91–100 of 194 posts
Still very impressive, but nowhere near the text chat revolution. OpenAI still trying to strike their second lightning
Earlier quoted context omitted.
Gemini is still my top choice within production software for typical data extraction from unstructured data. Gemini Flash Lite feels like a cheat code for speed, and it's really cheap. Some other Chinese models are also fast and cheap, but a harder sell in a U.S. production environment.
Yeah Gemini 3.5 Flash Lite is really good. Which Chinese models can you recommend?
Over the last two weeks, Qwen released two new models. Qwen3.8-Max is totally insane, but it’s only available through the Alibaba Cloud API. I wrote a similar blog covering Qwen3.8-Max: [https://blog.roboflow.com/qwen3-8-max/](https://blog.roboflow.com/qwen3-8-max/)
If you’re looking for something you can run locally, Qwen3.8-27B might be a great option. On Friday, I did a quick comparison between Qwen3.8-Max and Qwen3.8-27B: [https://x.com/skalskip92/status/2088411215441621469?s=20](https://x.com/skalskip92/status/2088411215441621469?s=20)
Ironically, the pill counting example selected to showcase "the best vision model" can be easily solved with OpenCV template matching, a technology created 25 years ago.
I'm sure a typical frontier model would also be happy to write that opencv script for you, and it would do it well. That is certainly pretty far from what was possible 25 years ago.
Earlier quoted context omitted.
Have you actually read the frontend design skill? It’s placebo at best. Very short and barely focused on design: https://github.com/anthropics/skills/blob/main/skills/fronte...
Have you actually tried using it?
Where are the Qwen benchmarks in this? I would be more interesting to see how Qwen performs.
So far I haven't seen a single model succeeding at transcribing sheet music, but I just tested it again with 5.6 Sol and it nailed the small test case. Fluently reading music requires multiple years of training for most people, but I feel like accurately following the horizontal lines trips up vision models in particular.
It's not an LLM, it's a custom thing we built. Here's a comprehensive list of support for various notation glyphs: https://www.soundslice.com/help/en/creating/pdf-import/294/s...
Penny sample shown looks like failed EXIF orientation registered by the model/harness. The coins are correctly marked, it's rotated 90 degrees.
In the third vision bench result, Sol is 100% correct but the expected has 1 error. Seems like an oversight. In the next bench, Sol looks like it’s correct again but the bboxes are rotated 90 degrees for some reason.