611–620 of 1001 posts
The Claude Pro subscription is basically useless at this point, in terms of usage limits with respect to the settings required to achieve actual useful output.
and to clarify, i don't sleep, i use this 24/7
I generated pelicans riding bicycles on both thinking level low and thinking level high: https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304... The high one is notably better - the bicycle frame is the correct shape, unlike thinking level low. For comparison, here's Opus 4.7: https://gist.github.com/simonw/afcb19addf3f38eb1996e1ebe749c...
I generated pelicans riding bicycles on both thinking level low and thinking level high: https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304... The high one is notably better - the bicycle frame is the correct shape, unlike thinking level low. For comparison, here's Opus 4.7: https://gist.github.com/simonw/afcb19addf3f38eb1996e1ebe749c...
Here's pelicans in all of the thinking levels - low, medium, high, xhigh, max https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
A rambling comment: I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5). So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that…
> My own experience w/ 4.6 and 4.7 are that I don't firmly grasp any capabilities improvements over my memory of 4.5, but it's all so fuzzy that it's truly difficult to tell. I've actually intentionally switched back to 4.5. I hated 4.7 so much that I decided to jump back all the way to 4.5. Now that I've been using 4.5 for a few weeks, I find it significantly more reliable but a bit more forgetful than 4.6/4.7. I'm…
I also recently moved to 4.6 since I started hitting the context limit too often with my current project.
Earlier quoted context omitted.
> This is a refreshing attitude! Well, I think the attitude is that costs are allowed to escalate faster and more steeply than the features delivered. From that perspective, semantic versioning is a handy tool for adjusting pricing strategies. IMHO, it (versioning) only makes sense for open-source projects, where you can clearly see the actual changes made with each version upgrade. Anything else is more than a littl…
The 4.8 model costs the same as it's 4.7 predecessor.
Earlier quoted context omitted.
I won't be surprised if the next gen frontier models are the last. There's orders of magnitude of low hanging juice to squeeze out of smaller models. It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely). It is far less clear that a 1.2T model will be meaningfully better enough to justify training it. As far as reasoning is con…
I'm frankly surprised the focus is still on these enormous "know everything in the world" models. I would think you could create an incredibly lean and smart "just React and React Native" model.
A rambling comment: I think this is the first time we've had a third minor version bump on a frontier Anthropic model. (I count the 0.5s as major here, because they've been issued non-sequentially and also corresponded to massive capability leaps, eg, Sonnet 3.5, Opus 4.5). So now the Opus 4.5 family has successors 4.6, 4.7, and 4.8, each posting fairly modest claimed gains. My own experience w/ 4.6 and 4.7 are that…
I won't be surprised if the next gen frontier models are the last. There's orders of magnitude of low hanging juice to squeeze out of smaller models. It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years (design not certain, probably unlikely). It is far less clear that a 1.2T model will be meaningfully better enough to justify training it. As far as reasoning is con…
The benchmarks need to change. The current coding benchmarks don't capture the realities of software engineering.
I had a bunch of images that got masked by some logic, I had to evaluate something on the original images, Claude 4.7 decided to inpaint the masked images instead of just fetching the actual unmasked images from upstream.
I had another model once that decided that because it couldn't figure out how to fill out a form to log into HuggingFace to download weights for some open source model that it was going to instantiate the model with random weights and run inference on a thousand images.
Its coding was fine, but the solution was not the right one.
I generated pelicans riding bicycles on both thinking level low and thinking level high: https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304... The high one is notably better - the bicycle frame is the correct shape, unlike thinking level low. For comparison, here's Opus 4.7: https://gist.github.com/simonw/afcb19addf3f38eb1996e1ebe749c...
For comparison, what's GPT-5.5 producing today?
On the contrary, they appear trained to say "Honestly" or "I have to be transparent with you" at inverse proportion to certainty.
Put another way, if they are certain, they don't use "Honestly", and if they are just wrong, or know they don't know, they don't use "Honestly".
They use "honestly" on the bubble, to the degree it's a tell that whatever it's asserting or doing is shakily grounded, sketchy or lazy work, or a host of other reasons you shouldn't trust it.
This training seems instead to be making it performatively punch up claims it cannot substantiate.