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The unbearable slowness of AI coding

joshuavaldez.com

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Re: The unbearable slowness of AI coding

#11

Post by AstroBen »

sc68cal wrote: post #3
falcor84 wrote: post #2

> ... I’ll keep pulling PRs locally, adding more git hooks to enforce code quality, and zooming through coding tasks—only to realize ChatGPT and Claude hallucinated library features and I now have to rip out Clerk and implement GitHub OAuth from scratch. I don't get this, how many git hooks do you need to identify that Claude had hallucinated a library feature? Wouldn't a single hook running your tests identify that?

They probably don't have any tests, or the tests that the LLM creates are flawed and not detecting these problems

Just tell the AI "and make sure you don't add bugs or break anything"

Works every time

Re: The unbearable slowness of AI coding

#12

Post by hodgehog11 »

AI tools seem excellent at getting through boilerplate stuff at the start of a project. But as time goes on and you have to think about what you are doing, it'll be faster to write it yourself than to convey it in natural language to an LLM. I don't see this as an issue with the tool, but just getting a better idea of what it is really good for.

Re: The unbearable slowness of AI coding

#13

Post by skeedle »

Even it's slow, you can run multiple agents. You can have one doing changes, while another writes documentation, while another does security checks, while another looks for optimizations. Persist finding to markdown files to track progress and for cross-agent knowledge sharing if need. And do whatever else while it's all running. This has been my experience.

Re: The unbearable slowness of AI coding

#14

Post by foobarbecue »

skeedle wrote: post #13

Even it's slow, you can run multiple agents. You can have one doing changes, while another writes documentation, while another does security checks, while another looks for optimizations. Persist finding to markdown files to track progress and for cross-agent knowledge sharing if need. And do whatever else while it's all running. This has been my experience.

OP says in 2nd paragraph that they are using multiple agents in parallel. In fact, that's what their app does.

Re: The unbearable slowness of AI coding

#15

Post by Nextgrid »

hodgehog11 wrote: post #12

AI tools seem excellent at getting through boilerplate stuff at the start of a project. But as time goes on and you have to think about what you are doing, it'll be faster to write it yourself than to convey it in natural language to an LLM. I don't see this as an issue with the tool, but just getting a better idea of what it is really good for.

The role of a software engineer is to condense the (often unclear) requirements, business domain knowledge, existing code (if any) and their skills/experience into a representation of the solution in a very concise language: a programming language.

Having to instead express all that (including the business-related part, since the agent has no context of that) in a verbose language (English) feels counter-productive, and is counter-productive in my experience.

I've successfully one-shotted easy self-contained, throwaway tasks ("make me a program that fills Redis with random keys and values" - Claude will one-shot that) but when it comes to working with complex existing codebases I've never seen the benefits - having to explain all the context to the agent and correcting its mistakes takes longer than just doing it myself (worse, it's unpredictable - I know roughly how long something will take, but it's impossible to tell in advance whether an agent will one-shot it successfully or require longer babysitting than just doing it manually from the beginning).

Re: The unbearable slowness of AI coding

#16

Post by [deleted] »

skeedle wrote: post #13

Even it's slow, you can run multiple agents. You can have one doing changes, while another writes documentation, while another does security checks, while another looks for optimizations. Persist finding to markdown files to track progress and for cross-agent knowledge sharing if need. And do whatever else while it's all running. This has been my experience.

[deleted]

Re: The unbearable slowness of AI coding

#17

Post by block_dagger »

My employer hosts one of the largest Ruby on Rails apps in the world. I've noticed that Claude Code takes a long time to grep for what it needs. Cursor is much better at this (probably because of local project indexing). Due to this, I favor Cursor over CC in my day to day workflows. In smaller code bases, both are pretty fast.

Re: The unbearable slowness of AI coding

#18

Post by ants_everywhere »

skeedle wrote: post #13

Even it's slow, you can run multiple agents. You can have one doing changes, while another writes documentation, while another does security checks, while another looks for optimizations. Persist finding to markdown files to track progress and for cross-agent knowledge sharing if need. And do whatever else while it's all running. This has been my experience.

if they are modifying the same code, then you have to merge all of different changes so it's not really parallel.

IME it's faster to not try to edit the same code in parallel because of the cost of merging.

Re: The unbearable slowness of AI coding

#19

Post by sarchertech »

skeedle wrote: post #13

Even it's slow, you can run multiple agents. You can have one doing changes, while another writes documentation, while another does security checks, while another looks for optimizations. Persist finding to markdown files to track progress and for cross-agent knowledge sharing if need. And do whatever else while it's all running. This has been my experience.

But then you have to keep all those tasks in your head and be ready to jump into any of them.

The check-ins are much more frequent and the instructions much lower level than what you’d give to a team if you were running it.

Do you have an example of a large application you’ve released with this methodology that has real paying users that isn’t in the AI space?

Re: The unbearable slowness of AI coding

#20

Post by doctoboggan »

When building a project from scratch using AI, it can be tempting to give in to the vibe and ignore the structure/architecture and let it evolve naturally. This is a bad idea when humans do it, and it's also a bad idea when LLM agents do it. You have to be considering architecture, dataflow, etc from the beginning, and always stay on top of it without letting it drift.

I have tried READMEs scattered through the codebase but I still have trouble keeping the agent aware of the overall architecture we built.

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