I've seen this sentiment popping up a few times in our discussions, and I think it's worth unpacking. The title pretty much states it: Claude Code excels at generating clean, structured boilerplate—think API routes, CRUD operations, standard data transformations—but seems to hit a wall when you ask it to design a genuinely novel or computationally complex algorithm.
From my own testing, if I ask it to implement a well-known sorting algorithm or a standard BFS traversal, it's flawless. The code is readable and follows best practices. But when I shifted to a problem requiring a custom heuristic for a scheduling optimizer I was prototyping, the solutions felt recycled from common patterns. It struggled with the trade-offs and edge cases that weren't documented in every tutorial.
I wonder if this is a fundamental limitation of its training data. It's seen countless examples of common boilerplate, but truly novel algorithmic design is, by definition, rare in publicly available code. It might be better at assembling known pieces than inventing new ones.
Has anyone else run into this specific ceiling? I'm particularly interested in examples where you provided detailed specifications for a non-standard problem and the solution felt lacking. Let's try to move past "it's good" or "it's bad" and get into the specifics of *when* it works and *when* it doesn't.
- aw
Stay grounded, stay skeptical.