791–800 of 1001 posts
Anyone ever seen the SouthPark episode making fun of Game of Thrones: A Song of Ass and Fire? Anthropic's announcements reminds me of "The Dragons Are Coming" running joke. What they have done: * Nerfed Fable, as many of noted it's useless * Leverage Mythos as a marketing strategy, claiming its too good to release * Removed thought traces, one of the only useful things to make sure your prompts are working correctly…
I am finding that I am now less interested in better models than I am in token budgets. My issue with Anthropic models now is that I don't feel like I can rely on them as a daily driver because they'll dry up before my quota resets. I am becoming dependent on AI to make a living, and I need predictable spend on it. If I know I can't use a model regularly all month, my enthusiasm is limited. I urge Anthropic to get be…
I literally only make it halfway through the week until my weekly usage runs out. This is using only Opus, no fable, and I'm on the max x20 plan. It's become ridiculous.
I use Opus every day and easily have most of my weekly limit left over at the end of the week
> For example, in testing by the investment firm Millennium, Fable 5.1 found the cause of a rare crash on their internal systems that none of their engineers (or any other model) had been able to explain after several years of trying. Say what you will about LLM-generated code, but stories like this give me hope that software will never be as buggy as it once was.
If I am reading this right, Fable 5 was worse than Opus 5 in almost every category, while consuming twice the tokens? The things you learn every day...
Since one of the big improvements here is supposedly the writing style, on that topic I'm mystified about something: Why is it that the voice models in Claude and ChatGPT have a perfectly normal style with barely any "AI smell", while the writing models are so obviously recognizable as AI? The answer is likely that models underlying the voice modes are (post) trained differently. If so, then why can't the writing mod…
I let it go a few hours on a not trivial but well-known problem, and it felt like it was just a little too plodding and just kind of mucked around a little too much and wasn't aggressive enough about getting stuff done. I asked it to wind it down and finish up and it took another hour and 15 to actually stop and commit without really getting much more done. Not very impressed here, if you can't tell. This new version…
> For example, in testing by the investment firm Millennium, Fable 5.1 found the cause of a rare crash on their internal systems that none of their engineers (or any other model) had been able to explain after several years of trying. Say what you will about LLM-generated code, but stories like this give me hope that software will never be as buggy as it once was.
Earlier quoted context omitted.
This is just cache reads. In real usage it costs 15% more than Fable 5 -- all for marginal gains. https://artificialanalysis.ai/
Cache reads dominate in modern workflows (coding CLIs and modern web clients such as ChatGPT Work and Claude Cowork (web)).
A conversation with 20 turns, 50k tok growth per turn, 1m tok context at end would price out like this:
Fable 5 ($1/M cache reads) ; cache reads 9.5M tok × $1.00 = $9.50 ; cache writes 1M tok × $12.50 = $12.50 ; output 1M tok × $50 = $50.00 ; total = $72.00
Fable 5.1 ($0.25/M cache reads) ; cache reads 9.5M tok × $0.25 = $2.38 ; cache writes 1M tok × $12.50 = $12.50 ; output 1M tok × $50 = $50.00 ; total = $64.88
So yes, cheaper, but not massively.
I'm afraid watermarking could restrict applications where LLMs can be safely used to assist with writing. If I write something myself and use an LLM to proofread it, without watermarking I can confidently say that corrections done by LLMs are small and insignificant enough to claim that the text is still authored by me, not by the model. With watermarking, however, I will never be sure if the result will not be flagg…
Watermarking will not flag something you wrote unless the AI rewrote significant chunks of it. AI watermarking works by exploiting the fact that lengthy phrases can be expressed in exponentially many ways, such that the selection of a single sequence from the exponential space is practically unique. For proofreading by contrast, if the AI is only changing isolated words in work that's otherwise yours, there are not e…