Same, tried Claud code over the past month. Let it do different tasks.

It’s useful as an built-in quick docs / search that can spit out small code fragments.

Every time I gave it more space results were disappointing.

I've been using Claude Code to decent results. Its not 10xing my productivity but it can do tasks like that. I haven't done much to configure it, I just regularly clear the context and @reference the relevant files.

What language are you using?

It greatly depends on what I am doing. If I am using common libraries especially when using JS it is particularly handy, similar with Python. When you branch out into the more obscure it's accuracy and knowledge ends up being unsurprisingly limited.

I used Cursor (with ChatGPT) for the first time last week to build two web apps and it worked well. Working results first go, then a few hours of tweaking and adding features. These were simple CRUD apps using PHP and SQLite on Apache. I had to clean up the front-end code a bit, but it all worked. I’m not strong on SQL, so it was neat to have all the database code working on the first pass, and helped me keep up the momentum and finish them, instead of getting bogged down and frustrated. Definitely plan to keep using Cursor, and try other AI tools.

Also feeling let down by it.

Have been using it to build a DSL in JS. Greenfield. I’ve followed the commonly touted “plan, act, evaluate” approach; I’ve got it to generate a clear project vision, scope, and feature checklist. Then told it to refer to that for context. I’ve been descriptive and explicit in my prompting, way more so than previously.

It has gotten the broad strokes right, I’ve got an exceptionally barebones DSL, made up of 5 entities, working…just.

It has now started to spin its wheels on small issues and can’t fix them without breaking something else. The codebase isn’t even big (~8 main functions across a few files). Troubleshooting the code is difficult because it’s convoluted and I lack the same intuition for it I would have had I written it myself. I’ve decided to rewrite everything with less control ceded to the LLM.

When it works, it feels great. When it doesn’t, which is often, the spell is broken and I feel I’ve wasted a bunch of time and have not much to show for it.

Maybe someone has better insights but what I have seen is that Claude Code is not amazing on greenfield. I mean it will generate something, which will probably work, but the solution can often be over-engineered, or hacky.

I think we have to build up enough code for it to start appearing like brownfield, before Claude knows how to engineer correctly. Which kind of makes sense if we view Claude Code as a junior engineer with infinite stamina.

I also actually like to spin up Claude Code and Gemini in parallel to see what each one comes up with. Gemini will often do the simpler approach, but not often fully featured, and my solution often ends up taking the 2 solutions and refining in Cursor to come up with the final solution.

I recently used claude code and go to build a metric receiving service for a weather station project. I had it build all the code for this. It created the SQL statements, the handlers, the deployment scripts, the systemd service files, the tests, a utility to generate api keys.

I think you are using the wrong language to be honest. LLMs are best at languages like Python, Javascript and Go. Relatively simple structures and huge amounts of reference code. Rust is a less common language which is much harder to write.

Did you give claude code tests and the ability to compile in a loop? It's pretty good in go at least at debugging and fixing issues when allowed to loop.