I’m a 10 year pro, and I’ve changed my workflows completely to include both chatgpt and copilot. I have found that for the mundane, simple, common patterns copilot’s accuracy is close to 9/10 correct, especially in my well maintained repos.

It seems like the accuracy of simple answers is directly proportional to the precision of my function and variable names.

I haven’t typed a full for loop in a year thanks to copilot, I treat it like an intent autocomplete.

Chatgpt on the other hand is remarkably useful for super well laid out questions, again with extreme precision in the terms you lay out. It has helped me in greenfield development with unique and insightful methodologies to accomplish tasks that would normally require extensive documentation searching.

Anyone who claims llms are a nothingburger is frankly wrong, with the right guidance my output has increased dramatically and my error rate has dropped slightly. I used to be able to put out about 1000 quality lines of change in a day (a poor metric, but a useful one) and my output has expanded to at least double that using the tools we have today.

Are LLMs miraculous? No, but they are incredibly powerful tools in the right hands.

Don’t throw out the baby with the bathwater.

Rocket chat I think checks those boxes

You can indeed have a virtual monitor from a mac mini, I have to initiate it from the mini though, no option comes up like it does on an m1 laptop

Haven’t tried steam link yet