If by Opus you mean Opus 4 and not Opus 4.8, then sure.

> If by Opus you mean Opus 4 and not Opus 4.8, then sure

I meant Opus 4.8 which is rather dumb and ineffective in coding harness, especially with higher thinking levels.

I can’t believe that anyone would actually think this. This

My experience was so much different to this, that I have the unfortunate impression that you're shilling. It really was not a capable model, it felt like the old oai models back when we were all excited but couldn't actually trust them even in the littlest ways. What harness were you using, did you do any work to make it better? What was I doing wrong? I just pointed opencode at it, with a pretty simple (large-ish) data cleaning project.

> My experience was so much different to this, that I have the unfortunate impression that you're shilling

I think you might both just be reading way too much into one-off random experiences that you've decided are evidence of significant and stable capability.

Have you actually used Opus 4.8 in Claude Code? It takes way too long to do any practical task on higher thinking levels due to over-engineering. And I am not the only one complaining. Lots of people downgrade to Opus 4.6 exactly for this reason.

Opus 4.8 training works well for agentic work. Not for code harness.

Works extremely well for us. I never know what other people are doing when we read these stories.

Yeah I found 4.7 and 4.8 to be downgrades from 4.6. I don't know if it was the model or just Anthropic's scaling issues though TBH. I found working with the Claude Code max (20x) sub would work awesome in between new models. A week before up to 2 weeks after, it would go to crap, dumber, slower, outages, harness churn, etc.

I'm finding the same with ChatGPT recently since the 5.6 release. Not as bad though, but sluggishness at times, harness churn (creating bugs and crashed), and occasional availability issues that cause me to downgrade to 5.5.

It's gotten to the point where I dread a new model release from these companies because it's guaranteed to be disruptive! I assume the pay per use API is less impacted.

Reddit is the absolute worst way to find real experience. Opus 4.8 is a very capable model and no chinese model can outdo it.

It’s so fascinating watching people on here bicker about models like fine wines. Wild times we live in. Wild times.

It's not a frontier LLM if it's not made in the Silicon Valley region of California, otherwise it's just a sparkling LLM.

Comments like this boggle my mind.

The model which everyone else raves about and is wildly successful with legions of programmers virtually demanding access while abandoning ChatGPT and Copilot in droves, is rather dumb?

Have you considered that it's more likely that you're doing something wrong?

My own experience is that the vast majority of programmers have experience with one model and maybe some short usage of earlier models from a competing choice but want to be using the model with the highest popularity and reputation. I've worked with people who actually had to test multiple choices for their team who didn't understand why they were pressured to select Claude for programmer morale.

I went from cycling between models all the time in Cursor (some would randomly be better at certain tasks than others) to just going pure Opus 4.5 when that came out - it was so far ahead of anything else at the time.

Interestingly with Fable vs GPT-5.6 I think they've lost their lead a bit. I'm finding Fable can't do certain work that 5.6 Sol Ultra can - especially when it comes to webpage design.

Grok 4.5 was fast but made mistakes that GPT/Fable just don't.

I'm curious to try Kimi.

When iOS users are given an Android phone, they complain about how awful of an OS it is.

In reality, they just aren't used to it.

What exactly is there to get used to? Do people really use each model very differently? There's a learning curve as in everything, but there is nothing that comes to mind for me when I use codex as opposed to claude

I realized how awful of an OS modern android is when I bought an iPad. I later fully switched to iOS and never looked back.

Point is, sometimes people just have different experiences from you.

Exactly what I’m thinking, too.

> is wildly successful with legions of programmers virtually demanding access while abandoning ChatGPT and Copilot in droves

Do you have a source for that? Codex went from 5 million users to 9 million users in the past few weeks since GPT 5.6 released. It was so popular that Claude was forced to extend Fable access by a week and then permanently for some plans.

  > At 10% of regular price it is a steal so far.

What price do you see?

Here standard plan has been discounted to $18.00, from $25.00/month.

What the difference between your experience and https://news.ycombinator.com/user?id=5701652400? ‘s?

