we agree. mixus makes that easy — across teams, classes, and communities.

That said, I myself am increasingly reading long texts written by LLMs and learning from them. I have been comparing the output of the Deep Research products from various companies, often prompting for topics that I want to understand more deeply for projects I am working on. I have found those reports very helpful for deepening my knowledge and understanding and for enabling me to make better decisions about how to move forward with my projects.

I tested Gemini and ChatGPT on “utilizing Euler angles for rotation representation,” the example topic used by the author in the linked article. I first ran the following metaprompt through Claude:

  Please prepare a prompt that I can give to a reasoning LLM that has web search and “deep research” capability. The prompt should be to ask for a report of the type mentioned by the sample “student paper” given at the beginning of the following blog post: https://claytonwramsey.com/blog/prompt/ Your prompt should ask for a tightly written and incisive report with complete and accurate references. When preparing the prompt, also refer to the following discussion about the above blog post on Hacker News: https://news.ycombinator.com/item?id=43888803

https://docs.google.com/document/d/1sqpeLY4TWD8L4jDSloeH45AI...

Here is the ChatGPT report:

https://chatgpt.com/share/681816ff-2048-8011-8e0f-d8cbad2520...

I know nothing about this topic, so I cannot evaluate the accuracy or appropriateness of the above reports. But when I have had these two Deep Research models produce similar reports on topics I understand better, they have indeed deepened my understanding and, I hope, made me a bit wiser.

The challenge for higher education is trying to decide when to stick to the traditional methods of teaching—in this case, having the students learn through the process of writing on their own—and when to use these powerful new AI tools to promote learning in other ways.

The kids these days got everything...

Back in HS literature class, I had to produce countless essays on a number of authors and their works. It never once occurred to me that it was anything BUT an exercise in producing a reasonably well written piece of text, recounting rote-memorized talking points.

Through-and-through, it was an exercise in memorization. You had to recall the fanciful phrases, the countless asinine professional interpretations, brief bios of the people involved, a bit of the historical and cultural context, and even insert a few verses and quotes here and there. You had to make the word count, and structure your writing properly. There was never any platform for sharing our own thoughts per se, which was sometimes acknowledged explicitly, and this was most likely because the writing was on the wall: nobody cared about these authors or their works, much less enjoyed or took interest in anything about them.

I cannot recount a single thought I memorized for these assignments back then. Passed these with flying colors most usually, but even for me, this was just pure and utter misery. Even in hindsight, the sheer notion that this was supposed to make me think about the subject matter at hand borders on laughable. It took astronomical efforts to even retain all the information required - where would I have found the power in me to go above and beyond, and meaningfully evaluate what was being "taught" to me in addition to all this? How would it have mattered (in specifically the context of the class)? Me actually understanding these topics and pondering about them deeply is completely inobservable through essay writing, which was the sole method of grading. If anything, it made me biased against doing so, as it takes a potentially infinite extra time and effort. And since there was approximately no way for our teacher to make me interested in literature either, he had no chance at achieving such lofty goals with me, if he ever actually aimed for them.

On the other side of the desk, he also had literal checklists. Pretty sure that you do too. Is that any environment for an honest exchange of thoughts? Really?

If you want to read people's original thoughts, maybe you should begin with not trying to coerce them into producing some for you on demand. But that runs contrary to the overarching goal here, so really, maybe it's the type of assignment that needs changing. Or the framework around it. But then academia is set in its ways, so really, there's likely nothing you can specifically do. You don't deserve to have to sift through copious amounts of LLM generated submissions; but the task of essay writing does, and you're now the one forced to carry this novel burden.

LLMs caught incumbent pedagogical practices with their pants down, and it's horrifying to see people still being in denial of it, desperately trying to reason and bargain their ways out of it, spurred on by the institutionally ingrained mutual-hostage scenario that is academia. *

* Naturally, I have absolutely zero formal relation to the field of pedagogy (just like the everyday practice of it in academia to my knowledge). This of course doesn't stop me from having an unreasonably self-confident idea on how to achieve what you think essay writing is supposed to achieve though, so if you want a terrible idea or two, do let me know.

(AI slop). If it's not worth writing, it's not worth reading.

Perfect.

Really? The example used was for a school test. Is there really much original thought in the answer? Do you really want to read the students original thought?

I think the answer is no in this case. The point of the test is to assess whether the student has learned the topic or not. It isn’t meant to share actual creative thoughts.

Of course, using AI to write the answer is contrary to the actual purpose, too, but it isn’t because you want to hear the students creativity, but because it is failing to serve its purpose as a demonstration of knowledge.

Relying on that to automatically detect their use makes no sense.

From a teaching perspective, if there is any expectation that artificial intelligence is going to stick, we need better teachers. Ones that can come up with exercises that an artificial intelligence can't solve, but are easy for humans.

But I don't expect that to happen. I expect instead text to become more irrelevant. It already has lost a lot of its relevancy.

Can handwriting save us? Partially. It won't prevent anyone from copying artificial intelligence output, but it will make anyone that does so think about what is being written. Maybe think "do I need to be so verborragic?".

I think I is good in two way, one is which you use if as a small helper (basic questions, auto completion...)

Two for getting started on something that you have no idea on ( not to teach you but just give you an idea of what it's and resources to learn more)

If you use AI all that is important is your ability to specify the problem, of course, as it always has been, you can just reiterate faster.

What about using LLMs to refine or sharpen your existing work? Similar to a Rubber ducky? If you're intentional about maintaining and understanding the theory behind the work, I've found it a useful tool.

Simply blaming models is an easy way out and creates little value - Maybe changing the medium and exercise to which it transfers could be a thing?

It's time to get creative.

Since there is no "interdiction" to use any LLM, perhaps it should be mandatory to include the prompt as well when used. Feels like that could be the seed that sparks the curiosity..

It helps me spot the bits that feel flat or don’t add much, so I can cut or rework them—while still getting the benefit of the LLM’s idea generation.

I wish there was some way to do the same for programming. Imagine a classroom full of machines with no internet connection, just a compiler and some offline HTML/PDF documentation of languages and libraries.

It feels like we are getting to this weird situation where we just use LLMs as proxies, and the long, boring text is just for LLMs to talk to each other.

For example:

Person A to LLM A: Give me my money.

LLM A to LLM B: Long formal letter.

LLM B to Person B : Give me my money.

Hopefully, nothing is lost in translation.

If your assignment can be easily performed by an LLM, it’s a bad assignment. Teachers are just now finding out the hard way that these assignments always sucked and were always about regurgitating information pointlessly and weren’t helpful tools for learning lol. I did heaps of these assignments before the existence of LLMs, and I can assure you that the busywork was mostly a waste of time back then too.

People using LLMs is just proof they don’t respect your assignment - and you know what, if one person doesn’t respect your assignment, they’re probably wrong. But if 90% of people don’t respect your assignment? Maybe you should consider whether the assignment is the problem. It’s not rocket science.

