I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.
A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
> AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
AI is, in my opinion, absolutely murdering the junior dev market. It's now much more efficient to pay for a senior or staff engineer and set them up with a subscription to claude or codex. I think things are going to remain fine for senior+ engineers, but I genuinely don't know what advice I'd give to graduating devs at this time.
Being a senior engineer doesn't really have anything to do with how long you've been developing. I've met amazing 21 year old developers who can out code people doing this for 20+ years in their sleep. I would say one of the reasons "seniors" are so much better with AI is because they have CS fundamentals. They know what to ask, they know systems design, they know what ACID is, they understand race conditions...
AI + CS allows you to develop with leverage I've never seen before.
I wonder how you'd do without your AI augmentation if you find yourself not being able to use it for whatever reason. I wonder what you retained when the AI is not working... or with a very expensive augmentation that wants more cut from your pay...
> A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
Yes and no.
I concur that it was never about the tools, although I've seen plenty of people advocating it had a more "practical" approach, using the same tools as companies use, and in the same way.
On the other hand it's not only about the basics.
In there you can go at depth in aspects that you will have to fight to use, like proper engineering practices, and might only be vital on the later stages of your career. And I feel that those learnings, plus the possibility of acquiring a breadth of knowledge that is hardly possible otherwise, is what gives CS graduates a leg up to other practitioners of the art of software development.
I do learn every day thanks to AI, I agree with that part. But I'm increasingly convinced that AI is likely to replace either me or some of my teammates in the near future.
In the last year, half of my team has been laid off (or left) already and won't be replaced, and we don't see any drop in productivity and the tools are getting better by the day. We need experts to drive all this, but not as many people as before.
Basically true for all degrees. I studied non-cs engineering before getting into coding and feel it often. At times have felt “should have stuck more with mech e where jobs are more stable and the tech moves slower” but know it’s kind of a fallacy.
Other than keeping up to date with how to best use AI/harnesses what else would be most important to stay on top of for retaining a software engineering job these days? Also AI seems generic to me. Could I bother you for a more detailed breakdown on the most important topics/skills you would consider necessary for a software engineer to be knowledgeable of?
Iff your goal is using a degree as a stepping stone, which seems to be getting less popular these days.
For some, the degree is the destination and that's more than fine as well. It's a marker that says "others agree I learned x to a certain level, and I had fun doing it". This tends to overlap well with those who just liked university as an extra method of learning rather than going primarily to form part 1(a) of their career or something.
I’m hearing this more and more from both students and teachers. It seems like everyone in education is frustrated and can’t figure out how to use AI for education productively yet.
TBH I’m also thinking about how to use AI at work for learning. (My work environment is 100% all-in on AI.) Everyone can see and admit that AI coding is making us lazier and that it produces too much code to do old-style line by line reviews. Some people are using agent teams to churn out millions of lines of code where there’s zero hope of reviewing it. We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix. Agents should get better at broad architecture in the future, but they’re definitely not there yet.
> The amount of work involved has also tripled
This has to be one of the big ironies of using AI. I’ve heard this from other students too. The problem is that old-style assignments where the level of difficulty was hand-crafted is way way too easy for an LLM, therefore teachers need to crank the level of difficulty so the student has to actually spend some time on the assignment. But LLMs are getting better every month and the difficulty is so high that no students can possibly do the assignment by hand and now small errors in assignment design can lead to large errors in effort and time required.
> We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix
Implement more versions with different languages, frameworks, libraries and AI agents. Then you can do differential execution for testing, which is easier than imagining testing scenarios from pure code. You can fuzz or use production data (shadow mode execution) to get notified early of divergences.
Keeping a population of diverse replicas might be the simplest way to derisk AI code. When 2 replicas diverge an agent can decide - was it a bug in one of them, or was the spec unclear? Either way, if you fix a bug or clarify the spec you come ahead.
A separate class of problems is related to intent divergence. For that my solution is to keep a full log of all user messages during the lifetime of the project. Even 10,000 messages collected over weeks and months can be read and summarized in a few minutes. That means you can check for intent divergence with agents, but you always start from the raw source (your human messages) not from intermediate summaries and diverged code.
