Fighting Pop-Under Tricks While Creating Educational Videos

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Hey HN, pop-ups are already very annoying, but pop-unders are worse. They usually stay in the background until a user closes all other windows, which is great for terrible ads and even better for things like coinhive. I have reverse engineered several tricks used by a pop-under library and reported the bugs to Chrome. After several months without a working pop-under technique, the library was updated with a new trick and it's working on Widows, Linux and Mac from the current version of Chrome up to the development version 68. While reporting the issues to Chrome is nice, I also create videos explaining the process and tools I use when analysing and working with obfuscated JavaScript. So here is the latest video: Custom Chromium Build to Reverse Engineer Pop-Under Trick (15min): https://www.youtube.com/watch?v=y6Uzinz3DRU Chromium Issue 833148: https://bugs.chromium.org/p/chromium/issues/detail?id=833148 If this was interesting for you, here are the previous videos on the topic - each one shows some other tricks/tools used: Reverse engineering obfuscated JavaScript - PopUnder Chrome 59: https://www.youtube.com/edit?o=U&video_id=8UqHCrGdxOM Reverse engineering PopUnder trick for Chrome 60: https://www.youtube.com/watch?v=PPzRcZLNCPY [Live] Reverse Engineering new PopUnder for Chrome 63 on Windows (very long/boring livestream): https://www.youtube.com/watch?v=VcFQeimLH1c Thanks for checking out the videos!

Visit product View on Hacker NewsApr 20, 2018

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

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

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

13%13% 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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