It's a direct translation without changing the overall code structure or data structures. I do think this process deserves a distinct name from blind whole-of-project vibe coding.
Translation does seem to be a strength of LLMs, and as they said in the post, the code at the function-level all still feels familiar to the team. They've also already moved users to the codebase without anyone noticing; that's a better result than typical vibe coding.
This may not be you, but the people who have said that to me irl walk significantly less than I do. And saying these things to people who don't yet exercise can make them far less likely to start as it's a far bigger step to do so. Podcasts and recorded lectures are what got me exercising in the first place, because I was excited to hear the next part. I now only have headprones on some of my walks, but the gateway drug of entertainment was a very useful stepping stone. And still often better than sitting at a desk.
I suspect it's the combination of wanting to capture market share (subsidised plans), being severely capacity limited in GPUs, and having bursty and absurd growth rates. There was speculation that Anthropic might be allocating fewer GPUs to training for a few days to allow people to use Fable.
The actual API pricing seems far more of a stable downward trend, if measuring by equivalent intelligence.
Sonnet 5 makes more sense when you pretend the higher thinking efforts don't exist. (His test was on xhigh)
Anthropic's own release announcement mentioned that it's less cost competitive per task than Opus at higher thinking levels. It's significantly cheaper at lower levels though.
I'm wondering if this is going to be a universal pattern of smaller models: they're less smart, so to achieve the same benchmark results they have to think a lot more and hence become expensive.
Benchmarks force models to solve the problem entirely by themselves, requiring thinking. But if you pair them with a smart model (who thinks and solves beforehand) they won't need to solve the hard parts and can run on low/med. I suspect that was Anthropic's intention.
I do think most of the "adversarial to their own customers" things are coming from a company in extreme compute crunch. Eg, if they stop abuse they have more compute to serve real customers. And some of it is coming from them being true believers that AI could be a risk to society when it gets smart enough (their talk about jobs is because they want society to prepare, because they think it will change jobs regardless of whether they make it or others).
Note that other providers are also training on the same copyright books.
I don't think anyone realistically thinks open weights can be banned, though it does raise interesting questions if the White House is going to keep banning models like Fable and GPT5.6 while open weights equivalents are floating around. Their reasoning seemed to be that they don't want foreign adversaries to have access to models that can find security issues, but a local ban on an open model wouldn't stop that.
I really don't think this is effective advertising, reactions have been negative virtually everywhere.
The security bugs were real (see the Open Source projects struggling to keep up) so I think gradual rollout was sensible originally before the ban. But people have always resented safety steps.
The grandparent claim was that they were surprised downloading books was legal, I was saying that it's not, as they did need to pay.
Whether the law is enough is another question (some cases are still ongoing), and whether the courts are awarding it widely enough is another, but they are facing genuine legal backlash that international firms aren't right now and are more cautious. Several billion is a genuine cost that can move their prices higher in a time of strong competition (see also other announcements with media firms, it's not just books).
It's true that "in the style of" (eg. Ghibli) is not currently legally protected, only actual character IP or using the Ghibli name. That's not inconsistent with US IP treatment.
I haven't used them in a while so my info may be out of date, but they tended to track whatever models were the best and auto-use them for each task (eg, one for planning, subagent for a code search, other frontier for implementing). Their CLI seemed very well thought out to make you do things "the correct way" -- for instance, `/handoff` instead of `/clear`.
It wasnt, that's why they paid a >billion dollar settlement over it, and now license/purchase them. I don't know if the people distilling are licensing those books/etc today, though
Translation does seem to be a strength of LLMs, and as they said in the post, the code at the function-level all still feels familiar to the team. They've also already moved users to the codebase without anyone noticing; that's a better result than typical vibe coding.