Combining the 2 ideas would make each replica agent derive the user intent separately from the same log.
This is a nice idea, but I’m in a big enough and low-level enough code base that using other languages or stacks isn’t very viable; we build the stack, so it’s software and hardware and compiler and driver. We do sometimes use multiple agents and adversarial review. The agents tend to come up with more complete corner-case testing than humans, the most common issue is that they don’t fully understand all design assumptions and parameters and they over-test for things that can’t happen using very complicated test setups, and they don’t test enough for simple mainline usage; they general don’t know what the common workflows are. I had the same complaint about humans before LLMs too. :P We often try to be clever about testing and design all this logical coverage while ignoring the boring & easy stuff, we think like engineers and not users.
I taught my Claude to teach me new skills, where I wanted to understand its work deeper. Specifically, I wanted to learn coccinelle semantic patching, so I told it to find potential places to apply it but withhold the solutions from me. Instead, it walks me through producing a patch myself.
Answers so far are assuming that this is complaining that their traditional CS course is not that useful for getting a job anymore.
That’s not what it’s about. It’s actually from someone who genuinely wants to learn for the sake of learning who’s talking about how AI is creating havoc in her studies.
I would argue the job thing is more at risk and opportunity for learning is at an all time high. You've got 24/7 access to a very skilled personal tutor.
AI can teach you too, you just have to put the effort in to tell it to teach you. In some contexts, I might want it to fire off a utility bash script and I have no interest in learning bash so I just run it and don't worry too much about the implementation. But if I'm getting it to help me write Java code for work, I review the output and ask it about parts that I don't understand. And it teaches me way better than the old way of reading dry/bad documentation.
This is a blog post where it’s easy to see which commenters did not read the blog before commenting.
The real villain in this story is the teaching staff. They need to get their act together and stop using generative AI for courseloads.
In other words, they need to do their job. The parts about AI mandates changing every 3 months reeks of an overly bureaucratic, admin-heavy structure where the students’ learning is a lower priority being performative about AI for your boss.
While AI is writing much much more of my code these days, I still need to be able to speak intelligently about what I am building, why I chose a given design and the tradeoffs between alternatives. When fixing things, it's still important to understand what broke and why. I don't think that will ever change in any organization that operates in high compliance environments or cares about delivering high quality products.
I don't think programming becoming 'easier' has make understanding programs any easier or less important. Even if all of the above goes away, I think computer science has taught me a lot about how to think about problems.
It’s probably time students shift further away from studying computer science and more toward software engineering. It provides real skills that are still relevant in the AI era. Less memorizing algorithms, more SDLC, team management, economics, and working with real customers on their requirements.
Being able to write a coherent critique that stays practical and doesn't stray into bombastic moralisation or interpersonal frustration is a rare skill that speaks well to your communication skill and taste. I think you will do just fine in a world of AI in engineering.
First off, congrats on spending time doing side projects in OpenProcessing. Keep working on projects that keep you motivated and keep you learning.
We've been there, lost in our learning paths. Even before AI there were times when we were overwhelmed with our CS journey. You'll overcome, don't you worry about that.
The CS degree was never the end of the learning journey. It was always _what you put into it_. You'll learn what you practice, and uni just happens to be a good place to practice things. Whether that's optimizing AI harnesses, understanding how CPU caches work, crunching pixels on a GPU or the principles of distributed computing... it will all make you a better thinker and good at what you do one day.
I used to have a lot of anxiety about Ai wiping out all the Software specific jobs, software development/testing/project management, etc. I actually do think that's still likely to be the case going forward. Pick an industry and try and become an expert in it. Someone will still have to prompt and dream up new features. There are still things that you can't prompt your way to, but they're on the relationship side, not the technical side. I'm not sure what I would say to someone currently doing a CS degree, Math degree, Physics degree, or wanting to do one. Most likely I would say if you want to have a stable income, get good at sales.
The uni I recently graduated is also very confused regarding what to do... Things have converged towards less hands on assignments and more theoretical exams, something I know for sure I would've hated during my studies.
It's awkward, you're very unlikely to stop students from using AI for certain assignments (i.e: first programming class) for the good of their fundamentals, but there's also no other teaching tool being used to get them equivalent experience to solving assignments without AI.
