Comment by p1necone

> I was more prepared and asked it to improve class by class, and for whatever reasons I got better answers

There is a learning curve with all of the LLM tools. It's basically required for everyone to go through the trough of disillusionment when you realize that the vibecoding magic isn't quite real in the way the influencers talk about it.

You still have to be involved in the process, steer it in the right direction, and review the output. Rejecting a lot of output and re-prompting is normal. From reading comments I think it's common for new users to expect perfection and reject the tools when it's not vibecoding the app for them autonomously. To be fair, that's what the hype influencers promised, but it's not real.

If you use it as an extension of yourself that can type and search faster, while also acknowledging that mistakes are common and you need to be on top of it, there is some interesting value for some tasks.

Interesting. From my experience, Claude is much better at stuff involving frontend design somehow compared to other models (GPT is pretty bad). Gemini is also good but often the "thinking" mode just adds stuff to my code that I did not ask it to add or modifies stuff to make it "better". It likes to 1 up on the objective a lot which is not great when you're just looking for it to do what you precisely asked it and nothing else.

My problem with Gemini is how token hungry it is. It does a good job but it ends up being more expensive than any other model because it's so yappy. It sits there and argues with itself and outputs the whole movie.

Breaking down requirements, functionality and changes into smaller chunks is going to give you better results with most of the tools. If it can complete smaller tasks in the context window, the quality will likely hold up. My go to has been to develop task documents with multiple pieces of functionality and sub tasks. Build one piece of functionality at a time. Commit, clear context and start the next piece of functionality. If something goes off the rails, back up to the commit, fix and rebase future changes or abandon and branch.

That’s if I want quality. If I just want to prototype and don’t care, I’ll let it go. See what I like, don’t like and start over as detailed above.

I have never considered trying to apply Claude/Gemini/etc. to Fortran or COBOL. That would be interesting.

Use Codex for coding work

Supply some relevant files of your codebase in the ClaudeAI project area in the right part of the browser. Usually it will understand your architecture, patterns, principles

You realize on some level all of these sort of anecdotes, though, are simply random coincidence .

By my tests (https://github.com/7mind/jopa) Gemini 3 is somewhat better than Claude with Opus 4.5. Both obliterate Codex with 5.1

How can you stand the excruciating slowness? Claude Code is running circles around codex. The most mundane tasks make it think for a minute before doing anything.

NME = "not my experience" I presume.

JFC TLA OD...

[dead]

Library/API conflicts are the biggest pain point for me usually. Especially breaking changes. RLlib (currently 2.41.0) and Gymnasium (currently 0.29.0+) have ended in circles many times for me because they tend to be out of sync (for multi-agent environments). My go to test now is a simple hello world type card game like war, competitive multi-agent with rllib and gymnasium (pettingzoo tends to cause even more issues).

Claude Sonnet 4.5 was able to figure out a way to resolve it eventually (around 7 fixes) and I let it create an rllib.md with all the fixes and pitfalls and am curious if feeding this file to the next experiment will lead to a one-shot. GPT-5 struggled more but haven't tried Codex on this yet so it's not exactly fair.

All done with Copilot in agent mode, just prompting, no specs or anything.

Try to ask it to write some GLSL shaders. Just describe what you want to see and then try to run the shaders it outputs. It can output a UV-map or the simple gradient right, but when it comes to shaders a bit more complex it most of the time will not compile or run properly, sometimes mix GLSL versions, sometimes just straight make up things which don't work or output what you want.

I posted this example before but academic papers on algorithms often have pseudo code but no actual code.

I thought it would be handy to use AI to make the code from the paper so a few months ago I tried to use Claude (not GPT, because I only have access to Claude) to recreate C++ code to implement the algorithms in this paper as practice for me in LLM use and it didn’t go well.

https://users.cs.duke.edu/~reif/paper/chen/graph/graph.pdf

Completely failed for me running the code it changed in a docker container i keep running. Claude did it flawlessly. It absolutely rocks at code reviews but ir‘s terrible in comparison generating code

Could you clarify what you mean by design questions? I do agree that GPT5 tends to have a better agentic dispatch style for math questions but I've found it has really struggled with data model design.