This is incorrect. The European Parliament elects the Commission President, not the European Council. In fact, von der Leyen narrowly squeaked through in 2019, getting 383 votes with 374 votes required (with her making it being attributed to her hawkish stance on Russia to an extent, which supposedly earned her additional Eastern European votes).
The European Council does nominate the candidate and infamously in 2019 overrode the informally agreed on spitzenkandidaten process, by which the nominee of the political group (EU Parliament equivalent of parties) with the most seats should have gotten the nomination.
That said, the EU Parliament is not bound by the nomination. It can very obviously reject any candidate it doesn't like, though of course the dynamics of candidate selection are heavily influenced by the European Council's nomination power.
But yes, there's nothing per se undemocratic about it and I'm a bit bewildered by people asking for direct election of the President rather than cleaning up the existing process, given that most EU member states elect their heads of government indirectly.
I think this oversimplifies the problem of dealing with performance issues. In my experience, there are three aspects that contribute to the software engineering cost of performance optimizations:
1. Identifying the cause of poor performance.
2. Implementation.
3. Architectural impact (performance is a classic case of a cross-cutting concern)
(I am specifically leaving out the case of realtime systems, hard or soft, where additional factors come into play.)
But the article seems to focus entirely on the second aspect, while largely ignoring the other two.
Most performance bugs are not difficult to fix [1, 2], but can sometimes be hard to identify. Implementation effort is not the driving limitation in those cases.
Conversely, other performance improvements may affect the overall design, e.g. 27% of all bugs identified in [3].
Having an obvious, self-contained optimization target with a benchmark where algorithmic optimization within a module is also the key problem seems to be the exception, not the rule.
Also, not all performance issues are created equal. Many have trivial cost.
In contrast, some of the most challenging performance issues are the ones that affect the design and architecture of the system. After all, the hard part of software engineering is not writing a small, self-contained application. It's managing system complexity, while maintaining (in the words of Fred Brooks) conceptual integrity. Fixing performance issues is at least in this regard not fundamentally different from fixing other software defects.
Unfortunately, this is an area that is also full of trade-offs, such as performance vs. architectural simplicity, or performance in one part of the system vs. performance in another part, all of which requires judgement.
For example, you may need to bypass an abstraction boundary or reorganize abstraction boundaries to improve performance. Or you may have to special-case something while keeping duplicated code at a minimum and easy to maintain.
This is not to say that agents cannot help here, too. In fact, agents can be very helpful at e.g. identifying bottlenecks that are not directly visible in a profiler or can be used quickly do comparative evaluations of the various options for an architectural change. But solving these issues is not, like with the regex example in the article, about hillclimbing towards better performance, but involves a combination of puzzle-solving and design skill, IMHO.
And finally, even a self-contained algorithmic improvement may come with an increased maintenance burden, especially around edge cases and through increased code complexity.
[2] Selakovic, Marija & Pradel, Michael. (2016). Performance issues and optimizations in JavaScript: an empirical study. 61-72. 10.1145/2884781.2884829.
[3] Zhao, Yutong & Xiao, Lu & Bondi, André & Chen, Bihuan & Liu, Yang. (2023). A Large-Scale Empirical Study of Real-Life Performance Issues in Open Source Projects. IEEE Transactions on Software Engineering. 49. 924-946. 10.1109/TSE.2022.3167628.
> I understand the argument of "people are not actively looking", but isn't the cost for such a scan getting lower by the week, and Anthropic's Project Glasswing is supposed to find them quite a while ago?
You have to consider that having an LLM scan for vulnerabilities is hardly infallible. It is a search guided by heuristics and given a large enough codebase, it is unlikely to identify all vulnerabilities.
Personally, I've had Fable 5, GPT 5.6 Sol, and GLM 5.2 all looking for correctness issues in an old abandoned WIP codebase of mine and all of them found some that the others hadn't discovered. Now, correctness issues aren't the same as vulnerabilities, but the same principle about using heuristics to find defects applies.
> [A]ll of them found some that the others hadn't discovered. Now, correctness issues aren't the same as vulnerabilities, but the same principle about using heuristics to find defects applies.
