MarkX – Effortless Bookmarking and Precision Highlighting

Hacker News

MarkX transforms the way you gather insights from the web. With a swift selection and a click, effortlessly save important text snippets from any webpage. No more cumbersome copying and pasting. Streamline your research process and focus on what matters most. I frequently consume content on various websites, including blogs and news articles. There are times when I wish to save specific content as a text string rather than just the URL of the entire site. This prompted me to create MarkX, a tool designed to streamline the process of bookmarking and highlighting content in a user-friendly manner. You can check it out here and give any feedback as long as I can help you improve your noting experiences : https://chrome.google.com/webstore/detail/markx-bookmarks-and-highl/ialfchbhcgcgpbpcihmceabgnnfoknjc

Visit product View on Hacker NewsSep 25, 2023

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Did not reach leaderboard

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

77%77% 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: google, user, new · Missing: mac, agents, macos

72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: google, way · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

11%11% 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.

Correct prediction on native model

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