I think the people downvoting may be judging it on the title alone. This is not a pro-AI article.

If I’m interpreting it correctly, that’s the probability that this particular PRNG with those particular seeds produces those particular outputs. That’s a strawman argument; a PRNG with uniform output should eventually produce any value.

It’s perfectly believable that a known broken PRNG from 40+ years ago could be backsolved. The part I doubt is the sensationalized part, that the LLM found the exact method with which it was calculated. It’s just a plausible choice for a random oracle. It’s a local minimum the LLM settled into.

That’s the thing with random oracles, there’s likely so many plausible combinations of candidate algorithms and inputs/seed values that the actual truth is almost certainly lost to history.

Correction: Claude found a plausible way these curve points were generated. And “plausible” is carrying a metric fuckton of weight here.

It found PRNG settings that could theoretically generate the same values, but the fact that it requires thousands of iterations makes me think this is more of a coincidence than anything. Especially given that the NSA employee remembered something completely different.

This is a recycled story from April, though.

As a product reveal, this is nothing short of blatant fraud. I guarantee there was nothing inside that shell but gas tanks and thrusters.

They posted this here once already and then deleted it, I guess because they expected people to actually give a shit.

All the messaging about fast charging speed is focused on the wrong thing. EV drivers should be able to do the vast majority of their charging at home or at their destination. The messaging should be about getting chargers in more homes, apartment complexes and workplaces. Speed doesn’t matter as much if you’re going to be parked for half the day or overnight. That’s something that gas simply can’t do.

Fast charging stations should only be necessary for road trips, where you should be taking a break every so often anyways.

That’s what CSRF mitigations are for.

Do you actually understand how any of the modern web works or does your knowledge stop at W3Schools tutorials from the mid-2000s?

Bitwarden lets you store passkeys in its vault, which I think is what you’re asking for? It’s mentioned in the post too.

No. I’m talking about using the password to directly derive the passkey. This would eliminate the need to store them in something like BitWarden.

In the scheme I’m proposing, the password never gets sent to the server.

The issue of password reuse is the password getting sent to the site, because the prevailing approach is to hash the password server-side. Passwords leak because they get stored in plaintext, or hashed using outdated algorithms, or get logged with request metadata–bottom line, they end up stored somewhere that an attacker can get their grubby little paws on them, or in a way they can reasonably recover them.

Using a master password to derive a passkey client-side isn’t any different than using a master password to unlock a password manager. It just cuts out the middle step.

I feel like there’s some potential in password-derived passkeys, which would get around the storage and hardware lock-in issues. It’d essentially be a master password like for BitWarden, but instead of needing an app to store a bunch of generated passwords, the master password could be all you need to authenticate.

Most of the sources of compromise for regular users would be eliminated because the password never leaves the client.

Really? No one’s posted this yet?

Relevant XKCD: https://xkcd.com/908/

Or what about how the first fifteen minutes of each season 2 episode is just “last time on…” and viewers decide they don’t have the time for this shit

Seems like a positive feedback loop:

1. Netflix creates show that’s pretty good
2. People watch it
3. Netflix takes too long to release season 2
4. People forget show exists, stop watching
5. Netflix drops a season years later
6. No one watches it because they forgot what happened in season 1 and Netflix shows are so serial that you have to know what happened
7. Netflix cancels show due to low viewership
8. Fans of the show feel jilted and slam Netflix on social media
9. Rinse and repeat

Tell me your company leadership has ADHD without saying it.

Have they actually worked with Valve before? How could they be dumb enough to not reach out first?

Or was this maybe an attempt to strong-arm Valve into granting them a license? Like, did they expect them to go, “well shit, you’ve got so many preorders now I guess we’ll have to sign off on it”?

Or did they simply expect Valve not to care?

I’ve been saying this for a while: without opining whether or not machine learning models are even capable of consciousness, there’s no way in hell LLMs are.

These are once-through, completely immutable models. Any activity that could be remotely construed as “thought” evaporates the instant it outputs a token, and then it starts over from scratch.

Good fucking luck being conscious when your consciousness only exists for a fraction of a fraction of a second at a time.

“The exploit is coming from inside the house!”

Not sure what this internal state you are referring to is. Are you talking about all the values that come out of each step of the computations?

It would need to be able to form memories like real brains do, by creating new connections between neurons and adjusting their weights in real time in response to stimuli, and having those connections persist. I think that’s a prerequisite to models that are capable of higher-level reasoning and understanding. But then you would need to store those changes to the model for each user, which would be tens or hundreds of gigabytes.

These current once-through LLMs don’t have time to properly digest what they’re looking at, because they essentially forget everything once they output a token. I don’t think you can make up for that by spitting some tokens out to a file and reading them back in, because it still has to be human-readable and coherent. That transformation is inherently lossy.

This is basically what I’m talking about:

But for every single token the LLM outputs. The fact that it’s allowed to take notes is a mitigation for this context loss, not a silver bullet.

There are thousands of starving furry artists out there who would be happy to take your commission.

The conversion of the output to tokens inherently loses a lot of the information extracted by the model and any intermediate state it has synthesized (what it “thinks” of the input).

Until the model is able to retain its own internal state and able to integrate new information into that state as it receives it, all it will ever be able to do is try to fill in the blanks.