[_the_inflator]

I disagree. It is not the model alone. It needs a system which capitalizes on it. And this is very complex. Hardware, software, architecture - it takes a lot to get it right.

Try running the latest OS models on a normal Mac or PC. Claude Fable and Mythos are systems not just pure models.

And of course marketing. Don't believe the hype.

I think Claude is often times underwhelming. Security concerns are also a concern companies have a blond spot for. The really toughest pro security (Yes, pro! Totally different framing!) company I know is Google after all.

What I can companies advise to do is, really having more than just bug bounties but a professional hacker team that does nothing else but attacking them the whole day and night 24/7. This needs to be coordinated with the government otherwise you might sound an alarm and will be SWATed for doing good. And I would pay them huge sums since the risk and fallout warrant such a treatment, not the standard wage.

Hackers are the real deal, not AI. Proof: Hackers using AI.

[zozbot234]

> Try running the latest OS models on a normal Mac or PC.

It can be done through the magic of SSD offload. The worst case involves seconds-per-token speeds, but that's OK if you only care about low volumes of slow unattended inference, which maximizes utilization for the hardware.

(The real worst case, where you're streaming the whole model from the cheapest storage you could feasibly think of, involves multiple minutes per token for a single inference, or even hours per token batch if you're doing many inferences in bulk. That's a lot less helpful, so there's a space for smaller models at the edge, even for unattended workloads.)

[trollbridge]

An LLM which provides an OpenAI or Anthropic API-compatible interface + a coding harness like OpenCode or oh-my-pi is a pretty easy "ecosystem" to replicate. Exactly what makes you say Fable or Mythos are "systems, not just pure models"?

[everforward]

Fable can delegate tasks to Opus or Sonnet, so it has some agentic properties and I believe it does them in parallel.

The parallelism is where this starts to fall apart on a local PC. Like I can run some Qwen quants, but I can’t run a decent Qwen model while also running another model smart enough to actually implement it. I’d have to do them in series, and given how long Fable seems to take even with parallelism, I’d probably be waiting days for an answer.

[trollbridge]

oh-my-pi can delegate tasks to other models too. I usually use DS4 Flash for low priority subagent tasks.

If Fable is "delegating" tasks, then there's actually an agent front end of whatever you think the API is.

We have a local instance of Qwen-3.6 which is more than adequate for running agents. You can mix and match local and cloud-hosted models. (My biggest use case for local models right now is vision models because they're quite small and I can avoid some data-locality issues my customers wouldn't be comfortable with if I sen them to a Chinese model.)

[nerdsniper]

> I disagree. It is not the model alone. It needs a system which capitalizes on it. And this is very complex.

AFAICT … despite saying you “disagree”, you appear to be agreeing with the parent comment that the model is less important and compute (all that complex infra) and data (also complex infra) are more important.

[ramblurr]

> > The bottleneck is compute and data, not the model.

> I disagree. It is not the model alone. It needs a system which capitalizes on it. And this is very complex. Hardware, software, architecture - it takes a lot to get it right.

What do you disagree with exactly?

[christkv]

For now I suspect however that the gigantic models are not needed and you will be able to do pretty much what you need in a specific domain with 120b or lower. There is so much trash in the frontier models. I don't need all the world's slam poetry for my coding tasks for example.

[ACCount37]

Wrong, mostly.

Model capability is a function of model size. Raising the bar raises model performance in every domain.

An "idiot savant" model that's overtrained for a specific domain would beat a generalist model of the same size. But scale the generalist up enough, and it'll trounce the specialist. Removing poetry data from a model training mix doesn't give you much - it might even cost you some performance - and "idiot savant" approach of overtraining for a domain has a hard ceiling.

So far, it seems like there's some equivalent of "g factor" in LLMs - a broad "intelligence" value that performance across many diverse domains correlates with. And, as a rule, larger models have more of it.

[everforward]

While I disagree with OP about removing stuff from the model, there’s a valid question about tradeoffs between intelligence and price.

Deepseek Flash is almost certainly wrong more often than Opus or Fable. It also costs like 5% as much.

The question becomes if I run Deepseek in a loop to fix the mistakes it made that Opus/Fable didn’t, can it fix its own bugs in few enough tokens that it’s still cheaper?

So far, the answer seems to be “yes, by a significant margin”. A lot of tasks are simple enough that both Deepseek and Opus or Sonnet can one-shot it, which is a huge cost win for Deepseek. Even on the long tail, it’s usually like 4x the tokens on Deepseek which is still way cheaper than Opus.

There are things that Opus can do that Deepseek just won’t ever really nail, but it happens so infrequently that I just don’t worry. Like most people, most of what I do is the same sort of “3 tier app with a React frontend” that doesn’t take a rocket scientist to work out.

[overfeed]

> Wrong, mostly.

> Model capability is a function of model size

Model effectiveness has improved across model sizes. You really should try the latest flash variants more. They have become my default for most tasks except for gnarly high-level planning.

[ACCount37]

"Capability per parameter" is rising, but parameter count remains an advantage. And small models remain bad, because "good" is a rapidly moving target.

A 2026 4B beats 2024 4B, but both are far behind the contemporary frontier. Which makes them bad. There is no such thing as "too much capability" - a "good" model is whatever the current frontier is.

In 2024, a "good" model is one that can be trusted to write a 800 line script. In 2026, it's a model that can be trusted to do gnarly high-level planning and execution both. In 2028, it's going to be something like a model you can point at an extremely involved task, abandon, and have it report back with a "done" in 3 weeks.

[overfeed]

> A 2026 4B beats 2024 4B, but both are far behind the contemporary frontier.

The thing about engineering is you don't just use the biggest bolt on the market on every bridge.

> In 2024, a "good" model is one that can be trusted to write a 800 line script. In 2026, it's a model that can be trusted to do gnarly high-level planning and execution both

This sounds a lot like having a single diamond-head hammer as the only tool in your toolbox. As suggested by the name, flash models are fast - sometimes I want to write the equivalent of fifty 800-line scripts. There is such a thing as good enough.

[ACCount37]

Good enough? That's a lie people tell each other because they lack imagination.

"It's good enough" was said about GPT-4, o1, o3, Opus 4 and more. Guess what happened? Newer models released, people updated their expectations of what LLMs can do, usage got more aggressive, and somehow, GPT-4 went from "good enough" to "obsolete trash".

If you have no imagination, then at least substitute your pattern recognition for it.

The world is hungry for capabilities. There are piles upon piles of tasks that aren't done by LLMs simply because LLMs aren't good enough to do them.

The thing a frontier model gives you is "you don't have to babysit a model to get it to do X", and that X gets more and more impressive release to release.

[trollbridge]

Right - the idea that "bigger model = better" might have been true a year ago, but the flash models are extremely effective right now. You simply use them for the tasks they are ideally suited for.