[onlyrealcuzzo]

I rage canceled Claude today.

After 2 weeks of Claude getting progressively worse and worse, today was the final straw.

I don't care if they have a phone app. The model is COMPLETE garbage after you subscribe long enough and they think they've "got you".

I can't code on my phone if the model literally moves in the wrong direction and does the opposite of what I tell it to. If I wanted to make my code worse, I'd just randomly commit garbage. I don't need a mobile app for that.

[couchdb_ouchdb]

I've seen a lot of this sentiment over the previous six months from people on reddit. I have yet to experience this myself as a developer with over 20 years of experience.

[fendy3002]

As always, I think this happen more to vibe coder. They don't understand that bigger project means worse AI performance. On top of that Opus felt being nerfed at understanding prompt so if your spec is bad you won't get good result.

[colechristensen]

What it does seem like is that they're tuning some knobs up and down or releasing new versions of models or system prompts that result in the model getting dumber and smarter in waves.

Opus has been dumb this week.

Claude was having a lot of capacity problems and downtime and then this week that has been much less obvious... and the model is dumber.

It could also just be luck and my impressions are false... who knows.

[Our_Benefactors]

It’s because it’s not true, there’s no evidence for it that passes the sniff test. No lab is “shipping a worse model once they’ve got you”. People have a bad few days and blame the model providers instead of stepping back to fix their workflow.

[raincole]

When it comes to something with random results (unfortunately that's what LLMs are), people will think the odds are rigged against them.

It's a good thing that hype-chasers are cancelling though. So we can use the services with a reasonable latency.

[dgellow]

Opus 4.7 has been a real downgrade for me. I’m back to mid 2025 when I had to catch all the completely intermediary goals/assumptions the model is creating for itself

[Wowfunhappy]

You can still use older versions of Opus if they work better for you. Just need to set the environment variable.

[chantepierre]

I felt that but find it worked way better by invoking it with `claude --effort max` only

[cdurth]

Yup I run Max only now. It's closer to the "old" Claude but still nerfed in my opinion.

[shomp]

My "mental scratchpad" needs to be as sharp as possible to maximize my intelligence. I think of the LLM as a scratchpad for my thinking, I hope the Anthropic team can see this.

[kaeluka]

it's sort of good at thinking, writing specs, etc.. Also debugging. But as a coder: I see no advantage to opus 4.6 and I preferred sonnet most times already over opus 4.6.

[johnfink8]

I see a lot of the "4.7 is a downgrade" sentiment. 4.7 does (mostly) what you ask it to do. 4.6 does what it thinks it should do. As someone with 20 years writing my own code I want the former, but the loud contingent online wants the latter.

When you're on a mature codebase with 500k+ lines of code, I haven't seen anything else be as effective as 4.7.

[onlyrealcuzzo]

I can tell you for a fact, Claude 4.7 was NOT doing what I told it to do (in fact the clear and complete opposite - repeatedly), a pretty simple architectural refactor, and that Codex did better and DeepSeek much better.

It was given very simple ways to verify success. It simply didn't do that and said it's at a good stopping point, despite moving in the WRONG direction not even doing 1% of the task, and being told to see the task through to completion.

Meanwhile, Codex broke it down into 3 steps and just got it done...

No, "I'm going to give it to you straight, this is a large risky commit that could go sideways, so I'm just not going to do anything instead."

Claude worked on it for almost 200 commits over 2 weeks, needing to typically prompt it 3x to even TRY to make any progress instead of just wasting tokens to ignore me and tell me how big and risky it is.

Maybe Claude is just particularly terrible at this type of refactor. I'm not sure why that would be.

[solenoid0937]

It's the same phenomenon as when you learn a new vocabulary word you see it everywhere.

People heard "Claude is nerfed" and now they see it everywhere, they notice failures a lot more than they would have otherwise.

Doesn't matter that Claude is not, in fact, nerfed. Perception is powerful and most humans are not rational.

[fendy3002]

Oh Opus is nerfed sure, but not that hard. Early this year opus 4.6 can understand your prompt and your intention easily, it got worse around mid April. Opus 4.7 even worse than that.

However that's just it, you just need to improve and make clearer of your prompt and it will perform just as good.

[fragmede]

Or just switch over to OpenAI. Codex-5.5 is quite good.

[arkadiytehgraet]

This account is an LLM-hype peddler, shilling for Anthropic (check comment history). If they say that Claude is not nerfed, then most likely it is, in fact, nerfed.

[solenoid0937]

I wouldn't call correcting misinformation and FUD "peddling hype" or "shilling" but I suppose we are in a post-truth world, where if you push back against the anti-AI emotions and vibes with grounded facts, you must be a shill.

