These posts are tiresome. Show us something. Go on.
The idea of AI being able to "code" is that it is able to do all this planning and architectural work. It cant. But its sold as though it is. Thats where the bubble is
For that, the model needs to learn all this architecture and structure itself from the huge repositories of human knowledge like the internet
Until then, reality will be below expectations, and the bubble will head towards popping
Not only the agents can complete trivial tasks on their own, leaving us just with reviewing (and often just focusing on the harnessing), but the new setup is very good for onboarding technical and non-technical staff: you can ask any question about both the product or its architecture or decisions.
Everything's documented/harnessed/E2Ed, etc.
Doing all of this work has much improved the codebase in general, proper tests, documentation and design documents do make a difference per se, and it further compounds with LLMs.
Which is my point in any case: if you start a new project just by prompting trivialities it will go off rail and create soups. But if you work on an established and well scaffolded project, the chances of going off rails and creating soups is very small.
And thus my conclusion: just fork existing projects that already do many of the things you need (plenty of them from compilers to native applications to anything really), focus on the scaffolding and understanding the project, then start iterating by adding features, examples and keeping the hygiene high.
We have successfully put Claude in huge multi-thousands pr long with projects.
But this meant that:
1. Solid architectural and design decisions were made already after much trial and error
2. They were further refined and refactored
3. Countless hours have been spent in documenting, writing proper skills and architectural and best practice documents
Only then Claude started paying off, and even then it's an iterative process where you need to understand why it tries to hack his way out, etc, what to check, what to supervise.
Seriously if you think you can just Claude create some project..
Just fork an existing one that does some larger % of what you need and spend most of the initial time scaffolding it to be ai friendly.
Also, you need to invest in harnessing, giving tools and ways to the LLM to not go off rails.
Strongly typed languages, plenty of compilation and diagnostics tools, access to debuggers or browser mcps, etc.
It's not impossible, but you need to approach it with an experimentation approach, not drinking Kool aid.