3 ms·

On the other hand, AI is good at following invariants and adversarially policing the system to pay back debt. The latter is too expensive for humans as a projec

On the other hand, AI is good at following invariants and adversarially policing the system to pay back debt. The latter is too expensive for humans as a project grows. Right now you can open Claude Code in your project and prompt it "start a workflow that fans out subagents, each with a narrow scope, to look for {simplifications, correctness by construction opportunities, <insert your goal>} ranked by impact vs confidence, and then collate the findings into result.md". If you have the tokens, do more workflows that find more issues and/or vet and polish the findings already in the file. I do that every time my weekly usage is about the reset to spend me last tokens. Maintenance is becoming trivial, and anyone who thinks AI dooms you to an ever growing mudball apparently hasn't considered using AI for anything other than appending more code to the mudball.

maccard 2mo ago
Are they? Because my experience is they’re really, really bad at following them and as context grows they get worse at following them.
- LtWorf 2mo ago
  I think probably something like 90% of developers are web developers doing some kind of web app that uses some remote API provided by someone else.
mikojan 2mo ago
This works great as long as you are not doing what the parent comment cautions you to not do: Let agents decide the architecture. The moment you do that, why even bother producing a result.md? Just let the Bot execute on its findings, you realistically won't be able to judge them anyway.
- hombre_fatal 2mo ago
  I've built many things this year letting sota models design the architecture, and they've been well-reasoned about it. I think what you describe is a lack of harness/infra/process around working with agents on a project. No effort to document ADRs. No effort to document invariants. No effort to design processes that ensure maintainable software even if it were five years ago and only humans were writing the code. Just because you delegate important decisions to AI doesn't obsolete practices as trivial as maintaining a log of potential issues in your codebase that you'd want to work through, keep track of, and verify.
mirsadm 2mo ago
And they're really good at adding more code to reduce complexity. In particular Claude is absolutely bonkers at the moment. I am having trouble taking this seriously especially because you mentioned Claude. Have you looked at anything it's done?
- hombre_fatal 2mo ago
  I use Claude model=fable effort=high to write most of my initial plans, especially the hard things like re-architecture. I recommend putting time into figuring out the "soft infrastructure" of your projects, usually things you've never been arsed to do in your life (like me until this year). Start with a docs/*.md folder that you link to from AGENTS.md where you can document things that would be important for you, any human, and especially AI to know. Start with a super basic ADR system where you document invariants over time, maybe a single DESIGN.md file that just lists them. Have a references/ directory that shallow clone all of the important deps you use for local agent reference (and tell the agents about them in AGENTS.md). Keep your plan files to higher level things like invariants, decisions, accepted risks, rejected ideas, that way you review the hard stuff, and sota models these days write crazy-good code. Look at using AI as a skill that you have to learn and develop, not like how we see CSS where it's the technology's fault if we suck at it for life while putting zero effort into developing the skillset. Or maybe there's some load bearing "make impossible states unrepresentable" platitude in my AGENTS.md I'm overlooking that's responsible for why I have such a great experience with AI and nobody else on HN apparently is.
  - munksbeer 2mo ago
    I have a good experience too. I do the same as you. I actively want to succeed with it, and therefore put time into making it work, and I have a good experience. As do the other 80+ developers at my company (it is a fintech). Pretty much every commit is written by claude currently, but I have to steer it constantly, and keep tweaking my setup. I think the difference is probably that some people just don't really like the idea, therefore put in the minimum of effort, see that results are sub-optimal (which they definitely can be), and declare it useless.
  - 2mo ago

maccard 2mo ago

Are they? Because my experience is they’re really, really bad at following them and as context grows they get worse at following them.

LtWorf 2mo ago
I think probably something like 90% of developers are web developers doing some kind of web app that uses some remote API provided by someone else.

mikojan 2mo ago

This works great as long as you are not doing what the parent comment cautions you to not do: Let agents decide the architecture. The moment you do that, why even bother producing a result.md? Just let the Bot execute on its findings, you realistically won't be able to judge them anyway.

hombre_fatal 2mo ago
I've built many things this year letting sota models design the architecture, and they've been well-reasoned about it. I think what you describe is a lack of harness/infra/process around working with agents on a project. No effort to document ADRs. No effort to document invariants. No effort to design processes that ensure maintainable software even if it were five years ago and only humans were writing the code. Just because you delegate important decisions to AI doesn't obsolete practices as trivial as maintaining a log of potential issues in your codebase that you'd want to work through, keep track of, and verify.

mirsadm 2mo ago

And they're really good at adding more code to reduce complexity. In particular Claude is absolutely bonkers at the moment. I am having trouble taking this seriously especially because you mentioned Claude. Have you looked at anything it's done?

hombre_fatal 2mo ago
I use Claude model=fable effort=high to write most of my initial plans, especially the hard things like re-architecture. I recommend putting time into figuring out the "soft infrastructure" of your projects, usually things you've never been arsed to do in your life (like me until this year). Start with a docs/*.md folder that you link to from AGENTS.md where you can document things that would be important for you, any human, and especially AI to know. Start with a super basic ADR system where you document invariants over time, maybe a single DESIGN.md file that just lists them. Have a references/ directory that shallow clone all of the important deps you use for local agent reference (and tell the agents about them in AGENTS.md). Keep your plan files to higher level things like invariants, decisions, accepted risks, rejected ideas, that way you review the hard stuff, and sota models these days write crazy-good code. Look at using AI as a skill that you have to learn and develop, not like how we see CSS where it's the technology's fault if we suck at it for life while putting zero effort into developing the skillset. Or maybe there's some load bearing "make impossible states unrepresentable" platitude in my AGENTS.md I'm overlooking that's responsible for why I have such a great experience with AI and nobody else on HN apparently is.
- munksbeer 2mo ago
  I have a good experience too. I do the same as you. I actively want to succeed with it, and therefore put time into making it work, and I have a good experience. As do the other 80+ developers at my company (it is a fintech). Pretty much every commit is written by claude currently, but I have to steer it constantly, and keep tweaking my setup. I think the difference is probably that some people just don't really like the idea, therefore put in the minimum of effort, see that results are sub-optimal (which they definitely can be), and declare it useless.
- 2mo ago