Such diametrically different ones.

Can we please include information of what languages we use when making claims like these?

It makes a huge difference if you're writing Javascript/HTML/CSS, Python, or C++/Rust.

Also the application type matters, e.g. user interfaces or scientific computing.

me: Go, Swift, Kotlin, bash k8s/gcloud

domain: typical web backend tier, mobile apps. not particularly complex, but requires OOP/architecture/system design.

It's useless to talk about models and harnesses without context and method. Depending on how you use the model and what the model is used for, experience may vary drastically. Also, different models with different harnesses require different approaches.

I've been using https://gitlab.com/gabriel.chamon/orisun which is my own simplified methodology, for coding web apps in python and elixir and have been very successful using qwen3.6 27b Q4 locally with help of larger models for architecture, so I get very suspicious when people talk how useless larger models are. They are either using it for a domain that models don't perform well or just not using it right.

I'm not sure about "useless" but from my experience agentic coding leads to death by a thousand cuts for all projects I've seen so far. Small decisions missed in a codebase that leads to degradation in correctness, reliability and performance. At some point it only takes one engineer to be careless, others skipping PR because they are AI generated...

I got into a bit of an argument a while back when I used the word "crass" to describe some of the code decisions I've seen Claude make (in someone else's project that I have to work with).

But it is how I feel and it feels like the right word for the job. Because as you say, good code projects start out with good decisions.

It's like when you see a CAD design with a sequence of features that exist only to fix problems caused by starting from the wrong principles or the wrong baseline.

Sure the resulting part may end up identical as a solid for that specific need, but it could have been done in a way that was more robust, simple, easier to understand and modify, and where the design doesn't break in an unexpected way due to a small change of an early measurement.

(CAD has made my instincts much more visible to me)

Exactly, and even when humans make bad decisions there is some friction, I feel that llm's don't have/notice that friction, they just bulldoze without caring about anything else.

Yeah. IME, LLMs actually introduces a lot of friction when you want to improve the design. They are 100% biased towards the status quo.

Not my experience. It does depend on the scale of the project, but for tens of thousands LOC, it's really good at taking instructions for architectural redesign.

LLM will take stuff at face value. If you tell it it's good, it'll gladly abide. Similarly if say it's crap it'll so the complete opposite.

Yes, it's good at taking instructions for architectural redesign, but when it's not doing an actual architectural redesign task, it will be heavily biased towards the status-quo, despite AGENTS.md and prompts trying to steer it.

My latest experience was towards a C++ to Rust migration and it failed miserably because the existing codebase enforced patterns I didn't want.

Ah yes, I understand, but that's expected and actually desirable, it's preferable than having inconsistent project patterns across the project introduced by multiple LLMs. Imagine the mayhem.

It's important to do a first pass cleaning up and preparing code before unleashing agents onto it, similar to how Working Effectively with Legacy Code advocates approaching consolidated codebases. After that the agent will actually pick up the new pattern and start propagating that to the rest of the code.

I believe it’s desirable as a default, but not when the prompting/steering asks not to.

> It's important to do a first pass cleaning up and preparing code before unleashing agents onto it

That’s a good idea in general, but the whole point of the rewrite was that Fable wasn’t capable enough of fixing the old codebase which we were using as a reference.

Another problem I had in another project was with having “sample” code purely written for testing being used as the architectural guidance, despite comments and repeated memories asking not to.

In the end, by the way, the solution was to simply blocking access to specific files, in the Claude settings.json.

But still Claude tried to cheat by running ‘cat file.cpp` a few times, so…

That's really interesting. I'll be more careful with RAG in the future then, thanks for the account

It's a solvable problem if you're willing to throw more tokens at it. Frontier models have gotten very good at cleaning up their own messes. You just need the right skills/loops, and to stick to models that consistently follow instructions (i.e. GPT-5.5/GPT-5.6-Sol).

All of which you had with only humans in the loop. Catalogue problems so they become technical debt and tackle them periodically. Seems to me like this is less of an AI problem and more of bad management.