Julia ranks pretty good on a recent ranking: https://langpulse.top/
According to the link, it’s more popular than MATLAB and Fortran combined, judged from the number of public repos with recent pushes, FWIW.
When I look at those charts the first question that comes to mind is, “What happened on May 18?”
All the languages had what looks like a step-function change right after May 17. This is peculiar.
One possibility (and it is only a guess) is that the ranking algorithm is tweaked at that time affecting all rankings. I’ve seen this same phenomena with PYPL. This really shows that you should not take short term changes in the rankings too seriously. As a case in point, if you look at the TIOBE graph at top you will see one outlier data point in late 2023 where the Julia ranking was about 0.85 which was enough to put Julia in the top 20. Guess what? there were a bunch of news posts about how Julia had cracked the top 20. Pure garbage reporting.
That’s sort-of what I was getting at. If you see that large of an unexplained artifact in the data it’s a sign that your data sources are not of the highest quality. I wouldn’t pay too much attention to such results.
Correct. But my gut feeling is that there is information to be gleaned in the long term (like a decade) trends especially if different rankings are compared and combined.
Language popularity matters considerably less than it used to. Of course network effects are still important, but LLM’s can help fill out the “boring” parts of the ecosystem which had less traction previously. Many of the things I wanted to do with Julia but couldn’t due to lack of support for this sort of boring stuff (HTTP/2, gRPC, pretrained vision backbones) flipped from blocking to reasonably achievable with a bit of a effort. Today it’s more important how a language and surrounding ecosystem stands out / is different, and Julia has no shortage of these sorts of things.
While true, another aspect that concerns me is that newcomers may not be as attracted to the expressivity of a language (for a human programmer) as they were in the pre-LLM times.
OTOH, newcomers may be less dissuaded by unfamiliarity. I find myself using (relatively) less python now that claude can write Rust for me
Yes, https://langrank.hexq.ru/ =)
I think it’s possible to combine several orthogonal rankings to obtain less noisy results. Ideally, we should apply a multidimensional Kalman filter to extract the signal.
FWIW, this month’s TIOBE (Sep 2026) ranks Julia 21, but probably more importantly, the front-page narrative is about Julia. It also refers to last month’s narrative about the fall of Matlab out of the top 20; at that point, Julia was one spot behind Matlab. So, while I’d take the numbers and even the instantaneous “rankings” with a grain of salt, the accompanying narrative provides some attention and perhaps a leading indicator of some movement in the scientific computing space.
October 2026:
October Headline: Julia Returns to the TIOBE Top 20
Julia is back in the TIOBE index top 20, having last reached this milestone three years ago. Back then, it might have been dismissed as a passing hype, but its renewed presence suggests that its popularity may be more sustainable this time.
Julia’s growing popularity is largely driven by the demand for modern programming languages that combine high performance with ease of learning, particularly for numerical computing. Python offers an accessible, high-level approach to numerical computing, but performance can become a bottleneck. C++, on the other hand, delivers excellent performance but comes with a steeper learning curve and a lower level of abstraction. Julia aims to bridge this gap by combining the best of both worlds. Let’s see whether Julia can maintain its position in the top 20 this time!
