So when the AI bubble burst, will there be coding jobs available to clean up the mess?
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I mean largely for most of us I hope. But I feel like the tech sector was oversatured because of all the hype of it being an easy get rich quick job. Which for some people it was.
The people talking about AI coding the most at my job are architects and it drives me insane.
According to Deutsche Bank the AI bubble is ~~a~~ the pillar of our economy now.
So when it pops. I guess that's kinda apocalyptic.
Edit - strikethrough
Only for taxpayers ☝️
I code with LLMs every day as a senior developer but agents are mostly a big lie. LLMs are great for information index and rubber duck chats which already is incredible feaute of the century but agents are fundamentally bad. Even for Python they are intern-level bad. I was just trying the new Claude and instead of using Python's pathlib.Path it reinvented its own file system path utils and pathlib is not even some new Python feature - it has been de facto way to manage paths for at least 3 years now.
That being said when prompted in great detail with exact instructions agents can be useful but thats not what being sold here.
After so many iterations it seems like agents need a fundamental breakthrough in AI tech is still needed as diminishing returns is going hard now.
If it wasn't for all the AI hype that it's going to do everyone's job, LLMs would be widely considered an amazing advancement in computer-human interaction and human assistance. They are so much better than using a search engine to parse web forums and stack overflow, but that's not going to pay for investing hundreds of billions into building them out. My experience is like yours - I use AI chat as a huge information index mainly, and helpful sounding board occasionally, but it isn't much good beyond that.
Oh yes. The Great pathlib
. The Blessed pathlib
. Hallowed be it and all it does.
I'm a Ruby girl. A couple of years ago I was super worried about my decision to finally start learning Python seriously. But once I ran into pathlib
, I knew for sure that everything will be fine. Take an everyday headache problem. Solve it forever. Boom. This is how standard libraries should be designed.
I disagree. Take a routine problem and invent a new language for it. Then split it into various incompatible dialects, and make sure in all cases it requires computing power that no one really has.
I think maybe a good comparison is to written papers/ assignments. It can generate those just like it can generate code.
But it is not about the words themself, but about the content.
I'd much rather write my own bugs to have to waste hours fixing, thanks.
AI coding is the stupidest thing I've seen since someone decided it was a good idea to measure the code by the amount of lines written.
More code is better, obviously! Why else would a website to see a restaurant menu be 80Mb? It's all that good, excellent code.
Even though this shit was apparent from day fucking 1, at least the Tech Billionaires were able to cause mass layoffs, destroy an entire generation of new programmers' careers, introduce an endless amount of tech debt and security vulnerabilities, all while grifting investors/businesses and making billions off of all of it.
Sad excuses for sacks of shit, all of them.
i use it for programming arduinos for my smarthome. its pretty nice but also aggravating.
LLMs work great to ask about tons of documentation and learn more about high-level concepts. It's a good search engine.
The code they produce have basically always disappointed me.
On proprietary products, they are awful. So many hallucinations that waste hours. A manager used one on a code review of mine and only admitted it after I spent the afternoon chasing it.
Those happen so often. I've taken to stop calling them hallucinations anymore (that's anthropomorphising and over-selling what LLMs do imho). They are statistical prediction machines, and either they hit their practical limits of predicting useful output, or we just call it broken.
I think the next 10 years are going to be all about learning what LLMs are actually good for, and what they are fundamentally limited at no matter how much GPU ram we throw at it.
Imagine if we did "vibe city infrastructure". Just throw up a fucking suspension bridge and we'll hire some temps to come in later to find the bad welds and missing cables.
I use AI as an entryway to learning or for finding the name or technique that I'm thinking of but can't remember or know it's name so then i can look elsewhere for proper documentation. I would never have it just blindly writing code.
Sadly search engines getting shitter has sort of made me have to use it to replace them.
Then it's also good to quickly parse an error for anything obviously wrong.
it's slowing you down. The solution to that is to use it in even more places!
Wtf was up with that conclusion?
I mean.. At best it's a stack overflow/google replacement.
At least when I'm cleaning up after shit devs who used Stack Overflow, I can usually search using a fragment of their code and find where they swiped it from and get some clue what the hell they were thinking. Now that they're all using AI chatbots, there's no trace.
These types of articles always fail to mention how well trained the developers were on techniques and tools. In my experience that makes a big difference.
My employer mandates we use AI and provides us with any model, IDE, service we ask for. But where it falls short is providing training or direction on ways to use it. Most developers seem to go for results prompting and get a terrible experience.
I on the other hand provide a lot of context through documents and various mcp tooling, I talk about the existing patterns in the codebase and provide sources to other repositories as examples, then we come up with an implementation plan and execute on it with a task log to stay on track. I spend very little time fixing bad code because I spent the setup time nailing down context.
So if a developer is just prompting "Do XYZ". It's no wonder they're spending more time untangling a random mess.
Another aspect is that everyone seems to always be working under the gun and they just don't have the time to figure out all the best practices and techniques on their own.
I think this should be considered when we hear things like this.
Writing apps with AI seems pretty cooked. But I've had some great successes using GitHub copilot for some annoying scripting work.
I think it's useful for writing mundane snippets I've written a million times or helping me with languages I'm less familiar with, but anything more compex becomes pretty spaghetti pretty quick.
AI is works well for mindless tasks. Data formatting, rough drafts, etc.
Once a task requires context and abstract thinking, AI can't handle it.
Eh, I don't know. As long as you can break it down into smaller sub-tasks, AI can do some complex stuff. Just have to figure out where the line is. I've nudged it along into reading multiple LENGTHY API documentation pages and written some fairly complex scripting logic.
But will something be done about it?
NOooOoOoOoOoo. As long as it is still the new shiny toy for techbros and executive-bros to tinker with, it'll continue.
“No Duh,” say senior developers everywhere.
I'm so glad this was your first line in the post
If not to editorialize, what else is the text box for? :)
I have been vibe coding a whole game in JavaScript to try it out. So far I have gotten a pretty ok game out of it. It's just a simple match three bubble pop type of thing so nothing crazy but I made a design and I am trying to implement it using mostly vibe coding.
That being said the code is awful. So many bad choices and spaghetti code. It also took longer than if I had written it myself.
So now I have a game that's kind of hard to modify haha. I may try to setup some unit tests and have it refactor using those.