Exactly. I see AI as a tool to automate the boring parts, if you try to automate the hard parts, you’re going to have a bad time.
Take the time to learn the tools you use thoroughly, and then you can turn to AI to make your use of those tools more efficient. If I’m learning woodworking, for example, I’m going to learn to use hand tools first before using power tools, but there’s no way I’m sticking to hand tools when producing a lot of things. Programming isn’t any different, I’ll learn the language and its idioms as deeply as I can, and only then will I turn to things like AI to spit out boilerplate to work from.
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
I can’t remember if I shared this earlier but I’m jolting down notes on the topic in https://fabien.benetou.fr/Content/SelfHostingArtificialIntelligence so I do also invest time on the topic. Yet my results have also been… subpar so I’m asking as precisely as I can how others actually benefit from it. I’m tired of seeing posts with grand claims that, unlike you, only talk about the happy path in usage. Still, I’m digging not due to skepticism as much as trying to see what can actually be leveraged, not to say salvaged. So yes, genuine feedback like yours is quite precious.,
I do seem to hear from you and others that to kickstart what would be a blank project and get going it can help. Also that for whatever is very recurrent AND popular, like common structures, it can help.
My situation though is in prototyping where documentation is sparse, if even existent, and working examples are very rare. So far it’s been a bust quite often.
Out of curiosity, which AI tools specifically do you use and do you pay for them?
PS: you mention documentation is both cases, so I imagine it’s useful when it’s very structured and when the user can intuit most of how something works, closer to a clearly named API with arguments than explaining the architecture of the project.
Alan Perlis said “A programming language that doesn’t change the way you think is not worth learning.”
So… if you code in another language without actually “getting it”, solely having a usable result, what is actually the point of changing languages?
I have a job to do. And I understand the other language conceptually, I am just rusty on the syntax.
Also the chat feature is invaluable. I can highlight a piece of code and ask what it does, and copilot explains it.
Exactly. I see AI as a tool to automate the boring parts, if you try to automate the hard parts, you’re going to have a bad time.
Take the time to learn the tools you use thoroughly, and then you can turn to AI to make your use of those tools more efficient. If I’m learning woodworking, for example, I’m going to learn to use hand tools first before using power tools, but there’s no way I’m sticking to hand tools when producing a lot of things. Programming isn’t any different, I’ll learn the language and its idioms as deeply as I can, and only then will I turn to things like AI to spit out boilerplate to work from.
Mind explaining a bit your workflow at the moment?
I’m not sure how to succinctly do that.
When I learn a new language, I:
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):
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:
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:
Super, thanks again for taking the time to do so.
I can’t remember if I shared this earlier but I’m jolting down notes on the topic in https://fabien.benetou.fr/Content/SelfHostingArtificialIntelligence so I do also invest time on the topic. Yet my results have also been… subpar so I’m asking as precisely as I can how others actually benefit from it. I’m tired of seeing posts with grand claims that, unlike you, only talk about the happy path in usage. Still, I’m digging not due to skepticism as much as trying to see what can actually be leveraged, not to say salvaged. So yes, genuine feedback like yours is quite precious.,
I do seem to hear from you and others that to kickstart what would be a blank project and get going it can help. Also that for whatever is very recurrent AND popular, like common structures, it can help.
My situation though is in prototyping where documentation is sparse, if even existent, and working examples are very rare. So far it’s been a bust quite often.
Out of curiosity, which AI tools specifically do you use and do you pay for them?
PS: you mention documentation is both cases, so I imagine it’s useful when it’s very structured and when the user can intuit most of how something works, closer to a clearly named API with arguments than explaining the architecture of the project.