531–540 of 1001 posts
Here are 18 pelicans - six each for Luna, Terra and Sol at the six different reasoning effort levels (plus the price to generate each one): https://static.simonwillison.net/static/2026/gpt-5.6-pelican... Or if you want to see some in 3D, OpenAI featured a pelican riding a tricycle, bicycle, pony and another pelican in their livestream this morning: https://www.youtube.com/live/Wq45rvPGNHs?t=1070s
Earlier quoted context omitted.
Information density of the prompt is the most important factor in my experience. And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default). Conciseness is usually a byproduct of information density though.
Lexical-priming->semantic-space-constraint;specialized-lexis+=sharp distributional-signature;∴ tight concept-cluster; generic-lexis->diffuse-activation, broad candidate-set;Attention-heads key/query-match domain-tokens;"Hamiltonian"->{operator,eigenstate,quantum,energy}->register+domain locked;Net:constrained-decoding,vocab=soft-prior over output-distribution; register-matching;#taskdef=decompress->continue
If the intent is not easy to understand, it's information sparse. Because it takes a lot of CPU (or brainpower) to interpret.
You can run gzip on an English sentence to make it more textually dense, but clearly it is not more information dense in this context.
Here are 18 pelicans - six each for Luna, Terra and Sol at the six different reasoning effort levels (plus the price to generate each one): https://static.simonwillison.net/static/2026/gpt-5.6-pelican... Or if you want to see some in 3D, OpenAI featured a pelican riding a tricycle, bicycle, pony and another pelican in their livestream this morning: https://www.youtube.com/live/Wq45rvPGNHs?t=1070s
Earlier quoted context omitted.
Time to dump this test. Probably not a coincidence every version has the same rolling green hills, gradient blue sky, sun in the corner, etc.
On the one hand: yes, pelicans on bikes are definitely in the training set at this point. On the other hand: the test is clearly not saturated, given that you can see a clear difference in output at the various reasoning levels / model versions.
Anyone else noticed the "Extended: Fable 5 is included in your weekly limit through July 12 blablabla" disappeared from claude code? Did they panic-delete the july 12th deadline ?
Not specific to OpenAI / Codex, but I'm curious what people are doing to protect themselves from any destructive actions by their coding agents? Just install and pray? Explicity approve all actions? Reconfigure for safety? Run in a sandbox (Docker) ?
More seriously, I was blindly trusting the auto-classifier from claude code (same as the middle option when you do `/permissions` in codex), and it actually allowed the agent to do pretty hardcore `rm` and `git push --force-with-lease` commands, which I would have expected to have to approve manually. Luckily no major issue from those yet.
The best option imo is the integrated cloud environments from claude code (not sure yet if there's a codex equivalent). It spawns a VM in the cloud where the agent runs, and you can open a PR from the app when it's done. Very smooth experience
Here are 18 pelicans - six each for Luna, Terra and Sol at the six different reasoning effort levels (plus the price to generate each one): https://static.simonwillison.net/static/2026/gpt-5.6-pelican... Or if you want to see some in 3D, OpenAI featured a pelican riding a tricycle, bicycle, pony and another pelican in their livestream this morning: https://www.youtube.com/live/Wq45rvPGNHs?t=1070s
Time to dump this test. Probably not a coincidence every version has the same rolling green hills, gradient blue sky, sun in the corner, etc.
Here are 18 pelicans - six each for Luna, Terra and Sol at the six different reasoning effort levels (plus the price to generate each one): https://static.simonwillison.net/static/2026/gpt-5.6-pelican... Or if you want to see some in 3D, OpenAI featured a pelican riding a tricycle, bicycle, pony and another pelican in their livestream this morning: https://www.youtube.com/live/Wq45rvPGNHs?t=1070s
https://www.google.com/search?client=firefox-b-d&q=pelican+1...
I really wish there was just an easy guide on when to use Sol vs Terra vs Luna, and it just moves further into confusing territory when it comes to naming. The naming convention is especially difficult to decipher depending on what your native language is. Of course a latin language speaker might be able to easily determine oh yeah each one is slightly bigger than the other but I still think it borderlines too confus…
You don’t know what sol means? You don’t understand the difference in sizes between Terra and sol? I’m genuinely asking.
Anthropic ships models with a helpful one-liner tag that makes the model hierarchy obvious. I think it wouldn't hurt if OpenAI did the same.
Earlier quoted context omitted.
Surely this comment is literally on every new model release post.
It's part of the pelican tradition at this point.