tech
I mentioned last year that I've slowly moved back to using Windows more and more. In the mean time, my transition is almost completely done. This year I've pretty much booted into ArchLinux to update it, about once a month and that's it. I
personal
It's hard to believe it's been more than half an year since I started my own company and became an independent machine learning consultant. It's been a very interesting ride. There have been plenty of moments where the predominant feeling was "what now?
blog
My blog has circled the Sun for another year. You got 37 more posts in the meantime. The Obsidian post was very popular, as was the Rust Codenames series. Vmmem issues are finding a solution on my blog as well. The second half of last year was slower than the
tech
Until this year, I usually had a laptop from my employer, on which I did work stuff and I had a personal desktop and laptop. The two personal devices got far too little usage coding wise, so I didn't really have a need to make sure I have
productivity
As I've started my freelance machine learning consulting business this year, I found I need better ways to organize my time. When I was employed as a software engineer, there was a task board I would choose what to work on. The tasks would be mostly decided at
machine learning
tl;dr: I'm launching an introductory course about machine learning in Romanian. It's aimed not just at developers, but at a more general audience. De ceva timp mă bate gândul să trec la următorul nivel de creare de conținut. Scriu pe blog de 10 ani și
learning
In programming there are so called design patterns, which are basically commonly repeated pieces of code that occur often enough that people thought it would be helpful to give them a name so that’s it’s easier to talk about them. One example is the iterator pattern, which is
machine learning
As much as machine learning developers like to think that once they've got a good enough model, the job is done, it's not quite so. The first couple of weeks after deployment are critical. Is the model really as good as offline tests said they are?
machine learning
One of the most impressive/controversial papers from 2020 was GPT-3 from OpenAI. It's nothing particularly new, it's mostly a bigger version of GPT-2, which came out in 2019. It's a much bigger version, being by far the largest machine learning model at the
machine learning
So the ML engineer presented the model to the business stakeholders and they agreed that it performed well enough on the key metrics in testing that it's time to deploy it to production. So now we have to make sure the models run reliably in production. We have
machine learning
So we finally got our data and we can get to machine learning. Without the data, there is no machine learning, there is at best human learning, where somebody tries to write an algorithm by hand to do the task at hand. This is the part that most people who
blog
2020 might have been a bad year outside, but it was a good year for my blog. I wrote 62 posts, almost as many as in the previous 3 years combined (63). Part of it was due to more time because of Covid, part of it was because of the