Why your link management tools fail, even with Notion, Pocket, etc.

Hacker News

Hey HN, I've been building Cokeep.me, a next-generation tool for saving and managing links powered by AI. I've used so many bookmarking and link management tools over the years (Notion, Pocket, Raindrop, Evernote, etc.) but they've all fallen short. Most of them are either overly complicated or too limited for teams. Most importantly, these tools often focus only on saving links without providing efficient ways to organize, search, or collaborate on them. While they are useful for storing links, they don’t do much to help me keep them organized or share them effectively with my team. That's why I decided to build Cokeep.me. I aimed to create a platform that is clean and simple, with powerful features for both personal and team use. Apart from just saving links, I added robust organizational tools, advanced search functionalities, and seamless collaboration features. You can create collections, tag links, and even share them with your team, all in one place. What sets Cokeep.me apart is its AI capabilities. The AI helps categorize and tag your links automatically, making organization effortless. Now, I can use Cokeep.me to manage my personal bookmarks and collaborate on projects with my team, without the clutter. If you’ve faced similar challenges, give Cokeep.me a try. Free too. Let me know if you have any feedback or questions!

Visit product View on Hacker NewsJul 29, 2024

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

84%84% 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 · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

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.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

41%41% 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

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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