GitHub Copilot: First Impressions
I’ve been lucky enough to use it for the past few weeks and so far has proven quite useful, having earned a place in my toolbox despite its rough edges. I also feel it signals a coming change in how we develop and reason about systems, a change which will allow us to go up a few layers of abstraction in the coming decades.
For starters, I’m increasingly convinced that in the near future, three to five years tops, we’ll all be writing a whole lot more comments, use a whole lot more descriptive names for everything, and write a whole lot less code.
GitHub Copilot has been described as ‘magical’, ‘god send’, ‘seriously incredible work’, et cetera. I agree, it’s a pretty impressive tool, something I see myself using daily. Especially once they add support for PyCharm. Heck, I’ve been using it daily while Cmd+Tabbing between PyCharm and VSCode, writing code in PyCharm whenever I wanted to think for myself and in VSCode whenever I wanted the algorithm to do it for me.
In my experience, Copilot excels at writing repetitive, tedious, boilerplate-y code. With minimal context, it can whip up a function that slices and dices a dataset, trains and evaluates several ml models, and, if you ask it nicely, also makes a nice batch of french fries. Not just that, it can look at an example and a list of items, and apply that example to each and every item in the list, the kind of stuff you’d record a quick macro to fix.
