Programming is easy and effortless when things go right. This happy path is where AI shines. There are sandboxed environments of development, coheasive ecosystems of tools, frameworks, libraries, which play well together to achieve a certain goal. There are also generally accepted patterns and ways of doing things. While you’re working within such a sandbox, exactly following the purposes it was built for, you’re on the happy path.

Two months ago I moved to Japan to pursue something I’ve always wanted to do. I’ve been a fan of Japanese culture for a long time, and always wanted to understand the language - so now I’m studying at a language school in Tokyo.

Being exposed to unbiased, relevant, high-quality news can open the door to professional opportunities, learning, quality personal time. How do we consume news and other content in this day and age? It is mainly via personalized feeds of social networks and notifications they send us.

I recently went through two remarkable tutorials on Kaggle: Feature Engineering and Model Explainability. They presented a bunch of different techniques to understand your data and models, which I’m going to summarise and connect in this article.

One can find enough software for personal finance on the Internet. Typically, this software focuses on reviewing and categorization of your past expenses. Often, there are features like automatic categorization, integration with bank apps, and reminders to pay recurring expenses.

This software typically focuses on the past and present financial situation. I did not find anything that would allow you to build awareness of your future financial situation given your planned expenses and income.