That seems like a normal academia dysfunction, AI is just another thing they are failing to adapt to. I graduated 10 years ago and on the job I had to unlearn most of what I learned from the Java course, for example.
I haven't seen a better example of "those who can't do, teach" than CS professors.
Isn't having something that can solve general problems based on prior art without the requisite effort in learning the prior art completely antithetical to education in general? Not saying they aren't useful tools, but asking a an AI to write bubble sort isn't learning bubble sort? The point was never to write bubbles sort it was to learn how and why.
I decided to finally get a CS degree at juuuust the right time (well, for my personal reasons of "love of the game" at least - individual interests may vary), having wrapped up ~2 years ago.
It was interesting to see things change towards the end. Some was the chaos from the lack of consistency the article highlights just starting to emerge. Mostly though, it was just the rampant surge in cheating and the classes not being prepared for it.
It's not that it's forbidden fruit, but it's akin to asking your mom for every answer on your 3rd grade homework. You get the correct answer and learn nothing.
Sure, in the real world outside of school, you'll be able to get help from many external tools. But when the entire point is to learn something, any tools that prohibit you from learning should be avoided.
It's not a double standard. It's the same standard applied to different things.
The best way we know of to learn something with AI is to do the work yourself, then watch AI blow you out of the water on the same task, learn its tricks and do the work again (better, even, since AI isn't that great at it yet)
Which means the teacher can use AI, and you can't.
This is also an entirely different subject to whether you should be learning that specific task in the first place or delegate it to AI.
>The best way we know of to learn something with AI is to do the work yourself, then watch AI blow you out of the water on the same task, learn its tricks and do the work again (better, even, since AI isn't that great at it yet)
Citation on this? There is something on HN front page right now that is contradicting.
Or "Karim lakhani how to learn with AI" and see Gemini's skim of the video.
The other thread is highly sensationalised and not really related. (Edit: sorry I thought it was a different thread, the one that's sublinked by OP in your thread. So disregard this paragraph. I can't contest either position, I'm a mere spectator. That being said, I don't see the contradiction, perhaps you could elaborate on that)
Wesley Crusher was kidnapped away to a planet with no children. Their bodies were decaying but their technology was superior to the Federation's so they needed to steal other people's kids.
They gave Wesley a laser scalpel, to teach him to chisel wood, with whatever his mind could picture.
Wesley complained, he said, "dood, this isn't art! i'm not even chiseling!"
And the mentor-kidnapper said, "No, Wesley, you are doing art. The art is in your mind. The tool is just doing the work."
And so then Wesley threw the perfect wooden dolphin at the wall, infuriated that all the pleasure had been removed from the art. But he lived the rest of his life on that planet, making perfect wooden laser sculpted dolphins, cause that was his job and his boss told him too bad.
For a little while Wesley made popsicle stick art during his free time at night to try and feel the sensation of choice and control, but eventually just gave up and played mariokart until he was an old man.
Why are more people (bots?) using bearblog. The landing page claims it's fast and privacy focused, but it offers so little... I could recreate it myself in a day so why aren't people running their own blogs, especially in CS where you could learn something doing it.
I'm also now reminded that this is ran by that same guy who complained about the MIT license allowing people to do whatever they wish.
File under "old man yells at clouds."
I think it's worth asking what a CS degree is for. Is it
to teach you how to code and make you obviously hire-able
by industry? Maybe it's to teach you how to think about
data and code? If you think it's the former, then AI is
probably going to be a problem for you. If the latter,
it's neutral to somewhat good.
I've been hiring CS grads for 4 decades (yikes!) and
there's definitely been a shift. Not all existing
programs are bad. Not all young software devs are bad.
But... the percentage of recent grads that seem to
understand what data processing systems are about has
definitely fallen.
Us old farts in industry must share some of the blame
here. Our industry seems to be pretty boom or bust.
Someone comes in with a stack of money and tells people to
build { the web | blockchain | AI | virtual worlds | etc }
and suddenly there's a rush. At the start of the gold
rush, we're not sure how to build everything that's going
to be the next google [[1]] so we fill the room with young
kids who have seen some recent tooling and will work
cheap. We don't develop them. We want them to be
compliant drones dancing to a tune played by a CTO and
VP/Eng who got their jobs because they're the children of
the fund investors who gave the startup a bunch of money.
