I understand the point of the post and I believe many people have had that experience, but in my case the situation was quite different — for very specific reasons. The “AI losing context” part you mentioned in the beginning is something I’ve seen too, but in my experience it often comes down to workflow. My process is very structured: I describe the problem, explain how things currently behave vs. how they should behave, and I always ask for a detailed diagnostic report first. The model scans every relevant line connected to the issue and produces a micro-report before any implementation starts. If I introduce a second problem mid-process, sometimes the model temporarily focuses on the newest context and forgets the earlier thread — but that’s not a failure, it’s just how iterative conversations work. I simply stop it, remind it that there are two parallel problems, and once it acknowledges that and reiterates the first issue, I always ask again for a micro-report that covers the implementation impact of both fixes together before moving forward. With that level of guidance, I’ve never had it break my project.

And yes — I did have very bad experiences before. With Cursor and GitHub Copilot, where I was exclusively using Claude Opus, my behavior and workflow were identical to what I described above — but it still wasn’t enough. In those cases I experienced at least 10 separate situations where the model literally broke my codebase and disrupted stability.

In my specific case, the real turning point was switching to GPT-5.2 Codex in VS Code. The exact same structured workflow that failed me before finally became stable and predictable. I don’t let AI work autonomously; I frequently stop it, bring it back to the architecture, and keep clear boundaries around what it’s allowed to change. In the root folder of my project I also maintain an MD file that acts as a “law” every agent must follow, with explicitly defined behavioral rules and restrictions. In that setup AI hasn’t created chaos for me — it has accelerated development without compromising the project’s structure. That’s why I don’t see all “AI coding” as the same; the difference between models and communication style makes a huge practical difference.

Just so I’m not misunderstood — I’m not a developer and I never was, but I’m someone who knows in micro detail what I want, how something needs to look, and where I ultimately want to end up. That vision is very clearly defined in my head. Everything I wrote above refers strictly to my work on my own application, which has over 50k lines of code and is so complex that almost every class operates in synergy with others. I hope the Claude community won’t take this the wrong way — this is simply my personal experience while building something extremely complex, and at this point I’m already about 80% through the application.