In the BERT era of language models, it was normalized that to get the best performance for a task, you probably needed targeted post-training
As models got bigger and instruction following got better, everyone jumped on the general capabilities of the model + prompting
We're approaching wall that needs to be overcome with a completely new and unheard of breakthrough, otherwise we're going to have to go back to specialized post-training (which lends itself to vertical solutions)
I think people are seeing that now with stuff like Devstral being posttrained specifically for OpenHands and massively over-performing for its size at agentic coding
Anthropic doesn’t have the universal name recognition of ChatGPT, so they’re going for an underdog strategy of building a portfolio of strong niches. Seems smart, sounds higher-margin.
The 30/50/100gb of random numbers that is a trained LLM is basically worthless - if it has any value at all on day 1, that value depreciates at multiple percentage points per day.
Anthropic more than OpenAi are going for the integrations, verticals and MCP - I think that is the right play. "OpenAi Inside" can replace the "Intel Inside" sticker but their marketcap needs to go 1/100x
Random numbers ?? Please stop showing your ignorance here because you have some weird bias against a technology. The utter contempt and dismissiveness of folks on this site is astounding.
As models got bigger and instruction following got better, everyone jumped on the general capabilities of the model + prompting
We're approaching wall that needs to be overcome with a completely new and unheard of breakthrough, otherwise we're going to have to go back to specialized post-training (which lends itself to vertical solutions)
I think people are seeing that now with stuff like Devstral being posttrained specifically for OpenHands and massively over-performing for its size at agentic coding