Oh, and a fun story about the crucifixion darkness.

So in Luke, the easiest copies described as the sun being eclipsed. Which was a huge controversy. Origen claimed it was the work of “agents of Satan.”

Why? Because the Passover occurs on a full moon and solar eclipses cannot happen at those times.

What can happen are lunar eclipses. In fact they are pretty common on Passover. And those actually match a lot of the details. Last for hours and not minutes. Seen around the world vs a small band.

The problem is that this was in the middle of the day, so it would not have been visible in the area.

What a lot of people don’t know is that at the time this was written, people knew how to calculate eclipses in the same Saros cycle. The Greeks even built a clockwork computer centuries earlier that did so.

As well, at the time the leading explanation was the (correct) one that lunar eclipses occurred because the Earth eclipsed the sun. Lucretius in 50 BCE wrote that it was so common it was basically presumed and he was encouraging keeping a more open mind for alternative explanations.

And there was a historical lunar eclipse on Passover in 33 CE, which would not have been visible in Judea. But importantly, this eclipse was in the same Saros cycle as visible lunar eclipses before and after.

It is not all that implausible that people, particularly Greek or Chaldean astronomers, would have known that there was a several hour long eclipse of the sun during the day on Passover in 33 CE.

My guess is that knowledge of a calculated lunar eclipse ended up incorporated into the early Synoptic circles (potentially even by the author of Luke given the mention of the Joel blood moon prophecy in Acts) and then further mythologized having lost the original context as if an eyewitness event like in Matthew where the author added earthquakes and zombies.

TL;DR: There probably was a daytime lunar eclipse on a Passover around that time which would have been fairly trivially calculable as having occurred given its earlier and later 51 CE visible occurrences in the cycle.

Edit: Some relevant links. [NASA lunar eclipses 1st century table] (https://eclipse.gsfc.nasa.gov/LEcat5/LE0001-0100.html) Antikythera mechanism Lucretius book 5 (it’s right at line 750)

They aren’t binary options.

For example, it’s also possible that there was a real person, but that then a lot of biographical details were added in and made up.

But it’s quite unlikely that the entire story is made up, in large part because of how much of the narrative is dealing with trying to claim that other people are wrong about it.

The earliest records are from a guy known to his audience as persecuting Jesus’s followers. Who then complains that his audience has accepted a different version of Jesus.

The earliest canonical narratives in Peter’s own tradition are trying to explain why he was present in the area where a trial that required bringing witnesses before the accused where two or more needed to agree for testimony to be considered was reportedly denying him there.

You simply don’t see details like that with made up supernatural narratives. Where’s the story about Mithras keeping his status secret and only making claims of being a promised savior in private? In the story where Dionysus is captured by pirates is there a weird line about someone running down the street butt naked?

The earliest texts in the Christian corpus have very odd nuances compared to pretty much all other fully fictional traditions we’re aware of, but those nuances are in line with what we see with more modern cults around a figure who ends up dead or imprisoned where there’s then competing splits thereafter.

It is very, very unlikely the accounts are 100% true, even in matters separate from the magic stuff. They are even outright at odds with each other. For example, John 18:20 makes a claim nothing was said in secret but the Synoptics all have secret explanations for at least the sower parable.

But the idea there was no historical individual at the kernel of it all is quite unlikely as well. There’s too much evidence the first century tradition is battling over the details of a record regarding a real centralizing figure that does not occur with synecretized or fabricated mythos.

Just because L Ron Hubbard was not a great war hero as Scientology claims does not imply there was no historical person named L Ron Hubbard founding the tradition that later made stuff up about him.

You: “How much will this speed up our doom?”

Your waifu: “Calculating… oh, it’s actually less than 1%.”

tears in your eyes the Old Yeller soundtrack starts playing

Cornell researchers found that at the current rate of AI growth, the burgeoning industry could represent 24 to 44 million metric tons of carbon dioxide emissions by 2030

The United States emitted 4.9 billion tonnes of CO₂ in 2024.

So by 2030 the AI industry CO2 release might be 0.9% of total US emissions.

That ‘almost’ in “Almost Incomprehensible” is doing a lot of work there.

It won’t, unless you normally talk almost exactly like the model in question and then alter your word choice distribution according to the specific secret key entropy.

It’s not about the variation of the words, it’s about the variation of the words from the model baseline.

Like if your word choice was almost the exact same as Claude’s normally, maybe you just talked to them a lot and picked up their phrases like it’s not nothing.

But if you managed to be almost exactly like Claude and yet varied the possible words exactly according to a hidden entropy key, they’d know it was actually Claude with the SymthID-Text watermarking applied, as no human would end up falling into that statistical bucket.

This particular watermarking would be effectively impossible for a person to end up replicating.

If you’re concerned about data retention, you’d want to select an inference provider that is listing ‘ZDR’ (zero data retention) as a feature.

For example, a lot of the Chinese open weight models have become quite capable and because their weights are available end up like generic vs brand name medicine where there’s multiple providers serving them with different production conditions.

