Comment by AstroBen
4 days ago
my issue hasn't been for a long time now that the code they write works or doesn't work. My issues all stem from that it works, but does the wrong thing
4 days ago
my issue hasn't been for a long time now that the code they write works or doesn't work. My issues all stem from that it works, but does the wrong thing
> My issues all stem from that it works, but does the wrong thing
It's an opportunity, not a problem. Because it means there's a gap in your specifications and then your tests.
I use Aider not Claude but I run it with Anthropic models. And what I found is that comprehensively writing up the documentation for a feature spec style before starting eliminates a huge amount of what you're referring to. It serves a triple purpose (a) you get the documentation, (b) you guide the AI and (c) it's surprising how often this helps to refine the feature itself. Sometimes I invoke the AI to help me write the spec as well, asking it to prompt for areas where clarification is needed etc.
This is how Beads works, especially with Claude Code. What I do is I tell Claude to always create a Bead when I tell it to add something, or about something that needs to be added, then I start brainstorming, and even ask it to do market research what are top apps doing for x, y or z. Then ask it to update the bead (I call them tasks) and then finally when its got enough detail, I tell it, do all of these in parallel.
Beads is amazing. It’s such a simple concept but elevates agentic coding to another levels
If it does the wrong thing you tell it what the right thing is and have it try again.
With the latest models if you're clear enough with your requirements you'll usually find it does the right thing on the first try.
There are several rubs with that operating protocol extending beyond the "you're holding it wrong" claim.
1) There exists a threshold, only identifiable in retrospect, past which it would have been faster to locate or write the code yourself than to navigate the LLM's correction loop or otherwise ensure one-shot success.
2) The intuition and motivations of LLMs derive from a latent space that the LLM cannot actually access. I cannot get a reliable answer on why the LLM chose the approaches it did; it can only retroactively confabulate. Unlike human developers who can recall off-hand, or at least review associated tickets and meeting notes to jog their memory. The LLM prompter always documenting sufficiently to bridge this LLM provenance gap hits rub #1.
3) Gradually building prompt dependency where one's ability to take over from the LLM declines and one can no longer answer questions or develop at the same velocity themselves.
4) My development costs increasingly being determined by the AI labs and hardware vendors they partner with. Particularly when the former will need to increase prices dramatically over the coming years to break even with even 2025 economics.
The value I'm getting from this stuff is so large that I'll take those risks, personally.
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I've said this multiple times:
This is why you use this AI bubble (it IS a bubble) to use the VC-funded AI models for dirt cheap prices and CREATE tools for yourself.
Need a very specific linter? AI can do it. Need a complex Roslyn analyser? AI. Any kind of scripting or automation that you run on your own machine. AI.
None of that will go away or suddenly stop working when the bubble bursts.
Within just the last 6 months I've built so many little utilities to speed up my work (and personal life) it's completely bonkers. Most went from "hmm, might be cool to..." to a good-enough script/program in an evening while doing chores.
Even better, start getting the feel for local models. Current gen home hardware is getting good enough and the local models smart enough so you can, with the correct tooling, use them for suprisingly many things.
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> 1) There exists a threshold, only identifiable in retrospect, past which it would have been faster to locate or write the code yourself than to navigate the LLM's correction loop or otherwise ensure one-shot success.
I can run multiple agents at once, across multiple code bases (or the same codebase but multiple different branches), doing the same or different things. You absolutely can't keep up with that. Maybe the one singular task you were working on, sure, but the fact that I can work on multiple different things without the same cognitive load will blow you out of the water.
> 2) The intuition and motivations of LLMs derive from a latent space that the LLM cannot actually access. I cannot get a reliable answer on why the LLM chose the approaches it did; it can only retroactively confabulate. Unlike human developers who can recall off-hand, or at least review associated tickets and meeting notes to jog their memory. The LLM prompter always documenting sufficiently to bridge this LLM provenance gap hits rub #1.
Tell the LLM to document in comments why it did things. Human developers often leave and then people with no knowledge of their codebase or their "whys" are even around to give details. Devs are notoriously terrible about documentation.
