andhuman

At the end of the blog post we get this nugget.

> The pace of progress from here will be fast. Stay tuned.

This benchmark expects models to not only do the happy path, but also edge cases, even though the ticket they give to the model doesn’t specify if. This to align more to real world tickets. Usually with benchmarks it’s the other way around: only implement what’s asked. So I welcome this type of benchmark because this is how I use the models.

Also, handing over the work to AI robs you of learning and understanding. Let’s say you write a ticket for a feature and that description is good enough that an AI agent can implement it from start to finish. The AI will then discover things while implementing the feature, and will use those learning to make the feature work. That learning will then be discarded once the AI is done. No one will be able to partake or share the knowledge with others. Sure, some of it can be saved in form of comments, but not everything.

I don’t see why they just don’t allow a smaller model to answer the question while letting the bigger one vet it. The vetting can be asynchronous and can be delivered after a few seconds (if it’s an easy query). If it’s a hard query, the UI can show the answer is currently being vetted or something.

What surprised me is that caffeine gets converted to other compounds that _also_ binds to the same receptors thus causing you to feel less tired. And that compound has its own half life. Now I’m just guessing, but it could be that one is more affected by one of the byproducts so to speak than the caffeine itself.

Why conscious thought as described in the article was thought to be signs of a delirious person was because that’s where you could find it in real life, is my guess. If someone blabbing about without a listener, they’re insane. So to describe someone’s inner thought would render them insane to the reader. But with psychology came greater understanding and value of the inner thoughts and now a character no longer was deemed insane by having innate thoughts.

Finally an alternative to the big dogs that a company can use. People have been asking for a way to run the Chinese models from a trusted provider. Here GitHub delivered!

The performance, if we trust the benchmarks, put it at Sonnet 4.6.

Let’s see if it’s worth it with GitHubs pricing.

The Vibe CLI is really bad on Windows, sure they don’t officially support it, so can’t blame them, but a FYI for anyone wanting to try it. It can’t get find and replace right.

I have good experience with Mistral Vibe.

I just listened to the Matrix OST and that one really gets me into a coding mood!

Oh man, this was the release I wanted to link, as it has a new feature (tiled follow link) that I actually started using right away. A new browser feature I find useful didn’t happen that often for me, so I got excited.

Be kind to others, but start with being kind to yourself.

Isn’t llama.cpp’s implementation of Qwen 3.5 better, or am I misinformed?