Comment by lmorchard
5 days ago
What you consider fun isn't universal. Some folks don't want to just tinker for half an hour, some folks enjoy getting a particular result that meets specific goals. Some folks don't find the mechanics of putting lines of code together as fun as what the code does when it runs. That might sound like paid work to you, but it can be gratifying for not-you.
For me it all the build stuff and scaffolding I have to get in place before I can even start tinkering on a project. I never formally learned all the systems and tools and AI makes all of that 10x easier. When I hit something I cannot figure out instead of googling for 1/2 hour it is 10 minutes in AI.
The difference is that after you’ve googled it for ½ hour, you’ve learned something. If you ask an LLM to do it for you, you’re none the wiser.
Wrong. I will spend 30 minutes having the LLM explain every line of code and why it's important, with context-specific follow-up questions. An LLM is one of the best ways to learn ...
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You can study the LLM output. In the “before times” I’d just clone a random git repo, use a template, or copy and paste stuff together to get the initial version working.
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This is just not true. I have wasted many hours looking for answers to hard-to-phrase questions and learned very little from the process. If an LLM can get me the same result in 30 seconds, it's very hard for me to see that as a bad thing. It just means I can spend more time thinking about the thing I want to be thinking about. I think to some extent people are valorizing suffering itself.
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I don't want to waste time learning how to install and configure ephemeral tools that will be obsolete before I ever need to use them again.
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Not necessarily. The end result of googling a problem might be copying a working piece of code off of stack exchange etc. without putting any work into understanding it.
Some people will try to vibe out everything with LLMs, but other people will use them to help engage with their coding more directly and better understand what's happening, not do worse.
I don't think "learning" is a goal here...
>> The difference is that after you’ve googled it for ½ hour, you’ve learned something.
I've been programming for 15+ years, and I think I've forgotten the overwhelming majority of the things I've googled. Hell, I can barely remember the things I've googled yesterday.
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Usually The thing you've learned after googling for half an hour is mostly that google isn't very useful for search anymore.
I don't think I'll learn anything by yet again implementing authentication, password reset, forgotten password, etc.
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Why train to pedal fast when we already got motorcycles? You are preparing for yesterday's needs. There will never be a time when we need to solve this manually like it's 2019. Even in 2019 we would probably have used Google, solving was already based on extensive web resources. While in 1995 you would really have needed to do it manually.
Instead of manual coding training your time is better invested in learning to channel coding agents, how to test code to our satisfaction, how to know if what AI did was any good. That is what we need to train to do. Testing without manual review, because manual review is just vibes, while tests are hard. If we treat AI-generated code like human code that requires a line-by-line peer review, we are just walking the motorcycle.
How do we automate our human in the loop vibe reactions?
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The difference is whether or not you find computers interesting and enjoy understanding how they work.
For the people who just want to solve some problem unrelated to computers but require a computer for some part of the task, yes AI would be more “fun”.
I don’t find this to be true. I enjoy computers quite a bit. I enjoy the hardware, scaling problems, theory behind things, operating systems, networking, etc.
Most of all I find what computers allow humanity to achieve extremely interesting and motivating. I call them the worlds most complicated robot.
I don’t find coding overly fun in itself. What I find fun is the results I get when I program something that has the result I desire. Maybe that’s creating a service for friends to use, maybe it’s a personal IT project, maybe it’s having commercial quality WiFi at home everyone is amazed at when they visit, etc. Sometimes - even often - it’s the understanding that leads to pride in craftsmanship.
But programming itself is just a chore for me to get done in service of whatever final outcome I’m attempting to achieve. Could be delivering bits on the internet for work, or automating OS installs to look at the 50 racks of servers humming away with cable porn level work done in the cabinets.
I never enjoyed messing around with HTML at that much in the 90s. But I was motivated to learn it just enough to achieve the cool ideas I could come up with as a teenager and share them with my friends.
I can appreciate clean maintainable code, which is the only real reason LLMs don’t scratch the itch as much as you’d expect for someone like me.
What I really enjoy in programming is algorithms and bit-twiddling and stuff that might be in Knuth or HAKMEM or whatever. That’s fun. I like writing Lisp especially, and doing cool, elegant functional programs.
I don’t enjoy boilerplate. I don’t necessarily enjoy all of the error checking and polishing and minutia in turning algorithms into shippable products.
I find AI can be immensely helpful in making real things for people to use, but I still enjoy doing what I find fun by hand.
See, I do though. I enjoy the act, the craft of programming. It's intrinsically fun for me, and has been for the 25 years I've been doing it at this point, and it still hasn't stopped being fun!
Different strokes I guess
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> The difference is whether or not you find computers interesting and enjoy understanding how they work.
I'm a stereotypical nerd, into learning for its own sake.
I can explain computers from the quantum mechanics of band gaps in semiconductors up to fudging objects into C and the basics of operating systems with pre-emptive multitasking, virtual memory, and copy-on-write as they were c. 2004.
Further up the stack it gets fuzzy (not that even these foundations are not; "basics" of OSes, I couldn't write one); e.g. SwiftUI is basically a magic box, and I find it a pain to work with as a result.
LLM output is easier to understand than SwiftUI, even if the LLM itself has much weirder things going on inside.
So, can you tell me everything that happens after you type www.google.com<RET> into the browser? ;)
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