tezza

The civil servants are addicted to their personal TV and only pause watching it compulsively when the boss comes around. As soon as the boss is out of sight they resume their fixation.

From memory it is a loop of a cowboy galloping on a horse.

Comment · 3 months ago

Yes, I see your point.

Your pelican output is thus both in the training set and yet still outside the capability of the model architecture.

And so you are tracking both the capability of the training and also the capability of the querying!

When you receive your first outstanding pelican it will track a gain of capability.

(btw I first mentioned simonw-pelican-into-training-set in May 2025 on twitter.)

My 3D-egyptology-explainer showed a massive uplift for Kimi K3 and this tracks a much improved 3D capability.

Comment · 3 months ago

Nice qualitative test!

Just like you I am super impressed by Kimi K3.

I do a qualitative benchmark series making 3D explainers and so here's Kimi K3 vs Claude Fable:

https://generative-ai.review/2026/07/kimi-k3-rush-test-vs-cl...

I've put links to the posts on GLM5.2, Opus 4.8, Chat GPT 5.5. I grab video screencaps so you can compare in detail. The full interactive Kimi output is at the bottom of the post if you want a comprehensive 3D play around

Comment · 3 months ago

well i’ve come across it loads, especially 1/2) during REPL style build outs and 2/2) calling libraries and frameworks you are not yet familiar with.

perl another offender… is it a hash? is it an arrayref? over time you get it right, but by trial and error and looping. json suffers this too, arrays different from strings, different from numbers etc, but opaque until checked and liable to change

Comment · 3 months ago

Absolutely, the change in quality over time is a great yardstick.

Nice to see you did the quality level comparisons and did three passes.

I've been using that technique myself on my image gen reviews[1] and it also works well in presentations and for personal study.

[1] https://generative-ai.review/2025/12/beast-mode-activated-op...

Comment · 4 months ago

I've just put GLM 5.2 through my __qualitative__ benchmark. I was quick enough to capture Claude Fable, so now you can compare GLM5.2 vs Claude Fable vs Opus 4.8 vs Chat GPT 5.5

https://generative-ai.review/2026/06/glm5-2-from-z-ai-vs-cla...

I've structured it side-by-side. You can clearly see where the private models excel, and where GLM 5.2 is still really good.

Comment · 4 months ago

There are benchmarks if you want quantitative results. Mine is qualitative, and clearly billed as such. Comparison and contrast still possible.

Comment · 4 months ago

check the backlinks[1][2] in the article before you start throwing around accusations. I am not (yet) a person that has advanced notice and access to models.

Fable just got announced and I did a rush out article because people are curious. I released the post mere hours afterwards and it takes time to create the output, slice into videos, make a wordpress article on top of taking my son to basketball training and eating dinner. I’m in London and this was all happening at 1am.

If you check the links my previous articles have all the juicy stuff you are criticising me for not having with little preparation.

How is a side by side direct comparison NOT precise?

[1] first in series from 2025: https://generative-ai.review/2025/05/vibe-coding-my-way-to-e... . This has all the background you are talking about in the Appendix

[2] https://generative-ai.review/2026/05/vibe-coding-my-way-to-e... . Second in series 2026 has a side by side table of what changed. This is what is possible with more than a few hours advanced warning.