Anyways, please take your discourse of calling people you disagree with "shills" back to Reddit. I'd much rather engage with someone debating the merits of an argument.

[arkadiytehgraet]

If you are an LLM-hype peddler, you really should not be offended at being called out. Also, this is the merit you are ostensibly looking for — since you are a shill, everyone should know this first before taking your words seriously.

You should also check your LLM prompt for HN comments, because the original comment you replied to was not anti-AI, and, in fact, very much pro-AI. The only criticism it had was about model being degraded, so they could not go as hard at AI-assisted development anymore as they used to before. I guess it's a bit difficult for LLMs to spot the difference and make proper conclusion for now.

Also even if taking you seriously — how does writing "no, model performance is not degraded because I say so" serve as correcting misinformation? It only does if you are shilling for Anthropic (which you do), otherwise it's just hot air.

[solenoid0937]

Not offended at all, but just ranting about how someone is a shill instead of responding to the substance of their argument is simply not the kind of discussion we have on HN. Read the guidelines.

> "no, model performance is not degraded because I say so" serve as correcting misinformation?

Because zero evidence has been provided other than feelings. That is not evidence of degradation, and we know they don't serve quants.

[mmusc]

All these tools have almost feature parity. The GitHub cli allows remote sessions and can run anthropic models anyway

[shomp]

When you say "code on your phone" ... you don't mean what I think you mean do you? Like, are you actually using your phone to make code commits?

[onlyrealcuzzo]

Yes, you can do that with Claude Code.

Tell it what to do.

Commit, push to origin, review on GitHub.

Tell it to make changes, amend the commit, push --force-with-lease.

I'm attempting to make a memory safe language like Rust but with a substantially lower learning curve and added safety (but non-zero cost abstractions) fully with AI, almost entirely from my phone, commuting, getting coffee, walking the dog, between sets at the gym, replacing doom scrolling before bed and during lunch, etc.

Mostly to test how much LLMs can actually scale development.

Depending on how long it takes them to clean up some architectural slop in the MIR lowering phase, the results could either be very impressive or not.

From a purely cost basis perspective, it's hard to argue they aren't killing it.

But from a multiplier perspective, it's up in the air how great they are.

It's proven to be a really nice experiment, because much of what I wanted to solve with a language is the problems inherent to LLM development.

So at the self hosting phase, I get a great opportunity to see if the language can actually deliver on what I dream for.

[shomp]

You review the code on github also from your phone?

[onlyrealcuzzo]

#1 -> part of scaling is you can't review every single line of code.

LLMs don't really scale if you're still the bottlneck, or they only scale as much as you reviewing every line of code - that's not that much scaling...

So I try to only review certain parts, like making sure they aren't changing tests to allow architecturally broken code to slip through (because they regularly try, even when given explicit instructions not to). Or if I'm watching them make changes on my phone and see that they are clearly doing the exact opposite of what they're supposed to be doing (regularly if I'm watching).

#2 -> if commits are small, GitHub's setup is good enough that you can review code on your phone.

#3 -> if they're huge, I can just review on my laptop at lunch or something.

Theoretically, all of this can be solved easily with orchestration and require minimal oversight.

If you're using LLMs to write code and you're carefully reviewing every line with a jade-handled magnifying glass, you're not really scaling - at least to the degree I'm interested in.

[zarzavat]

> LLMs don't really scale if you're still the bottlneck

This only works if there's no consequences if your code breaks. In the eyes of other humans you're responsible for what you commit. No amount of "scaling" will change that.

[onlyrealcuzzo]

> This only works if there's no consequences if your code breaks. In the eyes of other humans you're responsible for what you commit. No amount of "scaling" will change that.

You're only responsible for what you merge to master, not everything you commit to a feature branch no one is looking at...

If you have the testing and the infrastructure in place such that you can't ship broken code, then you just need to make sure your invariants are upheld - not that every single line is beautiful and perfect.

Further, I am working on a set of metrics that seems pretty good at identifying sloppy architecture. There's decent prior art at many different components of what "sloppy" architecture actually is, and ways to visualize it.

If you can rely on the consensus of several different models, plus your own judgement with the design and the testing in place to verify its implemented correct...

Then, 1) you don't need to code. 2) you only need to review 1/10th or less of the code written. That scales. Reading every line of code line by line doesn't really scale. LLMs aren't very fast at implementation outside of green-field projects. So you can often times implement something faster than they will if you did it by hand. Reviewing can take just as much time as implementing...