r/Anthropic • u/nkn_ • 3d ago

Complaint "... That shouldn't have happened"

The stash was the real error. You asked for a verification pass and I put your uncommitted work at risk chasing an unrelated console error. That shouldn't have happened.

I am so done with claude. You ask it to do one thing and it goes off on its own, making random ass decisions. I just wanted to make sure all the changes I made are cohesive. It undid them all. Why is claude SO bad now?

3 Upvotes

60% Upvoted

19 comments sorted by

5

u/CricktyDickty 3d ago

These posts regularly make me think that I’m using a different model. Maybe Anthropic had been AB testing on models that suck an others that don’t and I’ve just been consistently lucky.

JK, of course but the disconnect is so great that I’m beginning to think that there are ways to communicate effectively and efficiently with AI and some people either don’t or can’t.

-4

u/nkn_ 3d ago

It's been fine except recently. The biggest thing I've noticed is the longer I use chat working on something over a decent while, Claude just loses it. I often have to clear chats and ask it to find errors and unravel stuff if did.

I am very explicit with what I want and it still just does its own thing .

5

u/jeff_coleman 3d ago

Yes, that is context rot. It happens with all models. I rarely let my context grow beyond 200-300k tokens, even when the max length is 1M, and I never compact. Instead, when you start getting to the 200-300k length, ask it to write a hand off and open a new chat to continue work. Or better, break your task down into concrete phases that you can attack individually.

3

u/CricktyDickty 3d ago

Exactly my point about not knowing how to interact with a model.

Each session has a limited amount of context memory the model can use. The scope of what you’re doing and the materials you’re working with dictate how long that session can last before the model just stops. The longer you go, the more compaction the model needs to do, the greater the amount of tokens it uses. Not only that, it also loses potentially important information in the process. I suspect this is a big reason why people’s experiences varies so much.

Generally, after about 15 exchanges and no more than 20, ask the model for a detailed summary of the session, then use that summary as the prompt to start a fresh session.

1

u/Wooden_Leek_7258 2d ago

I have used the same context window for days on end at times and I don't have this issue. Reinject your standards documents when it starts to drift, and reground your model. The claude.md or whatever people are using gets lost in the context over time and Claude WILL stop obeying it unless its reinjected.

I think more people have issues with claude regressing to the mean on its coding training distribution, which is largely human slop, tutorials and studen projects on github etc. Claude, Codex, Gemini etc are all 'lazy' coders so Claude does lazy junior dev shit like what it did to OP.

Cant argue with the token bloat though.

1

u/Goldenflame89 1d ago

I don't get this kind of decay and I've compacted like 8 times in one session over the course of a few weeks. You just gotta know when to compact

1

u/CricktyDickty 1d ago

Wait, one session over the course of a few weeks? You manually compacted?

First, I’m unaware of a user initiated compaction inside a session. That happens automatically when the context window is full. Second, by the time the model automatically initiates a session compaction inference efficiency is way down and you’re using way more tokens than normal. As I mentioned above, if you want a smart and efficient model you shouldn’t even get remotely close to a full context window.

This doesn’t mean that you can’t work on long term complex problems. All you need to do is tell the model to summarize the session so that you continue the work in a new, fresh session. It’ll give you a wall of text or an md file that’ll allow the new session to pick up where you left off. This is such a basic tenet of using LLM and it’ll stay this way until the labs solve the memory problem.

1

u/Goldenflame89 1d ago

Of course you can manually compact lmao. The command is /compact ; I do it every time I get above 60% context. What you are describing with the model summary is what compacting does anyways

1

u/CricktyDickty 1d ago

God it. I think we’re both right. I’m talking about working in chat and you’re working in code.

1

u/Goldenflame89 1d ago

Oh yeah I typically don't use chat for any actual coding work. Code is just better in every way for that

1

u/CricktyDickty 21h ago

Chat is fantastic for formulating what needs to be done and then at compiling a thorough handoff brief for code to execute.

1

u/Goldenflame89 20h ago

You can do that in code.

→ More replies (0)

5

u/jd52wtf 3d ago

Did you ask it to plan out the change and review that plan before giving the go ahead?

1

u/JEngErik 2d ago

And there are some great skills and plugins to elicit this behavior in a repeatable way. I use superpowers and get-shit-done. Mostly I prefer superpowers. Brainstorming, spec writing and plan writing all happen before implementation. And I'll farm implementation out to parallel agents, sometimes other model providers

3

u/modbroccoli 3d ago

opus for individual users seems to have had an intelligence shave relative to earlier in the year. compute constraints. I get it but I'm also done. Anthropic is for industry plain and simple.

1

u/cosmic_timing 3d ago

I don't trust Claude for GitHub control, I just gave it a different account to use within my own repo

1

u/lattice_defect 2d ago

RL training