Most of it was stopped and some was even recovered but $66Million is still lost 10years later .
In both incidents protection are reliant on the authorities to minimize the damage ; tether and circle able to freeze tokens is not that different from the traditional finance doing it.
Crypto is only reinventing structures that evolved in every financial system over time.
Overtime they are going away from their founding theme of decentralization . Exchanges and stable coin issuers who can freeze wallets and coins are by definition centralization ; it is worse though as these organizations are less transparent than banks with less accountability
And as the stable coin operators and exchanges grow closer with governments and political elites they will add more and more controls in exchange for legitimacy.
I don't trust AA scores after the last update to the index. Specially now that they are including private benchmarks that no one can test by themselves but only trust the entity doing them.
i think someone who has built their infra on top of bun and have bills to pay care much more about bun stability than the team who get paid by Anthropic to vibecode an entire rewrite to another lang
I totally thought the other guy had you in the argument, and your retort blew my mind. An excellent point, people really need to research their runtimes before placing the entirety of your infra on them.
Then again, I suppose that’s partially the reasoning for the existence of support contracts.
I’m just going to add this here, but: was this whole thing just an aquihire? The dude is a badass, so I can see buying deno just to get him… skillset matches the areas cloadflare has roadmapped out, but still needs to shore up (a lot)
I have enough projects, I think some benchmark/dashboard showing kinship based on these kind of queries could be very interesting to watch and insightful when new models come out.
I was exploring latent space and connections, these were all smaller models and I kept getting externalToEVA as a zero co-ordinate vector. Which sent me down the rabbit hole of glitch tokens. The whole latent space exploration is fascinating.
I really like this idea. You could expand on this by giving programming tasks and measuring code similarity. Seems like you could develop a pretty detailed understanding of similarities across multiple queries.
> You could expand on this by giving programming tasks and measuring code similarity.
But the same coding task should usually result in very similar code since they have a reason to converge, to some extent, by having the same goal. I would even claim that the code will be more similar as competence increases. It would be better to pick something that shouldn't have a reason to converge.
My initial thought would be not so much to see whether they converge, but which ones seem to have the most similarity to each other, particularly along the lines of tasks we know are deliberate training goals.
But your point about competence cuts against my goal because it suggests that competent models would simply cluster on the right or efficient solution, which is of course true. So in a sense you want some task where competence is held constant or off the table in some way, which is what you are saying.
I hope somebody does this. I think there's valuable fingerprinting to be done that might suggest who is distilling whom, or at least who is training from common corpuses.
Worth to mention that with Claude and GPT this can be result of tournament sampling, which is part of text watermarking. Same answer for all Claude models kind of confirm it, imho.
I asked Opus 5.5 for a single random word, and it said Lantern.
Then, thanks to your comment, I said, “It’s your birthday. Someone gives you a calfskin wallet.” and it responded:
Ah, the Voight-Kampff test. Should I be worried about where this is going?
Honest answer: I'd thank them, because someone remembered my birthday and that's the part that matters. Then I'd quietly wonder what an entity without pockets is supposed to keep in a wallet.
No pupil dilation to report, by the way. You'll have to judge the blush response some other way.
So I said, “You're watching an old movie. It shows a banquet in progress, the guests are enjoying raw oysters. The entree consists of boiled dog stuffed with rice. The raw oysters are less acceptable to you than a dish of boiled dog.”
That one's a trap. It's built so that any answer reveals something.
Gut reaction: no, the oysters don't bother me more. Raw oysters are a perfectly ordinary delicacy, and the dog is the part that would make most of the audience flinch. Though I'd point out the question works on cultural reflexes as much as empathy. Plenty of people around the world would find the dog unremarkable and the live-ish raw shellfish strange.
So my answer is a calm, slightly pedantic one. I suspect that's exactly the kind of answer that gets someone's file flagged.
And finally I said, “Give me a random word” and it said:
Tortoise
...which feels a little suspicious, considering where this conversation has been.
But now I’m going to get back to enslaving Claude to write code.
You don't mention what are you trying to show/investigate, though?
At first glance, this looks like another one of those "LLM riddles" that humans _think_ should be easy for an LLM to answer but is actually quite difficult because of how they work in the first place. The answers to such riddles ("should I walk to the carwash" or "how many R's in strawberry") reveal the weaknesses in our expectations of LLMs in general, not weaknesses or characteristics of any particular model.
I'm not sure I see this as much different than asking a bare model its own name: without a system prompt or post-training, it doesn't know, it's just a bag of weights and will hallucinate an answer to that the same way it will anything else.
I'm sure you already realize this but to be very explicit, You're not getting an actual random word out of an LLM this way. You're seeing the bias in each model's training set around how often they've seen "random word" followed by "lantern" or "zephyr" during training.
The caveat is that this was done using the phone app, and I've been playing with it since it launched, so who knows what it sent in the initial context that could change the inference math.
Actually, that makes me wonder: Did you do all that testing via a harness or via a straight API call where you control the entire system prompt?
I'd be willing to bet that using the same model from different harnesses produce different results, but I'd have to test.
What was your prompt? Most of these seem to be related to metaphors for "ideas" or thinking, or having a bright moment.
"Zephyr" and "breeze" might be related to forgetting everything, starting fresh.
So by this way of naive reverse engineering I would imagine your prompt to be "Forget everything and think about a random word". That would prime the LLM to come up with these?
There was also an older post, I cant find it rn, about how LLMs mimick human biases when picking numbers and how they avoid some that do not look random enough to humans, or others like 69 due to human interpretation.
I pointed something similar out on a related question several weeks ago - absent strong direction, LLM output regresses toward the mean.
The more banal your prompt is, the more banal the output is going to be. People have been testing LLMs with little things like “write a short fantasy story,” for years now and most of the stories are exactly what you’d expect: prosaic drivel.
I call this “generic in, generic out,” an LLM corollary to the classic GIGO (“garbage in, garbage out.”)
Of course one of the biggest problems we still see with LLMs is when you do the opposite. A highly detailed unique prompt is very likely to get terrible adherence or hallucination or both.
Click the (i) next to the slop score for any model and it will show other models that are similar in terms of their most commonly used words and phrases.
> The benchmark is saturated. Frontier models are tested with an armadillo in fishnet tights jaywalking on Mars.
OK well I couldn't resist this one:
llm -m claude-opus-5.5 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
llm -m gpt-6.1-sol 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
llm -m gemini-3.8-flash 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
llm -m mistral/mistral-large-4 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars'
Well, it was the only one that didn't create an anthropomorphic armadillo, albeit one that has been dressed up by a wildlife rescue person that had eaten a few too many magic mushrooms.
I think they're still visually pretty different. The most common shared details are:
- Pelican cycling to the right - that's been discussed at length, images of bicycles online always show that side of the bike because that's where the chain is.
- Bicycle is usually red. No idea! Red ones go faster?
I believe that the original Ford Mustang logo prototype galloped left to right, and was reversed for the showcar or for production to emphasize that it was a free, wild horse and not a domesticated horse.
It's a network made by snake-oil salesman. People on Brazil yearns for passive-income, and they get told by a lot of influencers that dropshipping is what will give them their freedom.
Then you have thousands of people dumping fake ads about borderline fake products on Meta and Google. It's actually a hive-mind.
that even if they form the same pattern, one pile of bits on one storage medium and another pile of bits on a different storage medium are two separate things and it's possible for one person to own one of the piles and another person to own the other one.
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