And then we go back to Universities and tell them "OMG!
Your not bar-raising your grads by teaching them __fill in
the blank__!" [[2]]
So... I don't doubt it looks like AI is destroying the
degree. But maybe it's just making it hard to ignore
previous problems with the uni to industry pipeline.
And I didn't mention the Mythical Man Month once in this
comment. I'm getting better.
1. I'm not sure too many people want to be Google at the
moment, but over the last 25 years, they've certainly
done well.
2. I'm intentionally misspelling "you're" because in my
time in industry, I've found only about 15% of people
would pass a 7th grade spelling test.
This is a good article. AI just introduces new bureaucracy, workloads, useless discussions and is a hindrance.
No one needs AI in universities or software engineering. It only benefits the middle managers and mediocre people who jump on any bandwagon in order to help their careers.
No software has gotten any better since AI, except that some forgotten bugs from the 1990s were found, just as 20000+ bugs have been found by the Luddite OssFuzz system before AI.
This sounds awful, but the culprit is the terrible faculty at this institution:
> Whereas before, assignments were designed by a person who thought them through at least once, then approved by other people in the department, now I doubt they're getting read more than once (if that). Errors abound, often with corrections coming a week or two before the deadline.
This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.
From the title I thought I was going to be reading an article wondering how AI would devalue a CS degree, but this is actually a story about a CS program falling apart because the instructors are flailing about without AI.
> This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.
Why? How many times have these same assignments been created, semester after semester for decades before we see how wasteful this is? These are solved problems in 99% of cases and just busy work for the staff to justify their existence. Their job should be to lecture and assist students, not create endless assignments that just regurgitate what was already done a thousand times already.
> It's been a huge learning curve.
I have been doing this professionally for over twenty years, and I still learn something every day. Over the past couple years, with AI augmentation, I learn even more, move faster and go deeper into the systems that I work on.
A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
> AI is not replacing you or an education. AI is raising the bar of what you can comprehend and retain. From someone who is technically minded (and self-taught), all I have ever wanted is to keep learning.
AI is, in my opinion, absolutely murdering the junior dev market. It's now much more efficient to pay for a senior or staff engineer and set them up with a subscription to claude or codex. I think things are going to remain fine for senior+ engineers, but I genuinely don't know what advice I'd give to graduating devs at this time.
I agree with the caveat that a lot of "seniors" (in name only) will be discarded as well. Title inflation finally comes back go bite people.
Being a senior engineer doesn't really have anything to do with how long you've been developing. I've met amazing 21 year old developers who can out code people doing this for 20+ years in their sleep. I would say one of the reasons "seniors" are so much better with AI is because they have CS fundamentals. They know what to ask, they know systems design, they know what ACID is, they understand race conditions...
AI + CS allows you to develop with leverage I've never seen before.
I wonder how you'd do without your AI augmentation if you find yourself not being able to use it for whatever reason. I wonder what you retained when the AI is not working... or with a very expensive augmentation that wants more cut from your pay...
> A CS degree was never going to provide you with all of the tools that you need for a successful career. It provided you with the basics to get your foot in the door.
Yes and no.
I concur that it was never about the tools, although I've seen plenty of people advocating it had a more "practical" approach, using the same tools as companies use, and in the same way.
On the other hand it's not only about the basics.
In there you can go at depth in aspects that you will have to fight to use, like proper engineering practices, and might only be vital on the later stages of your career. And I feel that those learnings, plus the possibility of acquiring a breadth of knowledge that is hardly possible otherwise, is what gives CS graduates a leg up to other practitioners of the art of software development.
YMMV and a leg up can be easily overtaken, yes.
> AI is not replacing you or an education
I do learn every day thanks to AI, I agree with that part. But I'm increasingly convinced that AI is likely to replace either me or some of my teammates in the near future.
In the last year, half of my team has been laid off (or left) already and won't be replaced, and we don't see any drop in productivity and the tools are getting better by the day. We need experts to drive all this, but not as many people as before.
