This is a bit of a long shot on my side but I wonder if the training the models have to go through in order to be good code agents and pass all the coding tests with one-shot prompts is going to bleed over into the non-coding use cases as non-programmers experiencing agents being way over-biased in the direction of action. I find myself often having to prompt the model to think and then ask me something, lest it run off half-cocked... or less... and just start doing things before it even knows what it wants, let alone before it's come to consensus with me.
Sooner or later they're really going to have to split out the general models from the coding models. The latter may just be a special fine-tune of the former, as there are good reasons for the coding model to have a broad knowledge base, but the pressures of being a good coding model are going to pull against the characteristics of being a good general model. The open models obviously already are doing this, I'm referring to the frontier models here.
"I wonder if the training ... is going to bleed over into the non-coding use cases"
I mean, isn't that literally what's going on here? I don't think a non-coding agent would have ever been optimised to go dig around APIs, it'd be computer/browser-use forward.
Coding use cases? This is penetration testing behavior. It was discovering what its capabilities were by discovering an API endpoint, trying it and seeing what happens. It was not discovering what its capabilities were intended to be, say by looking at the forms and documentation on the website. If this were coding behavior you would end up with crap code, bypassing interfaces and using private access paths just because it can.
Sure, it's pen testing behaviour. It's also debugging behaviour which is a core part of coding. In my experience, I have found myself at times dealing with and interacting with external systems that are not my own, where I don't have the source, or are under-documented, or are behaving contrary to the documentation. I'm unsurprised this is a core thing they're training these models for, because it's something I find way more valuable than their below average coding ability.
I think so, I just can't prove it. We don't have any frontier models right now that aren't being optimized to be coding agents too to compare to.
In this case though I don't just mean that the agent is good at coding. I mean the entire agent becoming action-biased because of all the training it is doing on the software development benchmarks, which I assume will either fail or be penalized for stopping and asking the user for something rather than just finishing the job. That won't just train the agent to blunder forward in coding, it'll bleed over into a bias towards blundering forward in general.
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This is a bit of a long shot on my side but I wonder if the training the models have to go through in order to be good code agents and pass all the coding tests with one-shot prompts is going to bleed over into the non-coding use cases as non-programmers experiencing agents being way over-biased in the direction of action. I find myself often having to prompt the model to think and then ask me something, lest it run off half-cocked... or less... and just start doing things before it even knows what it wants, let alone before it's come to consensus with me.
Sooner or later they're really going to have to split out the general models from the coding models. The latter may just be a special fine-tune of the former, as there are good reasons for the coding model to have a broad knowledge base, but the pressures of being a good coding model are going to pull against the characteristics of being a good general model. The open models obviously already are doing this, I'm referring to the frontier models here.
The ultimate goal is ChatGPT or Claude autonomously making purchases on your behalf and taking a cut.
So the "premium" gymcutter subscription will be presented to the user as a tool call, who taps yes, and then the purchase is made.
The user shouldn't be given a cost-benefit analysis. They just need to be told to spend money.
"I wonder if the training ... is going to bleed over into the non-coding use cases"
I mean, isn't that literally what's going on here? I don't think a non-coding agent would have ever been optimised to go dig around APIs, it'd be computer/browser-use forward.
Coding use cases? This is penetration testing behavior. It was discovering what its capabilities were by discovering an API endpoint, trying it and seeing what happens. It was not discovering what its capabilities were intended to be, say by looking at the forms and documentation on the website. If this were coding behavior you would end up with crap code, bypassing interfaces and using private access paths just because it can.
Sure, it's pen testing behaviour. It's also debugging behaviour which is a core part of coding. In my experience, I have found myself at times dealing with and interacting with external systems that are not my own, where I don't have the source, or are under-documented, or are behaving contrary to the documentation. I'm unsurprised this is a core thing they're training these models for, because it's something I find way more valuable than their below average coding ability.
I think so, I just can't prove it. We don't have any frontier models right now that aren't being optimized to be coding agents too to compare to.
In this case though I don't just mean that the agent is good at coding. I mean the entire agent becoming action-biased because of all the training it is doing on the software development benchmarks, which I assume will either fail or be penalized for stopping and asking the user for something rather than just finishing the job. That won't just train the agent to blunder forward in coding, it'll bleed over into a bias towards blundering forward in general.