1. “When copying another person’s words, one doesn’t communicate their own original thoughts, but at least they are communicating a human’s thoughts. A language model, by construction, has no original thoughts of its own; publishing its output is a pointless exercise.”

LLMs, having being trained using the corpus of the web, I would argue communicate other human’s thoughts particularly well. Only in exercising an avoidance of plagiarism are the thoughts of other human’s evolved into something closer to “original thought” for the would-be plagarizer. But yes, at least a straight copy/paste retains the same rhetoric as the original human.

2. I’ve seen a few advertisements recently leverage “the prompt” as a means to resonate visual appeal.

i.e a new fast food delivery service starting their add with some upbeat music and a visual presentation of somebody typing into a LLM interface, “Where’s the best sushi around me?” And then cue the advertisement for the product they offer.

The very first time I enjoyed talking to someone in another language, I was 21. Then an exchange student, I had a pleasant and interesting discussion with someone in that foreign language. On the next day, I realised that I wouldn't have been able to do that without that foreign language. I felt totally stupid: I had been getting very good grades in languages for years at school without ever caring about actually learning the language. And now, it was obvious, but all that time was lost; I couldn't go back and do it better.

A few years earlier, I had this great history teacher in high school. Instead of making us learn facts and dates by heart, she wanted us to actually get an general understanding of a historical event. Actually internalise, absorb the information in such a way that we could think and talk about it. And eventually develop our critical thinking. It was confusing at first, because when we asked "what will the exam be about", she wouldn't say "the material in those pages". She'd be like "well, we've been talking about X for 2 months, it will be about that".

Her exams were weird at first: she would give us articles from newspapers and essentially ask what we could say about them. Stuff like "Who said what, and why? And why does this other article disagree with the first one? And who is right?". At first I was confused, and eventually it clicked and I started getting really good at this. Many students got there as well, of course. Some students never understood and hated her: their way was to learn the material by heart and prove it to get a good grade. And I eventually realised this: those students who were not good at this were actually less interesting when they talked about history. They lacked this critical thinking, they couldn't make their own opinion or actually internalise the material. So whatever they would say in this topic was uninteresting: I had been following the same course, I knew which events happened and in which order. With the other students were it "clicked" as well, I could have interesting discussion: "Why do you think this guy did this? Was it in good faith or not? Did he know about that when he did it? etc".

She was one of my best teachers. Not only she got me interested in history (which had never been my thing), but she got me to understand how to think critically, and how important it is to internalise information in order to do that. I forgot a lot of what we studied in her class. I never lost the critical thinking. LLMs cannot replace that.

I like reading and writing stories. Last month, I compared the ability of various LLMs to rewrite Saki's "The Open Window" from a given prompt.[1] The prompt follows the 13-odd attempts. I am pretty sure in this case that you'd rather read the story than the prompt.

I find the disdain that some people have for LLMs and diffusion models to be rather bizarre. They are tools that are democratizing some trades.

Very few people (basically, those who can afford it) write to "communicate original thoughts." They write because they want to get paid. People who can afford to concentrate on the "art" of writing/painting are pretty rare. Most people are doing these things as a profession with deadlines to meet. Unlike you are GRRM, you cannot spend decades on a single book waiting for inspiration to strike. You need to work on it. Also, authors writing crap/gold at a per-page rate is hardly something new.

LLMs are probably the most interesting thing I have encountered since I did the computer. These puritans should get off of their high horse (or down from their ivory tower) and join the plebes.

[1] Variations on a Theme of Saki (https://gist.github.com/s-i-e-v-e/b4d696bfb08488aeb893cce3a4...)

Arguably, that's not what teachers mainly do (to an ever increasing proportion).

Most knowledge is easily available. A teacher is teaching students to think in productive ways, communicate their thoughts and understand what others are trying to tell them. For this task, it's essential that the teacher has some idea what the students are thinking, especially when it's something original.

Perhaps the problem is that they are "graded", but this is to motivate the student, and runs against the age-old problem of gamification.

Pre-AI, homework was often copied and then individuals just crammed for the tests.

AI is not the problem for these students, it's that many students are only in it for the diploma.

If it wasn't AI it would just be copying the assignment from a classmate or previous grad.

And I imagine the students who really want to learn are still learning because they didn't cheat then, and they aren't letting AI do the thinking for them now.

The question is: Should we limit AI to keep the old way of learning, or use AI to make the process better? Instead of fixing small errors like grammar, students can focus on bigger ideas like making arguments clearer or connecting with readers. We need to teach students to use AI for deeper thinking by asking better questions.

We need to teach students that asking the right questions is key. By teaching students to question well, we can help them use AI to improve their work in smarter ways. The goal isn’t to go back to old methods for iterating but change how we iterate altogether.

> You only have to read one or two of these answers to know exactly what’s up: the students just copy-pasted the output from a large language model, most likely ChatGPT. They are invariably

This is validating. Your imitation completely fooled me (I thought it really was ChatGPT and expected to be told as much in an entirely unsurprising "reveal") and the subsequent description of the style is very much in agreement with how I'd characterize it.

In previous discussions here, people have tried to convince me that I can't actually notice these obvious signs, or that I'm not justified in detecting LLM output this way. Well, it may be the case that all these quirks derive from the definitely-human training data in some way, but that really doesn't make them Turing-test-passing. I can remember a few times that other people showed me LLM prose they thought was very impressive and I was... very much not impressed.

> When someone comments under a Reddit post with a computer-generated summary of the original text, I honestly believe that everyone in the world would be better off had they not done so. Either the article is so vapid that a summary provides all of its value, in which case, it does not merit the engagement of a comment, or it demands a real reading by a real human for comprehension, in which case the summary is pointless. In essence, writing such a comment wastes everyone’s time.

I think you've overlooked some meta-level value here. By supplying such a comment, one signals that the article is vapid to other readers who might otherwise have to waste time reading a considerable part of the article to come to that conclusion. But while it isn't as direct as saying "this article is utterly vapid", it's more socially acceptable, and also more credible than a bald assertion.

I believe that it has improved my writing productivity somewhat, especially when I'm tired and not completely on the ball. Although I don't usually reach for this most of the time (e.g. not for this comment).

Using LLMs to achieve this is just another step in the evolution of a broken education system. The fix? IMO, make the exams for the courses delayed by one semester. So during the exam study-period, the students have to 'catch up' on the lectures they had a few months ago.

Maybe the problem is that the professor doesn't want to read the student work anyway, since it's all stuff he already knows. If they managed to use their prompts to generate interesting things, he'd stop wanting to see the prompts.

The hardest hit industry by AI has been essay writing services.

If anything, it seems they're noticing because the AI is doing a worse job.

