randomifcpfan

Current frontier agents can one shot solve all 2024 AoC puzzles, just by pasting in the puzzle description and the input data.

From watching them work, they read the spec, write the code, run it on the examples, refine the code until it passes, and so on.

But we can’t tell whether the puzzle solutions are in the training data.

I’m looking forward to seeing how well current agents perform on 2025’s puzzles.

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.”

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.

College degrees from reputable colleges used to serve this purpose, but grade inflation has greatly weakened this signal.

Thanks for explaining! That’s quite different from the US English (and RFC English) meaning of “should”.

“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.

They identify 6 bugs/mistakes, of which, not doing staged releases, was the final mistake.

They stop short of identifying the real root issues of running at kernel level, and of not auto-backing-out updates that cause crashes, perhaps because those causes are harder to fix.

A tool for updating bazel build target dependencies. It inspects build files and source code, then adds/removes dependencies from build targets as needed. It requires using global include paths in C/C++ sources. It is not perfect, but it is pretty nice!

That’s certainly how it seemed from the Android side at the time. The Linux side was hoping that they could adapt the desktop Linux stack to work well on mobile devices, without introducing new concepts like wakelocks. It took them a long time to give up on that approach.