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Honesty isn't the issue, in my experience. The issue is unknowability. You usually can't know what clients actually want until you've given them a prototype and gotten their feedback. You often can't know the rough performance impact until you've done the first implementation. Sometimes you just have to work through a list of candidate solutions S1, S2, etc... and you can't know which one will eventually work. All of which means that forecasting beyond a couple of weeks is wasted effort, except for rote factory-line work.

This reminds me of when I rediscovered the track-listing of a CD titled "The Best Album of the Next Century Ever" which came with boxes of Golden/Cinnamon Grahams cereal. Had some real bangers like Josephine by Terrorvision and Come On Spring by Antenna, plus some very early tracks by Space and Muse who both went on to make it pretty big. I listened to it on repeat for months as a young teen. That kind of nostalgia probably isn't available to the generations below.

My guess would be that these particular problems were vulnerable to an attack which built on recent advances and potentially tied in something unexpected from a distant area of mathematics. "Harder" is becoming harder to define. Harder for humans is probably not harder for LLMs.

What do sorry points mean anymore.

Even if there's one dimension, this is not a logically correct statement. Some numbers are bigger than others, but there is no biggest number. Or it could be a partial ordering - say that a number x is "better" than y if y divides x. Then out of the first hundred numbers, some are better than others but there's no single "best".

I disagree. There is community opposition against individual data centers. There are calls for moratoriums until rules and regulations are improved. Calls for outright blocking data centers even with improved regulations are a small minority in my experience.

Fair point. If you divide the 163.7 billion gallons annually among the 5426 data centers, the figure does come out lower at ~30 million gallons or ~100 million liters annually. That's still far, far larger than the largest swimming pools.

The government obviously has the power to regulate data centers. That's why people are petitioning them to do so, and why the lack of data transparency is frustrating. I'm not sure what argument you're making.

I don't dispute your lived experience. But if you have evidence to cite in contrast to what I've cited, then please do so.

If the data center is built despite multiple demands to desist, then it may not be a surprise per se, but it can still be an unaccounted stress on local resources.

Firstly, who is deciding whether to "allow" open loop cooling? And secondly, from what I gather, closed-loop sites require an order of magnitude more power than open-loop to run. That's still going to have a large impact on local resources, especially as around 2/3 of power plants supplying data centers are also ocated in water-stressed regions [0].

[0] https://fieldreport.caes.uga.edu/publications/TP121/how-data...

This went into the weeds a little bit, but I've tried going over the figures again. I'm not sure if I'm missing something here or someone got some units wrong somewhere, but data centers seem to use far more water.

A medium size data center consumes 300k gallons of water per day [0]. That's over 400 million liters per year.

Even if we go with the highest estimate vidarh gave of 1.8 million liters per year for a full-size olympic size swimming pool, that's a tiny fraction of what a typical data center uses.

[0] https://www.eesi.org/articles/view/data-centers-and-water-co...

> A typical data center uses 300,000 gallons of water each day (equivalent to the demands of about 1,000 households), but large data centers can use an estimated 5 million gallons of water each day

https://www.brookings.edu/articles/ai-data-centers-and-water...

Well, OP said "small swimming pool". I guess it's ambiguous.

The point is that they create a heavy and volatile local water demand on those metropolitan areas.

I agree that utility planners have their work cut out to plan for data centers. It's a different paradigm than what they're used to. I think they are being caught off-guard, but it's a matter of process and infrastructure. They typically plan around averaged or linear trends, but data centers can have volatile usage spikes, and supply can depend on e.g. seasonal rainfall which is challenging to predict.

> we demonstrate that data centers are an overwhelmingly urban phenomenon, with 97.5% of facilities situated in metropolitan and micropolitan statistical areas

https://www.nature.com/articles/s44284-026-00487-z

Roughly 80% of the water evaporates into the atmosphere. The remaining 20% is flushed out of the system to remove mineral build-ups and sent to local treatment plants.

> the water usage is heavily overblown compared to the agriculture industry

Data centers tend to concentrate their water usage within urban areas, causing a strain on local resources. The value of their output is much less tangible. Their water consumption scales up around tenfold if you consider the indirect usage from generating the electricity.

But on the other hand; yes, it would also help enormously if people ate less meat.

A swimming pool requires maybe 60k liters a year...

The other problem is that stereotyping an entire social group simply alienates the members of that group. They perceive that society doesn't trust or value them, so in response they don't trust or value society. See e.g. racial profiling in the London Met's use of stop-and-search powers.

If your ultimate goal is harm-reduction, then stereotyping is irrational.

Don't you think that isolating people in poor conditions amongst hardened criminals might make them more likely to commit crime when they get released?

Tbf opposing gay marriage is quite an extreme opinion in much of the developed world. I suspect Eich's position on the matter is tied to his Roman Catholic faith, so although he showed a certain level of remorse in 2014, he never actually came out in support of gay marriage. He then later spread misinformation about mask-wearing during COVID, including suggestions that people could die from wearing a mask.

FWIW I think the concept of "interesting" is so subjective, time-dependent, and nebulous, that to say there's "only a finite number of interesting" mathematics problems feels absurd to me. What's interesting in ten years time will depend on what other interesting things have been discovered in the intervening time. But you're right, I shouldn't have responded with a cheap joke.

Godel's incompleteness theorem technically relates to individual axiomatic theories (i.e. the set of facts that logically derive from a given set of axioms). The numerical encoding you refer to applies to logical statements within that theory. Arguably, the kind of mathematics that humans do is not constrained to a single theory, but is a more general form of reasoning that is often flexible about which axioms may or may not be assumed.

There's been a lot of talk about the purpose of code review recently. It makes sense in the face of AI. Heres a link that was submitted a little while ago: https://mathstodon.xyz/@mjd/115096720350507897

And in response I wrote a non-exhaustive checklist of things that a code review can look for:

- Does it functionally achieve what it sets out to (as per tacker issue or PR description)?

- Does it have extraneous code? Leftover debug prints, private API keys etc...

- Does it have any obvious defects? Memory leaks, un-handled edge cases, security flaws, obsolete API calls, etc...

- Could it be more understandable? Add/remove abstractions, better variable/method names, more/less functional etc...

- Is the style consistent with the codebase and/or style guidelines?

- Are there obvious performance improvements? Hashset instead of list, lazy evaluations, etc...

- Is it sufficiently well tested?

I think LLMs are okay at most of these, and worst at the first.

Imo the counter-argument is that in principle you can research and understand the mechanics of climate change or medicine. You just don't have the free time and/or the motivation. With AI that changes - you could spend your entire life trying in vain to understand its reasoning.

It reminds me of how some religious people say that science is effectively no different to religion because we all take expert opinions on faith. But the difference is that there is a well-defined pathway to understanding, if you wish to do so.

There's an argument for improving communication and collaboration between empirical sciences and humanities. But it's false to claim that serious philosophers cannot or do not have sufficient grounding in mathematics and physics. Two immediate examples that spring to mind are Tim Maudlin and Jeremy Butterfield.

Reminds me of how Stephen Hawking says "philosophy is dead" whilst simultaneously engaging in philosophy: https://en.wikipedia.org/wiki/Model-dependent_realism

It's only been saved as that URL for the last two years.

You mentioned that you took up CrossFit after starting creatine. Did your previous fit phases include lifting heavy weights?