We can't draw conclusions from that study because it's been retracted on the basis that data has been faked.
On the other hand there are other similar studies that reach similar conclusions, and specifically try to control for aerodynamics e.g. [1] which says
> The weak positive relationship between vehicle registration year and splat rate suggests that newer vehicles are more efficient at sampling insects than older vehicles.
i.e. they saw more insects on newer cars compared to older ones in the same time period.
In general ecology studies aren't like lab physics, you can't control every possible confounding variable; the systems are too complicated and studies ex-situ have their own limitations. But refusing to engage with the data we do have because it's not perfect isn't going to help you make better decisions, and doesn't represent some moral high ground.
> 1. Why focus on Swifts as opposed to any other species in decline? They state that they are "iconic", so maybe that's the answer? Are they more "iconic" than any other specifies in Scotland?
They are a red-listed species whose population in the UK has declined by two thirds in 30 years. They are also a species for which there's an obvious measure that can be taken to reverse one of the changes which we know has happened over that time (improved building standards reducing the availability of nesting spaces).
If we can't take simple steps to protect swifts I don't think there's much chance that we'll protect anything. Conservation wise, this is really low hanging fruit.
> 2. Why are these bricks the best solution? Why not take that money that would be spent on bricks and instead preserve land, or just build them dedicated houses elsewhere?
Swifts are extremely site-loyal. You can't just hope that they will move elsewhere. Before buildings they nested in caves and tree cavities. Caves in particular don't move from year to year, so as a result the birds have a strong preference to return to the exact same location where they themselves were born and are slow to colonize other places.
Also your suggestions sound extremely expensive compared to this plan. Swift bricks cost like 30GBP retail. Yes, that's a lot more than the normal house brick they replace, but it's trivial compared to the other costs of building a house.
> 3. Why does this need to be done via government mandate versus voluntarily asking people to build Swift housing in existing buildings or land?
That's more or less the current situation in England, and perhaps unsurprisingly it's extremely rare for developers to actually install swift bricks. Indeed it's relatively uncommon for developers to actually follow through on their existing legally mandated ecological commitments [1].
Making something mandatory everywhere is also cheaper than making it only required in certain places: it eliminates all the bureaucracy around deciding whether this or that development is in the right area, and makes it extremely easy to follow and enforce the rules.
Now it is possible that the lack of nest sites isn't the dominant factor in the decline of swifts. For example it could also be related to the decline in flying insects, or changing weather patterns induced by climate change. We aren't really sure [2], however from that study: "it would be precautionary for conservation efforts to continue to focus on ensuring that safe and productive nesting sites are in sufficient supply", and we also know that the swift bricks will be used by many other bird species as well as swifts.
If you have specific sites that aren't working, please let us know and we can investigate and try to fix them.
The usual reporting channels are using https://webcompat.com or the "Report Broken Site" tool in the Firefox menu. Of course I"m also happy to take bug reports here if you (or anyone else) have them.
I think if prediction 1 is true (that it becomes cheap to clone existing software in a way that doesn't violate copyright law), the response will not be purely technical (moving to thin clients, or otherwise trying to technically restrict the access surface to make reverse engineering harder). Instead I'd predict that companies look to the law to replace the protections that they previously got from copyright.
Obvious possibilities include:
* More use of software patents, since these apply to underlying ideas, rather than specific implementations.
* Stronger DMCA-like laws which prohibit breaking technical provisions designed to prevent reverse engineering.
Similarly, if the people predicting that humans are going to be required to take ultimate responsibility for the behaviour of software are correct, then it clearly won't be possible for that to be any random human. Instead you'll need legally recognised credentials to be allowed to ship software, similar to the way that doctors or engineers work today.
Of course these specific predictions might be wrong. I think it's fair to say that nobody really knows what might have changed in a year, or where the technical capabilities will end up. But I see a lot of discussions and opinions that assume zero feedback from the broader social context in which the tech exists, which seems like they're likely missing a big part of the picture.
Your time-average power budget for things that run on phones is about 0.5W (batteries are about 10Wh and should last at least a day). That's about three orders of magnitude lower than a the GPUs running in datacenters.
Even if battery technology improves you can't have a phone running hot, so there are strong physical limits on the total power budget.
More or less the same applies to laptops, although there you get maybe an additional order of magnitude.
China has now had flat CO2 emissions for two years, and experienced a decline in overall CO2 emissions during 2025[1]. Part of this is that they're deploying way more renewables than basically any other large economy [2].
They've also pivoted their industrial strategy so that basically the entire green energy sector depends on Chinese supply chains. This is significantly contributing to their economic growth [3].
