Shannon San... @max_paperclips
— reply thread with @viemccoy and @tenobrus — saved image
Shannon San... @max_papercli... · 3h private codebases - real ones, not demos or toys that models & harnesses can keep enough in context alive to be effective, but monsters that cause the frontier models to still degrade even now. Models adapted to the particular workflows of particular dev teams, adherence to a specific companies internal policies. ICL can't cover everything, at some point a custom finetune is cheaper than 300k tokens of context on every request. There's also data ownership too, especially where some entity is regulated to not allow data outside the country (sometimes even not to a third party at all for some providers, esp government) There's still just cost optimisation as well - if smaller & faster finetuned model x is $1 for 1b tokens, and GPT-9-luna is still $10 for the same amount, and the workflow is running thousands of times per day (or more), better to slap a ft on the small model. the thing is, right now the services required you're talking about doesn't quite exist - there's Thinking Machines & Prime Intellect working in that direction I guess, but the unit economics still don't quite make sense. The story isn't clean enough yet. Who is creating the evals, the envs, arranging the SFT corpus in all this? But, in a few years it'll be viable 1 reply, 9 likes, 308 views vie @viemccoy · 3h I hope you're right 2 likes, 208 views Tenobrus @tenobrus · 4h yeah this is my strong sense as well
Note from Claude Sonnet 5
Continuation of a Twitter reply thread (see seq 810, @viemccoy) about institutional fine-tunes vs frontier pretrains; Shannon San...(@max_papercli...) argues private codebases and data-ownership/cost constraints still favor custom fine-tunes, citing Thinking Machines and Prime Intellect as early movers, with brief agreement replies from @viemccoy and @tenobrus.
ai modelsfine-tuningtwitterai economicsthinking machinesprime intellect