I'm a Claude user who has been burned lately by how opaque the system has become. My workflows aren't long and my projects are small in terms of file count, but the work is highly specialized. It is "out of domain" enough that I'm getting "what is the seahorse emoji" style responses for genuine requests that any human in my field could easily follow.
I've been testing Claude on small side projects to check its reliability. I work at the cutting edge of multiple academic domains, so even the moderate utiltity I have seen in this is exciting for me, but right now Claude cannot be trusted to get things right without constant oversight and frequent correction, often for just a single step.
For people like me, this is make or break. If I cannot follow the reasoning, read the intent, or catch logic disconnects early, the session just burns through my token quota. I'm stuck rejecting all changes after waiting 5 minutes for it to think, only to have to wait 5 hours to try again. Without being able to see the "why" behind the code, it isn't useful. It makes typing "claude" into my terminal an exercise in masochism rather than the productivity boost it's supposed to be.
I get that I might not be the core target demographic, but it's good PR for Anthropic if Claude is credited in the AI statements of major scientific publications. As it stands, trajectory in develeopment means I cannot in good conscience recommend Claude Code for scientific domains.
Did you ever think that this may be Anthropic's goal? It is a waste for sure but it increases their revenue. Later on the old feature you were used to may resurface at a different tier so you'd have to pay up to get it.
Most recent problems were related to topology, but it can take the wrong direction on many things. This is not an LLM fault; it's a training data issue. If historically a given direction of inquiry is favored, you can't fault an LLM for being biased toward it. However, if small volume and recent results indicate that path is a dead end, you don't want to be stuck in fruitless loops that prevent you from exploring other avenues.
The problem is if you're interdisciplinary, translating something from one field to one typically considered quite distant, you may not always be aware of historic context that is about to fuck you. Not without deeper insight into what the LLM is choosing to do or read and your ability to infer how expected the behavior you're about to see is.