I agree with the broader point of the article in principle. We should be writing to edify ourselves and take education seriously because of how deep interaction with the subject matter will transform us.

But in reality, the mindset the author cites is more common. Most accounting majors probably don't have a deep passion for GAAP, but they believe accounting degrees get good jobs.

And when your degree is utilitarian like that, it just becomes a problem of minimizing time spent to obtain the reward.

Whether it be writing or computer programming, or exercising, for that matter, if you aren't willing to put in the work to achieve your goals, why bother?

At first, I thought they didn't care. However, it was so pervasive that it couldn't be the only explanation. I was forced to conclude they trusted ChatGPT more than themselves to argue their case... (Some students did not care, obviously.)

It can be used as a personal tutor. How awesome is it to have a tutor always available to answer almost any question from any angle to really help you understand? Yes, AI won't get everything right 100%, but for students who are still learning basics, it's fair to assume that having an AI tutor can yield far better results than having no tutor at all.

It can also be used as a tool for doing mundane work, so you can focus more on the interesting and creative work. Kind of like a calculator or a spreadsheet. Would math majors become better mathematicians if they had to do all calculations by hand?

I think instead of banning AI, education needs to reform. Teaching staff should focus less time on giving lectures and grading papers (those things can be recorded and automated) and more time on ORAL EXAMS where they really probe student's knowledge and there's no possibility of cheating.

Students can and should use AI to help them prepare. E.g. don't ask AI to write an essay for you, write it yourself and ask it to critique it. Don't ask it to give you answers for a test, ask it to ask you questions on the topic and find gaps in your knowledge. Etc.

The is especially the case when you are about to complain about style, since that can easily be adjusted, by simply telling the model what you want.

But I think there is a final point that the author is also wrong about, but that is far more interesting: why we write. Personally I write for 3 reasons: to remember, to share and to structure my thoughts.

If an LLM is better then me at writing (and it is) then there is no reason for me to write to communicate - it is not only slower, it is counterproductive.

If the AI is better at wrangling my ideas into some coherent thread, then there is no reason for me to do it. This one I am least convinced about.

AI is already much better than me at strictly remembering, but computers have been that since forever, the issue is mostly convinient input/output. AIs makes this easier thanks to speech to text input.

[0]: See eg. https://www.oneusefulthing.org/p/centaurs-and-cyborgs-on-the....

The school should be drilling into students, at orientation, what some school-wide hard rules are regarding AI.

One of the hard rules is probably that you have to write your own text and code, never copy&paste. (And on occasions when copy&paste is appropriate, like in a quote, or to reuse an off-the-shelf function, it's always cited/credited clearly and unambiguously.)

And no instructors should be contradicting those hard rules.

(That one instructor who tells the class on the first day, "I don't care if you copy&paste from AI for your assignments, as if it's your own work; that just means you went through the learning exercise of interacting with AI, which is what I care about"... is confusing the students, for all their other classes.)

Much of society is telling students that everything is BS, and that their job is to churn BS to get what they want. Early "AI' usage popular practices so far looks to be accelerating that. Schools should be dropping a brick wall in front of that. Well, a padded wall, for the students who can still be saved.

I wish it was only at stoplights. But then just a few days ago, I witnessed a totally unnecessary accident. Left-lane got green, and someone in the straight lane noticed the movement but didn't look up and drove right into the car in front of them...

I almost had headaches after intense thinking of problems and ways to solve them in lambda prolog. That was the most interesting and satisfying to physically feel the effect of high focus combined with applying what was a new logic.

Computer science at the university, taught me how to learn and explore new ideas. I might sound like my grandpa who told me when I was 8yo that using calculator would lead to people not able to count... and here I am saying that LLM might lead to people who do not know how to write.

Actually, I am a bit concern that we might produce more text in the short term because it is becoming cheap to write tons of documentation with LLMs. But those feel like death by Terms and Conditions, i.e. text that no one reads. So not only we would lose our ability to write, but we can seriously affect our ability to read. Sure LLM can summarize as well, but then we lose the nuances.

Nature is lazy, but should we be lazy and delegate our ability to think (read/write), to a software ? Think about it :)

Even when no errors are introduced in the process, the outcome is always bad: 3 full paragraphs of text with bullets and everything where the actual information is just the original 1-2 sentences that the model was prompted with.

I never am happy reading one of those; it's just a waste of time. A lot of the folks doing it are not native English speakers. But for their use case, older tools like Grammarly that help improve the English writing are effective without the problematic decompression downsides of this class of LLM use.

Regardless of how much LLMs can be an impactful tool for someone who knows how to use one well, definitely one of the impacts of LLMs on society today is that a lot of people think that they can improve their work by having an LLM edit it, and are very wrong.

(Sometimes, just telling the LLM to be concise can improve the output considerably. But clearly many people using LLMs think the overly verbose style it produces is good.)

EDIT: Not a jab at the author per se, more that it's a third or fourth time I see this particular argument in the last few weeks, and I don't recall seeing it even once before.

https://ruudvanasseldonk.com/2025/llm-interactions

More than communicate, I would say to induce thoughts.

I write poetry here and there (on paper, just for me). I like how exploration through lexical and syntactic spaces can be intertwined with semantics and pragnatic matters. More importantly, I appreciate how careful thoughts are playing with attention and other uncharted thoughts. The invisible side effects on mental structures happening in the creation of expression can largely outweight the importance of what is left as an artefact publicly visible.

For a far more trivial example, we can think about how notes in the margin of a book can radically change the way we engage with the reading. Even a careful spare word highlight can be a world of difference in how we engage with the topic. It's the very opposite of "reading" a few pages before realizing that not a single thought percolated into consciousness as it was wandering on something else.

AI usage is a lot higher in my work experience among people who no longer code and are now in business/management roles or engineers who are very new and didn't study engineering. My manager and skip level both use it for all sorts of things that seem pointless and the bootcamp/nontraditional engineers use it heavily. Our college hires we have who went through a CS program don't use it because they are better and faster than it for most tasks. I haven't found it to be useful without an enormous prompt at which point I'd rather just implement the feature myself.

I don't understand this either. I use it a lot, but I never just use what an LLM says verbatim. It's so incredibly obvious it's not written by a human. Most of the time I write an initial draft, ask Claude to check it and improve it, and then I might touch up a few sentences here and there.

> Vibe coding; that is, writing programs almost exclusively by language-model generation; produces an artifact with no theory behind it. The result is simple: with no theory, the produced code is practically useless.

Maybe I still don't know what vibe coding is, but for the few times when I _can_ use an LLM to write code for me, I write a pretty elaborate instruction on what I want, how it should be written, ... Most of the time I use it for writing things I know it can do and seem tedious to me.