Basically true for all degrees. I studied non-cs engineering before getting into coding and feel it often. At times have felt “should have stuck more with mech e where jobs are more stable and the tech moves slower” but know it’s kind of a fallacy.
Other than keeping up to date with how to best use AI/harnesses what else would be most important to stay on top of for retaining a software engineering job these days? Also AI seems generic to me. Could I bother you for a more detailed breakdown on the most important topics/skills you would consider necessary for a software engineer to be knowledgeable of?
> all I have ever wanted is to keep learning
Good for you! Many people just want to be able to pay their bills.
Your post is entirely too dismissive of the chaos this new technology is introducing.
I don't think they're complaining about the learning curve.
Didn't say that to refute it. I chose that to make the point that the degree is not the destination.
Iff your goal is using a degree as a stepping stone, which seems to be getting less popular these days.
For some, the degree is the destination and that's more than fine as well. It's a marker that says "others agree I learned x to a certain level, and I had fun doing it". This tends to overlap well with those who just liked university as an extra method of learning rather than going primarily to form part 1(a) of their career or something.
I’m hearing this more and more from both students and teachers. It seems like everyone in education is frustrated and can’t figure out how to use AI for education productively yet.
TBH I’m also thinking about how to use AI at work for learning. (My work environment is 100% all-in on AI.) Everyone can see and admit that AI coding is making us lazier and that it produces too much code to do old-style line by line reviews. Some people are using agent teams to churn out millions of lines of code where there’s zero hope of reviewing it. We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix. Agents should get better at broad architecture in the future, but they’re definitely not there yet.
> The amount of work involved has also tripled
This has to be one of the big ironies of using AI. I’ve heard this from other students too. The problem is that old-style assignments where the level of difficulty was hand-crafted is way way too easy for an LLM, therefore teachers need to crank the level of difficulty so the student has to actually spend some time on the assignment. But LLMs are getting better every month and the difficulty is so high that no students can possibly do the assignment by hand and now small errors in assignment design can lead to large errors in effort and time required.
> We don’t yet know how to keep quality high and complexity low while doing agent coding, we’re just having them write lots of tests and hoping that when things break they’re easy to fix
Implement more versions with different languages, frameworks, libraries and AI agents. Then you can do differential execution for testing, which is easier than imagining testing scenarios from pure code. You can fuzz or use production data (shadow mode execution) to get notified early of divergences.
Keeping a population of diverse replicas might be the simplest way to derisk AI code. When 2 replicas diverge an agent can decide - was it a bug in one of them, or was the spec unclear? Either way, if you fix a bug or clarify the spec you come ahead.
A separate class of problems is related to intent divergence. For that my solution is to keep a full log of all user messages during the lifetime of the project. Even 10,000 messages collected over weeks and months can be read and summarized in a few minutes. That means you can check for intent divergence with agents, but you always start from the raw source (your human messages) not from intermediate summaries and diverged code.
Combining the 2 ideas would make each replica agent derive the user intent separately from the same log.
This is a nice idea, but I’m in a big enough and low-level enough code base that using other languages or stacks isn’t very viable; we build the stack, so it’s software and hardware and compiler and driver. We do sometimes use multiple agents and adversarial review. The agents tend to come up with more complete corner-case testing than humans, the most common issue is that they don’t fully understand all design assumptions and parameters and they over-test for things that can’t happen using very complicated test setups, and they don’t test enough for simple mainline usage; they general don’t know what the common workflows are. I had the same complaint about humans before LLMs too. :P We often try to be clever about testing and design all this logical coverage while ignoring the boring & easy stuff, we think like engineers and not users.
I taught my Claude to teach me new skills, where I wanted to understand its work deeper. Specifically, I wanted to learn coccinelle semantic patching, so I told it to find potential places to apply it but withhold the solutions from me. Instead, it walks me through producing a patch myself.
Answers so far are assuming that this is complaining that their traditional CS course is not that useful for getting a job anymore.
That’s not what it’s about. It’s actually from someone who genuinely wants to learn for the sake of learning who’s talking about how AI is creating havoc in her studies.
I would argue the job thing is more at risk and opportunity for learning is at an all time high. You've got 24/7 access to a very skilled personal tutor.
