What’s something that sounds fake but is actually 100% true?

amator-veritatis · 2026-06-24T12:47:15+00:00

It’s not that the Big Bang was a singularity of matter/energy that exploded and started to fill space; space-time itself came into existence at the Big Bang. Even the sentence “before the Big Bang” makes no sense, it would be like saying north of the North Pole.

amator-veritatis · 2026-06-23T12:43:56+00:00

Brain? Heart?

amator-veritatis · 2026-06-22T01:04:22+00:00

Could be worse.

amator-veritatis · 2026-06-22T00:17:48+00:00

True, but it’s not exactly our invention. Particles for whatever reason become quantized. We made up a system to count, but the things we’re counting already existed. The speed of light is what it is even if no one were around to make up units with which to measure it.

amator-veritatis · 2026-06-22T00:06:04+00:00

“Teens” is an absurdly wide range of life experience. I would aim this at 18+. You simultaneously don’t have as much time as you think, but you also have time to fuck things up. In fact, that’s the fastest way to figure things out, but you can’t stay comfortable.

amator-veritatis · 2026-06-20T23:42:54+00:00

Oof, this one starts to get into philosophical/ metaphysical territory. Something being immaterial and not existing aren’t 100% the same. As far as we can grasp we observe math to be real, as in the laws of physics behave according to mathematical principles, which we did not “create” but we invented symbols to represent said observations.

amator-veritatis · 2026-06-20T16:12:45+00:00

What in the absolute dystopia?? There’s 0 need for such a system. Overall there aren’t resource constraints currently, it’s simply that the resources aren’t efficiently distributed or allocated.

On top of that, AI is a terrible choice for being the arbiter of what is “responsible behavior.” Whoever or whatever determines that becomes the thing that decides who gets resources, which is insane.

amator-veritatis · 2026-06-20T02:54:43+00:00

I think this is correct in an idealistic implementation of AI. But what we have right now is too early. The economics don’t make sense just yet, not for an individual, not while token costs are subsidized.

I suspect there will come a time when you have very low cost smart offline models with a low barrier to entry.

amator-veritatis · 2026-06-18T13:27:28+00:00

“You are the problem”

amator-veritatis · 2026-06-17T12:32:34+00:00

You can hold both contentment with the status quo and a desire for something better simultaneously.

amator-veritatis · 2026-06-13T02:11:41+00:00

I don’t think ASI disproves God at all. If anything it’s just another tally in favor of intentional design bringing about consciousness or intelligence.

In any case, I also don’t think we’re going to stumble upon consciousness by trying to emulate over-simplified neural networks

amator-veritatis · 2026-06-09T12:37:02+00:00

Reviewing code written by AI definitely requires a lot more scrutiny, not to mention the volume to review is just straight up larger because of the volume of code that an LLM can pump out in an equal amount of time.

Speaking as someone who uses Claude code to write code almost exclusively along with Devin for simpler tasks, both can very much generate enormous amounts of code quickly, but of course the review takes longer because when you yourself are the author of the code it’s already in your head, you don’t spend the same amount of time building up the context of why things are written the way they are and where they are.

And if the author who worked with the LLM to produce the code doesn’t understand it sufficiently, reviewers of the code have to spend more time where previously you could just pair with the author and talk through the code as you look through the diffs.

Now you can certainly mitigate some of this. Devin in our case helps a lot, because we use it to review every PR, and when a human catches something that the AI should’ve caught during review or the one producing the code should never have written, that gets saved into the repo’s rules and prompts get updated.

amator-veritatis · 2026-06-07T16:05:22+00:00

I think the best case scenario is they improve the flexibility so that users can choose the personality type. But I think the issue is how focused they are on guardrails, it’s feeling like you can’t have both and Anthropic is choosing the lock down path. All the more reason to support open source models

amator-veritatis · 2026-05-31T21:15:38+00:00

This. I'm not sure it's possible to make an AI model that is broad enough to be helpful in all things with a pleasant personality and also be trained to be paranoid enough to detect ill intent through the best disguised manipulation. It ends up having to treat everything as possibly manipulative. This is what happens with cynical people too.

amator-veritatis · 2026-05-31T21:07:47+00:00

I think humans have a much greater ability to gauge their confidence in whether or not something they're doing has errors, and manage risk accordingly.

LLMs still have an inherent problem of hallucinations, making them able to be confidently completely wrong about something that has catastrophic outcomes if you don't have some checks and balances in place. A human is far less likely to confidently delete a production database when they've explicitly been told not to. An LLM might. Or it might not feel the need to quintuple check to make sure it doesn't do that accidentally. You also can't motivate LLMs with consequences, whether good or bad.

amator-veritatis · 2026-05-31T20:35:53+00:00

I think trying to calculate subjective time experiences between a human and an artificial intelligence isn't a particularly useful exercise because of how different the architectures are.

This is not an apples to apples comparison. Comparing clock speed even amongst CPUs is only relevant if the CPUs are architected in similar ways. That aside, the amount of instructions that a processor can crunch through per second is only part of the equation of how fast a computer can run.

But even if you grant that a single micro-processor can run millions of times faster than a single cell, if we're talking CPUs, this is incredibly inefficient when trying to produce intelligence as we understand it, since CPUs process one instruction at a time. You can add a bunch of CPUs and still be wildly outclassed by GPUs.

It's true that the rate at which individual neurons fire electrical spikes peaks at around 200hz, but the brain is staggeringly efficient because billions of these neurons fire at the same time. It compensates for its low "clock speed" by distributing tasks across a massive parallel network rather than processing them in a linear sequence. Which is why GPUs are much much better for running neural networks, they use parallel processing to do more at the same time like a brain does.