This makes perfect sense, but that conflicts with the impression put forward by Anthropic and OpenAI (in particular) that they alone occupy 'frontier model' spots. Frontier models should large dominate their competitors on a capability basis, but if GLM 5.2 (now 5.3) is routinely finding bugs / vulnerabilities missed by Fable and Sol then GLM might be genuinely a frontier-grade model by itself.
> This makes perfect sense, but that conflicts with the impression put forward by Anthropic and OpenAI (in particular) that they alone occupy 'frontier model' spots.
Not necessarily. Even near the frontier, we don't really have a total ordering of capabilities, but a partial order. And even frontier models make plenty of mistakes. Combined with the randomness inherent in searching large codebases for vulnerabilities or correctness issues, it is entirely plausible that even much weaker models (and GLM-5.2 isn't even weak) can stumble upon issues that stronger models missed.
My current hypothesis – for which I have only limited evidence, unfortunately – is that it is better to have multiple reasonably powerful (but not necessarily frontier) models looking for issues than just one very powerful one. And even then you're likely to miss out on some issues.
For normal software it would be as you say, but LLM progress is so ridiculously fast that things go from "bleeding edge" to "eh, you'll do" in about that timeframe, and "eh, you'll do" to "why even bother with this old rubbish?" in the same again.
Or, from a different perspective, we can expect some new frontier model from Anthropic in a week or two, and from OpenAI in a month or so.
I find that LLMs also generate a lot of false positives, or extremely minor issues that don't warrant a fix (that are always overstated by the LLM as very important!). Signal to noise is still not great and requires somebody to wade through and pick out the actual good findings.
The relevant legal provision is from §8 TDDDG [1]:
"(1) It is prohibited to possess, manufacture, make available on the market, import, or otherwise bring within the territory to which this Act applies, telecommunications equipment that, by its appearance, purports to be another object or is disguised as an object of everyday use and that, because of these circumstances or because of the way it functions, is particularly suited and intended to intercept, without that person’s knowledge, the non-publicly spoken words of another person or to record images of another person without that person’s knowledge."
IANAL, but this sounds like it's directly applicable.
Violations can be punished with up to two years in prison or a fine.
Glasses that record video have been around for decades, they just weren't as pervasive. It sounds like this is a general law against disguised surveillance cameras, which I'm sure has historically included camera glasses, so it seems like kind of a slam dunk unless Meta can somehow argue that smart glasses have become common enough to be readily recognized as a recording device.
> American workers still get higher salaries than elsewhere in the OECD and growth in such industries isn't out competing those same industries in other OECD countries.
I used to think that also, probably because FAANG salaries had skewed my perception, but after looking at the data, this does not seem to be actually true, at least not in general.
For example, Germany has a somewhat higher annual median gross salary for full-time employees than the US (PPP-adjusted, BLS/DESTATIS salary data, OECD PPP values).
Of course, this is the median salary. America absolutely offers higher salaries at the top end (and I mean much higher, often by a factor of 2-3 for highly qualified professionals, such as software engineers and doctors). But that also means correspondingly lower salaries at the low end. And of course, labor is taxed heavily in Germany, so discretionary/disposable income may look different in the end on a case-by-case basis.
First of all, yes, labor is taxed much higher in Germany than in the US (PPP adjustment already takes VAT on prices into account, so that doesn't matter in this case). In fact, Germany has one of the highest taxations of labor in the world.
What makes this analysis tricky is disposable vs. discretionary income. The US clearly comes out on top when it comes to disposable income (labour is taxed too much in Germany compared to capital income and wealth).
The picture gets more complicated when you look at discretionary income, which also accounts for regular bills, such as rent, college tuition, out of pocket expenses for health care, childcare, and such. All the taxes you pay in Germany do also pay for something, after all. There is unfortunately very little data for this type of comparison.
