41–50 of 134 posts
Maybe, but a specific example showing how to do it would have been a more compelling argument.
It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from higher priced tokens or they’ll implode.
The big lab models are academically interesting but business wise they seem toast long term. There’s no way for these model companies to compete when the models are becoming a pure commodity and others offering options that are orders of magnitude cheaper. It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from high…
There is so much opportunity for purpose built models like this. Ideally a harness should spin up a subagent to offload to targeted models for specific tasks like this. I know this is not a novel idea. Claude code does some of this by handing off the "explore" agent work to haiku. I just love seeing that specialized LLMs are being developed.
I feel like the future is people building applications with tightly integrated LLMs that work hand in hand with the application's own lifecycle and code. I also didn't realize that people were using agentic harnesses for search, it's an interesting idea. If the context length is short enough it should be fairly cheap compared to running "normal" agentic coding workloads where you have O(100k) context length for doing…
One thing that plagues [insert current FAANG] is the large amount of corpus knowledge that is outdated/misleading or just plain wrong. I'm curious how this addresses that if it's deriving the reward function from the corpus itself.
a few ideas i have yet to validate are: - prioritize recently updated docs when generating the training questions (assumption those docs are more correct than others) - actually including contradicting documents that talks about the exact same topic might be a good training example - ideally the model should surface all the relevant info it can find, and explain what it has found. (usually contradiction comes from the fact that the later document is the updated stance) - you could also mine high quality Q&A from public slack / communication channels where questions were asked and someone else in the team linked some docs / answer. those are strongly validated "ground truth" answers
The big lab models are academically interesting but business wise they seem toast long term. There’s no way for these model companies to compete when the models are becoming a pure commodity and others offering options that are orders of magnitude cheaper. It’s not that the big labs couldn’t theoretically just also put out 100x cheaper options but their business model requires them to generate huge revenues from high…
castform founder here. despite our bet on fine-tuned smaller open-source models, i'm still quite bullish on the big labs. i think scaled closed models will continue to dominate for more general purpose use-case like codegen, search, etc. but intelligence has lots of long-tail applications and i think for these longer-tail applications, finetuned custom models will rule
https://www.linkedin.com/posts/introducing-ontology-1-ugcPos...
Edit: more direct links, sorry:
There is so much opportunity for purpose built models like this. Ideally a harness should spin up a subagent to offload to targeted models for specific tasks like this. I know this is not a novel idea. Claude code does some of this by handing off the "explore" agent work to haiku. I just love seeing that specialized LLMs are being developed.
> Claude code does some of this by handing off the "explore" agent work to haiku. That is not handing off to a specialized model, its just handing off to a lighter and interior model (compared to the parent model). That by itself can create issues like the lighter model not capturing all the data that the parent needs. The idea is that we get specialized models that are better then general purpose models. But its rar…
https://shopify.engineering/sidekicks-continual-learning-loo... https://thinkingmachines.ai/news/learning-to-replicate-exper... https://castform.com/blog/elsa/
at the end of the day, models are only as good as the data they're trained on. and if one has access to proprietary data, it should yield specialized models that do better than larger general models
There is so much opportunity for purpose built models like this. Ideally a harness should spin up a subagent to offload to targeted models for specific tasks like this. I know this is not a novel idea. Claude code does some of this by handing off the "explore" agent work to haiku. I just love seeing that specialized LLMs are being developed.
> There is so much opportunity for purpose built models like this. OpenAI etc could themselves do this, and maybe they already do? Where the public-facing interface delegates to multiple little goblins behinds the scenes
Earlier quoted context omitted.
castform founder here. despite our bet on fine-tuned smaller open-source models, i'm still quite bullish on the big labs. i think scaled closed models will continue to dominate for more general purpose use-case like codegen, search, etc. but intelligence has lots of long-tail applications and i think for these longer-tail applications, finetuned custom models will rule
Product search model: https://www.linkedin.com/posts/introducing-ontology-1-ugcPos... Edit: more direct links, sorry: https://onton.com/research/ontology-1 https://onton.com/research/ontology-1-benchmarks