1. I'm integrating ChatGPT extensively into https://CoCalc.com. This integration makes a lot of sense, because cocalc is a platform in which relatively inexperienced students use Jupyter notebooks, linux terminals and Latex. So far, the most popular feature by far is a "Help me fix this" button that appears above stacktraces in Jupyter notebooks.

2. One software engineering challenges is that ChatGPT often outputs code in markdown blocks. I've had to emphasize in prompts that it should explicitly mark the language. I then got inspired to make it possible to evaluate in place the code that appears in these blocks using a Jupyter kernel, and spent a week making that work (so, e.g., if you type a question into the chatgpt box on the landing page at https://cocalc.com, and code appears in the output, often you can just evaluate it right there). There seem to be endless surprises and challenges though. For example, a few minutes ago I realized that sometimes the giant tracebacks one gets when using Python in Jupyter notebooks are so big (even doing simple things with matplotlib) that they end up resulting in too much truncation: https://github.com/sagemathinc/cocalc/issues/6634

3. I'm mostly using GPT-3.5-turbo rather than GPT4, even though I have a GPT4 api key. Aside from costs, GPT4 takes about 4x as long, which often just feels too long for my use case. The average time for a complete response from GPT-3.5 for my application is about 8 seconds, versus over 30s for GPT4.