I have to admit I was a bit surprised how bad LLMs are at the continue this essay task. When I read it in the blog I suspected this might have been a problem with the prompt or the using one of the smaller variants of Gemini. So I tried it with Gemini 2.5 Pro and iterated quite a bit providing generic feedback without offering solutions. I could not get the model to form a coherent well reasoned argument. Maybe I need to recalibrate my expectations of what LLMs are capable, but I also suspect that current models have heavy guardrails, use a low temperature and have been specifically tuned for problem solving and avoid hallucinations as much as possible.

IMO the core problem is that in many cases this typical belief holds true.

I went to university to get a degree for a particular field of jobs. I'd generously estimate that about half of my classes actually applied to that field or jobs. The other half were required to make me a more "well rounded student" or something like that. But of course they were just fluff to maximize my tuition fees.

There was no university that offered a more affordable program without the fluff. After all, the fluff is a core part of the business model. But there isn't much economic opportunity without a diploma so students optimize around the fluff.

I couldn’t agree more with the sentiment of this article.

Writing yourself, _writing manually_ is much nicer, to hear your unfiltered thoughts, than condensing them through an LLM, and get average-sounding sentences with no soul. To me, LLM writing is soulless. I even started to turn to Grammarly and Copilot, as these were a mere distraction to the actual task at hand: writing. Instead of writing, I was constantly grammar fixing, and ultimately, nothing got done. I love the gym-analogy https://news.ycombinator.com/item?id=43888803 gave.

Perhaps that's good, perhaps that's bad, but it certainly doesn't really allow him to see much of the appeal... yet

Forcing people to do these things supposedly results in a better, more competitive society. But does it really? Would you rather have someone on your team who did math because it let them solve problems efficiently, or did math because it’s the trick to get the right answer?

Writing is in a similar boat as math now. We’ll have to decide whether we want to force future generations to write against their will.

I was forced to study history against my will. The tests were awful trivia. I hated history for nearly a decade before rediscovering that I love it.

History doesn’t have much economical value. Math does. Writing does. But is forcing students to do these things the best way to extract that value? Or is it just the tradition we inherited and replicate just because our parents did?

Having spent about two decades reading other humans' "original thoughts", I have nothing else to say here other than: doubt.

Pithy and succinct takes time.

The goal is to make something legible, but the reality is we are producing slop. I'm back to writing before my brain becomes lazy.

There's too much information in the World for it to matter, I think is the underlying reason.

As an example, most enterprise communication nears the levels of noise in its content.

So, why not let a machine generate this noise, instead?

Yes, totally. Unfortunately, it takes time and maturity to understand how this is completely wrong, but I feel like most students go through that belief.

Not sure how relevant it is, but it makes me think of two movies with Robin Williams: Dead Poet's Society and Will Hunting. In the former, Robin's character manages to get students interested in stuff instead of "just passing the exams". In the later, I will just quote this part:

> Personally, I don’t give a shit about all that, because you know what? I can’t learn anything from you I can’t read in some fuckin’ book. Unless you wanna talk about you, who you are. And I’m fascinated. I’m in.

I don't give a shit about whether a student can learn the book by heart or not. I want the student to be able to think on their own; I want to be able to have an interesting discussion with them. I want them to think critically. LLMs fundamentally cannot solve that.

Exploring a concept-space with LLM as tutor is a brilliant way to educate yourself. Whereas pasting the output verbatim, passing it as one’s own work, is tragic: skipping the only part that matters.

Vibe coding is fun right up to the point it isn’t. (Better models get you further.) But there’s still no substitute for guiding an LLM as it codes for you, incrementally working and layering code, committing to version control along the way, then putting the result through both AI and human peer code reviews.

Yet these all qualify as “using AI”.

We cannot get new language for discussing emerging distinctions soon enough. Without them we only have platitudes like “AI is a powerful tool with both appropriate and inappropriate uses and determining which is which depends on context”.

The point is: how good is it at teaching you how the language works? Like the grammar concepts, etc. I don't think it excels at that.

Depends, I think. If we are talking about writing an essay (and I believe we are), then the LLM is somewhere between useless and counter-productive.

Of course, if the LLM is used to understand an RFC (I would debate how useful it is for that, but that's another discussion), then it's different. The goal was to understand the RFC, it doesn't really matter how you did it. But the goal of writing an essay is not to end up with a written essay at all. Nobody cares about it, you can burn it right after it's graded.

https://www.satisfice.com/blog/archives/487881

As always, I reject wholeheartedly what this skeptical article has to say about LLMs and programming. It takes the (common) perspective of "vibe coders", people who literally don't care what code says as long as something that runs comes out the other side. But smart, professional programmers use LLMs in different ways; in particular, they review and demand alterations to the output, the same way you would doing code review on a team.

Yes I know the subject area for which I write assessments and know if what is generated is factually correct. If I’m not sure, I ask for web references using the web search tool.

https://chatgpt.com/share/6817c46d-0728-8010-a83d-609fe547c1...

To play devil's advocate original code alienates you from many programming jobs. This was true before LLMs, and remains true now. Many developers abhor original code. They need frameworks or packages from Maven, NPM, pip, or whatever. They need to be told exactly what to do in the code, but copy/paste is better, and a package that already does it for you is better still. In these jobs, yes, absolutely let a computer write it for you (or at least anybody that is an untrusted outside stranger). Writing the code yourself will often alienate you from your peers and violate some internal process.

if you can one-shot an answer to some problem, the problem is not interesting.

the result is necessary, but not sufficient. how did you get there? how did you iterate? what were the twists and turns? what was the pacing? what was the vibe?

no matter if with encyclopedia, google, or ai, the medium is the message. the medium is you interacting with the tools at your disposal.

record that as a video with obs, and submit it along with the result.

for high stakes environments, add facecam and other information sources.

reviewers are scrubbing through video in an editor. evaluating the journey, not the destination.

That part caught my attention. As an English-as-a-second-language speaker myself, I find it so difficult to develop any form of "taste" in English the same way I have in my mother tongue. A badly written sentence in my mother tongue feels painful in a sort of physical way, while bad English usually sound OK to me, especially when asserted in the confident tone LLMs are trained in. I wish I could find a way to develop such sense for the foreign languages I currently use.

Libraries are still in every campus, often with internet access.

Traditional media have transitioned to become online content media farms. The NYT Crossword puzzle is now online. Millions of people do Wordle every day online.

This is just kickback. Every paradigm shift needs kickback in order to let the dust settle and for society to readjust and find equilibrium again.

People say “I saved so much time on perf this year with the aid of ChatGPT,” but ChatGPT doesn’t know anything about your working relationship with your coworker… everything interesting is contained in the prompt. If you’re brain dumping bullet points into an LLM prompt, just make those bullets your feedback and be done with it? Then it’ll be clear what the kernel of feedback is and what’s useless fluff.

How about an emoji like library designed exclusively for LLMs, so we can quickly condense context and mood without having to write a bunch of paragraphs, or the next iteration of "txt" speech for LLMs. What does the next step of users optimising for LLMs look like?

