I think the key problem is that usage of MCP servers is not 'baked' into the LLM training - but API's and CLI's are already a part of training. So to use your MCP server, the LLM has to use additional intelligence which could have been used to do the actual work instead.
I think the only relevant part to note here is that this model showed improved text-only performance after multimodal training. Wonder if this translates to Llama models also ? Is it possible to extend Llama 3.1 405b with multi-modal training to create another SOTA large model ?
I think the answer here is "it depends." The Llama-3.2 series is an extended version of the Llama-3.1 series with multimodal (image) training, but they kept the language model weights frozen and only updated the new image weights. So in the end, the 3.2 series benchmarks identically to 3.1 on text-only tasks; the image weights provided no value to the language model weights.
Allowing the language model weights to be updated during training could potentially result in better performance on both tasks, though, if Nvidia's result replicates. I could believe that it might: after all, more diverse data is more diverse data, and the model will be forced during training to generalize more.
Can you explain how is this different from the other offerings. I believe there are quite a few RAG solutions out there, but its not all clear from your description how you guys stand out.
AI should assist, not replace, skilled coding. It's crucial to blend AI efficiency with human craftsmanship for clean, sustainable code. From my consulting experience, excessive AI reliance results in bloated code that’s a nightmare to maintain. This echoes the problem Java shops faced with class and object overuse, but now it’s amplified.
reply