Posted by sshh12 11/2/2025
My concern with hardcoding paths inside a doc, it will likely become outdated as the codebase evolves.
One solution would be to script it and have it run pre commit to regenerate the Claude.md with the new paths.
There probably is potential for even more dev tooling that 1. Ensure reference paths are always correct, 2. Enforces standard for how references are documented in Claude.md (and lints things like length)
Perhaps using some kind of inline documentation standard like jsdoc if it’s a ts file or a naming convention if it’s an Md file
Example:
// @claude.md // For complex … usage or if you encounter a FooBarError, see ${path} for advanced troubleshooting steps
You’ll also end up dealing with merge conflicts if you haven’t carefully split the work or modularized the code.
Most of the time I'm just pasting code blocks directly into raycast and once I've fixed the bug or got the properly transformed code in the shape that I aimed for, then I paste it back into neovim. Next i'm going to try out "opencode"[0], because I've heard some good things about it. For now, I'm happy with my current workflow.
I recommend using it directly instead of via the plugin
This is how we are doing it too, for the same reasons. For now, much easier to administer than trying to figure out who spends enough to get a real Claude Code seat. The other nice thing about using API keys is that you basically never hit rate limits.
For example, if you're writing a command line tool in Python, it doesn't really matter what model you use since they're all really great at Python (LOL). However, if you're writing a complicated SPA that uses say, Vue 3 with Vite (and some fancy CSS framework) and Python w/FastAPI... You want the "smartest" model that knows about all these things at once (and regularly gets updated knowledge of the latest versions of things). For me, that means Claude Code.
I am cheap though and only pay Anthropic $20/month. This means I run out of Claude Credits every damned week (haha). To work around this problem, I used to use OpenAI's pay-per-use API with gpt5-mini with VS Code's Copilot extension, switching to GPT5-codex (medium) with the Codex extension for more complicated tasks.
Now that I've got more experience, I've figured out that GPT5-codex costs way too much (in API credits) for what you get in nearly all situations. Seriously: Why TF does it use that much "usage". Anyway...
I've tried them all with my very, very complicated collaborative editor (CRDTs), specifically to learn how to better use AI coding assistants. So here's what I do now:
* Ollama cloud for gpt-oss:120b (it's so fast!)
* Claude Code for everything else.
With Claude Code, it's like magic: You give it a really f'ing complicated thing to troubleshoot or implement and it just goes—and keeps going until it finishes or you run out of tokens! It's a, "the future is now!" experience for sure.
With gpt-oss:120b it's more like having an actual conversation, where the only time you stop typing is when you're reviewing what it did (which you have to do for all the models... Some more than others).
FYI: The worst is Gemini 2.5. I wouldn't even bother! It's such trash, I can't even fathom how Google is trying to pass it off as anything more than a toy. When it decides to actually run (as opposed to responding with, "Failed... Try again"), it'll either hallucinate things that have absolutely nothing to do with your prompt or it'll behave like some petulant middle school kid that pretend to spend a lot of time thinking about something but ultimately does nothing at all.
GPT5-codex (medium) is such a token hog for some reason
It wasn't possible before for me to do any of this at this kind of scale. Before, getting stuck on a bug could mean hours, days, or maybe even weeks of debugging. I never made the kind of progress I wanted before.
Many of the things I want, do already exist, but are often older, not as efficient or flexible as they could be, or just plain _look_ dated.
But now I can pump out react/shadcn frontends easily, generate apis, and get going relatively quickly. It's still not pure magic. I'm still hitting issues and such, but they are not these demotivating, project-ending, roadblocks anymore.
I can now move at a speed that matches the ideas I have.
I am giving up something to achieve that, by allowing AI to take control so much, but it's a trade that seems worth it.
This is basically a "thinking tax".
If you don't want to think and offload it to llm they burn through a lot of tokens to implement in a non-efficient way something you could often do in 10 lines if you though about it for a few minutes.
If you tell me I didn’t really need a LLM to be able to do all that in a week and just some thought and 10 lines of code would do, I suspect you are not really familiar with the latest developments in AI and just vastly underestimates the capabilities they have to do tricky stuff.
Thats why it took a week with llm. And for you it makes sense as this is new tech.
But if someone knows those technologies - it would still take a week with llm and like 2 days without.
Before LLMs we simply wouldn't implement many of those features since they were not exactly critical and required a lot of time, but now when the required development time is cut signifficantly, they suddenly make sense to implement.