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Comment by jjice | Hacker News Reader

The last two-ish years have been insane for Python performance. Something clicked with the core team and they obviously made this a serious goal of theirs and the last few years have been incredible to see.

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It’s because the total dollars of capitalized software deployed in the world using Python has absolutely exploded from AI stuff. Just like how the total dollars of business conducted on the web was a big driver of JS performance earlier.

But all the AI heavy lifting is done in native code.

AI heavy lifting isn't just model training. There's about a million data pipelines and processes before the training data gets loaded into a PyTorch tensor.

also done in native code

From personal experience, no. I ended up writing rust bindings to call from python that turned minutes of loading into seconds.

Ehhh... if you're lucky. I've seen (and maybe even written) plenty of we-didn't-have-time-to-write-this-properly-with-dataframes Python data munging code, banged out once and then deployed to production. I'll take performance gains there.

That is the exact value proposition of Mojo.

Pythonesque (sane) syntax, great AOTC language features, great performance, memory safety, excellent Python interoperability. What’s not to like?

If you think of something, contact Modular…

When Node.js started really taking off, I was actually a bit worried that Python would become No Longer Best Practice, but now it definitely seems to be still going strong.

Python is Best Practice (tm)? What do you mean?

Right now, if you develop something in Python, not many people complain at you, although some might say you shoulda used JS and some of them might be right....

Maybe not absolute best practice, but it's not like PHP or Ruby or Perl, where there's rarely ever any new projects everyone blogs about using them.

All the big web frameworks are still maintained, there are still new coders learning it, it's not a language that will make people be like "Oh ew, I'm not learning that language just to work on that", etc.

Microsoft are paying core devs to work on it full time, for one.

Which is strange considering how bad the tooling to use Python on Windows is. There's a few workflows where people have gone down the beaten path before (Conda, etc.), but outside of that you have to just pretend you're on Linux and use the cygwin toolchains and even that doesn't always work so well. Better support on Linux was a top 5 reason for me making the switch to using it full time when I went off to college, and it hasn't changed substantially in the 8 years since then.

Not sure what you mean. I use Python for Windows all the time at work, and on Linux at home. I've not noticed any meaningful difference in my workflow.

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