Not everything needs to have "traction", "excitement" or the biggest community. D is a useful, well designed programming language that many thousands of people in this vast world enjoy using, and if you enjoy it too, you can use it. Isn't that nice?

Oh a programming language certainly needs to have traction and community for it to succeed, or be a viable option for serious projects.

You can code your quines in whatever you'd like, but a serious project needs existence of good tooling, good libraries, proven track record & devs that speak the language.

"Good tooling, good libraries, proven track record" are all relative concepts, it's not something you have or don't have.

There are serious projects being written in D as we speak, I'm sure, and the language has a track record of having been consistently maintained and improved since 2001, and has some very good libraries and tooling (very nice standard library, three independent and supported compiler implementations!) It does not have good libraries and tooling for all things; certainly integrations with other libs and systems often lag behind more popular languages, but no programming language is suitable for everything.

What I'm saying is there's a big world out there, not all programmers are burdened with having to care about CV-maxxing, community or the preferences of other devs, some of them can just do things in the language they prefer. And therefore, not everything benefits from being written in Rust or whatever the top #1 Most Popular! Trending! Best Choice for System Programming 2026! programming language of the week happens to be.

D has three high quality compiler implementations. It has been around for ages and is very stable and has a proven track record.

Zig has one implementation and constant breaking changes.

D is the far more pragmatic and safer choice for serious projects.

Not that Zig is a bad choice but to say that a unstable lang in active development like Zig would be a better choice for "serious projects" compared to a very well established but less popular lang shows the insanity of hype driven development.

>Oh a programming language certainly needs to have traction and community for it to succeed, or be a viable option for serious projects.

You are totally right, sir.

>You can code your quines in whatever you'd like, but a serious project needs existence of good tooling, good libraries, proven track record & devs that speak the language.

Now, you HN user who calls yourself 4gotunameagain (which type of name is already a red flag for those in the know, because usually used by trolls), do us a favour by posting the link to at least one of your so-called serious projects here. Put your money where your mouth is.

Python was first released in 1991. It rumbled along for about 20 years until exploding in popularity with ML and the rise of data science.

That's not how I remember it. Excitement for python strongly predated ML and data science. I remember python being the cool new language in 1997 when I was still in high school. Python 2.4 was already out, and O'Reilly had put several books kn the topic already it. Python was known as this almost pseudo code like language thst used indentation for blocking. MIT was considering switching to it for its introductory classes. It was definitely already hyped back then -- which led to U Toronto picking it for its first ML projects that eventually everyone adopted when deep learning got started.

It was popular as a teaching language when it started out, along side BASIC or Pascal. When the Web took off, it was one of a few that took off for scripting simple backends, along side PHP, JS and Ruby.

But the real explosion happened with ML.

I agree with the person you're replying to. Python was definitely already a thing before ML. The way I remember it is it started taking off as a nice scripting language that was more user friendly than Perl, the king of scripting languages at the time. The popularity gain accelerated with the proliferation of web frameworks, with Django tailgating immensely popular at the time Ruby on Rails and Flask capturing the micro-framework enthusiast crowd. At the same time the perceived ease of use and availability of numeric libraries established Python in scientific circles. By the time ML started breaking into mainstream, Python was already one of the most popular programming languages.

As I remember it there was a time when Ruby and Python were the two big up-and-coming scripting languages while Perl was in decline.

That is correct. I "came of age" in 2010-11 during the Web 2.0 era of web apps beginning to eat the world. Ruby was just starting to come down from its peak as the new hotness, and Python thanks to Django + Google's support/advocacy was becoming the new Next Big Thing for the web and seemed like a no-brainer to learn as my main tool back at the time.

At the time Java was the mature but boring "enterprise" alternative to both, but also beginning its decline in web mindshare as Ruby/Python (then JavaScript/Node) were seen as solving much of the verbosity/complexity associated with Java.

There was a lot of worry that the Python 2->3 controversy was threatening to hurt its adoption, but that concern came from Python in a position of strength/growing fast.

Python's latter day positioning as the ML/scientific computing language of choice came as its position in the web was being gobbled up by JavaScript by the day and was by then well on the downswing for web, for a variety of technical/aesthetic reasons but also just simply no longer being "cool" vs. a Node/NoSQL stack.