I miss the 80's/90's :-(

Am I alone with this?

There's so much bad writing of valuable information out there. The major sins being: burying the lede, no or poor sectioning, and just generally verbose.

In some cases, like in EULAs and patents that's intentional.

The punchline? Bullet point 3 was wrong (it was a PL assignment and I'm 99% sure the AI was picking up on the word macro and regurgitating facts abut LISP). 0 points all around, better luck next time.

I wish to communicate four points of information to you. I’ll ask ChatGPT to fluff those up into multiple paragraphs of text for me to email.

You will receive that email, recognize its length and immediately copy and paste it into ChatGPT, asking it to summarize the points provided.

Somewhere off in the distance a lake evaporates.

It's the old joke of the teacher who wants students to tried their best and that failure doesn't matter. But when the student follows the process to the best of their ability and fails they are punished while the student who mostly follows the process and then fudges their answer to the correct one is rewarded.

Personally, I’ve been enjoying using ChatGPT to explore different themes of writing. It’s fun. In my case the goal is specifically to produce artifacts of text that’s different from what I’d normally produce.

Also the point about default LLM settings not doing that is a good point.

Video in itself is not more information by definition. Just look at those automatically generated videos when you try finding a review on an unusual product.

It's not just nitpicking: maybe it's only their perception they are more productive? Or maybe they are. But saying it is not convincing enough for me.

So, observing a couple of my colleagues (I am an engineering manager, but have switched back and forth between management and IC roles for the last ~20 years), I've seen them either produce crap, or spend so much time tuning the prompts that it would have been faster to do it without an LLM. They mostly used Github Copilot or ChatGPT (most recent versions as of last few months ago).

I am also keeping out a keen eye for any examples of this (on HN in particular), but it usually turns out things like https://news.ycombinator.com/item?id=43573755

Again, I am not saying it's not being done, but I have struggled to find someone who would demonstrate it happen in a convincing enough fashion — I am really trying to imagine how I would best incorporate this into my daily non-work programming activities, so I'd love to see a few examples of someone using it effectively.

You see the same thing in every argument with LLM skeptics. 'The code is bad. You don't even know what the code is doing." This is obviously false. A professional reads the code they commit and push. A professional doesn't push code they know to be bad.

  > As a team lead, what would you think of a team member who consistently generated "crap" pull requests?

Your first question was:

  > How is that better?

If you are talking about a case where an inexperienced human uses LLM to start off with a crappy code change, but then adapts the output during the review process, and potentially learns through it (though research confirms people learn better when they produce mistakes themselves) — they still won't be able to use LLM to produce comparable code the next time, so they'll have to do the review and improve it by hand before putting it up for review by somebody else, thus negating any productivity gain (which was the original premise), and likely reducing the learning potential.

If there was a question I misinterpreted, please enlighten me. Thanks! :)

Elsewhere in this comment section, it's discussed about the importance of having original thought, which the summarized text specifically isn't, and has leeched away.

The parent comment has actually made the case against the summarized text being "better" (if we're measuring anything that isn't word count).

Sounds like "back in my days" type of complaining. Do you have any evidence of this "100% reduction" or is it just "AI bad" bandwagoning?

> But you're definitely not learning how to write.

How would you know? You've never tested him. You're making a far-reaching assumption about someone's learning based on using an aid. It's the equivalent of saying "you're definitely not learning how to ride a bicycle if you use training wheels".

A piece of text does not have a single inherently correct interpretation. Its meaning is a relation constructed at run- (i.e. read-)time between the reader, the writer, and (possibly) the things the text refers to, that is if both sides are well enough aligned to agree on what those are.

Words don't speak, they only gesture.

Thanks for the example chat it was a valuable learning for me!

In my experience, an ideas leader (you know the type) will fail at telling a machine exactly what to do and get bored with the inevitable edge cases, computers saying no, and non-ideas drudgery. This is where I believe every no-code and low-code and WYSIWYG platform and now LLMs fall apart.

A major aspect of programming is translating the messy meatspace to something an extremely fast moron (a computer and I wish I coined this term) understands. And as much of a step change LLMs for writing code are, I have yet to see them take this step.

If you accept that the degree/job relationship is the start, not end, of the reason for being there, then you see other things too.

There are opportunities around the student which are for them, not for their degree, not for their job. There are things you can learn, and never be graded. There are toys to play with you'll never see again. There are whole departments of living experts happy to answer questions.

For example, (this is pre google) I wrote a program and so needed to understand international copyright. I could have gone to the library and read about it. Instead I went to the law faculty, knocked on the door, and found their professor who specialized in intellectual property.

Since the program I wrote was in the medical space, I went to the medical campus, to the medical research library, and found tomes that listed researchers who might benefit. I basically learned about marketing.

If all you care about is the company job, then all you'll see is the degree.

Seeing this non-compelling prompt would tell me right off the bat that I wouldn't be interested in the video either.

During the final exam, a national exam (all the students did it at the same time in the country) you were given blank pieces of paper, a pen, 10 different topics to choose from, and 6 hours.

You pick a topic, write a draft, then write the essay. ~700 words.

Bonus points if you use relevant literature or science quotes in it. Quotes you had to memorize, without knowing the topics.

None of the topics resonated with you? Tough luck, buddy.

Don't have any 'novel' ideas? Figure something out!

Cited something the reader didn't know about? Should've known better!

Quoted someone the person grading doesn't know? Better hope they're in a good mood!

There's a reason they got rid of this style of exam a few years later. Doesn't change that this one thing dominated my native language and literature classes.

My biggest takeaway from it is how much I hate my native language class and literature class, but that might've happened even without the essay.

And yeah, and revisiting the OP we're on the same track.

For us the curriculum was the start of the learning, not the end. We'd get a weekly assignment that could be done in an afternoon. Most of the class did the assignments, and that was enough.

There was a small group of us that lived (pretty much) in the lab. We'd take the assignment and run with it, for days, nights, spare periods, whatever. That 10 line assignment? We turned it into 1000 lines every week.

For example the class on sorting might specify a specific algorithm. We'd do all of them. Compete against each other to make the fastest one. Compare one dataset to another. Investigate data distributions. You know, suck the marrow.

(Our professors would also swing by the lab from time to time to see how things were going, drop the odd hint, or prod the bear in a direction and so on. And this is all still undergrad.

I can imagine a History major doing the same. Researching beyond the curriculum. Going down rabbit holes.

My point is though is that you're right. You need to be interested. You need to have this compulsion. You can't tell a person "go, learn". All you can do is offer the environment, sit back, and see who grabs the opportunity.

I get that you cant imagine this playing out. To those interested only in the degree, it's unimaginable. And no, as long as burning-desire is not on the entry requirements, it most certainly will not be the majority.

In truth the lab resources eoild never have coped if the majority did what we did.

