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Steer for a talent transformation strategy (and avoiding AI fatigue)

 There was a debate on whether to feature the term “AI” in the title of this article. Honestly, a key motivation for pursuing the research that led to this post was sparked by the widespread excitement about AI appearing constantly in our LinkedIn feed, to the point of feeling the fatigue, and even a bit disappointed in the algorithm of this, and the others, social media and content curated apps.  We soon discovered that there is an entire concept called "AI fatigue", not exactly how we were feeling it, but more about the mixed emotions people in the workforce have regarding the use of AI tools. Photo by Mart Production via Pexels (background updated with AI and Adobe  tech) From micro blog posts to video podcasts, lately, most of the tech content we encounter revolves around AI. They often sound or read very similar, usually mentioning the same few top providers. The articles (and social posts... at least the popular ones with paid-campaigns behind it) tend to focus less...
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Designing Habit Forming Mobile Application

Anonymous
Mobile Applications have become an integral part of our daily lives - we use mobile apps as alarm clocks to wake us up in the morning, to create to do lists when we start our day, to communicate with our colleagues at work via apps like Skype. We even check reviews of restaurants to visit on apps like Yelp and we seek entertainment on apps like Netflix and spotify. So what drives us to use these apps so seamlessly in our daily lives? Why we prefer some apps over others? Is there a science behind designing successful mobile apps like Facebook?  Photo by Peter C from Pexels A study in US revealed that a user between the age of 18 and 44 visits the Facebook app on average 14 times a day [1]. This shows that using the Facebook app is a daily routine for many of its users. This makes Facebook a great example of a habit forming mobile app which is designed with human psychology in mind that encourages habit forming behavior in its users .   I recently attended a seminar ...
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Building MCP with TypeScript

MCP servers are popular these days. We’ve been researching and exploring a few code repos, some where missing modularity, others just not having pieces that we were looking for… therefore we decided to build our own, simple and foundational that could be a starting point for those trying to solve for the similar things we were… and we decided to share it with the community, via our public github. MCP host, server,data sources     Before we start.  Using Typescript and NodeJS was one of our requirements. This proved somewhat challenging because I don't code as frequently these days due to my leadership responsibilities, and I typically prefer working with C# or Python. Colleagues in my tech community have been working with their teams on some of their MCPs going the Python route. Therefore, I said, “I guess we are trying the other route” 😊. One of our reasons to go with TypeScript was due to the need of the integration with APIs, and based on the research, it seems t...
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Assembling MLOps practice - part 2

 Part I of this series, published in May, discussed the definition of MLOps and outlined the requirements for implementing this practice within an organisation. It also addressed some of the roles necessary within the team to support MLOps. Lego Alike data assembly - Generated with Gemini   This time, we move forward by exploring part of the technical stack that could be an option for implementing MLOps.  Before proceeding, below is a CTA to the first part of the article for reference. Assembling an MLOps Practice - Part 1 ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success... Take me there Components of your MLOps stack. The MLOps stack optimises the machine learning life-cycle by fostering collaboration across teams, delivering continuous integration and depl...
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Agreed