You would also need to adjust for the huge medical expenses Americans face if you are doing that as Germans don't have that. Maybe the 401K savings Americans do for retirement (if Germans have a better societal retirement payout than Americans). And the other social benefits that are accounted for by German taxes that Americans still have to account for in some way. You can't just lump those in on the Germans and not account that Americans pay it some way as well.
It means that this is a cabinet decision, not (yet) legislation. It still has to go through the Bundestag. Given the opposition within the SPD and the idea being very unpopular among voters, it is not yet clear whether this will actually become law.
It is still very worrying and the unfortunate result of a lot of things going wrong at the cabinet level.
It hasn't even been formally approved by the cabinet. Right now it's just a political agreement between the coalition party. A law still has to be drafted that the cabinet can then submit to the Bundestag.
I am not sure I'm buying this. The raison d'être for our existing software engineering methods is that humans make mistakes and we needed to contain the effects of these mistakes; and without an appropriate methodology to do that, software defects will just accumulate over time. Worse, once they show up, nobody understands the code well enough to do anything about them, or at least not without considerable time investment.
This does not change with agents doing the coding. Coding agents make mistakes also. Not very often nowadays, but neither do competent human programmers. And without a methodology to keep problems in check your agentic code will also accumulate software defects over time and result in code that becomes less and less maintainable, because you have no mental model of the software.
Antirez is correct in pointing out that slop existed before we started to use LLMs for programming; I've worked with my share of really ugly legacy code myself. But the problems do not magically disappear in the LLM age, no matter how good your model is. They remain, as every model is ultimately a heuristic (albeit a very powerful one), and no heuristic is 100% accurate.
This does not mean that coding agents are useless; used correctly, they can be enormously powerful accelerators for the software development (and validation!) process, because combining your strengths with those of a modern LLM is generally a substantial net gain. But that must still happen as a part of an approach that results in maintainable software with minimal defects.
Personally, I primarily use agents as virtual pair programmers these days, which I find very useful. This is an iterative process with relatively small and contained changes, where "looking at the code" is just part and parcel of following along and building a mental model of the resulting piece of software.
My concern with most of these visual benchmarks, popular as they are, is that they are likely more indicative of knowledge (i.e. how comprehensive the training data is and how well it can be retrieved from the model) than of reasoning ability. I don't see in particular how a model would construct a CoT that mapped somehow to a representation of the cube geometry and its animations in latent space without a large chunk of that being pre-existing information.
> without a large chunk of that being pre-existing information.
Is there any evidence that novel reasoning is present in LLM? I've never been able to make that work, and I believe Apple's paper some time ago was good evidence that it doesn't exist. In my experience, sparse latent spaces result in a complete, comical, failure in reasoning.
True, but that's an unknown internal model, without details of the architecture. We'll have to see if the LLM model, itself, was responsible for the "novel" bits, or if it was stuffs bolted onto the LLM that made it possible. I suppose "LLM" is maybe no longer sufficient to describe the systems that LLM are being integrated into, so maybe my point is pedantic/semantic.
I mean, Western providers such as Fireworks AI/Microsoft Foundry (US) or Tensorix (EU) already are offering many of these models on their own hardware with all the typical compliance boxes ticked through a standard API. Either as open weight models or through partnerships with Chinese firms, or both. DeepSeek etc. do not have to do anything here other than making their models available to Western partners (either as open weights or through a licensing agreement).
Heritability has a very specific meaning in quantitative genetics [1], which in many ways is not what your intuition would suggest [2]. It is this usage that the article talks about that.
That said, there are plenty of critiques of this definition of heritability, and not just because it is different from what a layperson would expect it to mean.
For example, the way it is used also usually has a big problem in that the standard formula assumes that Cov(G, E) = 0 (or at least is negligible), whereas in practice that is not actually true [3, 4].
This definition of heritability is also mathematically flawed in that it assumes (without evidence) that P = G + E, or at least can be reasonably approximated this way. Given that human development is the result of a feedback loop involving genetic and environmental factors, one would expect a model closer to something like a Markov chain. Proposed justifications of a simple additive model as an approximation (e.g. via the central limit theorem for highly polygenic traits) have to my knowledge never been tested.