Sure, but the point was that it being used for web backends was years after it was invented, an area in which it never ruled the roost. ML is where it has gained massive traction outside SW dev.

Python was common place long before ML. Ever since 1991, it would jump in popularity every now and then, collect enough mindshare, then dives again once people find better tools for the job. It long took the place of perl as the quick "linux script that's too complex for bash" especially when python2 was shipping with almost all distros.

For example, python got a similar boost in popularity in the late 2000s and early 2010s when almost every startup was either ruby on rails or django. Then again in the mid 2010s when "data science" got popular with pandas. Then again in the end of 2010s with ML. Then again in the 2020s with LLMs. Every time people eventually drop it for something else. It's arguably in a much better place with types, asyncio, and much better ecosystem in general these days than it was back then. As someone who worked on developer tools and devops for most of the time, I always dread dealing with python developers though tbh.

> I always dread dealing with python developers though tbh.

Out of curiosity, why is that?

There are plenty of brilliant people who use python. However, in every one of these boom cycles with python I dealt with A LOT of developers with horrific software engineering practices, little understanding of how their applications and dependencies work, and just plane bizarre ideas of how services work. Like the one who comes with 1 8k line run.py with like 3 functions asking to “deploy it as a service”, expecting it to literally launch `python3 run.py` for every request. It takes 5 minutes to run. It assumes there is only 1 execution at a time per VM because it always writes to /tmp/data.tmp. Then poses a lot of “You guys don’t know what you’re doing” questions like “yeah, it takes a minute, but can’t you just return a progress bar?” In a REST api? Or “yeah, just run one per machine. Shouldn’t you provide isolation?”. Then there is the guy who zips up their venv from a Mac or Windows machine and expects it to just run on a Linux server. Or the guy who has no idea what system libs their application needs and is so confused we’re not running a full Ubuntu desktop in a server environment. Or the guy who gives you a 12GB docker image because ‘well, I’m using anaconda”

Containers have certainly helped a lot with python deployments these days, even if the Python community was late to adopt it for some reason. throughout the 2010s where containers would have provided a much better story especially for python where most libraries are just C wrappers and you must pip install on the same target environments, python developers I dealt with were all very dismissive of it and just wanted to upload a zip or tarball because “python is cross platform. It shouldn’t matter” then we had to invent all sorts of workarounds to make sure we have hundreds of random system libs installed because who knows what they are using and what pip will need to build their things. prebuilt wheels were a lot less common back then too causing pip installs to be very resource intensive, slow and flaky because som system lib is missing or was updated. Still python application docker images always range in the 10s of GBs

Thanks for the detailed reply. I wrote and deployed a few dev-opsy python scripts in my last job that I wasn't massively proud of, but after reading that I all of a sudden don't feel so bad lol

Python crossed the chasm in the early 2000s with scripting, web applications, and teaching. Yes, it's riding an ML rocket, but it didn't become popular because it was used for ML, it was chosen for ML because it was popular.

Oh? How about Raymond's "Why python?" article that basically described the language as the best thing since sliced bread? Published in 2000, and my first contact with python.

Python had already exploded in popularity in the early 2000s, and for all sorts of things (like cross-platform shell scripting or as scripting/plugin system for native applications).

Not really, back in 2003 when I joined CERN it was already the offical scripting language on ATLAS, our build pipeline at the time (CMT) used Python, there were Python trainings available for the staff, and it was a required skill for anyone working in Grid Computing.

I started using Python in version 1.6, there were already several O'Reilly books, and Dr.Dobbs issues dedicated to Python.

This is not true. It took about 20 years for Python to reach the levels of its today's popularity. JavaScript also wasn't so dominant and omnipresent until the Chrome era.

Also, many languages that see a lot of hype initially lose most of their admirers in the long run, e.g. Scala.

> Never has an old language gained traction, its all about the initial network effects created by excitement.

Python?! Created in 1991, became increasingly popular – especially in university circles – only in the mid-2000s, and then completely exploded thanks to the ML/DL boom of the 2010s. That boom fed back into programming training, and it's now a very popular first language too.

Love it or hate it, Python was a teenager by the time it properly took off.