I’ve been able to contribute to projects recently using ai. And another project banned this so I didn’t bother. The second was a bug fix for an issue I was having. The first was a new feature.
Either way my bug got fixed but if a project maintainer doesn’t want it who am I to fight it.
You can always fork it. I suspect my contributions are higher quality because I use different model providers to review each other but whatever.
QGIS is not your run of the mill project, though. This is specialized software and a rare breed. It's in their right to decide either way, and this is the right call for this niche, IMHO.
GIS is basically "serious mapping software", and when it's used for city planning or other large GIS-relevant activities, accuracy and precision are non-negotiable requirements.
IOW, your city might be using QGIS to plan the city and verify everything being done on it, down to cm level accuracy. So, any subtle bug is a big deal for these kinds of software packages.
While doing construction work (regardless of infrastructure or buildings), the plans are laid and verified to that level. The haphazard spray paints on the ground are indeed big deal, and has a digital copy in the GIS software.
It's not only used by municipalities and/or governments either. Research teams use GIS packages extensively as well. I briefly worked with a research team working at Antartica. These GIS software and related ecosystem is their both bread and butter and foundation to anything they do.
A patch generated by an AI model breaking these software packages have cascading and important effects. So, the software needs to stay dependable and reliable.
Long answer: These software packages generally have a couple (or more) graybeards on the team knowing the intricacies of the domain and the software pretty well, so they can read a patch, see the effects, make an informed decision and give required feedback, or sometimes get the patch, mangle it to correct form and merge.
LLMs tend to write overly verbose code with many file edits at the same time, accompanied by walls of text to justify itself. This overwhelms these kinds of communities. So, they prefer targeted, smaller patches arriving at a slower rate.
...and maybe that big patch can be written as a one-liner somewhere else, just because AI failed to find where the problem originates and just patches where the problems happen, making thing way more complicated than before?
These are complex software packages and are sometimes things looking correct are wrong, and things looking wrong are correct, esp. on the math side.
I developed software using boundary element method to evaluate things, and it was complex to put it mildly. Verifying everything down to 32 significant digits after comma, and tearing the code down when the tests deviated for no apparent reason is something I did regularly.
GIS is no different. You're modeling the world, down to the earth's curvature, and putting things on it with cm level accuracy, and a couple of people knowing what they are doing is way more effective than a statistical model which emits code with no bounds and understanding of the domain.
Sounds like llm's have made is so easy for anyone without a shred of know how to submit anything that people prompt one or two times and consider the job done, not knowing any better becausue they don't have to.
there's a big difference between now and then. we built up capacity. dark fiber. and couldn't use until we got broadband. In general. ai capacity is being used right now today.
That’s not exactly true, a lot of GPUs are sitting in warehouses because they are lacking prerequisites to being installed, in some cases this is even just power.
I don’t think the article you cite supports your point with confidence you convey. Yes other dimensions can complicate deployment, but incentives to work around those issues are high, given the cost of GPUs
That’s not the same. Those GPUs are allocated to real projects with real demand. If they could turn them on today they would do it and they would have customers.
It’s not the same as dark fiber capacity being overbuilt for demand that wasn’t there yet.
> If they could turn them on today they would do it and they would have customers.
Is this really true? Other than the occasional outages, the frontier labs don't seem to be getting overloaded by API pricing customers at peak times, and with Opus 5.5 using even less tokens (which translates into less GPU time and less money spent on the same tasks), it seems actual capacity isn't literally strained right now. If current needs are met by currently available capacity, where is this demand going to come from?
There appear to be a lot of uninstalled GB200s (looking at you Azure), but the power efficiency of those is ~10x worse than a Vera Rubin. Does it really make sense to install them, when your data center is power/cooling limited, and it's not built yet?
Maybe somebody will use them, but not the original owners.
Yes. I have 3 GPUs in the other room, sitting in boxes, waiting to be powered on, because I dont have the electrical circuit installed in the room yet.
I can directly confirm this fact, and I'm just some random dude out here in suburbia, who can only imagine what the Indiana Jones warehouse of glowing GPUs looks like in a data center.
We're all salivating over this imagined future where an AI toilet seat greets us every morning.