AI can teach you too, you just have to put the effort in to tell it to teach you. In some contexts, I might want it to fire off a utility bash script and I have no interest in learning bash so I just run it and don't worry too much about the implementation. But if I'm getting it to help me write Java code for work, I review the output and ask it about parts that I don't understand. And it teaches me way better than the old way of reading dry/bad documentation.
It's what you make of it.
This is a blog post where it’s easy to see which commenters did not read the blog before commenting.
The real villain in this story is the teaching staff. They need to get their act together and stop using generative AI for courseloads.
In other words, they need to do their job. The parts about AI mandates changing every 3 months reeks of an overly bureaucratic, admin-heavy structure where the students’ learning is a lower priority being performative about AI for your boss.
You don’t know what pressures the teaching staff is under.
While AI is writing much much more of my code these days, I still need to be able to speak intelligently about what I am building, why I chose a given design and the tradeoffs between alternatives. When fixing things, it's still important to understand what broke and why. I don't think that will ever change in any organization that operates in high compliance environments or cares about delivering high quality products.
I don't think programming becoming 'easier' has make understanding programs any easier or less important. Even if all of the above goes away, I think computer science has taught me a lot about how to think about problems.
It’s probably time students shift further away from studying computer science and more toward software engineering. It provides real skills that are still relevant in the AI era. Less memorizing algorithms, more SDLC, team management, economics, and working with real customers on their requirements.
Being able to write a coherent critique that stays practical and doesn't stray into bombastic moralisation or interpersonal frustration is a rare skill that speaks well to your communication skill and taste. I think you will do just fine in a world of AI in engineering.
Hey.
First off, congrats on spending time doing side projects in OpenProcessing. Keep working on projects that keep you motivated and keep you learning.
We've been there, lost in our learning paths. Even before AI there were times when we were overwhelmed with our CS journey. You'll overcome, don't you worry about that.
The CS degree was never the end of the learning journey. It was always _what you put into it_. You'll learn what you practice, and uni just happens to be a good place to practice things. Whether that's optimizing AI harnesses, understanding how CPU caches work, crunching pixels on a GPU or the principles of distributed computing... it will all make you a better thinker and good at what you do one day.
Stay curious.
I used to have a lot of anxiety about Ai wiping out all the Software specific jobs, software development/testing/project management, etc. I actually do think that's still likely to be the case going forward. Pick an industry and try and become an expert in it. Someone will still have to prompt and dream up new features. There are still things that you can't prompt your way to, but they're on the relationship side, not the technical side. I'm not sure what I would say to someone currently doing a CS degree, Math degree, Physics degree, or wanting to do one. Most likely I would say if you want to have a stable income, get good at sales.
The uni I recently graduated is also very confused regarding what to do... Things have converged towards less hands on assignments and more theoretical exams, something I know for sure I would've hated during my studies.
It's awkward, you're very unlikely to stop students from using AI for certain assignments (i.e: first programming class) for the good of their fundamentals, but there's also no other teaching tool being used to get them equivalent experience to solving assignments without AI.
That seems like a normal academia dysfunction, AI is just another thing they are failing to adapt to. I graduated 10 years ago and on the job I had to unlearn most of what I learned from the Java course, for example.
I haven't seen a better example of "those who can't do, teach" than CS professors.
Isn't having something that can solve general problems based on prior art without the requisite effort in learning the prior art completely antithetical to education in general? Not saying they aren't useful tools, but asking a an AI to write bubble sort isn't learning bubble sort? The point was never to write bubbles sort it was to learn how and why.
I decided to finally get a CS degree at juuuust the right time (well, for my personal reasons of "love of the game" at least - individual interests may vary), having wrapped up ~2 years ago.
It was interesting to see things change towards the end. Some was the chaos from the lack of consistency the article highlights just starting to emerge. Mostly though, it was just the rampant surge in cheating and the classes not being prepared for it.
The double standard of "YES WE'RE USING AI BUT THAT DOESN'T MEAN YOU SHOULD!" needs to be called out much more. It isn't some forbidden fruit.
It's not that it's forbidden fruit, but it's akin to asking your mom for every answer on your 3rd grade homework. You get the correct answer and learn nothing.
Sure, in the real world outside of school, you'll be able to get help from many external tools. But when the entire point is to learn something, any tools that prohibit you from learning should be avoided.
