Plurrrr

I started a new job recently so I had the opportunity to get one of the new M1 MacBooks, I decided to go with the Air. The reviews have been very positive and I’m here to tell you: it is indeed an amazing device. The performance feels a lot better than my MacBook Pro 16”, which is only a year old and about 3x the price.

When I got the Mac I set out with the goal of avoiding Intel builds of software as much as possible and using native whenever possible unless it’s absolutely impossible.

Deep Learning is an extremely fast-moving field and the huge number of research papers and ideas can be overwhelming. Even seasoned researchers have a hard time telling company PR from real breakthroughs. The goal of this post is to review those ideas that have stood the test of time, which is perhaps the only significance test one should rely on. These ideas, or improvements of them, have been used over and over again. They're known to work.

When getting started with containers, it’s pretty easy to be shocked by the size of the images that we build. We’re going to review a number of techniques to reduce image size, without sacrificing developers’ and ops’ convenience. In this first part, we will talk about multi-stage builds, because that’s where anyone should start if they want to reduce the size of their images. We will also explain the differences between static and dynamic linking, as well as why we should care about that. This will be the occasion to introduce Alpine.