git
As a freelancer, I work for many clients, who have their source code in many different places, often self hosted. I generally create new SSH keys for each separate platform. Unfortunately, Git doesn't provide an option for what keys to use, you have to configure this in the
programming
I work on many different projects and I use some ActivityWatch for automatic time tracking. AW has a feature where you can use regexes to match window titles to assign them automatically to different projects. But, unfortunately, Jupyter Notebook have a "{filename} - Jupyter Notebook" title in the
programming
When running long running code in Jupyter, I want to get notified when it finished so that I can get back to it. There is an extension to do that with browser notifications, but there are times when I leave the computer while waiting for an ML training to finish.
til
I use tmux as a multiplexer and for running some long-running commands on servers. Today I encountered a weird issue: when trying to attach to an existing tmux session (or when trying to do anything with tmux), I would get the following error: can't create socket: Permission denied
programming
As I work from home 90% of the time, I run into a small issue during meetings: I sometimes speak too loudly. Before my daughter Gloria arrived, this was something that annoyed my wife and others in the house, but now, when Gloria is sleeping, this is not just an
gdcr
After 8 years, I finally went again to a Code Retreat, but this time as a facilitator, not as a participant. As far as I know, it was the first time it was done in Oradea, so kudos to the Oradea Tech Hub team for organizing it. My co-facilitator was
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
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
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