I don't know to what extent taxation in Europe contributed to China's decision making here, but it presumably created an market for green energy and therefore helped solidify the economics.
This is of course not to say that there's nothing to criticize in China's environmental policies; there certainly is. But the trope of "why should we do anything because China won't" turns out to be spectacularly ill-informed. Indeed I think it makes more sense to ask the opposite: what are the likely consequences now that China has positioned itself as the global centre of green energy, and what should other countries be doing to ensure that they're not left behind?
But the point of comparison is something like the HTML specification. That's supposed to be a document that is detailed enough about how to create an implementation that multiple different groups can produce compatible implementations without having any actual code in common.
In practice it still doesn't quite work: the specification has to be supplemented with testsuites that all implementations use, and even then there often needs to be a feedback loop where new implementations find new ambiguities or errors, and the specification needs to be updated. Plus implementors often "cheat" and examine each other's behaviour or even code, rather than just using the specification.
Nevertheless it's perhaps the closest thing I'm familiar with to an existing practice where the plan is considered canonical, and therefore worth thinking about as a model for what "code as implementation detail" would entail in other situations.
And yet, the premise of the question assumes that it's possible in this case.
Historically having produced a piece of software to accomplish some non-trivial task implied weeks, months, or more of developing expertise and painstakingly converting that expertise into a formulation of the problem precise enough to run on a computer.
One could reasonably assume that any reasonable-looking submission was in fact the result of someone putting in the time to refine their understanding of the problem, and express it in code. By discussing the project one could reasonably hope to learn more about their understanding of the problem domain, or about the choices they made when reifying that understanding into an artifact useful for computation.
Now that no longer appears to be the case.
Which isn't to say there's no longer any skill involved in producing well engineered software that continues to function over time. Or indeed that there aren't classes of software that require interesting novel approaches that AI tooling can't generate. But now anyone with an idea, some high level understanding of the domain, and a few hundred dollars a month to spend, can write out a plan can ask an AI provider to generate them software to implement that plan. That software may or may not be good, but determining that requires a significant investment of time.
That change fundamentally changes the dynamics of "Show HN" (and probably much else besides).
It's essentially the same problem that art forums had with AI-generated work. Except they have an advantage: people generally agree that there's some value to art being artisan; the skill and effort that went into producing it are — in most cases — part of the reason people enjoy consuming it. That makes it rather easy to at least develop a policy to exclude AI, even if it's hard to implement in practice.
But the most common position here is that the value of software is what it does. Whilst people might intellectually prefer 100 lines of elegant lisp to 10,000 lines of spaghetti PHP to solve a problem, the majority view here is that if the latter provides more economic value — e.g. as the basis of a successful business — then it's better.
So now the cost of verifying things for interestingness is higher than the cost of generating plausibly-interesting things, and you can't even have a blanket policy that tries to enforce a minimum level of effort on the submitter.
To engage with the original question: if one was serious about extracting the human understanding from the generated code, one would probably take a leaf from the standards world where the important artifact is a specification that allows multiple parties to generate unique, but functionally equivalent, implementations of an idea. In the LLM case, that would presumably be a plan detailed enough to reliably one-shot an implementation across several models.
However I can't see any incentive structure that might cause that to become a common practice.
> But at a national level the data is compelling. I'm convinced by the Environmental Kuznets Curve.
Which data do you find compelling?
For people who don't know the Environmental Kuznets Curve is basically the hypothesis that as economies grow past a certain they naturally start to cause less environmental damage.
As far as I can tell the main empirical evidence in favour of this is the fact that some western countries have managed to maintain economic growth whilst making reductions to their carbon emissions. This has, of course, partially been driven by offshoring especially polluting industries, but also as a result of technological developments like renewable energy, and BEVs.
On the other hand, taking a global sample it's still rather clear that there's a strong correlation between wealth and carbon emissions, both at the individual scale and at the level of countries.
It's also clear that a lot of the gains that have been made in, say, Europe have been low-hanging fruit that won't be easy to repeat. For example migrating off coal power has a huge impact, but going from there to a fully clean grid is a larger challenge.
We also know that there are a bunch of behaviours that come with wealth which have a disproportionately negative effect on the environment. For example, rich people (globally) consume more meat, and take more flights. Those are both problems without clear solutions.
(FWIW I agree that solar power is somewhat regressive, but just for the normal "Vimes Boots Theory" reasons that anyone who is able to install solar will save money in the medium term. That requires the capital for the equipment — which is rapidly getting cheaper — but also the ability to own land or a house to install the equipment on. The latter favours the already well off. There are similar problems with electric cars having higher upfront costs but lower running costs. The correct solution is not to discourage people from using things, but to take the cost of being poor into account in other areas of public policy).