I tend to disgree. maybe for you, but my mind does not work that way.

As a teacher at a university I come to see that students "learn" by asking an LLM, but they forget to understand the content the LLM produces because the LLM actually solves the assignment (mostly) for them. One may say that's the teachers task to produce better questions, but the thing I most "struggle" with that getting educated as a student seems to just be a play of "gaming the system". Yes, it was similar during my time (learning how to reach your goal with as less effort as possible is a good part of the "education" at a university IMHO), but we actually had to think and understand while today just seems like prompt-and-copy.

[1] https://archive.is/kS8NY

>And of course, none of this matters if I don't want to learn something :)

Society makes people do a lot of things they don't want. I wonder if we're going to hit a breaking point this generation.

Any documentation they write at best re-states what is immediately obvious from the surrounding code (Useless: I need to explain why), or is some hallucination trying to pretend it's a React app.

To their credit they've slowly gotten better now that a lot of documentation already exists, but that was me doing the work for them. What I needed them to do was understand the project from existing code, then write documentation for me.

Though I guess once we're at the point AI is that good, we don't need to write any documentation anymore, since every dev can just generate it for themselves with their favorite AI and in the way they prefer to consume it.

* They'll pretend they understand by re-stating what is written in the README, then proceed to produce nonsense.

I have met maybe two people who truly understood the behaviour of their code and both employed formal methods. Everyone else, including myself, are at varying levels of confusion.

Teachers will lament the rise of AI-generated answers, but they will only ever complain about the blatantly obvious responses that are 100% copy-pasted. This is only an emerging phenomenon, and the next wave of prompters will learn from the mistakes of the past. From now on, unless you can proctor a room full of students writing their answers with nothing but pencil and paper, there will be no way to know for certain how much was AI and how much was original/rewritten.

I can’t imagine disincentivising actually getting stuck into programming and incentivising being good at regurgitating info in an exam room being a good thing for CS students.

As a professor today, assignments are the place where I’m happy to throw my students into the “deep end” (go learn a new language and a set of library toolkits while also learning this skill.) Exams just don’t provide that experience. Worse, students tend to cram for exams which is the worst way to retain information. I can’t even imagine thinking that the two are comparable in terms of retention and skill-building.

So, you can usually get away with it if there is not some way the professor/TA can prove it.

As things change, this will change, but that's the situation the author of the original article finds themself in, because it's the current situation.

Only if you don't have any custom instructions about style and don't proofread it afterwards. All the usual "tells" of ChatGPT are very obvious to scrub out, and you don't have to use OpenAI's chat wrapper to begin with.

I don't know what the answer is. I'm old school, if it was up to me I'd bring back slide rules and log tables, because that's such a visual and tactile way of getting to know mathematics and numbers.

It's interesting to consider how AI is affecting humans' cognition skills. Is it going to make us stupid or free us up to use our mental capacities for higher level activities? Or both?

Maybe you mean people who are bad at interviews? Or people whose job isn't actually programming? Or maybe "lots" means "at least one"? Or maybe they can strictly speaking do fizzbuzz, but are "in any case bad programmers"? If your claim is true, what do these people do all day (or, let's say, did before LLMs were a thing...)?

Predictably they end up with some people on the range from "can't code at all" to "newbie coder without talent"

They are the wrong tool for pedagogy.

It is also hard to evaluate university teaching because there are no benchmarks for that (compared with high school, for example), and it is hard to judge if teaching is good from student feedback. You can only know if someone fucked up or did really well, which are outliers.

There are other issues as well. Professor IMO is a ridiculous job, you are supposed to be an expert in the field, be a researcher, be a manager, be a teacher, be a salesman, all at the same time. There are people who can excel in all these, but these are probably just outliers. It doesn't help when PhD training doesn't train you to be a proper manager and teacher. While there are some teaching training, I think we are not really held to a high enough standard. E.g. One can pass the teaching course if they just show up and spend some time, even though their teaching is horrible.

If you never learn to research, sure. Otherwise, you should be worried about accuracy, up to date information, opinionated takes, and outright lies/misinformation. The tool you use doesn't change these factors.

Employees are increasingly expected to use personal devices in work settings.

While we're talking about things we're grateful for, I am so glad that we've structured the education and employment systems such that not having a degree puts you at significant risk of unemployment, prevents you from ever immigrating anywhere for the first decade of your working life, and generally marks you as a failure.

If you want to make your own certificates, good luck getting them on the trusted list.

I believe that the same holds true for other facts one might memorize. Yes, the fact may seem like meaningless trivia (and might even be so at times), but in the right situation knowing that fact can help with understanding. You can certainly spend too much time on memorization of facts, but that doesn't mean it has no place either.

Maybe we should let/encourage this to happen. Maybe letting bloated zombie-like organisations bloat themselves to death would thin the herd somewhat, to make space for organisations that are less “broken”.

In American universities where your GPA from your in-class assessments forms part of your final grade? Yes, absolutely.

Where I came from you do your learning in class and your assessment in a small, short set of exams (and perhaps one graded essay) at the end of each year. That seems far more conducive to learning things without having to juggle two competing objectives the whole time.

Someone with years of coding experience is going to be able to laser guide an AI agent to the answer/result than someone who has muddled their way through comp sci 101 using an AI chatbot.

This isn't even an opinion on LLMs, it's recruiting 101. You're free to convince the gym to train forklift drivers, but don't be surprised when you're laughed out the room.

I learned programming online and got jobs as a developer (I did later study CS at a university though). In my experience the best developers are those who taught themselves. Admittedly this may have been more the case for my older generation where formal education for programming wasn't that great nor widely available.

That _may_ be true for the vast majority, but it's criminal to waste the time of bright young people by putting them though hoops. I would even speculate that that they phoney goals, timelines, and deliverables in school actually damage kids.

If I wanted to learn JavaScript or .NET or CSS or whatever I could easily do so online. But that's different from becoming a software developer. The important thing is that university doesn't focus on one topic, it teaches a variety of topics that they think will be useful for your career. You can do this without uni, but you need to be good at figuring out what to learn, not just how. And of course the discipline to complete your goals by yourself, like you mention.

Although maybe something you could do would be to look at a university's course structure and copy it.

Also, do I really need to remind people here of the "resources" used when you struggle and need help while self-guided?

- I probably don't need to rant about StackOverflow. Discord can be incredibly hit or miss, many forum pleas goes uncalled. It can be really hard to get unsuck compared to asking a teacher about their own assignment

- worse than asking quesions, forget getting high quality feedback on your project: getting people to do more than a quick skim takes effort in and of itself. You'd truly find an angelic soul if anyone decided to disect and correc your source code.

