YHK - yet another hacker news clone

> No benchmarks, no info on which models are used, […]

The benchmarks are here → https://echo.tracerml.ai/eval/

They are not good benchmarks but at least they exist.

It's easy enough to copy and paste in a prompt, no?

Eval tests while giving general indicators might not be similar for each use case.

The evaluator is public here: https://echo.tracerml.ai/eval/

It currently exposes 907 stored rows across seven benchmark families, with prompts, outputs, grades, and cost records. More benchmarks are coming soon.

Echo does not disclose its per-request routing decision because that policy is the product. We can, however, publish some of the eligible open-weight model pool, version dates, aggregate allocation mix, and evaluation settings without exposing the request-level recipe.

New video is also being made.

It's basically trying to replicate OpenRouter, which works pretty well and has a lot of nice features to abstract away any single provider, such as failover, metering, autoswitching, etc. It's actually a really smart infrastructure abstraction.

I just wish this were solving an actual problem rather than being a fairly transparent attempt to say something approximating, "Hey VCs, OpenRouter just became a unicorn but I can basically vibe code it"

Calling it "Fable-level" feels intellectually lazy / dishonest, but then again, what do you expect when there's so much money on the table.

Unrelated - but reminds me of my favorite quote by tenderlove:

"microservices turn function calls into distributed computing problems"

You are right that the privacy wording was too broad. We are fixing it now so it states explicitly that Echo does not use customer prompts, files, chats, or outputs to train or fine-tune models. We are updating the matching Terms language at the same time.

Echo also starts users with free credit and does not require a credit card to try it. The current signup flow did not make that clear enough, so we are fixing that presentation too.

Thanks for calling both out.

I assume trying without a credit card will bankrupt him immediately.

If you want to try a similar idea that you can chat with immediately (free tier uses open weight models), I'm working on this: http://pellmell.ai

You might be interested in a project that I'm working on, which is kind of like OpenRouter Fusion, but instead of waiting for all models to synthesize, we stream the best model immediately and background the rest. The background models then reconcile with an emoji reaction and optional reply. It gets similar results to Fusion and is a lot faster! There's a free version that leverages open weight models here: http://pellmell.ai.

how does this differs from Sakana Fugu?

The entirety of "agents" and tool calls is a process of combining LLMs to get better results. Is it the same LLM in many cases? Yes. But it doesn't have to be.

It's the natural move that happened after people realized you couldn't throw away half a century of AI research.

Most of these focus on costs. But it is simply the case that the one-shot output did not scale for harder problems on workflows.

Looks like it but with open-weights only.

In MoE systems the routing decision is made per-token, not per prompt or task. It’s one of ML’s many confusing naming conventions.

Even more confusingly, there are older pre-LLM MoE systems which ensemble and pool the predictions from multiple sub-components. For example in a random forest you could take the majority vote of the decision trees or the average of their numerical predictions.

After that, we developed neural net architectures for predicting a single thing like whether the user will click on your ad. An MMoE is in the same family.

And so now we are at massive MoE networks for LLMs which have similarities with MMoE in that the “decision” is about the very next token to predict.

Because that is a short term solution, it won’t be offered forever. Large organisations have to purchase credits at $/tokens. Eventually everyone else will too.

doesnt exist for enterprise plans

You may want to consider the incomes of developers outside the US, students, unemployed. $200/month is a lot to a lot of people.

Enterprise plans pay API costs, they're only subsidizing individual accounts and that's only because they have to compete with open-source models.

When they are successful at making those illegal/inaccessible, both Anthropic and ChatGPT are going to rip the band-aid off and inference will only be sold to those that can afford it.

The good ole American way.

haven't done yet the public yc launch, currently in s26