471–480 of 540 posts
https://jqlang.org/manual/#ascii_downcase-ascii_upcase
However GLM-4.7 insists that is called ascii_down().
I tried to correct it and gave the exact version number, but still, after a long internal monologue, This is its final world:
"In standard jq version 1.7, the function is named ascii_down, not ascii_downcase.
If you are receiving an error that ascii_down is not defined, please verify your version with jq --version. It is possible you are using a different binary (like gojq) or a version older than 1."
GLM-5 gives me the correct answer, ascii_downcase, but I can get this in the Chat Window. Via the API I get HTTP Status 429 - too many requests.
Pelican generated via OpenRouter: https://gist.github.com/simonw/cc4ca7815ae82562e89a9fdd99f07... Solid bird, not a great bicycle frame.
The bird not having wings, but all of us calling it a 'solid bird' is one of the most telling examples of the AI expectations gap yet. We even see its own reasoning say it needs 'webbed feet' which are nowhere to be found in the image. This pattern of considering 90% accuracy (like the level we've seemingly we've stalled out on for the MMLU and AIME) to be 'solved' is really concerning for me. AGI has to be 100% righ…
Lets not miss that MiniMax M2.5 [1] is also available today in their Chat UI [2]. I've got subs for both and whilst GLM is better at coding, I end up using MiniMax a lot more as my general purpose fast workhorse thanks to its speed and excellent tool calling support. [1] https://news.ycombinator.com/item?id=46974878 [2] https://agent.minimax.io
Then I gave two models a Real World Task.
The "Best" model took 3x longer to complete it, and cost 10x more. [0]
Now I define Best Model as "the smallest, fastest, cheapest one that can get the job done". (Currently happy with GLM-4.7 on Cerebras, at least I would be if the unlimited plan wasn't sold out ;)
I later expanded this principle when model speed crossed into the Interactive domain. Speed is not merely a feature; a sufficient difference in speed actually produces a completely new category of usage.
[0] We recently arrived at an approximation of AGI which is "put a lossy solver in an until-done loop". For most tasks we're throwing stuff at a wall to see what sticks, and the smaller models throw faster.
Pelican generated via OpenRouter: https://gist.github.com/simonw/cc4ca7815ae82562e89a9fdd99f07... Solid bird, not a great bicycle frame.
That's a bike that's ergonomically designed for pelicans. It is unreasonable to expect pelicans to ride human bikes, they have different anatomy.
Draw a pelican on a bicycle ergonomically designed for pelicans.
It's live on openrouter now. In my personal benchmark it's bad. So far the benchmark has been a really good indicator of instruction following and agentic behaviour in general. To those who are curious, the benchmark is just the ability of model to follow a custom tool calling format. I ask it to using coding tasks using chat.md [1] + mcps. And so far it's just not able to follow it at all. [1] https://github.com/rus…
I love the idea of chat.md. I'm developing a personal text editor with vim keybindings and paused work because I couldn't think of a good interface that felt right. This could be it. I think I'll update my editor to do something like this but with intelligent "collapsing" of extra text to reduce visual noise.
I couldn't decide on folding and reducing noise so I'm stuck on that front. I believe there is some elegant solution that I'm missing, hope to see your take.
There is a well-known CLI tool for JSON processing called jq. I have just asked GLM-4.7 for the name of jq's built function to convert a string to lowercase. It is called ascii_downcase() according to the manual: https://jqlang.org/manual/#ascii_downcase-ascii_upcase However GLM-4.7 insists that is called ascii_down(). I tried to correct it and gave the exact version number, but still, after a long internal monologue…
I have also realized that I get faster and correct answer to the ascii_downcase question (even from GLM-4.7) when I submit to open.bigmodel.cn endpoint rather than the z.ai API endpoints (using the same API key). I get a mix of Chinese and Western characters in error responses from open.bigmodel.cn though, while the z.ai endpoint does only contain Western Characters.
(Just assuming that both websites are operated by the same company).
Grey market fast-follow via distillation seems like an inevitable feature of the near to medium future. I've previously doubted that the N-1 or N-2 open weight models will ever be attractive to end users, especially power users. But it now seems that user preferences will be yet another saturated benchmark, that even the N-2 models will fully satisfy. Heck, even my own preferences may be getting saturated already. Op…
Quantization the better approach in most cases, unless you want to for instance create hybrid models ie. distilling from here and there.
Earlier quoted context omitted.
I specifically do not use the CN/SG based original provider simply because I don't want my personal data traveling across the pacific. I try to only stay on US providers. Openrouter shows you what the quantization of each provider is, so you can choose a domestic one that's FP8 if you want
Funny, living in Europe, I prefer using EU and Chinese hosts because as I don't want my data going to the US. The trust in US firms and state is completely gone.
Earlier quoted context omitted.
That's a bike that's ergonomically designed for pelicans. It is unreasonable to expect pelicans to ride human bikes, they have different anatomy.
The next frontier: Draw a pelican on a bicycle ergonomically designed for pelicans.