Earl Lee

@earlvlee

thinking traces of a 33bn param model trained on @hightouchdata @costanoavc @yale @harvard

nyc

instagram.com/earlleeinsights

Replies Reposts Media

Earl Lee
@earlvlee
Jul 31
i tend to believe raw model intelligence and inference will become commoditized. frontier intelligence gets distilled and state of the art gets reverse engineered or socialized across the industry. this means that long-term, frontier labs can't make the bulk of their revenue
1
Earl Lee
@earlvlee
Jun 14
chat is actually a great ui for many ai use cases and workflows. it's lazy to say otherwise just because it was the first breakout interface to ai. makes me very bullish on linq and apple messages for business becoming an "ai app store"
2
Earl Lee
@earlvlee
Jun 14
consumer ai apps can’t subsidize inference forever. the endgame may be a shared local inference layer: install ollama or lm studio once, download models once, then use hundreds of apps on top. less like every app bundling its own model. more like steam / app store for local ai.
Earl Lee
@earlvlee
Jun 14
the more i see intelligence scale with model size and training, the more i feel the need to simply read a lot. apply the bitter lesson to yourself.
Earl Lee
@earlvlee
Feb 23
the really cool thing about ai is that there's so much whitespace for simple engineering solutions to stepwise increase performance, quality, etc e.g. instead of having models make multi-turn tool calls, ask it to write a script to make multiple tool calls at once
Jason Zhou
@jasonzhou1993
Feb 22
Replying to @jasonzhou1993
1/ Programmatic tool call Most of us have been using LLM to glue together functions via tool call Instead of forcing LLM output tool calls, just give it an environment that have access to all tools and write script to call those functions Anthropic shows achieving ~37% token
00:00