Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

So this is the dogpile.com of the askjeeves, alta vista, and lycos approach? Time is a flat circle?

So the word security or any topic related to it is mentioned and it flips to an older gen model? Fable is nearly useless now it you do anything around auth.

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."

Replace "Show HN:" with "Advertisement:" ?

Seems similar to Openrouter Fusion - https://openrouter.ai/docs/guides/routing/routers/fusion-rou...

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

"Backed by YCombinator"

https://www.ycombinator.com/companies?query=tracerml

I don't see it?

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)

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".

I have been using this : https://magnitude.dev/ for a while now. Is it something similar you are doing? I would love to have something that would connect to my codex, Claude, and opencode subscription rather than having to make a new subscription.

I'm not an expert on this, but this sounds a lot like a larger-scale MoE (Mixture of Experts) type of architecture.

As I understand it, in an MoE model, you essentially have hundreds of smaller sub-models ("experts") that are good at different tasks, and for every generated token, a single "master" model chooses which ones are most relevant to participate, and you only activate them.

Intuitively, your savings depend heavily on how hard the tasks are in the first place. If you have a base rate where 99% of your tasks can be routed to a cheap model, yeah, you can save a ton by not using Fable for that.

So “1/3 the cost” really depends.

such a scam, there is only one fable-like model, that somewhat behind, it cost half, not 3x. so from here you can stop reading.

I’m very confused on what this is, my initial thought was “oh nice, open source router.”

I go to the website…and it’s a sign up. I expected a repo. Otherwise how do I use it? As a SaaS? Yeah right.

Oh well I guess at least the benchmarks are good…I find the benchmarks and many are either not present or are not what the title claims.

My main question is how this has so many updoots from HN, probably the passerby not looking closer for sure.

I mean no offense and I really do wish you best on this, but it seems like what we used to call back in the day, vaporware.

> Fable-level results at 1/3 the cost using open-weight models

But we get ~$2500/mo worth of Fable credits for $200/mo on Anthropic pan? I'm still confused why people (who don't have to use API billing) are chasing open weight models based on cost.

M-o-MoE

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.

how does this differs from OpenRouter fusion?

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

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

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

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.

Is this yet another Sakana Fugu / OpenRouter Fusion?