I think a more nuanced take is appropriate here. It‘s true that computers were invented with the express goal of speeding up military and corporate computing (back when computers were still people), but their influence on our culture and society extended far beyond those initial applications. The telephone was invented as a means of long-distance communication, but it shaped our values surrounding communication as well. Therefore it may be hard to predict what will ultimately become of a technology.
I agree that there are a lot of overhyped technologies though. Quantum computing has been in the works for decades now, with little to show for it in the popular perception.
Back when I went to school in Germany, we had a locker at school, but I just took the books I needed for assignments home with me. I haven't heard of schools that don't let you take (loaned) books home.
As far as I know the code was ported to use @floops, with minor optimisations in addition to that.
I think it's quite possible that it's an allocation issue, that's something we're looking into, although I don't have any specific results for Julia yet.
Yes, that's the paper my predecessors worked on! I replicated the measurements with an upgraded version of Julia (1.12), but despite the claimed performance benefits, Julia still performed poorly.
Thank you for the article! We're mainly interested in floating-point performance and energy consumption w/r/t to solving differential equations and tridiagonal systems of equations, while running on a 128-core compute node. Our current results will likely only be presented in May, but here are last year's results: https://www.cs.uni-potsdam.de/bs/research/docs/papers/2025/l...
Our Julia code is parallelised with FLoops.jl, but so far Numba has shown surprising performance benefits when executing code in parallel, despite being slower when executed sequentially. Therefore I can imagine that Julia might yield better results when run in a regular desktop environment.
We have been running benchmarks to compare different languages relevant to high-performance computing and unfortunately Julia still lags behind even Numba-JIT-compiled Python. Perhaps my understanding of Julia is limited, but even the Rodinia SRAD program, which was originally written in Julia, performs faster in all other implementations that aren't Julia.
I agree with you. Having lost a close family member to cancer over a period of years, the only regret I have is not trying more to improve her quality of life for as long as I could. Putting in effort to understand the diagnosis is warranted, but there are no miracle cures, even if there are miracles sometimes.
I feel like the use of the term "anti-AI hype" is not really fully explored here. Even limiting myself to tech-related applications - I'm frankly sick of companies trying to shove half-baked "AI features" down my throat, and the enshittification of services that ensues. That has little to do with using LLMs as coding assistants, and yet I think it is still an essential part of the "anti-AI hype".