I’ve been following OpenAI’s models for a while, and I was excited to read yesterday’s GPT-5.6 announcement. Sol is the model I’m most curious about. The wider release follows a limited preview, so this feels like the point where more people can actually start trying it.
OpenAI describes improvements in coding, science, and working through longer tasks. What interests me is quite ordinary: could it help me get past the small things that slow down a project? The release announcement is where I started reading.
As a mechanical engineering student, I often have an idea before I have all the programming knowledge to try it. I might want to plot a sensor signal, compare two designs, or make a small interface for an experiment. There can be a lot to learn before the first useful result appears.
What I want to try
I’d like to give it a messy but small problem. Something like turning a set of vibration readings into plots, checking the units, and explaining why a peak appears. Then I could compare its explanation with what I know about the system.
That last part matters to me. A plot can look convincing even when a sampling rate or a unit is wrong. If I don’t understand what the code is doing, I’ve only moved the confusion somewhere else.
I’m also curious about building simple learning tools: a page where I can change a controller gain and see what happens, or a script that helps me compare experimental runs. These are small ideas, but they’re the kind I would actually use.
Why I’m excited
I don’t expect a model to understand a physical setup from a vague description. I still need to measure things, read, and check the result. But having help with the unfamiliar parts could make it easier to start.
For now, these are things I want to explore, rather than results from a comparison I’ve run. I’m excited because there are a few more ideas I might be able to try for myself.