> a pretty big context window if you are feeding it the right context.

Yup. There's some magical "right context" that will fix all the problems. What is that right context? No idea, I guess I need to read a yet-another 20 000-word post describing magical incantations that you should or shouldn't do in the context.

The "Opus 4.5 is something else/nex tier/just works" claims in my mind means that I wouldn't need to babysit its every decision, or that it would actually read relevant lines from relevant files etc. Nope. Exact same behaviors as whatever the previous model was.

Oh, and that "200k tokens context window"? It's a lie. The quality quickly degrades as soon as Claude reaches somewhere around 50% of the context window. At 80+% it's nearly indistinguishable from a model from two years ago. (BTW, same for Codex/GPT with it's "1 million token window")

> There's some magical "right context" that will fix all the problems.

All I can tell you is that in my own lived experience, I've had some fantastic results from AI, and it comes from telling it "look at this thing here, ok, i want you to chain it to that, please consider this factor, don't forget that... blah blah blah" like how I would have spelled things out to a junior developer, and then it really does stand a really solid chance of turning out what I've asked for. It helps a lot that I know what to ask for; there's no replacing that with AI yet.

So, your own situation must fall into one of these coarse buckets:

- You're doing something way too hard for AI to have a chance at yet, like real science / engineering at the frontier, not just boring software or infra development

- Your prompts aren't specific enough, you're not feeding it context, and you're expecting it to one-shot things perfectly instead of having to spend an afternoon prompting and correcting stuff

- You're not actually using and getting better at the tools, so you're just shouting criticisms from the sidelines, perhaps as sour grape because you're not allowed by policy / company can't afford to have you get into it.

IDK. I hope it's the first one and you're just doing Really Hard Things, but if you're doing normal software developer stuff and not seeing a productivity advantage, it's a fucking skill issue.

It's like working with humans:

  1) define problem
  2) split problem into small independently verifiable tasks
  3) implement tasks one by one, verify with tools

With an LLM usually 1) is just a markdown file, 2) is a markdown checklist, Github issues (which Claude can use with the `gh` cli) and every loop of 3 gets a fresh context, maybe the spec from step 1 and the relevant task information from 2

I haven't ran into context issues in a LONG time, and if I have it's usually been either intentional (it's a problem where compacting wont' hurt) or an error on my part.

I realize your experience has been frustrating. I hope you see that every generation of model and harness is converting more hold-outs. We're still a few years from hard diminishing returns assuming capital keeps flowing (and that's without any major new architectures which are likely) so you should be able to see how this is going to play out.

It's in your interest to deal with your frustration and figure out how you can leverage the new tools to stay relevant (to the degree that you want to).

Regarding the context window, Claude needs thinking turned up for long context accuracy, it's quite forgetful without thinking.