inspect and enables manual tweaking of the model’s suggested actions. On top of that, code output avoids lock in and is straightforward to extend and integrate with larger projects. Autotab runs as a Chrome extension. As you navigate in the browser, autotab generates the Selenium code to reproduce your actions. You can copy that code into your own project or use our starter GitHub repo to get your automation up and running in <5 minutes: https://github.com/Planetary-Computers/autotab-starter . We'd love to hear what you think!

Visit product View on Hacker NewsOct 19, 2023

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

84%84% 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

73%73% 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

53%53% 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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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