Probably a first world problem, but with Opus 5.5's efficiency, the limits on the 5x plan are simply sufficient for my everyday work, even when running 2-3 sessions at a time. So I wonder when I would use Sonnet 5.5.
More concurrency than that isn't really practical for me if I want to retain some semblance of understanding. Perhaps it's different for purely web app or frontend tasks, where the outcome is more relevant than the process, I don't have much experience there (and also don't want to belittle these domains, I might be underestimating their complexity).
So surprisingly, my own work is at least for the time being almost saturated by the model capabilities. I am not sure how I'd scale from here. Sure I could run all requests at max effort to burn tokens for the sake of it, but that can't be it. And for many tasks, I am not really able to define so clear cut success criteria or self-verification loops that I could benefit from letting an agent (or a fleet thereof) autonomously run for a day.
So I realize it's a skill issue on my side, but I can't be the only one. I wonder if there is a limit to token demand, at least short term. Feels like either they accelerate to AGI and RSI, where the AI can find uses for token, or things might plateau at some point.
Note I don't think this because I'm an AGI skeptic or think there's a ceiling to intelligence, but there might simply be a valley of economic hardship for the companies where the supply of tokens outpaces the demand, due to a lack of ideas of what to do with them. And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
I find that "vibe coders" (that is, people who do not know anything about programming, but nevertheless produce useful tools for themselves and others) are using a lot more tokens than we do as programmers.
I think this is partially because we're still attached to pre-LLM notions of architecture, good design and code quality (which are still important, but maybe less important than they once were and that we think they are), partially because their projects are in a messy state, so models have to work around the technical dept.
They're essentially trading off programmer time for LLM time (which is a good trade financially speaking).
It's because they don't know data structures.
"Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious." - Fred Brooks, The Mythical Man-Month (1975).
and essentially the same sentiment, three decades later:
"Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds, git mailing list, 2006.
These things have not changed even though everything else is topsy-turvy. As-of current writing, I have yet to see an LLM make good data structure choices; they go for something that is superficially plausible but profoundly ill-considered (or rather, not considered at all), and then commonly burn tokens treating this implementation detail as a design invariant and trying to deal with the consequences by writing more code, instead of iterating directly upon the ill-fitting data at the root its problems.
If you're wondering, "does he mean the schema of let's say a db or other persistent store, or does he mean abstract/algebraic structures", the answer is yes to both, I think coding models are today shockingly weak when it comes to design reasoning in both domains.
Fortunately, their suggestibility means the same models will readily accept direction on the matter (perhaps even more so than on the structure of code), so I recommend doing just that, and (bonus!) this means your CS degree is still relevant.
Sol- · · focus · HN ↗
More concurrency than that isn't really practical for me if I want to retain some semblance of understanding. Perhaps it's different for purely web app or frontend tasks, where the outcome is more relevant than the process, I don't have much experience there (and also don't want to belittle these domains, I might be underestimating their complexity).
So surprisingly, my own work is at least for the time being almost saturated by the model capabilities. I am not sure how I'd scale from here. Sure I could run all requests at max effort to burn tokens for the sake of it, but that can't be it. And for many tasks, I am not really able to define so clear cut success criteria or self-verification loops that I could benefit from letting an agent (or a fleet thereof) autonomously run for a day.
So I realize it's a skill issue on my side, but I can't be the only one. I wonder if there is a limit to token demand, at least short term. Feels like either they accelerate to AGI and RSI, where the AI can find uses for token, or things might plateau at some point.
Note I don't think this because I'm an AGI skeptic or think there's a ceiling to intelligence, but there might simply be a valley of economic hardship for the companies where the supply of tokens outpaces the demand, due to a lack of ideas of what to do with them. And this might slow down the funding enough that they never reach escape velocity with the training run scaling. But we'll see.
miki123211 · · focus · HN ↗
I think this is partially because we're still attached to pre-LLM notions of architecture, good design and code quality (which are still important, but maybe less important than they once were and that we think they are), partially because their projects are in a messy state, so models have to work around the technical dept.
They're essentially trading off programmer time for LLM time (which is a good trade financially speaking).
inopinatus · · focus · HN ↗
"Show me your flowcharts and conceal your tables, and I shall continue to be mystified. Show me your tables, and I won’t usually need your flowcharts; they’ll be obvious." - Fred Brooks, The Mythical Man-Month (1975).
and essentially the same sentiment, three decades later:
"Bad programmers worry about the code. Good programmers worry about data structures and their relationships." - Linus Torvalds, git mailing list, 2006.
These things have not changed even though everything else is topsy-turvy. As-of current writing, I have yet to see an LLM make good data structure choices; they go for something that is superficially plausible but profoundly ill-considered (or rather, not considered at all), and then commonly burn tokens treating this implementation detail as a design invariant and trying to deal with the consequences by writing more code, instead of iterating directly upon the ill-fitting data at the root its problems.
If you're wondering, "does he mean the schema of let's say a db or other persistent store, or does he mean abstract/algebraic structures", the answer is yes to both, I think coding models are today shockingly weak when it comes to design reasoning in both domains.
Fortunately, their suggestibility means the same models will readily accept direction on the matter (perhaps even more so than on the structure of code), so I recommend doing just that, and (bonus!) this means your CS degree is still relevant.
avmich · · focus · HN ↗
[deleted] · · focus · HN ↗
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