Who here has already asked AI how to make your own DRAM ? I know you have. Thats a sign of peak interest in a subject or market.
> We're all salivating over this imagined future where an AI toilet seat greets us every morning.
I'm not, but I can certainly believe the surveillance industry vectoralists are salivating over a future where their AI toilet seats greet each of their data subjects every morning.
> In general. ai capacity is being used right now today.
Not according to this article[1], which says:
"I believe we are now in an inevitable overbuild situation, one with no neat, tidy Dot-Com Bubble-style exit story. Demand for NVIDIA GPUs — and those from Broadcom, AMD and other semiconductor companies — is driven by speculative capital believing that the AI industry will become magnitudes larger than it is today, largely driven by the fact that everybody believes there’s far more demand for compute capacity than actually exists. ... NVIDIA has created a remarkable illusion perpetuated by the media — that GPU sales are a direct measurement of the actual demand for AI compute, rather than a measurement of how a few companies are willing to invest in an idea two years in advance, using circular financing as a means of creating the sense that you must buy these GPUs now, or you’ll miss out on the future. ... At the very least, hyperscalers are going to be burdened with brutal depreciation charges or onerous write-offs for years to come, whether their capacity turns into revenue or not. ... I don’t see how 90%+ of NVIDIA’s sales ever end up generating a single dollar of revenue, and considering the amount of project financing-backed data center debt deals, there’s very little that exists to protect investors if AI compute demand never arrives."
In 5 years from now every device. Even your oven or washing machine has somekind of local LLM running for sensor reading and smart decision models. I can already think of quite some useful use cases for those devices. So I do believe that AI becomes magnitudes bigger than today. It's a logical step in our digital journey.
Why would you need the LLM to be running on the device itself? Wouldn't it be more efficient for the intelligence to be more centralized? Maybe in one central computer per home, or (probably even more desirable for corporations) for the devices to phone home to a centralized server at the service provider in the cloud somewhere.
Oh of course thats certainly also gonna happen. But i believe there its still useful to have local dedicated AI with low latency. For example regulators that need constant adjustments. Also in cameras local AI could be extremely useful. Same for audio processing and other sensor processing. Cloud solutions wont work in those cases.
That's Ed Zitron, and his calls on AI matters routinely get outperformed by that of a broken clock.
The man saw the market demand for saying "AI bubble is going to pop and AI tech is going to wither away and die", and went to meet it on the supply side - truth be damned.
So far, AI companies still keep getting bottlenecked on compute, AI utilization increases - driven by, among other things, increased price-performance of AI making it viable in more and more roles. And the demand for both AI inference and AI hardware in general shows no signs of stopping.
This does not seem to be the case. The very first VeraRubin NVL72 systems are barely installed and won't be running real workloads until beginning of '27.
Colossus II appears to be the largest user of Blackwell Ultra NVL72s and those are also only recently on-line in September of this year with the remaining 3/4 of them not until end of the year. The delays in datacenter deployment are very long. Even their GB200s didn't come on-line until January of '26. Hopper H100/200s still dominate.
The other hyperscalers are even further delayed. CoreWeave has some VeraRubin, but not in volume (as I understand).
A lot of its current use is just basic questions that could be handled by local llms just as well, yet they are being processed on GB200s on the other side of the world.
I keep hearing this, but the retail price of AI compute keeps dropping, which seems counterintuitive for a commodity that is supposed to be in such tight supply. It suggests that AI demand is extremely elastic, and belies the urgency of deploying new data centers. AI is something that AI labs think everyone should find incredibly valuable, but the market seems to be saying otherwise.
But it's missing the point: it is not always about "better" or "worse".
And in fact humans are special in a very straight-forward way: we share one thing with each other that no AI will share with us, which is our common biological ancestry and its consequences.
So, when looking at a human-made painting - which could look much worse than flawless AI counterpart - I'm not just looking at a painting, but I'm also thinking about what it took to produce, how much the artist was struggling to balance the need of survival with the need to create, how big was the price they had to pay, how it's similar or different to my own life, whether I'd be willing to pay such a price, and a lot more.
