No benchmarks, no info on which models are used, ai generated video, just a signup page with nothing else.

Anyhow, this kinda reminds me of that quote about architecture: "We replaced our monolith with micro services so that every outage could be more like a murder mystery."

I think approaches like this have potential. Only time will tell. This reminds me of the mixture of experts taken by deepseek r2 (I think it was r2, at least), but less specific models I guess.

I have often wondered how tools like GHCP choose the best model for the job when set to "auto".

The HumanEval+ tests, the sole code benchmark, consist of thirty-one (31) programming questions designed to create a function that solves a straightforward algorithm problem. I find none of these particularly challenging for a model of Fable caliber, so I’m confused by the necessity of the comparison.

Also, when clicking on the “Inspect” button in each of the 31 code tests, there’s a section that supposedly displays the answer, but it’s always empty so the only comparison one can make is whether the code appears identical or at least similar to the one generated by Claude Fable 5.

And one more, there’s a test where Fable supposedly gave the wrong answer while the @OP’s system was correct. However, when I input the same prompt to my instance of Claude Fable 5, as well as all the Mythos variants I have access to (5 in total), all of them return the correct code, which suggests that the @OP may not have run the prompts with enough iterations.

I don’t find the recent spate of blog posts and systems delegating and combining LLMs to get better performance particularly interesting. Especially given that anyone who’s taken an ML 101 course has learned about ensemble methods.

While an LLM isn’t what you’d traditionally consider a weak learner, the theorems on learning systems clearly point to them being so in this context. The feigned surprise at combining them to yield better results seems disingenuous.

Even so, the work to predict which models are best suited for which task, how to delegate, and how to combine their outputs is interesting, especially if you’re placing a cost minimization objective on it. That said, this isn’t too far off from what many AI labs are already doing.

This reminds me on OpenRouters report that combining multiple different models gave comparable performance to Fable 5. I think this approach has lots of potential. Maybe OpenAi was ahead of its time with GPT-5 (it being a router to different models rather than just being one new model)

No single signin. Privacy policy allows training. No try it first without credit card. It's a good idea, but this looks premature.

Might want to rethink the name to avoid an Amazon issue.

Fable-level, yea, but can it run gstack?

how does this differs from OpenRouter fusion?

That’s basically the same idea IBM advertises with Bob?

If you copy Perplexity, they let you have the first few rounds of chat for free to get you going before asking to sign up.

[dead]