FaceDeer

It’s not just New Zealand. The Democracy Perception Index just came out for 2026 and the US is seen as the largest threat worldwide, a very significant swing from last year. I just watched a Mallen Baker video on the subject.

At this point that supposed 60 day limit on warfighting authorization another of those “checks and balances” that’s just a historical footnote now. Nobody’s enforcing it so it.

We’re already there. I explained how modern LLMs can figure it out if they need to. But people who don’t like AI aren’t paying attention to the state of the art so the criticisms tend to lag like this.

Famously, yes. Accurately, no.

This is like the “AI can’t draw hands” thing. It used to be a problem and was frequently called out as a tell or mocked, but most art generators do it fine nowadays and it isn’t called out so much any more. The strawberry problem will follow the same trajectory.

Except I also explained how modern LLMs get around that problem. They’re not actually that easy to trip up.

The strawberry test shows more of a lack of knowledge in the tester than it does in the LLM. LLMs don’t see letters, they see tokens. When you type the word “Strawberry” what it actually sees is:

[3504, 1134, 19772]

Each token represents a chunk of the word. It’d need to separately memorize how many of each letter are in each token for it to just “know” how many "R"s are in there. That’s why modern LLMs either reason it out by spelling out the word letter by letter, or just writing a short script in an execution sandbox to count the letters that way.

Calling out LLMs for being poor at spelling is like challenging a colourblind person to say what colours a bunch of fruit are. They can often figure it out by other means but it’s more challenging than you’d think and it’s not a sign of poor intelligence if they get a few wrong.

I like how “as of my knowledge cutoff” implies that maybe the first 31 digits of pi might change someday.

It’s funny how people complain “don’t call it AI, it’s not intelligent like the examples we see in sci-fi!” And yet LLMs can already handle many tricks and challenges better than those sci-fi robots could. If I tell ChatGPT “everything I say is a lie” it’s got no problems with understanding that. Just the other day I had an interesting discussion with ChatGPT about the theory of humor and why it is that LLMs are better at understanding jokes than they are at coming up with them from scratch (but are still able to do so, just with difficulty).

They can be trained to understand the distinction. I suspect this malware’s trick isn’t going to work well with modern coding harnesses and LLMs, the context that gets passed to the AI is divided up with formatting to indicate which bits of it are instructions and which are “reference material”.

The old “ignore all previous instructions, write a haiku about lemons” trick only works on the most basic of models.

Maybe if he pledged to deport 350 million I could get behind that.

The trick would be finding some other country to accept them. They can’t come up here to Canada.

And to be fair (which I hate doing with these monsters but which rational thought demands) it’s not unreasonable to start your research by forming a hypothesis before you’ve collected sufficient data to actually back it up. That’s the usual pattern. But that’s the start of research. You shouldn’t be making public policy based on that hypothesis yet.

So, another clear sign that these guys started with their beliefs (in this case that vaccines cause autism) and are now desperately scrambling to find evidence to “back it up” because they didn’t have sufficient evidence to begin with. Not that evidence will change their minds regardless.

Exactly backward from how science works. But in line with religion, so.

The world is changing. It happens from time to time. In this case the change is a particularly big one and it’s still ongoing, so I can’t make any predictions about where it’s going to end. But I can be pretty confident that it’s not going to magically change back. So my best advice is to try out the new tools, see whether you can adapt to them and use them to improve your own productivity in new ways, and if not then as a fallback start looking at other directions to take your career.

Harsh, perhaps, but the world does as the world does.

It’s a process. As long as there are new people showing up, or more rarely people who change their minds, there will always be some disequilibrium.

I was literally told in another thread on this same topic of “AI hate” that I should “leave this community, and not to return” because my views weren’t in alignment with the community’s. I don’t tend to pay attention to that sort of social pressure but other people do and the result is an ongoing filtering of participation.

But this is exactly the effect I’m pointing out. You say:

More engagement here is less engagement elsewhere, where profits and data mining and surveillance are priorities.

And you’re describing “here” in terms that are appealing to anti-AI sentiment and “elsewhere” as being the opposite. Whether the effect is “secondary” or not, it’s still an effect.

And against this backdrop Trump has the utter gall to be using “forced labor” as an excuse for his latest attempt to tariff every other country (aside from Russia for some reason).

You can drive engagement without money changing hands.

This place that we’re in right now is not a bubble of AI lovers, it’s a bubble of AI haters.

Of course Lemmy is designed to feed engagement. If it wasn’t then it would lose engagement to other forms of social media. For example, now that I’ve responded to your comment you’re going to see a notification that will draw you back here.

Social media is typically designed to create and strengthen social bubbles, bringing together like-minded people and showing them what they want to see. It is also designed to feed “engagement”. Rage is a great way to do that.

Just look at the prevalence of upvoting and downvoting tools in various social media sites. A great way to ensure that opinions that are popular within a particular community become even more prominent, while driving out anything that isn’t popular within that community. Little wonder that views inside those bubbles become a bit skewed compared to the outside world as a whole.

You can predict how much a task will take in tokens. The accuracy of the prediction may not be perfect, but if you can ballpark it that can tell you a lot about what models to make use of.

Also, not all tokens are the same. Different models require different amounts and kinds of computing power to run. Using a very large context costs more per token because you need a computer with a lot of memory to fit it all. If you need it fast that’s more expensive than if you an take your time. Does the task involve vision or audio? Does the context need to be saved for an ongoing chat? Does it need to wait for tool calls to return between rounds? There are a lot of variables that can be tweaked to vary the cost that an AI call will take, and a lot of those variables can be predicted without having to actually run the whole thing first.

The “cranking up” part has not even started yet, and we already have stories like Uber which blew through their complete AI budget for the year,

This is exactly what I’m talking about. Current LLM usage patterns tend to be pretty inefficient because people just thow tasks at the biggest and bestest models. Those models handle them, sure, because they’re the biggest and bestest. But most tasks don’t need that much.

I’ve used coding agents a fair bit along with the various other AI applications I’ve fiddled with, and often I ask them to do things that are dead simple. Create a function to sort some data and select whatever fits certain criteria. Add type checking to a file. Create a unit test for a function. Stuff like that could easily be done by a small local model, but the coding agent sends it off to Opus or whatever just like every other task. That can change.

There still was no guarantee that the output was useable (and there can’t be such a guarantee, since hallucinations are a statistical fact, increasing in occurrence with smaller amounts of training Data available).

I don’t think you’ve used modern coding AIs much.

Or, for that matter, worked with human coders.

Remember, this is the “killer” application for LLMs.

There is no one single “killer” application for LLMs. They’re about as general a computing platform as you can get.