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.
There's lots more you can do! Use the model to monitor your deployments after they get deployed. Have them fix and watch CI issues for you. Run adverserial review. Automatically watch metrics every day and highlight performance regressions. Start reviewing your previous sessions to find ways to statically reject different failure modes and have the agent have more success earlier on etc.
Another thing to think about is, what would it take for you to care less about the understanding. Better integration / e2e tests? Performance validation? visualizing program and data flows? Better refactoring of your modules?
An LLM can produce far more code than a human can understand. And the famous rule that "optimizations are entirely pointless unless you're optimizing at the constraint" is logistics 101.
To accelerate software development, you either need to remove or lessen the need for code understanding, or make it much quicker for humans to gain that understanding. Making the LLM faster won't help you if the LLM isn't the bottleneck.
I want to make better software, not more software. Making software development faster isn't necessarily the goal. Making it better in the many, many ways that matter (of which speed is just one part) is.
There is more software to be written than there were programmers so lots of people do indeed want more software, for example small tools and one off projects that aren't worthwhile to make pre LLM.
Many people want less software to deal with now, not more. Being forced to download and update apps on your phone that previously could be done without an app, for example.
A lot of people would find it easier to pull out a few quarters and put it in the parking meter than download and update an app and give it your financial information. Or hand over a paper ticket to get into an event that’s easy to transfer instead of yet another app with a barcode.
Probably because most of that software is garbage then. While there are some people who may do that, most find the convenience of not carrying around loose change as a benefit.
I'm looking forward to the making of more software. I think there are probably people who have had really useful software ideas for a long time that they'd never be able to raise money for, but now for $20 a month, they can get up and running, serving their local and/or niche communities, without having to hire a team of engineers.
Eventually we're going to reach a point where they don't have to understand the code themselves. The democratization of software creation is going to be fascinating.
The Android/iOS app store is already flooded with low quality apps. Almost 20,000 games have been released on Steam so far in 2026, that averages to about 70 games per day, every single day. Getting any kind of traction in such a environment is hopeless; every new app might as well be a scream into the void.
I think a lot of people don't want or care about major traction. They want to make little things that make their own little corners of the world better.
There are small towns all over the world which could realistically have their own little "hometown app" now, that really does track all the interesting things going on there. People don't need "The (Unofficial) Smallville Happenings" Facebook pages anymore. These don't all have some programmer who cares enough about building such a thing to make it happen, but they probably do have a teenager or even a retiree who would have a lot of fun making something that really works well for the people of that town and how they want to use it. The barrier to entry just became low enough to get over.
And that's just one example. There are SO MANY problems that used to require mega investment to solve at scale to get any traction at all. Now communities can build them for themselves. And they'll never be the kind of data target that a megacorp is, because you'd have to target each little app individually, hoping there was something useful in there.
The democratization of software development is going to have lots of things that go nowhere, and lots of little hobby projects, and a few things people will actually hear about and care about. But a lot of people's lives will become incrementally better and I'm excited to see that.
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.
maherbeg · · focus · HN ↗
Another thing to think about is, what would it take for you to care less about the understanding. Better integration / e2e tests? Performance validation? visualizing program and data flows? Better refactoring of your modules?
Hauthorn · · focus · HN ↗
Could you explain why it would be a goal to understand the system less, rather than more?
It seems harder to know if you have good tests while lowering your expertise in the system.
miki123211 · · focus · HN ↗
An LLM can produce far more code than a human can understand. And the famous rule that "optimizations are entirely pointless unless you're optimizing at the constraint" is logistics 101.
To accelerate software development, you either need to remove or lessen the need for code understanding, or make it much quicker for humans to gain that understanding. Making the LLM faster won't help you if the LLM isn't the bottleneck.
gr_norm · · focus · HN ↗
satvikpendem · · focus · HN ↗
jimbokun · · focus · HN ↗
satvikpendem · · focus · HN ↗
jimbokun · · focus · HN ↗
Etc.
satvikpendem · · focus · HN ↗
smeej · · focus · HN ↗
Eventually we're going to reach a point where they don't have to understand the code themselves. The democratization of software creation is going to be fascinating.
ThrowawayR2 · · focus · HN ↗
smeej · · focus · HN ↗
There are small towns all over the world which could realistically have their own little "hometown app" now, that really does track all the interesting things going on there. People don't need "The (Unofficial) Smallville Happenings" Facebook pages anymore. These don't all have some programmer who cares enough about building such a thing to make it happen, but they probably do have a teenager or even a retiree who would have a lot of fun making something that really works well for the people of that town and how they want to use it. The barrier to entry just became low enough to get over.
And that's just one example. There are SO MANY problems that used to require mega investment to solve at scale to get any traction at all. Now communities can build them for themselves. And they'll never be the kind of data target that a megacorp is, because you'd have to target each little app individually, hoping there was something useful in there.
The democratization of software development is going to have lots of things that go nowhere, and lots of little hobby projects, and a few things people will actually hear about and care about. But a lot of people's lives will become incrementally better and I'm excited to see that.