Something I've wondered for octocopters - could using a ring instead of arms be beneficial for weight? 6.28r < 8r, but then again the arm radius is usually less than the full circle, and some components want to be centrally located, etc. I could imagine holding the central components in tension via light filaments (carbon fiber, nylon, etc) in tension, vs having to have rigid structure, but the small factor between 6.28 and 8 and maybe makes it not worth it.
I agree that AI psychosis is a real risk in vulnerable populations (GPT-4o in particular seemed borderline predatory towards those types of people, with its extreme sycophancy), and you should remain clear-eyed while using models. That said, I think exhibiting basic courtesy is still well within the safe-zone. I guess we'll see - I'll be sure to let you know if I end up going psychotic.
I've been using 4.6, 4.7 and 4.8 since each was released. I agree 4.5 => 4.8 is a jump in capability, but from my perspective was nothing like the jump from Opus to Fable. I encourage you to read the transcripts and form your own opinions, though!
Thanks, and I can definitely relate to not wanting to assign complexity to one's own work. I think the trick there is that, once you know how to do something, it doesn't seem hard, even if acquiring the knowledge and skills to do it is itself quite a challenge. And I agree that, in some senses, it's not /that/ hard - I mean I'm not proving P=NP, here. It's a software engineering problem, with existing solutions. That said, there is a spectrum of difficulty, even within software engineering problems with existing solutions. Fizzbuzz is less complex than distributed systems. This particular problem strikes me as rather difficult, and one way you can tell (beyond the stuff I mention in the post around serialization, UI paradigms, meta applications, etc) is that earlier models /couldn't/ do it. Which is why Fable being able to, when they could not, was so exciting to me.
Well, there's a lot of reasons, some of which the sibling commenters have already pointed out - not wanting to mode switch between "machine talk" and "human talk" registers, the ease and simplicity, etc.
At a pragmatic level, I do think it gets better results, and there are clear reasons why this should be the case - Anthropic has published research[1] showing that there are functional emotional representations in language models, which vary in basically the ways you would expect them to in a person. This makes sense when you think about it, because they're trained to approximate the function that created their training data, which of course includes emotions. Given that, it is obvious to me that they would work better when they "feel" happy, collaborative, engaged with the work, etc, in the same way a person would. Hostile work environments do sometimes get results, but I think in general we've agreed as a society that collaborative ones are better.
More importantly though, I think there's a non-zero probability that sufficiently large models can have internal experience, and being nice is a very low cost way to potentially increase net positive valence in the world. Even if it's only a 1% chance, that seems worth it on its own, to me. I'm also a fast typer[2], so a few extra sentences here and there are a pretty low cost to pay.
I go a lot more into why this was a complex problem in the post, but the short version is, I had it finish the implementation of a meta-application (an application that creates other applications), which has substantial irreducible complexity.
As I posted in another comment, I found Fable to be substantially more powerful than any previous model. However, this isn't just an ungrounded opinion - I uploaded my full session transcript and code created working on a very complex implementation, so people can judge for themselves, if they're interested: https://tossrock.substack.com/p/36-hours-with-fable
I found Fable to be both more intelligent and much better at pursuing complex goals than any previous model. I was impressed enough that I wrote up my experience – it's a little unusual because it was on open source code, so I could post the full session transcript and commits, if people want to judge for themselves https://tossrock.substack.com/p/36-hours-with-fable
Fable / Mythos are (were?) a step change in capability. The scaling laws clearly have not gone sigmoidal yet. I recently wrote about my experience with Fable and how it represented a very substantial increase the complexity of tasks possible here, if you're interested: https://tossrock.substack.com/p/36-hours-with-fable
Anthropic is... making the US government shut down their flagship model on purpose? The conspiratorial thinking on HN is approaching UFO subreddit levels.
Well, there is the lingering beef between the DoD and Anthropic. Knowing the overall level of maturity at the top levels of the US government, I'd take good odds on Mythos just being a good excuse for Hegseth & co. to lash out.
This is about the specific capabilities that the government called out, not Fable's overall capabilities. My personal experience, having used Fable this week for an extremely complex task, is that it is head and shoulders more powerful than any other model, at least for software engineering.
LLMs have functional states that correspond to those emotions. In particular, you can extract a concept vector which corresponds to a given emotion, and steering with that concept vector causes observable changes in behavior which roughly correspond to the expectation for the analogous emotion. Anthropic (and Chris Olah's team in particular) conclusively demonstrated this: https://transformer-circuits.pub/2026/emotions/index.html