I really like how you put it down! This is how I'd have imaged to be the Apple car, not a Ferrari. The "irony" of people defending this monstrosity are saying thing like "this is the type of comments of someone who cannot afford this", but they are completely missing the point. If it doesn't making people dream about it, then it's not worth of being a real Ferrari product.
> Don't focus on what you prefer: it does not matter. Focus on what tool the LLM requires to do its work in the best way.
I noticed that LLMs will tend to work by default with CLIs even if there's a connected MCP, likely because a) there's an overexposure of CLIs in training data b) because they are better composable and inspectable by design so a better choice in their tool selection.
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I think they are exposing how fragile and vulnerable in reality they are, and I wonder when it will happen that a group of highly motivated individuals will organize to create a truly community driven distilled models.
I think they are exposing how fragile and vulnerable in reality they are, and I wonder when it will happen that a group of highly motivated individuals will organize to create a truly community driven distilled models.
> We find testing and evals to be the hardest problem here. This is not entirely surprising, but the agentic nature makes it even harder. Unlike prompts, you cannot just do the evals in some external system because there’s too much you need to feed into it. This means you want to do evals based on observability data or instrumenting your actual test runs. So far none of the solutions we have tried have convinced us that they found the right approach here.
I'm curious about the solutions the op has tried so far here.
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FloydHub is a YC start-up building AI infrastructure and tools. We have a popular platform with a highly satisfied and growing user base.
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