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Point 2 is 1000% not true, the models have both gotten better at the overall act of coding, but have also gotten WAY better at USING tools. This isn't tool orch
by XenophileJKO 1y ago
Point 2 is 1000% not true, the models have both gotten better at the overall act of coding, but have also gotten WAY better at USING tools. This isn't tool orchestration frameworks, this is knowing how and when to use tools effectively and it is largely inside the model. I would also say this is a fundamental model capability.
This improved think->act->sense loop that they now form, exponentially increases the possible utility of the models. We are just starting to see this with gpt-5 and the 4+ series of Claude models.
- emp17344 1y agoYes, the models have gotten better at using tools because tech companies have poured an insane amount of money into improving tools and integrating them with LLMs. Is this because the models have actually improved, or because the tools and integration methods have improved? I don’t think anyone actually knows.
- XenophileJKO 1y agoThe models have improved. They are using "arbitrary tools" better.
- emp17344 1y agoI don’t know what you mean, because arbitrary tools don’t integrate with LLMs in the first place. Are you referring to MCP?
- remich 1y agoBut, isn't improving tools and the LLM's integration with them improving the model? Caveat that we don't fully understand how human intelligence works, but with humans it's generally true that skills are not static or siloed. Improving in one area can generate dividends in others. It's like how some professional football players improve their games by taking ballet lessons. Two very different skills, but the incorporation of one improves the other as well as the whole. I would argue that narrowly focusing on LLM performance via benchmarks before tool use is incorporated is interesting, but not particularly relevant to whether they are transformative, or even useful, as products.