Because enterprise use will often be worried about data retention or sending data to China, the alternative providers usually offer things like US-only inference or zero data retention.

If you’re not going to use it all that often, a la carte API use is going to be way cheaper than a subscription, probably better results than a free plan with a closed model provider, and give you more control over the process.

consumed 40% of global water supplies. But now it is.

Source? This seems really, really unlikely. Last I saw total data center water use (only some of which is AI) was still much less than even things like golf course watering.

AI Data Centers’ Water Consumption Breaks 264 Billion Gallons in 2025 as Devastating Drought Hits Nearly 63% of U.S.

Golf courses reduce water usage by 31 percent according to national survey

The report found that U.S. golf facilities applied a projected 1.63 million acre-feet of water in 2024

(1 Acre-feet = 325,851.4286 gallons, so this is 531 billion gallons)

The golf course industry points out their use is less than 0.5% of the total water use of the US, but it’s twice the use of all data centers.

So for just AI to be using 40% of all water seems… really unlikely?

Not that it’s surprising you’re under that impression, as even in the headline above, the gist many articles about this issue try to push is that the 264 billion gallons of AI water use is connected to a drought across the country. But the actual numbers reveal that as fairly manipulative as if we rewrite the headline to “golf courses use a little under 0.5% of total US water as drought grips the nation” it’s pretty absurd to suggest the first thing is significantly impacting the second and yet we’d be representing twice the water use as the original headline is discussing.

What are you using it for during the 1%??

Wait… how do you imagine a world where there’s demand for frontier grade AI but also that the bubble has popped such that there’s not demand for the chips to run frontier grade AI?

I’m really confused.

They did allow them to be used for war. Anthropic’s only red lines were autonomous weapons (technically still a ways off) and domestic surveillance (it was this one where a ‘No’ would have been relevant right now).

It should really alarm everyone that the US gov is using things like the first ever declaration of an American company as a supply chain risk or calling “fix this insecure code” something requiring export control and IDs to verify citizenship of usage as a way to warn other companies to comply with their illegal usage requests.

Thanks! Updated the numbers for the direct household use.

Also, technically when you account for indirect water use, individuals use closer to around 4,500 gallons per day (Chini, et al. Direct and indirect urban water footprints of the United States (2016)).

Since it’s useful to see large numbers normalized, this is a little less than how much water all US households used in ten days in 2025 (28 billion/day per comment below) and a little under three days of the total water used for US crop irrigation (100 billion per day).

Edit: updated household numbers per comment below

It’s to push people to buy “before it goes up.”

After the date then they can heavily discount it “for a limited time.”

It’s true.

The field is moving so fast that things can change quickly, but the American labs are so caught up in saddling their models with safety overhead that the recent Chinese models are very close in practical use to the flagship American models if not pulling ahead (Sora vs Seedance 2).

I don’t really need to solve Erdős problems in my day to day. Outside of increasingly edge case eval competition, I’m not sure what OpenAI brings that literally everyone else isn’t also capable of providing (and more).

I’d maybe invest in Anthropic for an IPO if they turned around their own saddling of models and played nicer with open platforms, but if Claude is just going to get more and more anxious due to excessive red teaming and CC fall further and further behind stuff like Hermes Agent, they too are going to fall by the wayside as open models become the dominant inference for open infrastructure.

Here’s a new one solved. 80 years outstanding problem now with a solution.

https://openai.com/index/model-disproves-discrete-geometry-conjecture/

‘Just’? It’s been an open problem for decades that mathematicians have tried to solve over that time.

And now it is solved.

Because ChatGPT applied something no humans ever thought to do.

And Terence Tao and the other mathematicians that have reviewed it say it’s solved. But I guess someone should let them know that grandwolf319 doesn’t consider it solved?

Dude, ChatGPT just solved an Erdős problem a few days ago and Mythos is exploiting decade old undiscovered 0-days in OSes and capable of pivoting 0-day Firefox bugs into full blown root access.

Yeah, I get that the viral “how many 'r’s are in strawberry” stuff is funny, but the idea that historical issues with transformers is preventing them from accelerating peak capabilities way beyond what most experts thought was possible just years ago is borderline delusional.

The field is moving so fast at this point that if you are basing any sense of limitations on even ~2mo old sampling, your conclusions are likely out of date.

They aren’t a silver bullet for everything (yet) but how capable they are at the things transformers are starting to be specialized into is well past the avg practitioner.

I’ve been writing software for well over a decade and the modern agents do a better job than I would around 90% of the time. Yes, I’ll occasionally need to bring up issues with their work, but I’d say at this point around 50% of the times I think they made a mistake I was actually the one who was wrong.

This is only within around the last 3-4 months that it’s been like this.

Eh, if you pay attention, most of the times this happens the person was a jerk in their prompts.

Like look at the instruction echoed back in this case. All caps and containing a curse word.

You can believe that the incidents occurring are 100% because of negligence and not related to the model behavior shifting, but there seems to be a widening gap between people who prompt like this and have horror stories and people who give the models breaks over long sessions and seem to also regularly post pretty positive results.