My ideal curriculum would be to go through the entire evolution of computing, and at the final years you end up in modern computing. In the end we kind of went over all those topics, but it would have been a very straight forward curriculum. You start at basic electricity and the Turing machine, in the middle somewhere you learn about neural networks (I learned that around 2000, and it was old technology then).
When you graduate, you have a full understanding from bottom to top.
That's how I would have loved it, but maybe for others that would have been too boring, so they mixed it up.
In the end I got great value from my master in CS. All the practical things you learn at the job anyway, and I definitely learned a lot those first few years. But my education allows me at certain occasions to go further when other developers reach their limit.
Yea I think a general history of computing that teaches from first principles would be great. Could help students realize neural networks and transformers aren’t really new concepts just needed the data and hardware to catch up. Can dispell a lot of myths and magical thinking about AI.
I do agree with you, but don't underestimate the projects where you can actually apply this 10x. For example, I wanted to get some analytics out of my database. What would have been a full weekend project was now done in an hour. So for such things there is a huge speed boost.
But once software becomes bigger and more complex, the LLM starts messing up, and the expert has to come in. That basicaly means your months project cannot be done in a week.
My personal prediction: plugins and systems that support plugins will become important. Because a plugin can be written at 10x speed. The system itself, not so much.
Yes, definitely. I also don't think every project is able to create a plugin platform. Sometimes you just have a lot of interconnected components, where they kind of influence each other.
What I was trying to say is that in future developments, as a developer, one of the extra questions on your mind should be: can we turn this into a platform with separate plugins? Because you know those plugins can be written fast, cheap, and don't require top notch engineering work.
But I think I get what you are saying: what you gain in plugin simplicity, you pay in effort to design the platform to support them.
I guess it will depend from project to project, and so the typical "it depends" applies :).
People have been thinking about that a long time though. For that objective, LLMs don't seem to open up any new capabilities. If that problem could be solved, with really clean abstractions that dramatically reduce context needed to understand one "module" at a time, sure LLMs will then be able to take that an run. But it's a fundamentally hard problem.
Exactly my experience to, and I'm doing hiring at the moment. We used to filter out the worst with a hacker rank test, but now the idiots cheat with AI, and then we have to waste our time in an interview. It's difficult at the moment.
>The bar for human juniors is now way higher than it used to be.
What do you think that is now? How does someone signal being 'past the bar'? If I hand wrote a toy gaussian splat renderer is that better than someone who used AI to implement a well optimized one with lots of features in vulkan?
'past the bar' means you have to be smarter than AI, simple as that. You need to be able to tell when it delivers good work, and when not. If you are not smarter than AI, you will not be able to tell the difference. And then what is your added value?
One thing I want to mention here is that you should try to write a test that not only prevents this bug, but also similar bugs.
In our own codebase we saw that regression on fixed bugs is very low. So writing a specific test for it, isn't the best way to spend your resources. Writing a broad test when possible, does.
Not sure how LLM's handle that case to come up with a proper test.
Didn't a bunch of kids in NYC get STIs cuz of this like a decade or two ago? A bunch of rabbis were biting baby dicks, oh sorry I mean performing a religious ceremony, and giving kids STIs.
I think it all depends on the use case and a luck factor.
Sometimes I instruct copilot/claude to do a development (stretching it's capabilities), and it does amazingly well. Mind you that this is front-end development, so probably one of the more ideal use-cases. Bugfixing also goes well a lot of times.
But other times, it really struggles, and in the end I have to write it by hand. This is for more complex or less popular things (In my case React-Three-Fiber with skeleton animations).
So I think experiences can vastly differ, and in my environment very dependent on the case.
One thing is clear: This AI revolution (deep learning) won't replace developers any time soon. And when the next revolution will take place, is anyones guess. I learned neural networks at university around 2000, and it was old technology then.
I view LLM's as "applied information", but not real reasoning.
When you graduate, you have a full understanding from bottom to top.
That's how I would have loved it, but maybe for others that would have been too boring, so they mixed it up.
In the end I got great value from my master in CS. All the practical things you learn at the job anyway, and I definitely learned a lot those first few years. But my education allows me at certain occasions to go further when other developers reach their limit.
reply