spent a weekend “hacking” the UI of WhatsApp, then published two addons

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I had a need for two missing features in WhatsApp and Messenger, and as I'm used to modifying the sites I use the most (JavaScript injection with TamperMonkey) I eventually went ahead and started the projects. It took just a few minutes!... for me to realize I had greatly underestimated the difficulty of the tasks. Those apps, it turns out, don't use regular inputs for the users to type messages; they use a weird combination of divs and spans, probably to avoid security issues. I took it as a challenge to at least, make sure that it wasn't possible. But the more I pushed, the more fun I had! Screenshot https://i.imgur.com/tMf531B.png These were some of the projects I enjoyed the most, and I was left wondering if being a hacker feels as fun as that. I made the code open-source, with MIT licenses: https://github.com/ivocass/quick-emojis https://github.com/ivocass/whatsapp-quick-reply-button Demo videos: https://youtu.be/Ixha7icPXkc https://youtu.be/seTnOZlyEBs

Visit product View on Hacker NewsJul 17, 2020

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

65%65% 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

59%59% 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: apps, video, users · Missing: mobile apps, ios, personal

38%38% 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

34%34% 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

14%14% 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.

Incorrect prediction on native model

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