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I finally replaced my old Thinkpad E480 with a new HP ProBook 440 G11. The laptop comes with an integrated Intel Arc GPU and as a machine learning and AI person I immediatelly wanted to know if I can run AI workloads on it.

When people encounter some complex concept from mathematics, they often ask this question. So is mathematics actually useful? I offer two answers and some food for thought.

I often find myself examining the similarity between multiple pieces of text. I calculate embeddings and determine their cosine distance. Most recently, I was thinking about hacking together a simple tool that would filter out social media posts lacking factuality and resources.

I noticed that I repeatedly write the same code whenever I need to do something with text embeddings. I figured, enough is enough.

So today I am introducing my own Embeddings playground. This simple tool lets you quickly enter different texts, calculate their embeddings, and then determine the distance of different texts from the reference text.

I don’t have to tell you that making good decisions is important both in business and your personal life. What you may not know is that we have a scientific field developing decision-making methods based on mathematics with the singular goal of helping you make better decision.

But why on Earth should you bother with complicated mathematical formulas instead of relying on our gut instincts? True, gut instincts are useful. If you simply need to buy something to eat, pulling out Excel to determine the optimal decision is an overkill. And even worse, if you find yourself in a fight, you simple don’t have the time for a detailed analysis.

In May 2024 I successfully defended my dissertation and ended a 5-year-long adventure that was doing a PhD. Knowing what I know now, would I do it again?

Answering this question is anything but straightforward. So instead of doing it directly, I want to talk about different aspects of doing a PhD including traveling, teaching, money, writing paper, and dealing with bureaucracy. I hope this reflection helps those of you who are currently thinking about starting a PhD, but are unsure if it’s the right path for you.

Testing is crucial for producing reliable software. As an industry, we know a lot about testing, or at least about testing conventional software. The recent democratization of AI and LLMs enabled developers and software companies to easily integrate text or image generation features into their products. LLMs possess unique properties that make testing LLM-powered software quite challenging. Why is this the case, and how can we test such software properly?