commentto, "Pinterest for text"

Hacker News

Hello everyone at HN, I'd like to show HN my startup, commentto. Using commentto, you can comment-to, bookmark and save webpages and also parts of webpages, called excerpts. How it works: user signs up, downloads extension (available for Chrome and Firefox) and starts using commentto right away. To use it, the user just selects some content on a page, and saves it to commentto. Problems commentto solves: 1. Commenting - using commentto, a user can comment anywhere. 2. Bookmarking - bookmarks can be imported and organized right in commentto. Excerpts can be easily created using the commentto addons. 3. Saving information - when a user finds some information to save, he/she can just select it, and "Save" it. This quick how-to videos shows how commentto works: http://www.youtube.com/watch?v=oSwsHDWabJ8 You can try it out by going to commentto.com. I'd love to know what you think. Thank you for your feedback. Please upvote the clickable links if you can, thanks! :)

Visit product View on Hacker NewsJan 30, 2012

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Actual performance

3points

3comments

Did not reach leaderboard

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

82%82% 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: created · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

46%46% 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: video, way · Missing: mobile apps, ios, personal

33%33% 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

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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