You made a scale-free claim about increasing greenness with increasing CO2 concentration. That implies a monotonic relationship.
> The only debate is over what might happen in the future, which, again, is fortune telling
The idea that using models of physical systems to predict their future evolution is "fortune telling" will surprise many scientists. Indeed, you yourself have proposed a simple model and used it to make a prediction about the future ("the world will be greener in a high-CO2 environment"), and used linear extrapolation of the past to justify the adequacy of your model.
That's not necessarily a bad starting point, but when actual studies with more complex models show different behaviours you should consider there's a possibility you're over-confident in your predictions.
Anyway, I suspect this conversation has become rather pointless. It's always unclear online to what extent people are engaging in good faith, but if it was then I'm rather sure you've now mentally pigeonholed me as a "doomer" who can't be reasoned with.
> you can't easily separate out CO2 concentration from the other impacts of increased CO2
>> I never said you could?
I took the fact that you explicitly mentioned "high-CO2 environment" and claimed there was no room for argument over the "fact"s as an indication that you were trying to separate out the impact of CO2 from other factors caused by climate change such as heat stress and drought. If that wasn't the case then apologies for misunderstanding.
> That paper is talking about a net reduction in biomass due to projected losses in places with temperature increases exceeding 10 degrees C.
The abstract says:
| with great biomass reductions in regions where mean annual temperatures exceeded 10 °C
Unless the abstract is especially badly written that suggests that it's not 10°C _change_ but 2°C change leading to biomass loss in areas that are already at 10°C on average.
> IPCC report
Thanks, that's a useful reference! Do you have a link to the final report? That one seems to be a draft and I didn't find the right published version (but there are many so I'm sure I'm missing it).
I note the paragraph you quoted concludes:
> The increased greening is largely consistent with CO2 fertilization at the global scale, with other changes being noteworthy at the regional level (Piao et al., 2020); examples include agricultural intensification in China and India (Chen et al., 2019; Gao et al., 2019) and temperature increases in the northern high latitudes (Kong et al., 2017; Keenan and Riley, 2018) and in other areas such as the Loess Plateau in central China (Wang et al., 2018). Notably, some areas (such as parts of Amazonia, central Asia, and the Congo basin) have experienced browning (i.e., decreases in green leaf area and/or mass) (Anderson et al., 2019; Gottschalk et al., 2016; Hoogakker et al., 2015). Because rates of browning have exceeded rates of greening in some regions since the late 1990s, the increase in global greening has been somewhat slower in the last two decades
So it sounds like a combination of the CO2 increases up to about the year 2000, along with agricultural intensification and various other factors have indeed increased the amount of plant cover, but we are already seeing changes to that picture with further rises to CO2 levels.
> You spent a lot of words arguing with me about things I didn't say.
Well you started with
> The world will be greener in a high-CO2 environment. There’s no legitimate argument over that fact.
And my central point is that the model you're implying there is one in which there's a monotonic relationship between CO2 levels and plant growth. However in reality things are clearly more complex than that, and there is indeed legitimate argument over what factors are dominant in different scenarios.
Your claim that things will only change over long-enough timescales so that you don't have to worry about also seems to lack evidence. In systems with significant feedback loops it seems dangerous to assume that changes will only happen slowly unless you're very confident that you fully understand all the system dynamics. With climate change it's clear that we don't fully understand the system, and some changes are happening faster than earlier models predicted. So _maybe_ we have a few centuries to figure out how to move global agriculture to northern latitudes, and deal with more variable conditions, but from a risk-analysis point of view it seems like a rather poor strategy.
> The world will be greener in a high-CO2 environment. There’s no legitimate argument over that fact.
However it's important to remember that world isn't a high school physics experiment, and you can't easily separate out CO2 concentration from the other impacts of increased CO2:
| Climate change can prolong the plant growing season and expand the areas suitable for crop planting, as well as promote crop photosynthesis thanks to increased atmospheric carbon dioxide concentrations. However, an excessive carbon dioxide concentration in the atmosphere may lead to unbalanced nutrient absorption in crops and hinder photosynthesis, respiration, and transpiration, thus affecting crop yields. Irregular precipitation patterns and extreme weather events such as droughts and floods can lead to hypoxia and nutrient loss in the plant roots. An increase in the frequency of extreme weather events directly damages plants and expands the range of diseases and pests. In addition, climate change will also affect soil moisture content, temperature, microbial activity, nutrient cycling, and quality, thus affecting plant growth.
