Or are the subagents generating your training data using a closed/paid model?

For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.

The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.

Think of it as distillation, but focused on a specific task.

I ask cause would this be a kind of model distillation?

I have a small model I'm looking to train on some data, and I have some real live data but I'd love to be able to extend it.

edit: others have asked any you have said "soon (tm)"

we dont know what the result is and how its impressive.

https://huggingface.co/vwdubb/Qwen3.8-27B-Fable-Distill-NVFP...

side quest, are fable distillations only wrong when it's another country?

Aside on the aside, I welcome this new era of really personal software. Not Ai's being sycophants, rather being able to easily and quickly change, adapt, or extend software I am not familiar with.

This is the part where the narrator looks at the camera and says "Don't try this at home, kids!"

[Search: Can I refund Google cloud?]

It looks like we’re not able to ask for a refund since we did actually use all of that compute intentionally.

Would you like me to write you a pleading email to send to the support team?

Are you confusing this with an OAuth token or something?

https://docs.cloud.google.com/billing/docs/how-to/budgets-sp...

I had to intervene a few times. For instance, as smart as the models are said to be (Astra), it would copy the full training run, train on the server, pull every checkpoint to the local machine, then run tests, update. So, the bandwidth bill was as high as training bill for the first 6 hours. It could have simply tested each checkpoint on the server, saved time and money, didn't occur to it until I said.

I wouldn't put my house on it. Brave.

Edit: will do as soon as possible

—"Benchmarks!"

...I'll tell that they can be gamed so easily, and they are on a consistent basis.

Ember isn't picked yet. In planning, Opus 5.5 wins under the planning weights. In code, GPT-6 Sol dominates it: also 10/10, but with a higher quality score and a lower estimated cost. Ember has no intelligence index, so its starting score is only 0.73, which holds its 10/10 down to 0.954 against Sol's 0.975.

[1] https://philippdubach.com/posts/jev-model-router-for-pi/

6 or 5.6? Because 6 is hot garbage

I suspect the advantage that catapulted Linux ahead of the establishment was less technical potential and talent and more organizational advantage. That's not to diminish the technical talent of the Linux crew, but them being unencumbered gave them more degrees of freedom. The rest is history.

So as long as the AI companies don't succumb to "big company" dynamics, they can outlead. To wit: Open AI and Anthropic are kicking Google's ass.

in one month, Google actually went cash-negative. [0] even still, they are subsidizing their stuff a lot less, have the most opaque and variable limits, and increase adoption through bundling and shuffling features. I can't even share my Google One storage without subscribing to a Google AI plan anymore, but previously any plan except Google One Lite was shareable.

if you tell me that's not enough to go after frontier, then how much money are Anthropic and OpenAI burning?

[0]: https://www.techspot.com/news/113214-google-records-first-ne...

Indeed. And when you have freedom to play, you are able to find new stepping stones that you didn't anticipate. And you can combine stepping stones in new ways to make new discoveries.

Greatness cannot be planned.

The lock-in is less pronounced as it is with AWS or MS.

https://en.wikipedia.org/wiki/Exapunks?wprov=sfti1

I can also see the argument for providing a post-training service from a customer acquisition perspective: "hey, we can fine-tune this open weights model, so it both gives better/more predictable results than OpenAI/Anthropic and also is cheaper. And btw, once we've won your business, please run this model on our infra."

But what I'm struggling to understand is fireworks spending a bunch of money (on salaries and compute) releasing a frontier model that is going to rapidly fall behind the frontier. Is this "just" advertising for them, both for customers and also for hiring? Or are they actually trying to stay on the frontier? If so, to what end?

I'd expect that their business strategy is to compete in more markets, and if successful, they can capture more value. This is the "easiest" for them as they already have GPUs, a training environment etc. For that platform it's not the worst if there's an internal customer team that can help shape the future and provide immediate feedback, and if it results in a good model, even better.

Other things I'd not be surprised they offer in the future in the same vein: A multi-model harness, coding agent (cloud and local), and maybe at a later point in time even a CPU-only cloud compute product.

It's unclear if they can do this systematically and it's unclear if they can do it better than others. But, lots of things are unclear in AI at the moment, this doesn't seem outrageous on the surface. And, it could just be marketing. And it could be the first option with the backup of the second.

This is partly the appeal of Jev et al; having a quick model for simple tasks, that doesn’t require that much thinking

It’s amazing all the workflows that models like that can unlock. And yes, classifiers and other ML models have been around for a while for these types of tasks, but Jev has made it easy and cheap to play and experiment. This in turn, is incentivizing people to try them for a bunch of stuff, unlocking creativity and producing a lot of new cool (and eventually potentially very useful) applications

1. Evals (once you have your rubric defined and tuned using a reasoning model, jev can be great for running periodic evals especially those that run daily.

2. e-commerce catalog classification 3. quick search using anything as context and query mapping to a pre-defined set.

For example, a typical/stock LLM can’t really play Doom in real time, but a Jev-like model can. Just because of latency

Of course, if you want the best Doom player, there are way better and faster adhoc models

Analysis paralysis stifles not just human intelligence, but other intelligences too.

The more options you have, the harder it becomes to be satisfied with the one you picked.

> task and environment feedback

> on-policy planning and learning

> feedback connects decisions to their consequences

These are deliberately the least informative phrases you could possibly use to describe what you have done, while still being in the realm of words that go over a generic investor who has no idea whats going on and may be dazzled by sciencey sounding language.

Cursor compose 2.5 article where they used and described on policy self distilation was actual alpha.

Not suggesting this is right or wrong, but is sort of the nature of the technology.

The words of a license are what the license is.

The pareto frontier needs clearer distinction. Benchmarks miss half the story. What, if any, capability is lost by the token reduction (for example, was it like super awesome at Golang before and now kind of sucks? that kind of distinction).

Kimi K3 itself isn't FOSS. Speaking of reciprocity: Fireworks is presumably paying Moonshot serious money for the right to do what they are doing here, since Kimi's license[0] excludes commercial inference providers (such as Fireworks) from gratis use. It requires them to: "...enter into a separate agreement with Moonshot AI before using the Software or its derivative works..."

[0] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE#...

I see that with Opus 5, it started thinking like crazy in the last few days , I don't think my workflow is that complicated, still it gets into thinking mode and stays there

It's pretty important to understand if your own work domain is one where the last 5% matters. In a lot of day-to-day software engineering tasks, it doesn't, and one can get crazy mileage out of the cheaper models. OTOH, if you are performing novel research, that last 5% may be worth whatever it costs...

On the other hand, if it's cheap to tell whether you got a good solution, and you think the 90 and 95% apply to your task blend, then it's almost always worth trying the cheap model first.

> creative

Choose one.

"Pareto": 8 hits

"Opus 5.5": zero hits

That's just vibes, though.

Also, did I miss a memo? Suddenly every article on AI seems to be talking about the Pareto frontier - or have I just not been paying attention?

Kimi K3 with less reasoning tokens isn't exactly exciting either, and particularly so if the license is less open than original Kimi K3.