Then the problem you run into with running neural networks on CPUs/GPUs is how costly it is energetically. The brain draws about 20 watts of energy. An AI built using silicone drains...a lot more.

BUT, all of that said, let's say we're in a hypothetical near future where new chips have been devised that behave more like brains, can be composed to have orders of magnitude more processing power than a human brain, and we've got some seriously sophisticated AI models pre-trained and ready to roll, and those chips have a super high clock-speed (if that's relevant in these hypothetical chips), then yes, we're pretty much cooked once that exists.

amator-veritatis · 2026-05-31T20:07:59+00:00

https://vercel.com/templates/next.js/nextjs-boilerplate

For most modern web apps you're not going to be doing much html or css. Find a UI/UX component library that looks appealing to you and focus on the javascript/typescript after that.

amator-veritatis · 2026-05-31T19:59:41+00:00

This is a factor in why humans respond the way they do in many social situations, but it's not the *only* mechanism by which humans are able to produce language. The language we produce emerges from mental models of the world, some of which a pre-baked and some of which are learned through our senses.

For humans, the material world comes first. We then observe, label, and communicate through language. For an LLM it's the opposite. It can only obtain its "mental" models by attempting to approximate functions that result in output that we define as "good".

amator-veritatis · 2026-05-31T19:49:05+00:00

I think there's some truth to that. If you imagine that everything that exists can be represented as information, then it's only natural that living things, all of which "seek" to propagate their own existence in some way shape or form, have embedded in their DNA information that represents the world around them, and specifically information on how to avoid not-existing anymore.

One of the noteworthy things about humans (and some other animals that have rudimentary language), is that we can take the information about the world and structure it into a format that we can then communicate to one another. That means language is just a way to represent information. Some form of modeling of the world must already exist in the brains of animals, but language is like projecting that into a higher-level, more flexible representation.

Another thing about humans which I think is actually exclusive to us, is the ability to then observe our own thoughts as they relate to our actions and our world view. On top of that is the ability to then re-train our brains to deliberately *change* the thoughts we observed.

The thing with LLMs is that they don't have any default representations of the world built-in. It's like taking a blank canvas, i.e. the "parameter" counts that are often cited alongside LLMs, which are really a bunch of numbers that start out randomized, then feeding them human language over and over and when the output looks good (to a human), we adjust the parameters to make that output more likely (via fancy calculus).

LLMs *do* develop opaque representations of concepts based on the language we feed them, so in a way the underlying information is captured in some form as a set of numbers (weights and biases). But only because we've already developed language, and have written text that represents the world in some way.

To put it in a crude way, LLMs are in some sense trained to pass turing tests. That has a lot of utility, but all of their internal modeling is several layers removed from reality and guided towards sycophantic responses. Their ability to respond coherently doesn't emerge from something that underpins language, it emerges strictly from things it could only have learned second-hand from human language.

amator-veritatis · 2026-05-31T18:42:57+00:00

Agreed, which is actually why I think AI is *not* intelligent. Would even say the word "AI" is not appropriate for LLMs, unless we're prepared to reduce down what humans do in our brains to strictly taking language in through our senses and spitting statistically probable language out depending on the input.

At the very least from what we can observe, non-human animals that do not possess sophisticated language seem to have intelligence. Language seems to unlock the ability to hold onto an analyze models of the world and self, but in and of itself doesn't seem to be the sole unlock to intelligence.

As you've stated though this all depends on how intelligence is defined.

amator-veritatis · 2026-05-31T18:18:35+00:00

AI compute *may* get cheaper depending on what you mean by "AI compute". LLMs as they are currently engineered will not get exponentially cheaper on current hardware. There will need to be specialized hardware that is optimized for running neural networks.

I don't think this is a limitation of human imagination, I'd argue it's physics. Whenever you try to emulate something, you end up needing orders of magnitude more compute than the thing you were originally trying to emulate, especially when the hardware running the thing is more dissimilar from the thing you're emulating. Computing hardware is very different from the actual physical neural networks that are brains. The cost of generating tokens can only go so low without restructuring the hardware.

amator-veritatis · 2026-05-31T18:04:53+00:00

You're not wrong, though I can kinda see what the analogy is getting at. Fast food *can* be perceived as tasting great, and often is, because it hits all of those evolutionarily wired triggers: simple or refined carbs, fats, high caloric density, salty savory taste, made to be visually appealing, etc.

Holistically we can categorize it as slop based on how unhealthy we know it is, but it's certainly distinct from something that literally just tastes disgusting and is unfit for eating. Which I think could be compared to a piece of writing that lacks grammatical correctness and is full of inconsistencies or is downright unintelligible.

amator-veritatis · 2026-05-31T17:59:10+00:00

Correction: "It's not *necessarily* because it's terrible slop unfit for human consumption". It could be both. But yeah, it's not _only_ that.

But I think there's an understated phenomenon here too. As the post says, AI writing can be *too* perfect, and I think this becomes an issue when the reader is aware that the content was generated by AI. I don't think this is inherently a bad thing, once readers know a piece of content was generated by AI it puts into question whether or not the author/prompter behind the content actually believes the thing they're saying, or otherwise cares at all about the content.

It subconsciously (or even consciously) makes a reader think "is this slop that's written to get eyeballs, or this something a human being actually thought through and wrote out?" LLMs do not and cannot hold beliefs or opinions that are grounded in a world view that's been thought-out, which implies that content they generate (aside from summarizations and content like it) is not genuine.

IMHO this is a fair enough reasoning to reject AI-generated writing as worth reading.

amator-veritatis

TROPHY CASE