The study points out, “Python and Rust are the two most popular languages used by Advent of Code participants. This may explain why Rust fares so well.”
Here’s a study that found that for small problems Gemini is almost equally good at Python and Rust. Looking at the scores of all the languages tested, it seems that the popularity of the language is the most important factor:
In my application, code generation, the distilled DeepSeek models (7B to 70B) perform poorly. They imitate the reasoning of the r1 model, but their conclusions are not correct.
The real r1 model is great, better than o1, but the distilled models are not even as good as the base models that they were distilled from.
The DeepSeek R1 paper explains how they trained their model in enough detail that people can replicate the process. Many people around the world are doing so, using various sizes of models and training data. Expect to see many posts like this over the next three months. The attempts that use small models will get done first. The larger models take much longer.
Small r1 style models are pretty limited, so this is interesting primarily from an “I reproduced the results” point of view, not a “here is a new model that’s useful” pov.
“You should consider using this in your requirements” implies that this is not a hard rule, it’s just an ignorable suggestion. It would be interesting to audit gov.uk web pages over time to see whether this advice is being followed.
I remember seeing the PERQ at trade shows. The best thing about the PERQ was its monitor, which was unusually sharp for that era. It used a yellow-white long persistence phosphor. A CMU grad student friend told me that the monitor designer was “a close personal friend of the electron”, implying that the analog circuitry of the PERQ monitor was especially high quality.
Which aims to match SQLite quality and provide new features (free encryption, multiple simultaneous writers, and bitflip resistance.)