Linkz.ai – Live link previews on hover

Hacker News

Hi, I'm Vittorio, and I'm improving the Internet browsing. However, I'm not building another web browser. As a big Wikipedia & Google Docs user, I liked how a minor addition of link previews on hover positively enhanced the UX. My goal is to similarly improve the UX of other websites & the Internet overall. Generating link previews on hover at scale fast is quite a problem. However, I solved similar scalability problems at MS. With a lot of serverless & caching - Linkz.ai was born. Linkz.ai live link previews allow website owners, bloggers & publishers to maximize visitor retention. Each hyperlink gets a popup with a link summary; And for hyperlinks to videos, rich media, articles & files - Linkz.ai will automatically extract & show hyperlinked content or iframe inside your website. The goal is to provide visitors with enough extra context to keep them on the original website. As a result, end-users are following fewer links, increasing retention for website owners, reducing tab-overload for themselves, and making the Internet greener :) PS To address the elephant in the room, there is no AI in the product at the moment. I know it contradicts with the name; I just believe it adds intelligence to a website that installs the product. [1] https://linkz.ai [2] Demo: https://www.youtube.com/watch?v=Qr9WErZCjVY

Visit product View on Hacker NewsMar 21, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, context · 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: maximize · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, 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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

15%15% 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.

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