Chrome headless support without selenium for Mink and Behat

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I wrote a mink driver which communicates directly with chrome over the devtools protocol, allowing you to control chrome without selenium. With chrome 59+ you can also eliminate the need for xvfb by running it headless. Our behat suite of 1800 scenarios and 19000 steps takes just over 60 minutes to run in Chrome 58 with selenium and xfvb. With chrome 60 headless it takes less than 18 minutes. https://gitlab.com/DMore/chrome-mink-driver Any feedback and contributions would be greatly appreciated.

Visit product View on Hacker NewsMay 13, 2017

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AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

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

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

18%18% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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