OTOH I don't care at all about the "struggle" of the ML model. I don't care about GPUs and TPUs sweating while climbing their "mountains". It might produce outputs that are aesthetically pleasant, but it will never be art.
I doubt any company shares your moral values. Though a few of them might pay some lip service do some of your moral values. You tell me, have you audit all the companies you do business with to see if they share your moral values.
Agreed. It’s not like a particular word is any different when it’s made by ai.
Would we care if someone used a typewriter vs a computer to write it.
This looks like a weird snobbishness. The fact that it’s by Europeans makes it more funny since they have been left out of the AI race.
In the end you have a final product. Can critique it on its own. He used ai to help write it and you couldn’t tell before, says more about the product and perhaps you don’t like what it says about you .
Creatine is not available from plants and the 0.5 g is not insignficant for normal health. The 5 grams you're quoting is generally for people who supplement for athletic performance.
Creatine is created within the body so a vegetarian eating plant precursors can help create their own baseline supply.
A non-aggressive, curious question: can you give a citation for the "significant" health benefits of 0.5g creatine? That seems like quite a claim. (I'm reading "not insignificant" as "significant", perhaps a step too far?)
Note that there have also been brain benefits from creatine, but those have only been observed in ~20g/day studies (its not just athletic performance).
I'm not making a claim for a significant health benefit from 0.5g of creatine I'm just countering your bald claim that it's insignficant - we are getting into word lawyer territory. But anyway to establish what I'm claiming, 0.5g of creatine, I feel, is a useful amount - the numbers you are fixated on are for athletes or neurotropic nerds.
"Although the human body produces a portion of its daily creatine needs, approximately 50 % must be obtained through the diet – predominantly from animal-based foods like red meat and fish"
So in that context, a vegetarian can benefit from small amounts of supplementation if they aren't getting it in their diet.
Ah. I guess the term we're looking for is "physiologically meaningful"; I wasn't talking about "statistical significance". Something can be statistically significant but have a clinically worthless effect size.
FYI the standard null hypothesis is that differences are not significant until shown otherwise. E.g. no doctor will prescribe 50mg metformin to a prediabetic even if it might have an effect; it will be physiologically meaningless.
> the numbers you are fixated on are for athletes or neurotropic nerds
If you have other numbers/studies to back "0.5g of creatine, I feel, is a useful amount", then I'm willing to "fixate" on those numbers.
you are either doing it for moral reasons. in that case all these facts are irrelevant. or you can eat red meat for a month and see if you can tell the difference. if you can't there's your answer.
citations are bullshit for this context. Unless you are doing research.
Good grief. I say absolutely nothing about vegetarianism: you're the one who keeps bangin on about about it.
> you are either doing it for moral reasons
Or red meat isn't easily available or cheap in the place where you live? There are many reasons why red meat isn't in your digestive system that are outside "morals". Beef is literally at an all-time high in the US. https://www.macrotrends.net/3141/us-ground-beef-prices
Summary of this thread:
Me: 0.5 g of creatine is small
Other: No it isn't
Me: Citation please?
Other: no
Me: null hypothesis
You: Incorrect interpretation of null hypothesis
Me: Correction
You: "citations are bullshit"
There are many people who feel morally grey about eating meat yet still choose to do so. E.g. there are science-based discussions in plant-based-diet social media; relatively few plant-based diet discussions revolve around morality. Facts matter.
I agree that n=1 experiments are important and often _overrule_ any RCT for an _individual_. If that's your point, I _agree_. But you should say so instead of me trying to infer whatever it is you're arguing...? Is that even what you're saying?
The goalpost shifting is immense. All I wanted was a citation for 0.5g creatine being meaningful, and instead I get "facts are irrelevant", "citations are bullshit", and "morals".
(To be clear: I'm not saying that you're saying "facts are irrelevant"; I'm annoyed that you seem to think that _I'm_ arguing "facts are irrelevant". See this? This is me giving you grace. I would appreciate the same in kind.)
The analysis might be better on Artifical Analysis' however the way the information is presented by terrydjony is so much better. I'm able to get the exact actionable info I want. Whereas on artificial Analysis I'm analyzing and figuring it out.
It's the equivalent of ai slop writing in a data format.
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