Surprised pikachu face
I’m not sure how to succinctly do that.
When I learn a new language, I:
- go through whatever tutorial is provided by the language developers - for Rust, that’s The Rust Programming Language, for Go, it’s Tour of Go and Effective Go
- build something - for Go, this was a website, and for Rust it was a Tauri app (basically a website); it should be substantial enough to exercise the things I would normally do with the language, but not so big that I won’t finish
- read through substantial portions of the standard library - if this is minimal (e.g. in Rust), read through some high profile projects
- repeat 2 & 3 until I feel confident I understand the idioms of the language
I generally avoid setting up editor tooling until I’ve at least run through step 3, because things like code completion can distract from the learning process IMO.
Some books I’ve really enjoyed (i.e. where 1 doesn’t exist):
- The C Programming Language - by Brian Kernighan and Dennis Richie
- Programming in Lua - by Roberto Ierusalimschy
- Learn You a Haskell for Great Good - by Miran Lipovača (available free online)
But regardless of the form it takes, I appreciate a really thorough introduction to the language, followed by some experimentation, and then topped off with some solid, practical code examples. I generally allow myself about 2 weeks before expecting to write anything resembling production code.
These days, I feel confident in a dozen or so programming languages (I really like learning new languages), and I find that thoroughly learning each has made me a better programmer.
Thanks for that, was quite interesting and I agree that completion too early (even… in general) can be distracting.
I did mean about AI though, how you manage to integrate it in your workflow to “automate the boring parts” as I’m curious which parts are “boring” for you and which tools you actual use, and how, to solve the problem. How in particular you are able to estimate if it can be automated with AI, how long it might take, how often you are correct about that bet, how you store and possibly share past attempts to automate, etc.
I honestly don’t use it much, but so far, the most productive uses are:
- generate some common structure/algorithm - web app, CLI program, recursive function, etc
- search documentation - I may not know what the function/type is, but I can describe it
- generate documentation - list arguments, return types, etc
But honestly, the time I save there honestly isn’t worth fighting with the AI most of the time, so I’ll only do it if I’m starting up a big greenfield project and need something up and going quickly. That said, there are some things I refuse to use AI for:
- testing - AI may be able to get high coverage, but I don’t think it can produce high quality tests
- business logic - the devil is in the details, and I don’t trust AI with details
- producing documentation - developers hate writing documentation, which is precisely why devs should be the ones to do it; if AI could do it, other devs could just use AI to generate it, but good docs will do far more than what AI can intuit
