Caveat: I didn’t do enough testing to find the edge cases (eg, negation).

I am finding my writing prompt style is naturally getting lazier, shorter, and more caveman just like this too. If I was honest, it has made writing emails harder.

While messing around, I did a concept of this with HTML to preserve tokens, worked surprisingly well but was only an experiment. Something like:

> <h1 class="bg-red-500 text-green-300"><span>Hello</span></h1>

AI compressed to:

> h1 c bgrd5 tg3 sp hello sp h1

Or something like that.

Have you tried just adding an instruction to be terse?

Don't get me wrong, I've tried out caveman as well, but these days I am wondering whether something as popular will be hijacked.

But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working. I'm seeing a lot of goodwill for Codex and a ton of bad PR for CC.

It seems like 90% of Claude's recent problems are strictly lack of compute related.

I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.

"Opus 4.7 uses an updated tokenizer that [...] can map to more tokens—roughly 1.0–1.35× depending on the content type.

[...]

Users can control token usage in various ways: by using the effort parameter, adjusting their task budgets, or prompting the model to be more concise."

Codex isn’t as pretty in output but gets the job done much more consistently

Perhaps they need the compute for the training

I cancelled my subscription and will be moving to Codex for the time being.

Tokens are way too opaque and Claude was way smarter for my work a couple of months ago.

I have been getting better results out of codex on and off for months. It's more "careful" and systematic in its thinking. It makes less "excuses" and leaves less race conditions and slop around. And the actual codex CLI tool is better written, less buggy and faster. And I can use the membership in things like opencode etc without drama.

For March I decided to give Claude Code / Opus a chance again. But there's just too much variance there. And then they started to play games with limits, and then OpenAI rolled out a $100 plan to compete with Anthropic's.

I'm glad to see the competition but I think Anthropic has pissed in the well too much. I do think they sent me something about a free month and maybe I will use that to try this model out though.

OpenAI bet on more compute early on which prompted people to say they're going to go bankrupt and collapse. But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working.

It seems like 90% of Claude's recent problems are strictly lack of compute related.

From that it's pretty likely they were training mythos for the last few weeks, and then distilling it to opus 4.7

Pure speculation of course, but would also explain the sudden performance gains for mythos - and why they're not releasing it to the general public (because it's the undistilled version which is too expensive to run)

If they are indeed doing this, I wonder how long they can keep it up?

> /model claude-opus-4.7

  ⎿  Model 'claude-opus-4.7' not found

     /model claude-opus-4.7
      ⎿  Model 'claude-opus-4.7' not found

Related features that were announced I have yet to be able to use:

    $ claude --enable-auto-mode 
    auto mode is unavailable for your plan

    $ claude
    /memory 
    Auto-dream: on · /dream to run
    Unknown skill: dream

Edit: Not 30 seconds later, claude code took an update and now it works!

https://reddit.com/r/ClaudeAI/comments/1smr9vs/claude_is_abo...

This story sounds a lot like GPT2.

https://www.youtube.com/watch?v=BzAdXyPYKQo

""If you show the model, people will ask 'HOW BETTER?' and it will never be enough. The model that was the AGI is suddenly the +5% bench dog. But if you have NO model, you can say you're worried about safety! You're a potential pure play... It's not about how much you research, it's about how much you're WORTH. And who is worth the most? Companies that don't release their models!"

But if it'll actually stick to the hard rules in the CLAUDE.md files, and if I don't have to add "DON'T DO ANYTHING, JUST ANSWER THE QUESTION" at the end of my prompt, I'll be glad.

I was researching how to predict hallucinations using the literature (fastowski et al, 2025) (cecere et al, 2025) and the general-ish situation is that there are ways to introspect model certainty levels by probing it from the outside to get the same certainty metric that you _would_ have gotten if the model was trained as a bayesian model, ie, it knows what it knows and it knows what it doesn't know.

This significantly improves claim-level false-positive rates (which is measured with the AUARC metric, ie, abstention rates; ie have the model shut up when it is actually uncertain).

This would be great to include as a metric in benchmarks because right now the benchmark just says "it solves x% of benchmarks", whereas the real question real-world developers care about is "it solves x% of benchmarks *reliably*".

So the answer to your question, we don't know. It might be a cherry picked result, it might be fewer hallucinations (better metacognition) it might be capability to solve more difficult problems (better intelligence).

The benchmarks don't make this explicit.

A more quantifiable eval would be METR’s task time - it’s the duration of tasks that the model can complete on average 50% of the time, we’ll have to wait to see where 4.7 lands on this one.

This is like a user of conventional software complaining that "it crashes", without a single bit of detail, like what they did before the crash, if there was any error message, whether the program froze or completely disappeared, etc.

My statusline showed _Opus 4_, but it did indeed accept this line.

I did change it to `/model claude-opus-4-7[1m]`, because it would pick the non-1M context model instead.

Eep. AFAIK the issues most people have been complaining about with Opus 4.6 recently is due to adaptive thinking. Looks like that is not only sticking around but mandatory for this newer model.

edit: I still can't get it to work. Opus 4.6 can't even figure out what is wrong with my config. Speaking of which, claude configuration is so confusing there are .claude/ (in project) setting.json + a settings.local.json file, then a global ~/.claude/ dir with the same configuration files. None of them have anything defined for adaptive thinking or thinking type enable. None of these strings exist on my machine. Running latest version, 2.1.110

Not to say I see this as the right approach, in theory the two forces would balance each other out as both white hats and black hats would have access to the same technology, but I can understand the hesitancy from Anthropic and others.