I hadn't considered that. I'm using it almost exclusively to validate logic, kind of like a fuzzer in nature ("What if we need to do this with this logic/someone tries to do that/what am I missing/etc"), or to fill in specifications ("what feature would compliment this/what could be trimmed to achieve MVP more easily/does this spec appear to be missing anything according to this set of requirements"), which requires a lot of review, and using more expensive models like Opus doesn't appear to provide meaningfully better results. After prompting it, I typically have a lot to think about and the terminal goes quiet, or I prompt it on a related matter that will similarly require my eyes and brain for long enough that I won't be able to limit out.

I can see how work involving larger contexts and deeper consideration would lead to exhausting limits a lot faster though, even if you aren't using it like a slot machine.

"find a list of social events in my area"

Isn't this something you can do with a simple Google search? Or Perplexity?

No need to shove by far the most expensive LLM (Claude Opus 4) at it.

When you say not even in the running, is that including Deep Research on o3-pro?

Switching to Opus is an eye-opening experience. You hit limits often, and need to get creative to avoid burning through limits, but the difference is seriously impressive. You'll waste a lot less time with dead ends and bad code.

I think this is it. They use it like a slot machine, and when something isn't quite what they wanted, they provide broad instructions to refine and do better. Progress is slow and questionable, but anticipation and (sometimes) reward is increased.

The issue could be, in part, that a lot of users don't care to be efficient with token usage and maintaining condensed, efficient, focused contexts to work with.

Me too. I've also found that even when trying to restrict models meant for these tasks, they tend to go on tangents and waste tremendous amounts of tokens without providing meaningfully better outputs. I'm not yet sold on these models for anything outside of fuzzy tasks like "does this logic seem sound?". They tend to be good at that (though they often want to elaborate excessively or propose solutions excessively).

I use this approach because I like to work on features or logical components in isolation and then bring them together. I still can't limit out most of the time because I need to actually look at the outputs and think about what I'm building. At the moment I have 3 directories in my work tree. Sometimes I prompt in more than one at a time, especially at interfacing code, but that could mean 30–90 minutes of reviewing and implementing things in each directory. Over a work day I apparently send an average of ~40 messages according to `claude --resume`

I respectfully consider this analogy void, but welcome an explanation of why I'm wrong.

I haven't yet seen anyone doing anything remarkable with their extensive use of Claude. Without frequent human intervention, all of it looks like rapid regression to the mean, or worse.