7 ms·
I see a lot of discourse about it being fast-to-deprecation. But I see it a different way personally. Modern LLMs are trying to do more with less. Focus on doi
by dabbz 1mo ago
I see a lot of discourse about it being fast-to-deprecation. But I see it a different way personally.
Modern LLMs are trying to do more with less. Focus on doing the right thing the first time. Even if we squeeze dumb LLMs, the significantly faster speed means quicker iterations. So a bad decision doesn't cost the time and inference costs that it cost before. It theoretically changes the scale of errant token spend.
I compare it to the 1 thousand monkeys on a typewriter. In this case it's 1,000 monkeys with stale training data of everything ever written and the ability to search the web.
- vel0city 1mo agoI agree with this take in a lot of ways. If you slash the token cost and increase speed for each token 1,000x, who cares if it takes even 20x as many tokens to achieve the goal? And also, there are lots of tasks where models today are fine with doing. If you think of these things like appliances, who cares if it's not quite as powerful as the next generation? It was purchased to do a task, it still does that task very well. It feels like being in the 90s and asking "why buy a server today when they're going to be faster next year? Just keep renting mainframe time." Well maybe I just need a box to run our HR and payroll system, and this box manages to run it fine today.
- mafuy 1mo agoSounds to me like you would hire 20 barely-paid interns instead of 2 competent programmers.
- dabbz 1mo agoWeird conclusion to draw from my statement. Feels intentionally hostile of an assessment about me, but I digress.
- mafuy 24d agoI perhaps went overboard, Sorry.
- ruined 1mo agoif it fits, it ships
- surrealist 1mo agoI would hire malleable interns fit for replication, and teach them all the things they would need to know. But for some reason I was forbidden to do that, so I'm forced to settle for cheap LLMs instead.