Fast Q&A discussion on every web page

Hacker News

Hi HN We have developed a chrome extension which lets users ask or answer questions on any webpage. https://chrome.google.com/webstore/detail/anywyse/gfohefaikkfjlddgdigfpjhijljmneik http://www.anywyse.com When you are on a website, a number of times you have to open another tab to search about things you found on the website you are currently on. e.g. news/media websites, GitHub repos, online shopping, university course/admissions pages etc. Most people think about similar questions. If a site is visited by millions of users a week, we would have thousands of people genuinely seeking similar information. Anywyse gives you a platform that can help the whole community to share notes quickly in the form of Q&A. The idea is simple, when you are visiting a web page, click the extension and it will show you questions and answers asked on that very page. You can add a question or add an answer to a previously asked question. Short answers are preferred, currently we have limited answers to 200 words only. Our inspiration is Reddit and StackOverflow. Both the platforms are great for simply consuming genuine and quality information. We hope Anywyse helps you find information faster. We are just launching so content will build up when users contribute. As we progress we will also have AI generated Q&A. Appreciate your valuable feedback on the product. Please suggest scenarios where the product could be helpful. Thanks! Feel free to reach out at crtechmbd@gmail.com

Visit product View on Hacker NewsFeb 25, 2021

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Launch Intel predictions

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

85%85% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

48%48% 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

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.

Correct prediction on native model

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