Posted by martinald 1 day ago
It doesn't need to pass whole conversation history as context (unlike /model), you can ask follow up to that forked model (which sub agents in claude doesn't support AFAIK), and you can ask models from opencode while using claude.
That’s like a gas station saying they have 90% margin over pumps but still losing money.
is there a market saturation point for intelligence? how about for software? it seems like the more you have the more you want because you're trying to do more things.
as the models get smarter I get busier because I'm doing more things...
Then you have things like CRUD apps, where a model needs to write some SQL, make a service endpoint, serialize some JSON, etc. Here a local model might have a bit more trouble juggling all the pieces, but any hosted model will do just fine. If your day to day job involves working on CRUD apps, then it's basically a solved problem now.
The cases where frontier models matter are when you're solving genuinely complex problems, but that's not what most people are doing day to day. So, paying an order of magnitude for a model that has capabilities to solve problems outside the range of problems you actually work on becomes a waste of money.
There's going to be a market for these models from people who really do work on complex things on regular basis, but the question is how big that market is. Additionally, open models keep getting better, and GLM 6 or DeepSeek v5 could end up being another big jump in capability where they fully close the gap with Fable. At that point, even more of the market becomes covered by these models leaving truly complex cases on the frontier.
Another thing to consider is that most big problems can be broken down into smaller ones. That's the basis for how programming languages are structured. We have primitives which are arranged into functions, that get bundled into classes or namespaces, and so on. So, you don't need an infinitely capable model to solve big problems. You just need to be able to break large problems into smaller ones, and a model that's smart enough to decompose a problem to the point where it becomes tractable.
I had GLM 5.2 do the same, and it performed exceptionally better, but when it got stuck on something it would be trial and error mode going forward and have zero foresight for future issues that might occur due to fixes it was trying. the model severally lacks prompt understanding, and testing .
I’ve had good results with Tavily so far, might be worth checking as an alternative for agent search.
Somehow no one talks about LLM speed.
Partnership you mean?, Cerebras went public and are trading at around 45B in market cap.
While OAI could in theory cough up that kind of money, it would massively hamper their existing committed capital outlays.
maybe i should get some cerebras stock then, ty for the pointer
When I've raised speeds about local inference I've been told 60-75 t/s is perfectly usable. It makes sense that people aren't talking about speed yet since you either already have a response fast enough to wait for, or you go do something else and check back in a few minutes.
I would love to wait for the latter type of tasks though, because those are typically the ones that require the most work from me to verify and I don't want my attention divided with multitasking.
As the token bills start to come in, those economics will be harder to ignore (regardless of the origin of the LLM); especially as there will be many CIOs sweating over their quick and costly AI initiatives showing little ROI.
My hope is that the EU also steps up their own competition in the frontier model space so that it’s not just China v USA.
I don't feel like I'm missing out after cancelling my personal Claude subscription, whereas I used to feel that way a few months ago.
Sure, "it's just frontend", but that's actual use enough for me to take it seriously.