Hacker News: jondea

New comment by jondea in "Seven countries now generate nearly all their electricity from renewables (2024)"

jondea — Mon, 13 Apr 2026 07:44:16 +0000

A single energy source having a capacity factor of 10% does not imply that gas plants will have to run 90% of the time.

It ignores storage, over-provisioning, aggregation of uncorrelated sources etc.

Not to mention that wind typically has a much higher capacity factor than 10%.

I don't know what the true number is, but I think this is a low effort take.

New comment by jondea in "US and TotalEnergies reach 'nearly $1B' deal to end offshore wind projects"

jondea — Mon, 23 Mar 2026 23:25:06 +0000

Could you give some sources for this? I can't seem to find anything with a cursory search, but I'd be interested in reading.

New comment by jondea in "Julia: Performance Tips"

jondea — Fri, 27 Feb 2026 11:28:47 +0000

The JuliaParallel/rodinia repo says that the focus of those benchmarks is the CUDA versions. I suspect that the CPU versions have not had much optimization effort spent on them. Julia isn't a magic wand, but you can usually get within a factor of 2 of C++ with similar effort.

New comment by jondea in "Julia: Performance Tips"

jondea — Fri, 27 Feb 2026 08:49:24 +0000

I broadly agree that it can be hard to nail down Julia's behaviour but it does have static typing and I think it is more subtle. Function arguments and variables can be concrete types e.g. if you were implementing an approximation for sin, you could restrict arguments to Float32 if you knew it was only suitably accurate for that type.

New comment by jondea in "How Israeli actions caused famine in Gaza, visualized"

jondea — Thu, 02 Oct 2025 20:32:43 +0000

Do any of the Scottish National Party, Plaid Cymru or Sinn Féin count? I have no doubt there are similar elsewhere...

New comment by jondea in "Removing yellow stains from fabric with blue light"

jondea — Thu, 11 Sep 2025 08:13:52 +0000

I'm surprised it isn't mentioned in the article, but you can get rid of yellow stains by putting your clothes out in the sun.

New comment by jondea in "A woman who can smell Parkinson's is inspiring research into diagnosis (2020)"

jondea — Wed, 14 Feb 2024 22:09:23 +0000

If it helps, the meta analysis is called: Volatile organic compounds analysis as promising biomarkers for Parkinson’s disease diagnosis: A systematic review and meta

Absolutely, it may not be useful as a screening test on the general population, but it may be useful as a piece of evidence for diagnosis alongside other pieces of evidence. Even for a test with a lot of false positives, the P(Parkinson's|positive) > P(Parkinson's|negative).

I generally agree that a lot of media doesn't accurately portray uncertainty in medical advancements, buts it's not as simple as having a sample size threshold. It really depends on the significance and the strength of the effect. Also, I want to hear about the exciting preliminary stuff, provided that it's properly caveated. There's just a lot of incentives to sensationalise.

New comment by jondea in "A woman who can smell Parkinson's is inspiring research into diagnosis (2020)"

jondea — Tue, 13 Feb 2024 22:10:48 +0000

I'm not an expert, but a very quick search showed a meta analysis[1] which considers the false positives of using volatile biomarkers as a diagnosis. The original paper[2], of which Joy is co-author has a much smaller sample size, but also has a control group to measure false positives.

Again, I'm not an expert, but from personal experience I know that Parkinson's can be hard to diagnose definitively until there are serious symptoms. This test may be relatively poor but still be useful as a piece of evidence.

[1]: https://www.sciencedirect.com/science/article/abs/pii/S03038...

[2]: https://pubs.acs.org/doi/full/10.1021/acscentsci.8b00879

New comment by jondea in "Choose Your Weapon: Survival Strategies for Depressed AI Academics"

jondea — Fri, 14 Apr 2023 22:02:36 +0000

Computing convolutions using FFTs is efficient for large kernels (or filters). Most convolutions in popular ML models have small kernels, a regime where it is typically more efficient to reformulate the convolution as a matrix multiplication.

I think your complexity argument is correct for N=pixels=kernel size. But typically, pixels>>kernel size.

Disclosure: I work at Arm optimising open source ML frameworks. Opinions are my own.