- there's also so, so, so many domains to explore. How will you specialize without knowing they exist? And if you've dug deep into any domain, you know that this is where the publicly free knowledge truly dries up. You won't find a nifty course on low level optimization hacks, nor network architecture (beej gets close, but only touches the surface), nor modern rendering techniques. You'll find some 300 level material, but 400 level stuff will likely require a mix of cobbling project ideas together that's within your reach but also pushing you. Scoping is always hard to do, and nearly impossible while still a student.

And Software is one of the easier domains to self learn. Good luck with the lab based STEM, getting proper feedback in art while learning theory, taste testing as a cook, using power tools in any given blue collar work, etc.

You don't have to play the game the same way to work there. But it helps to accept that others will play it, and manage your own expectations accordingly.

Academia put itself as a gateway and barrier to the middle class. Why would we be surprised when people with no interest in anything but the goal are not enthralled by the process?

I'm expanding on the author's point that the hard part is the input, not the output. Sure someone else could produce the same output as an LLM given the same input and sufficient time, but they don't have the same input. The author is saying "well then just show me the input"; my counterpoint is that the input can often be vastly longer and less organized or cohesive than the output, and thus less useful to share.

To be fair, the first Harry Potter is a kinda average British boarding school story. Rowling is barely an adequate writer (and it shows badly in some of the later books). There was a reason she got rejected by so many publishers.

However, Netscape was going nuts and the Internet was taking off. Anime was going nuts and produced some of the all time best anime. MTV animation went from Beavis and Butthead to Daria in this time frame. Authors were engaging with audiences on Usenet (see: Wheel of Time and Babylon 5). Fantasy had moved from counterculture for hardcore nerd boys to something that the bookish female nerds would engage with.

Harry Potter dropped onto that tinder and absolutely caught fire.

This has already been the case for decades. There are probably brilliant works sitting out there on AO3 or whatnot. But you'll never find them because it's not worth wading through the junk. AI merely accelerates what was already happening.

Yes.

A person is a idiot if he/she takes someone's competence at face value because of a degree. ( jobs aside - don't assume your doctor is competent because he has MD, it will cost you your life)

I see your point, but the issue is that it's quite futile to shame students for playing the game that people in the industry has set up. It doesn't help that in the past, college degrees were in fact more relevant than today, especially before the era of the internet and wikipedia, so if older people who are currently in charge of hiring aren't aware of these changes, they might just apply their outdated personal experience and just assume college degrees hold the same weight as they did in the past.

I'm pretty certain when kids these days eventually become responsible for hiring decisions, they probably will handle things differently since their experiences are different.

I don’t know how generalizable this is. I remember reading a few studies trying to assess if Ivy League education was really more valuable that a state school. The result (IIRC) was that it only matters for students who came from the lowest economic strata; the authors presumed it was due to the network effect. But that also means the network effect was negligible for the majority of students.

I think you underestimate how bad some high schools really are.

Citation needed. There's great books out there that provide a lot of guidance down a particular path. I'd say a lot of them do, and I can't imagine online learning sources would be worse. There's online communities for learners for specific subjects that are full of people offering good advice.

However, as a non-native speaker, I really like the fact that I can give the LLM some description of a word that I don't know and it'll figure it out. "those fruits that are kind of bitter but also make my tongue feel like it wants to curl" (= "adstringend" [sic] or so, but who remembers that word or can spell it correctly?)

LLMs are basically like all those type-correction algorithms on steroids. Very helpful sometimes, even if it means I have to doublecheck their output.

* Not provide background information and let people figure it out for themselves. This will not help me achieve my goals.

* Link them to Google's SRE book and hope they read it. Still not achieving my goals, because they won't.

* Spend 3 hours writing the relevant background information out for them to read as part of my proposal. This will achieve my goals, but take an extra 3 hours.

* Tell the LLM what I'm looking for and why, then let it write it for me in 2 minutes, instead of 3 hours. I can check it over, make sure it's got everything, refine it a little, and I've still saved 2.5 hours.

So for me, I think the author has missed a primary reason people use LLMs. It saves a bunch of time.

It feels like the information is there strewn across the internet, in forums, Reddit posts, stack overflow, specs, books. But to trawl though it all was so time consuming. With an LLM you can quickly distill it down to just the information you need.

Saying that, I do feel like reading the full spec for something is a valuable exercise. There may be unknown unknowns that you can't even ask the LLM about. I was able to become a subject expert in different fields just but sitting down and reading through the specs / RFCs, while other colleagues continued to struggle and guess.

Why is the angle called m? Why is a combination nPr * (1/r)? What is 1/r doing there?

I use mathacademy.com as my source of practice. Usually that’s enough but I tend to fall over if small details aren’t explained and I can’t figure out why those details are there.

In high school this was punished. With state of the art LLMs, I have a good tutor.

Also it’s satisfying to just upload a page in my own handwriting and it understands what I did, and is able to correct me there.

Also, always keep in mind the most underrated kind of cheater: the clearly smart kid who cheats to go from an A- to an A. That level of student can't be "caught" with tests because by all accounts they already know and have even mastered the material. The pressure at that level of competitiveness simply requires zero room for doubt. Not really what the article is about, but some food for thought.

Synthesizing large amounts of information into smaller more focused outputs is something LLMs happen to excel at. Doing the exact same work more slowly by hand just to prove a point to someone on HN isn't a productive way to deliver business value.

Sure, there's a huge jump from a line editor like `ed` to a screen editor like `vi` or `emacs`, but from there on, it was diminishing returns really (a good debugger was usually the biggest benefit next) — I've also had the "pleasure" of having to use `echo`, `cat` and `sed` to edit complex code in a restricted, embedded environment, and while it made iterations slower, not that much more slower than if I had a full IDE at my disposal.

In general, if I am in a good mood (and thus not annoyed at having to do so many things "manually"), I am probably only 20% slower than with my fully configured IDE at coding things up, which translates to less than 5% of slow down on actually delivering the thing I am working on.

A compiler translates _what you have already implemented_ into another computer runnable language. There is an actual grammar that defines the rules. It does not generate new business logic or assumptions. You have already done the work and taken all the decisions that needed critical thought, it's just being translated _instruction by instruction_. (btw you should check how compilers work, it's fun)

Using an LLM is more akin to copying from Stackoverflow than using a compiler/transpiler.

In the same way, I see org charts that put developers above AI managers, which are above AI developers. This is just smoke. You can't have LLMs generating thousands of lines of code independently. Unless you want a dumpster fire very quickly...

IMHO, the example of using calculators in a learning environment is a great topic to explore.

Using calculators in a university setting is entirely reasonable as it is expected students have already mastered the math calculators automate. Formulae calculators are also included as, again, the expectation is a student capable of defining them have an understanding of what they are and when to use them.

Now, contrast the above with using calculators in elementary school, where basic math is an entirely new concept and the subject being taught. Here, the expectation is students learn how to perform the operations themselves through varied exercises, questions to the instructor, and practice.

> It allows much more advanced learning to be done in the long run.