More recent genome-wide association studies [5] have actually shown a considerable gap between heritability estimates from genotype data and heritability estimates from twin studies, known as the "missing heritability problem".
OP has another post on the definition of heritability, which I really liked: https://dynomight.net/heritable/ . I'm a layman, though, so since you seem knowledgeable, I would love to hear your thoughts on that article!
For instance, OP's definition H = Var[G] / Var[P] seems to bypass the issues you mentioned:
> For example, the way it is used also usually has a big problem in that the standard formula assumes that Cov(G, E) = 0 (or at least is negligible), whereas in practice that is not actually true [3, 4].
> This definition of heritability is also mathematically flawed in that it assumes (without evidence) that P = G + E, or at least can be reasonably approximated this way.
> For instance, OP's definition H = Var[G] / Var[P] seems to bypass the issues you mentioned:
No, this is exactly the definition I am talking about. The problem is that while theoretically you could work with Var(G)/Var(P) even if Cov(G, E) ≠ 0, studies are not designed to capture that.
In fact, the standard ACE model [1] used in twin studies explicitly assumes among other things that there is no gene-environment correlation. This means that it gets silently added to one or more of the ACE components; not because of any ill intentions, but simply because if you included covariance, the resulting system of equations would be underdetermined and could not be solved [2].
But to make matters worse, gene-environment correlation/interaction itself is disproportionately absorbed by the A and C components rather than E. All this can lead to inflated heritability estimates.
And to clarify, I am not making any pronunciations about how much relevance or magnitude that effect has; for all I know, this could in the end be a minor effect. My point here is that there is a lot of mathematical handwaving going on with very limited testability of the modeling.
[2] If you want to be precise, you need to actually distinguish between gene-environment correlation and interaction and use P = G + E + (G x E), but that makes the system even more underdetermined, because now we have both Cov(G, E) and Var(G x E) to worry about.
> Heritability has a very specific meaning in quantitative genetics [1]
Literally the first paragraph of that page is
> Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is not explained by the environment or random chance?"
That matches what I assumed it meant, and it seems like OP and the post are arguing that that is some kind of surprising interpretation.
> OK, but check this out: Say I redefine “hair color” to mean “hair color except ignoring epigenetic and embryonic stuff and pretending that no one ever goes gray or dyes their hair et cetera”. Now, hair color is 100% heritable. Amazing, right?
Uhm, no. That is exactly what I (and I think most people) would expect the answer to be.
> That matches what I assumed it meant, and it seems like OP and the post are arguing that that is some kind of surprising interpretation.
The unintuitive part is that in quantitative genetics, heritability is defined in terms of variance in traits at the population level, not as the passing of traits from parents to offspring (that would be heredity [1]). Of course, I may have misinterpreted what you said in your OP when you cited the wiktionary definition of "[g]enetically transmissible from parent to offspring", and if so, I apologize, but at the time it seemed to me that you were talking about heredity.
> Uhm, no. That is exactly what I (and I think most people) would expect the answer to be.
What the article is talking about is that if you fix Var(E) = 0, then Var(P) = Var(G) in the standard heritability model, i.e. all phenotypic variance is explained entirely by genotypic variance (because in that model, Var(P) = Var(G) + Var(E)).
Fun fact (even if only tangentially unrelated): In Western countries, wearing glasses is a highly heritable trait, because wearing glasses is a strong proxy variable for refractive error [2], such as nearsightedness, which is highly heritable. It is often brought up as another example of how the quantitative genetics definition does not match conventional use of the word.
The European Council does nominate the candidate and infamously in 2019 overrode the informally agreed on spitzenkandidaten process, by which the nominee of the political group (EU Parliament equivalent of parties) with the most seats should have gotten the nomination.
That said, the EU Parliament is not bound by the nomination. It can very obviously reject any candidate it doesn't like, though of course the dynamics of candidate selection are heavily influenced by the European Council's nomination power.
But yes, there's nothing per se undemocratic about it and I'm a bit bewildered by people asking for direct election of the President rather than cleaning up the existing process, given that most EU member states elect their heads of government indirectly.
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