> Certainly it’s more favorable for growth of plants that make food
That does not seem to be what agricultural researchers believe:
| In wheat a mean daily temperature of 35°C caused total failure of the plant, while exposure to short episodes (2–5 days) of HS (>24°C) at the reproductive stage (start of flowering) resulted in substantial damage to floret fertility leading to an estimated 6.0 ± 2.9% loss in global yield with each degree-Celsius (°C) increase in temperature
| Although it might be argued that the ‘fertilization effect’ of increasing CO2 concentration may benefit crop biomass thus raising the possibility of an increased food production, emerging evidence has demonstrated a reduction in crop yield if increased CO2 is combined with high temperature and/or water scarcity, making a net increase in crop productivity unlikely
| When the combination of drought and heatwave is considered, production losses considering cereals including wheat (−11.3%), barley (−12.1%) and maize (−12.5%), and for non-cereals: oil crops (−8.4%), olives (−6.2%), vegetables (−3.5%), roots and tubers (−4.5%), sugar beet (−8.8%), among others
Notice that it says "almost all programs" and not "almost all _C_ programs".
I think if you understand the meaning of "crash" to include any kind of unhandled state that causes the program to terminate execution then it includes things like unwrapping a None value in Rust or any kind of uncaught exception in Python.
That interpretation makes sense to me in terms of the point he's making: Fil-C replaces memory unsafety with program termination, which is strictly worse than e.g. (safe) Rust which replaces memory unsafety with a compile error. But it's also true that most programs (irrespective of language, and including Rust) have some codepaths in which programs can terminate where the assumed variants aren't upheld, so in practice that's often an acceptable behaviour, as long as the defect rate is low enough.
Of course there is also a class of programs for which that behaviour is not acceptable, and in those cases Fil-C (along with most other languages, including Rust absent significant additional tooling) isn't appropriate.
As someone who's been quite heavily involved with web-platform-tests, I'd caution against any use of the test pass rate as a metric for anything.
That's not to belittle the considerable achievements of Ladybird; their progress is really impressive, and if web-platform-tests are helping their engineering efforts I consider that a win. New implementations of the web platform, including Ladybird, Servo, and Flow, are exciting to see.
However, web-platform-tests specifically decided to optimise for being a useful engineering tool rather than being a good metric. That means there's no real attempt to balance the testsuite across the platform; for example a surprising fraction of the overall test count is encoding tests because they're easy to generate, not because it's an especially hard problem in browser development.
We've also consciously wanted to ensure that contributing tests is low friction, both technically and socially, in order that people don't feel inclined to withhold useful tests. Again that's not the tradeoff you make for a good metric, but is the right one for a good engineering resource.
The Interop Project is designed with different tradeoffs in mind, and overcomes some of these problems by selecting a subsets of tests which are broadly agreed to represent a useful level of coverage of an important feature. But unfortunately the current setup is designed for engines that are already implementing enough feature to be usable as general purpose web-browsers.
In addition, the colleges have a lot of data about the people they interview and how well they do during the degree programme.
My understanding (based on a discussion with one Natural Sciences admissions tutor at one Cambridge college nearly 20 years ago, so strictly speaking this may not be true in general, but I'd be surprised if it wasn't common) is that during the admissions process, including interviews, applicants are scored so they can be stack-ranked, and the top N given offers. Then, for the students that are accepted, and get the required exam results, the college also records their marks at each stage of their degree. To verify the admissions process is fair, these marks are compared with the original interview ranking, expecting that interview performance is (on average) correlated with later degree performance.
I don't know if they go further and build models to suggest the correct offer to give different students based on interview performance, educational background, and other factors, but it seems at least plausible that one could try that kind of thing, and have the data to prove that it was working.
Anyway my guess is that of the population of people who would do well if they got in, but don't, the majority are those whose background makes them believe it's "not for the likes of me", and so never apply, rather than people who went to private schools, applied, and didn't get a place.
(also a Cambridge alumni from a state school, FWIW),
> The UK’s electricity market operates using a system known as “marginal pricing”. This means that all of the power plants running in each half-hour period are paid the same price, set by the final generator that has to switch on to meet demand, which is known as the “marginal” unit.
> While this is unfamiliar to many people, marginal pricing is far from unique to the UK’s electricity market. It is used in most electricity markets in Europe and around the world, as well as being widely used in commodity markets in general.
The thing that's unique about the UK is that the marginal price is almost always (98% of the time) set by the price of gas. That means when the gas price increases, the wholesale price of electricity, and hence consumer bills, increase in direct response.
Of course the situation is also made worse by the fact that gas is used directly for heating and cooking in a high proportion of British homes.