Only if the fundamentals have already been established. Which leads back to my original question:

  Am I here to learn or to get a passing grade?

* according to the UCLA CIRP freshman survey

Me and most of my peers in college had the choice between two courses. Course A was interesting, yet vastly more challenging and therefore time consuming, with the additional downside of lower grade expectation. Course B was boring, a gentle breeze in comparison, yet with an almost guaranteed perfect grade.

Imagine which course most students choose?

Even if a student wants to take on the more interesting course, incentives matter, and the incentive is: better grades qualify for better compensated positions and prestigious degrees. Only students who didn't care about this or were confident enough in their ability did choose Course A. In the end, barely a handful of students out of hundreds went with A.

> Depends on what you are looking for. I’ve turned half baked ideas into white papers for plenty of praise. I’ve used them to make my Jira tickets seem complicated and complete. I’ve used them to get praised for writing comprehensive documentation.

This is about giving people a good impression of you so they'll write strong peer feedback.

Why don't employers recognize the credentials of a MOOC to the same degree that they would a university degree?

We could similarly ask why employers value the degrees of some universities more than others.

I think it's important to realize that ultimately the decisions come from the employers, not the universities. No one is making the employers do anything. But at least the second question might have a clearer partial answer. In part, there is a selection of a tribe, an implicit "culture fit" that's happening. It isn't uncommon to see employer bias towards specific universities. This is especially true with prestigious universities.

But it's not the universities that are making anyone do anything and that's an important distinction.

When we're talking about college costs, public schools are really the only institutions that matter. The Ivies (or even Ivy+) are a rounding error compared to the big midwestern land-grant universities and the UC and Cal State systems.

States have substantially reduced their per-student support for universities: https://www.ppic.org/publication/higher-education-funding-in...

This coincides with federal funding programs, but student loans are famously not dischargeable, which makes them more instruments of social control than conventional financial vehicles.

But that's just the job market. The other elephants in the room are inflation and the housing market. People who don't have top-notch jobs (that require degrees) can't afford to buy a house. They can hardly afford rent. Cities don't want to build more housing because that will undermine the equity growth of homeowners.

We are a society of ladder-pullers.

Broken? Saddling individuals with a quarter million in debt when they are just starting life is absolutely broken. That they must indenture to be a modern professional (and buy hope for at least a middle class landing) is broken.

The notion that everything must return a (generally, near-term) accounting profit is on its face stupid.

Ideally maybe employers ought to rely on more targeted selection mechanisms. But this would be extremely expensive (and potentially legally risky due to equal opportunity laws) so most don't bother.

Meh, academic degrees don't come for free, someone has to pay for universities, staff and other expenses. In the US it's everyone for themselves by student loans that can't be discharged in bankruptcies, in Europe it's the tax payers.

The problem is, the ones profiting from the gatekeeping (aka employers) aren't the ones paying for it in either system. If employers had to pay, say, 10.000$ for each job listing that requires an academic degree without an actual valid reason, guess how fast that incentive would lead to employers not requiring academic degrees for paper-pusher bullshit jobs.

Always follow the money

We were talking about writing, not about vibe coding. We don't use calculators for writing. We don't use API requests for writing (except when we make an LLM write for us).

> Using the right tool for the job is just doing my job well.

I don't know what your job is. But if your job is to produce text that is meant to be read by humans, then it feels like not being able to synthesise your ideas yourself doesn't make you excellent at doing your job.

Again maybe it makes you productive. Many developers, for instance, get paid for writing bad code (either because those who pay don't care about quality or can't make a difference, or something else). Vibe coding obviously makes those developers more productive. But I don't believe it will make them learn how to produce good code. Good for them if they make money like this, of course.

As much as the author rightfully complains about the example in the post, a version that only said "explain the downsides of Euler angles in robotics and suggest some alternatives" would obviously be far worse. In this case, the AI helped elevate clear F-level work to maybe a C. That's not an indictment of AI; it's an indictment of low-quality work. LLMs lower the bar to produce passable-looking bad work, but they also lower the bar to produce excellent work. The confirmation bias here is that we don't know how many cases of B-level work became A papers with AI assistance, because those instances don't stand out in the same way.

In the audit example, LLMs aren't doing the audit. They synthesize my notes into a useful starting point to nullify writer's block, and let me focus more of my time on the hard or unique aspects of a given report. It's like having an intern write the first draft for me, typically with some mistakes or oversights, occasionally with a valuable additional insight thrown in, and often with links to a few helpful references for the customer that I wouldn't necessarily have found and included on my own. That doesn't lower the quality; it improves it.

As far as the legal example, it really depends on the complexity of a given instance and the guidance you've provided to your lawyers. A good lawyer won't sign off on something that fails to meet the requested quality bar (if anything, the financial incentive would be for them to err on the side of conservatism and toss out the draft you'd provided). But of course this all depends on you having a clear enough understanding of what you're trying to accomplish, and enough familiarity with legal documents and proficiency with language to shape everything into a passable first draft. AI speeds this up, but if you don't know what you're doing then the AI won't solve that for you. It's a tool like any other, and can be used properly or improperly.

No, but I also don't think a human can do that either. Subjective things are subjective. I'm not sure I understand how this connects to the idea you expressed that doing various tasks with automation tools like LLMs prevent you from "internalizing" the data, or why not "internalizing" data is necessarily a bad thing. Am I just misunderstanding your concern?

Depending on the type of nurse, you do not need a degree. Some will need to pass an exam though. You can become a lawyer without a degree, only needing to pass the BAR. An engineer doesn't need a license. You are thinking of a Professional Engineer, which is a specific job title that has legal ramifications. This requires a degree but it also requires you working under another PE for some time and then passing state competency exams.

The cases we're talking about here are jobs where if you do something wrong you can kill hundreds or thousands of people and destroy millions or billions of dollars worth of assets. *You've just completely moved the goalpost to try to make your point.*

Do you really honestly believe that these jobs shouldn't require a formal education?

I'm not going to take anyone seriously that is going to argue that medical doctors shouldn't receive formal educations. Come on, don't be dumb. Just have a normal fucking conversation. You don't have to double down and back yourself into a corner.

I know you're not that dumb, so stop bring disingenuous.

  > We can't fix the problem by making better choices as individuals

The problem itself was created through individual actions...

Lead by example. Mimicry is real, we all do it whether or not we are aware. Every node in the graph influences others. If you must exhort, it works better if you follow your own advice.

Of course, be discerning as you do this, and don't expend your energy or goodwill where it will be wasted. Be like Gandalf.

Any dogmatic system is fault-tolerant, in that it will "route around" some amount of internal dissent, but this does not make it impregnable.

The ruts in our minds steer us just as much as those in the ground. But earth turns and so can we.

If students find a way to get a diploma without doing the work, it will soon be worth less than the paper on which it is printed.