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).
If you have well-specified tasks, you can easily reach 10 simultaneous agents working on disjoint parts of the code in worktrees. That consumes tokens pretty quickly!
I created an orchestration skill for myself (using herdr but any persistent mechanism works). So I then only interact with a front session and it will triage and dispatch each request to the relevant spaces (each of them can have multiple worktrees of the same project), summarize movements and pending decisions for me all at once. I do not directly interact with a multiplexer or any dashboard.
This seems complex and expensive, and I suppose the only reason to do this is because you want to generate code faster? Do you really have so much code to write that a single LLM is too slow?
I don't do this at my day job (coworkers would be pretty mad). This is for software ideas that comes up weekly that I need to execute to at least MVP before I ever need branching/merging.
Not complex at all, only one extra session other than the ones doing work and it's on a dumb model and can be thrown away & restarted because it only dispatches work, not doing anything.
I do everything in there, collecting requirements, kick off research, branching, merging, not one other agent on top. I considered making that orchestration command llm-powered but it's not justified at my current use.
It's not more expensive, in fact I could have just chugged along with the slow and manual session by session work but I have a claude subscription and another GLM one (the most low cost basic tier, not even much), that just sit there collecting dust if I don't put them to use in a more efficient way.
And doing session by session would face your problem when context switching too much become unscalable.
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).
bensyverson · · focus · HN ↗
thunky · · focus · HN ↗
nsonha · · focus · HN ↗
thunky · · focus · HN ↗
nsonha · · focus · HN ↗
Not complex at all, only one extra session other than the ones doing work and it's on a dumb model and can be thrown away & restarted because it only dispatches work, not doing anything.
I do everything in there, collecting requirements, kick off research, branching, merging, not one other agent on top. I considered making that orchestration command llm-powered but it's not justified at my current use.
It's not more expensive, in fact I could have just chugged along with the slow and manual session by session work but I have a claude subscription and another GLM one (the most low cost basic tier, not even much), that just sit there collecting dust if I don't put them to use in a more efficient way.
And doing session by session would face your problem when context switching too much become unscalable.