Eugeleo

Writing research code (in my case ML/AI) is very different to writing production code. The goals are different, and thus so are the best practices, patterns, and values.

What's your favorite resource on how to write code in research? What are the research-code-specific equivalents of Rich Hickey's talks or SPJ's posts or the many many SWE blogposts posted to HN?

I've read through the MacBook threads and seen one question repeated again and again: which specs to go for in the new MacBooks. I figured it might be worth it to make a dedicated post to discuss this.

To start this off, consider the following scenario:

- 50% of my work is web or mobile app development

- another 50% is training ML and deep learning models (currently on a cluster, but I'd prefer do it on my notebook of course)

- I also do raw photo editing work, as a hobby

In this case, what specs would you choose for your Mac? Specifically:

- M1 Pro with [14, 16] GPU cores, [8, 10] CPU cores, and [16, 32] GB of unified memory

- M1 Max with [24, 32] GPU cores, 10 CPU cores, and [32, 64] GB of unified memory

With the new MacBooks around the corner, I'm torn between the 13" and 15" (or more likely, 14" and 16") versions. All things besides size and weight equal, what notebook screen size do you prefer and why? Doesn't have to be MacBooks of course, I'm interested in all thoughts on this topic, especially from people who tried both.

I can't decide whether being on the move often is an argument for the smaller version (lighter), or the bigger one (more comfortable to work without an external monitor).

(forgot to include the url, please delete this)