> 3) Gradually building prompt dependency where one's ability to take over from the LLM declines and one can no longer answer questions or develop at the same velocity themselves.
You can't develop at the same velocity, so drop that assumption now. There's all kinds of lower abstractions that you build on top of that you probably can't explain currently.
> 4) My development costs increasingly being determined by the AI labs and hardware vendors they partner with. Particularly when the former will need to increase prices dramatically over the coming years to break even with even 2025 economics.
You aren't keeping up with the actual economics. This shit is technically profitable, the unprofitable part is the ongoing battle between LLM providers to have the best model. They know software in the past has often been winner takes all so they're all trying to win.
> With the latest models if you're clear enough with your requirements you'll usually find it does the right thing on the first try
That's great that this is your experience, but it's not a lot of people's. There are projects where it's just not going to know what to do.
I'm working in a web framework that is a Frankenstein-ing of Laravel and October CMS. It's so easy for the agent to get confused because, even when I tell it this is a different framework, it sees things that look like Laravel or October CMS and suggests solutions that are only for those frameworks. So there's constant made up methods and getting stuck in loops.
The documentation is terrible, you just have to read the code. Which, despite what people say, Cursor is terrible at, because embeddings are not a real way to read a codebase.
I'm working mostly in a web framework that's used by me and almost nobody else (the weird little ASGI wrapper buried in Datasette) and I find the coding agents pick it up pretty fast.
One trick I use that might work for you as well:
Try that with your own custom framework and it might unblock things.
If your framework is missing documentation tell Claude Code to write itself some documentation based on what it learns from reading the code!
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In a circuitous way, you can rather successfully have one agent write a specification and another one execute the code changes. Claude code has a planning mode that lets you work with the model to create a robust specification that can then be executed, asking the sort of leading questions for which it already seems to know it could make an incorrect assumption. I say 'agent' but I'm really just talking about separate model contexts, nothing fancy.
Cursor's planning functionality is very similar and I have found that I can even use "cheap" models like their Composer-1 and get great results in the planning phase, and then turn on Sonnet or Opus to actually produce the plan. 90% of the stuff I need to argue about is during the planning phase, so I save a ton of tokens and rework just making a really good spec.
It turns out that Waterfall was always the correct method, it's just really slow ;)
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Even better, have it write code to describe the right thing then run its code against that, taking yourself out of that loop.
And if you've told it too many times to fix it, tell it someone has a gun to your head, for some reason it almost always gets it right this very next time.
If you're a developer at the dawn of the AI revolution, there is absolutely a gun to your head.
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I think it's worth understanding why. Because that's not everyone's experience and there's a chance you could make a change such that you find it extremely useful.
There's a lesser chance that you're working on a code base that Claude Code just isn't capable of helping with.
Correct it then, and next time craft a more explicit plan.
The more explicit/detailed your plan, the more context it uses up, the less accurate and generally functional it is. Don't get me wrong, it's amazing, but on a complex problem with large enough context it will consistently shit the bed.
The human still has to manage complexity. A properly modularized and maintainable code base is much easier for the LLM to operate on — but the LLM has difficulty keeping the code base in that state without strong guidance.
Putting “Make minimal changes” in my standard prompt helped a lot with the tendency of basically all agents to make too many changes at once. With that addition it became possible to direct the LLM to make something similar to the logical progression of commits I would have made anyway, but now don’t have to work as hard at crafting.
Most of the hype merchants avoid the topic of maintainability because they’re playing to non-technical management skeptical of the importance of engineering fundamentals. But everything I’ve experienced so far working with LLMs screams that the fundamentals are more important than ever.
It usually works well for me. With very big tasks I break the plan into multiple MD files with the relevant context included and work through in individual sessions, updating remaining plans appropriately at the end of each one (usually there will be decision changes or additions during iteration).
It takes a lot of plan to use up the context and most of the time the agent doesn't need the whole plan, they just need what's relevant to the current task.