> Sorry but I don't understand how keeping contact information private or public has any effect on the compliance of people to stay in quarantine?
Because if you know who's supposed to be in quarantine you can take steps to verify that they actually are. And also because people are simply less likely to comply with rules when they know no one can tell if they're following them.
> I don't understand why you think an app that can promise to keep people's personal data private would gain lower adoption than one that was explicitly collecting such data. Maybe I've misunderstood what you're trying to say here but it would make more sense to me that an app that can preserve privacy would have higher adoption, higher trust and therefore higher compliance levels.
Again, because you're comparing "opt-in and private" with "opt-in and non-private". I'm comparing "opt-in and private" with "mandatory and shares user data with the health authorities" (which is different from "[opt-in|mandatory] and public" in that in the latter case any member of the public can get information about infections; I think South Korea are/were running a system with that level of sharing).
> I really don't understand all these discussions about something that might save lives. IMO the real discussion should be whether these app are privacy preserving enough. Are there (realistic) attacks on these apps? How can we mitigate them?
The problem is there's likely a pretty direct tradeoff between preserving privacy and efficacy. So I think people whose main concern is privacy are taking the position that the apps can't work, without being clear about the fact that it really depends on what assumptions you make around enforcement.
Clear disclaimer: I'm not anything like an expert here. Much of what I've written below is likely wrong or misleading and I'd welcome corrections.
A simple, bad, toy model is that the fraction of infection chains you prevent is (fraction of people using the app)^2 x (fraction of transmissions detected) x (fraction of people who comply). So if your model is "app usage must be opt-in" and "it must be impossible to tell who is complying with the app's recommendations" you do indeed find that the app only detects a small fraction of transmission chains (e.g. assuming 50% of the population opts in, 80% of transmission events are detected, and there's 50% compliance rate, you only stop 10% of infection chains). But if it's mandatory to use the app, and there's a mechanism to ensure enforcement you can do much better e.g. with 80% of people using it and 95% compliance you're closer to stopping 50% of transmission chains.
Of course that model is far too simple to be correct. But https://045.medsci.ox.ac.uk/files/files/report-effective-app... models this in more detail. Their simulation finds that you can suppress the overall transmission in the UK with 80% of smartphone users using the app (56% of population, comparable to the most popular apps), 80% detection of transmission, and 2% dropout rate per day on a quarantine period for all individuals flagged as contacts, which lasts up to 2 weeks. I don't know what they used to come up with that number for dropout rate, but it seems highly optimistic if the information about who has registered as a contact is kept private. They also find that up to half the population might be in quarantine at any one time, which I suspect is higher than most people are anticipating for a post-lockdown period. To combat this, they propose various going through a process of optimising the algorithm based on feedback, but again, if the most-privacy-preserving approach is taken for the data collection, performing such optimisations may become difficult or even impossible, since you won't be able to followup on individuals.
So, my — non-expert — conclusion at this point is that privacy-preserving digital contact tracing probably isn't going to be that useful because people either won't use it or won't follow the advice when doing so is inconvenient (e.g. because it puts their employment at risk). But digital contact tracing in general seems like it can help, if you can drive both usage and compliance high enough.
So there may be a pretty direct tradeoff here between our ability to trust the health services with personal data and our ability to prevent further rounds of unchecked exponential growth of this virus. And that is worrying, because — as a British person — I'm very unexcited about giving a government that has a history of using targeted data from social media to achieve policy ends even more data to work with. But I don't know that I can construct a satisfactory argument that it's OK for people to die, or even for those that don't to suffer an additional year of economic disruption, in order to avoid the privacy implications of a system of contact-tracing-with-enforcement. And I also worry that those who would be happy to collect this data will use the launch of an initial ineffective app, and a corresponding second wave of infections, as a way to make this kind of data privacy socially unacceptable.
Maybe there's an argument that by focusing on legal limits to the use of the data rather than to technical controls on its availability, we will end up in a better position in five years time, not just on the pandemic, but also on privacy.
On the other hand there are other similar studies that reach similar conclusions, and specifically try to control for aerodynamics e.g. [1] which says
> The weak positive relationship between vehicle registration year and splat rate suggests that newer vehicles are more efficient at sampling insects than older vehicles.
i.e. they saw more insects on newer cars compared to older ones in the same time period.
In general ecology studies aren't like lab physics, you can't control every possible confounding variable; the systems are too complicated and studies ex-situ have their own limitations. But refusing to engage with the data we do have because it's not perfect isn't going to help you make better decisions, and doesn't represent some moral high ground.
[1] https://cdn.buglife.org.uk/2022/05/Bugs-Matter-2021-National...