Anyone paying attention has known that demand for all type of compute than can run LLMs (i.e. GPUs, TPUs, hell even CPUs) was about to blow up, and will remain extremely large for years to come. It's just HN that's full of "I hate AI" or wrong contrarian types who refuse to acknowledge this. They will fail to reap what they didn't sow and will starve in this brave new world.
This reads like a weird cult-ish revenge fantasy.
And what about you? Show your "I used AI today" badge, right now!
I don't know what Gas Town is, but Claude Code Agent Teams is what I was doing for a while now. You use your main conversation only to spawn sub agents to plan and execute, allowing you to work for a long time without losing context or compacting, because all token-heavy work is done by sub agents in their own context. Claude Code Agent Teams just streamlines this workflow as far as I can tell.
With stuff like this, might be that all the infra build-out is insufficient. Inference demand will go up like crazy.
Anyone paying attention has known that demand for all type of compute than can run LLMs (i.e. GPUs, TPUs, hell even CPUs) was about to blow up, and will remain extremely large for years to come. It's just HN that's full of "I hate AI" or wrong contrarian types who refuse to acknowledge this. They will fail to reap what they didn't sow and will starve in this brave new world.
Oh yeah I mean if you're a webdev and you haven't built several data centres already you're basically asking to be homeless.
I absolutely cannot trust Claude code to independently work on large tasks. Maybe other people work on software that's not significantly complex, but for me to maintain code quality I need to guide more of the design process. Teams of agents just sounds like adding a lot more review and refactoring that can just be avoided by going slower and thinking carefully about the problem.
You definitely have to create some sort of PLAN.md and PROGRESS.md via a command and an implement command that delegates work. That is the only way that I can get bigger things done no matter how „good“ their task feature is. You run out of context so quickly and if you don’t have some kind of persistent guidance things go south
I tried doing that and it didn't work. It still adds "fallbacks" that just hide errors or the fact that there is no actual implementation and "In a real app, we would do X, just return null for now"
I absolutely cannot trust Claude code to independently work on large tasks. Maybe other people work on software that's not significantly complex, but for me to maintain code quality I need to guide more of the design process. Teams of agents just sounds like adding a lot more review and refactoring that can just be avoided by going slower and thinking carefully about the problem.
You definitely have to create some sort of PLAN.md and PROGRESS.md via a command and an implement command that delegates work. That is the only way that I can get bigger things done no matter how „good“ their task feature is. You run out of context so quickly and if you don’t have some kind of persistent guidance things go south
It's not sufficient, especially if I am not learning about the problem by being part of the implementation process. The models are still very weak reasoners, writing code faster doesn't accelerate my understanding of the code the model wrote. Even with clear specs I am constantly fighting with it duplicating methods, writing ineffective tests, or implementing unnecessarily complex solutions. AI just isn't a better engineer than me, and that makes it a weak development partner.
I absolutely cannot trust Claude code to independently work on large tasks. Maybe other people work on software that's not significantly complex, but for me to maintain code quality I need to guide more of the design process. Teams of agents just sounds like adding a lot more review and refactoring that can just be avoided by going slower and thinking carefully about the problem.
There is research[0] currently being done on how to divide tasks and combine the answers to LLMs. This approach allows LLMs reach outcomes (solving a problem that requires 1 million steps) which would be impossible otherwise.
I'm not anti-whimsy, but if your project goes too hard on the whimsy (and weird AI-generated animal art), it's kind of inevitable that someone else is going to create a whimsy-free clone, and their version will win because it's significantly less embarrassing to explain to normal people.