Glasp is a social online highlighter that allows users to annotate and organize web-based quotations and ideas in one place, share their learning, and access the annotated content of others with similar interests. In June of 2021, the Glasp team set out to create a platform that would make it easier for anybody to benefit from the knowledge and expertise of others. Glasp's ultimate goal is to make it possible to quickly and easily access all information from the highlighted parts of the world. You may highlight the original page in one of four colors with the help of the Glasp browser plugin (available for Chrome, Safari, Brave, Edge, Opera, and more). As well as importing notes and highlights from other services like Obsidian, Roam Research, Notion, and Readwise, you can export them to .txt,.md, .html,

Cheetah uses supervised learning techniques to catch its prey. That's a bizarre, random out-of-the-blue statement you may say. A cheetah has adapted a very refined approach to hunting by honing its skills through practice, observation, experience, and computation. Much like training datasets to create a spectacular AI model. They're trained and taught continuously until they're able to operate on their own.

Rapid developments in technology require professionals to upgrade their skills for technology-centered jobs of tomorrow. Srikanth Vidapanakal, who has been into data for more than 18 years, was inquisitive to learn about new technologies. He did a Self-Driving Car Engineer Nanodegree that helped him acquire advanced skills and landed him with a job in automation sector. Srikanth is an example of lifelong learning where staying relevant in the age of rapidly changing technologies is the need of the hour. In 2017, research suggested that AI and robotics could collectively take over 800 million jobs worldwide by 2030.

If you're getting thumb strain from trying to swipe your way to the perfect partner on Tinder, the latest update to the dating app could be the solution for you. Tinder is piloting a new feature, dubbed'Picks', that ditches the need to constantly swipe left or right to trawl through users' profiles on the dating service. Instead, Tinder Picks highlights a handful of fellow lonely hearts that it believes will be a good match, based on similar career, hobbies and interests. Although any Tinder user can see the profiles picked-out for them by the app, only those who subscribe to the Los Angeles-based dating company's £7.49 Tinder Picks will highlight a handful of dating app users who share similar interests, hobbies, and jobs.

Travel-planning app Gogobot will help you find fun things to do, whether you are traveling for work, on vacation, or just hanging out in your home town. The company just released a new version makes that's even smarter in finding out exactly what you might want to do. It's now using "artificial intelligence to predict what you are looking for and highlight recommendations before you even start typing," CEO Travis Katz tells tells us. Gogobot's claim to fame is a concept called "tribes" in which you match yourself to others with similar interests. This might be foodies, budget-conscious students, adventurer and/or families.

Social tagging systems have emerged as an effective way for users to annotate and share objects on the Web. However, with the growth of social tagging systems, users are easily overwhelmed by the large amount of data and it is very difficult for users to dig out information that he/she is interested in. Though the tagging system has provided interest-based social network features to enable the user to keep track of other users' tagging activities, there is still no automatic and effective way for the user to discover other users with common interests. In this paper, we propose a User Recommendation (UserRec) framework for user interest modeling and interest-based user recommendation, aiming to boost information sharing among users with similar interests. Our work brings three major contributions to the research community: (1) we propose a tag-graph based community detection method to model the users' personal interests, which are further represented by discrete topic distributions; (2) the similarity values between users' topic distributions are measured by Kullback-Leibler divergence (KL-divergence), and the similarity values are further used to perform interest-based user recommendation; and (3) by analyzing users' roles in a tagging system, we find users' roles in a tagging system are similar to Web pages in the Internet. Experiments on tagging dataset of Web pages (Yahoo!~Delicious) show that UserRec outperforms other state-of-the-art recommender system approaches.