It's not a double standard. It's the same standard applied to different things.
The best way we know of to learn something with AI is to do the work yourself, then watch AI blow you out of the water on the same task, learn its tricks and do the work again (better, even, since AI isn't that great at it yet)
Which means the teacher can use AI, and you can't.
This is also an entirely different subject to whether you should be learning that specific task in the first place or delegate it to AI.
>The best way we know of to learn something with AI is to do the work yourself, then watch AI blow you out of the water on the same task, learn its tricks and do the work again (better, even, since AI isn't that great at it yet)
Citation on this? There is something on HN front page right now that is contradicting.
https://news.ycombinator.com/item?id=50353192
It's a potentially murdered paraphrase from a Harvard Business School professor that works in the field.
I don't have a very high quality source but this does cover and expand on it: https://youtu.be/vFOnDsy58NU
Or "Karim lakhani how to learn with AI" and see Gemini's skim of the video.
The other thread is highly sensationalised and not really related. (Edit: sorry I thought it was a different thread, the one that's sublinked by OP in your thread. So disregard this paragraph. I can't contest either position, I'm a mere spectator. That being said, I don't see the contradiction, perhaps you could elaborate on that)
Wesley Crusher was kidnapped away to a planet with no children. Their bodies were decaying but their technology was superior to the Federation's so they needed to steal other people's kids.
They gave Wesley a laser scalpel, to teach him to chisel wood, with whatever his mind could picture.
Wesley complained, he said, "dood, this isn't art! i'm not even chiseling!"
And the mentor-kidnapper said, "No, Wesley, you are doing art. The art is in your mind. The tool is just doing the work."
And so then Wesley threw the perfect wooden dolphin at the wall, infuriated that all the pleasure had been removed from the art. But he lived the rest of his life on that planet, making perfect wooden laser sculpted dolphins, cause that was his job and his boss told him too bad.
For a little while Wesley made popsicle stick art during his free time at night to try and feel the sensation of choice and control, but eventually just gave up and played mariokart until he was an old man.
Why are more people (bots?) using bearblog. The landing page claims it's fast and privacy focused, but it offers so little... I could recreate it myself in a day so why aren't people running their own blogs, especially in CS where you could learn something doing it.
I'm also now reminded that this is ran by that same guy who complained about the MIT license allowing people to do whatever they wish.
I think it's a terrible time to get a CS degree and an amazing time to learn CS on your own with AI help.
Since you are (likely) young - here's a tip when writing such articles:
Provide examples.
It's hard for readers to gauge whether you're complaints are legitimate or overreactions.
You work in the industry? If so, look up from your agent harness for 2 minutes and you'll see the complaints are legitimate.
This is a good article. AI just introduces new bureaucracy, workloads, useless discussions and is a hindrance.
No one needs AI in universities or software engineering. It only benefits the middle managers and mediocre people who jump on any bandwagon in order to help their careers.
No software has gotten any better since AI, except that some forgotten bugs from the 1990s were found, just as 20000+ bugs have been found by the Luddite OssFuzz system before AI.
If just another bot, then just another bot. If an actual throwaway, why bother? Nobody gives weight to such thoughts from a faceless account.
Wrong. It was upvoted to 2 before the AI Gestapo intervened.
EDIT: zamadatix is an administrator. They felt offended by the swipe at mediocrities.
This sounds awful, but the culprit is the terrible faculty at this institution:
> Whereas before, assignments were designed by a person who thought them through at least once, then approved by other people in the department, now I doubt they're getting read more than once (if that). Errors abound, often with corrections coming a week or two before the deadline.
This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.
From the title I thought I was going to be reading an article wondering how AI would devalue a CS degree, but this is actually a story about a CS program falling apart because the instructors are flailing about without AI.
> This is entirely unacceptable. Professors who are using AI to generate assignments and course loads need to be discipline and removed if they can’t start doing their job.
Why? How many times have these same assignments been created, semester after semester for decades before we see how wasteful this is? These are solved problems in 99% of cases and just busy work for the staff to justify their existence. Their job should be to lecture and assist students, not create endless assignments that just regurgitate what was already done a thousand times already.