In this benchmark, I compare the models on various axes. But what do they mean, and what do they matter? As I said in the first post in this series, the benchmark produced various metrics. In this second post, I will go into some of these numbers and discuss what they mean.
How helpful are benchmarks for choosing LLM models? What do they tell you that you can translate into answers to the questions you have for your specific use cases? For me, the answer was "not much", and so I chose to "benchmark" some of the models to find my own answers.
How much autonomy are you prepared to yield to enjoy the convenience and streamlined experiences the big players are building? Renouncing your autonomy should at least be a conscious choice, but it will require you to stay alert. Because they won't spell it out for you.
The central missing brick for creating your own OpenClaw-like functionality within Claude Code was just quitely released. With /loop you can create proactive agents
Many companies make the mistake of treating the adoption of AI in the company the same way they have treated adoption of other new technology through the years. This is a mistake. The introduction of AI will affect all layers of busines and the society, and comes with its own unique challenges.
The last few weeks, I have talked to many people in the tech sphere, and a few from other areas. Experienced developers, managers, architects. My impression is that most people are absolutely not prepared for what's coming. Let's take a step back to see why they're not.