Well before AI co-pilots something happened to the good old admins--they started to disappear only to be replaced by "AWS devops" (their job titles) who have never wired a network using routers, switches, and cables. I noticed that they started lacking basic networking knowledge and couldn't set up networking inside AWS. They just didn't know what a gateway, NAT, or subnet is.

Similar things are happening with AI co-pilots. We have an increasing number of people who "write code", but the number of people who can understand and review code is not increasing. There is also a problem of injecting ethics and politics into those tools, which can produce silly results. I asked Bard to write me a Python function to turn the US Constitution into a palindrome. Bard refused and gave ma a lecture on how the US Constitution is too important to be played with in such trivial fashion. I then asked it to produce code that turns the US national anthem into a palindrome, it refused again. So I asked it do the same but with the Russian national anthem and it spat out code without telling me off. I then asked it to generate code for simple tasks and it did an OK job, except the formatting and the fonts used were different every time, because it just lifted code from different webpages and recombined it like a massively hungover student waking up to realise he's supposed to hand in the assignment in one hour.

I see the same arguments about higher level languages. All of the tech industry is about standing on the shoulders of giants. I do believe that lower level knowledge can help a great deal but using this argument I can say something like:

Kids these days don’t know basic assembly, they have no idea what an or/and/nor/xor gate is, they’ve never built a computer from components (no, not cpu/mb/ram/etc, I’m talking transistors and soldering).

Maybe LLMs are different but I don’t think they are, they are yet another tool that some people will abuse and some will use wisely. No different from an IDE or a higher-level language in my book.

I have next to zero idea how many things I use daily work (like my car for example), it doesn’t stop me from being able to drive where I need to go. I have configured lower level networking equipment in the past and I couldn’t be happier that I don’t have to do that drudgery anymore.

All I’m saying is this is a dangerous argument to make because someone can always one-up (one-down?) you. “Oh you had NAT? Luxury! In my day we didn’t even have a network, we had to….” (See also: Four Yorkshiremen [0])

[0] https://youtu.be/ue7wM0QC5LE?si=Mm-FY1oooq2XkCQz

> Kids these days don’t know basic assembly

It is a problem. Knowing assembly is crucial if we want to have more efficient, faster hardware. Someone has to write kernels and drivers. The efficiencies happening on the lower level of the stack make the upper layers of the stack faster, more stable. It is also important that we have a large pool of devs who know how to do it. Otherwise that knowledge will be taken in-house and shared under strict NDAs preventing devs working for competition and effectively killing all the efforts of the Open Source community to wrangle free access the computer out of the claws of corporations.

This is not a problem that AI has brought about first, but co-pilots are accelerating it.

Just to provide some context, I could not wait for 16-bit, 32-bit, and then 64-bit architectures to become popular. I was the happiest dev around when object oriented programming tools became available, same to Linux, web, all frameworks and even cloud computing. But AI as it is currently being sold is a shit tool. If it stays and doesn't disappear after the VC money runs out, we will have to adapt our ways of working and educational courses to teach devs to review work of others recompiled by AI (it can't write shit on its own). Not sure it is worth the effort, because every time a new framework is introduced the results generated by AI will be of low quality until the models get trained on exaples written by humans... What's the point of using AI in that case?

If I was the same kid now, I'd get into a bootcamp or self teach and get a web dev job because the money is lucrative. Yeah sure, maybe i'll dabble in foundationa litems here and there, but most won't. It doesn't advance your career in web/software.

IMHO we will see a brain drain in computer/electronics engineering in 20 years when the pipeline of graduates dwindle and those with experience retire. Hope LLMs can innovate enough to make up, I guess.

That sure sounds like it has nothing to do with LLMs and everything to do with factors not all related to even computer science. “Capitalism” or maybe the more trendy “late-stage capitalism” is probably more to blame here than anything else.

Also I reject the idea that there are no kids today that get involved in tech/computers for the joy of learning/exploration.

I bristle whenever I see arguments about “kids these days didn’t learn exactly how I did so it must be wrong”. It reeks of “old man yells at cloud” (note, I was also a 90’s kid). It’s the same BS I heard as a kid about how computers would rot your brain or they were horrible for .

I’ve heard this anecdotally from every educator I know at the high school and college level. CS students are increasingly entering without any computer proficiency whatsoever so it has to be built up from scratch.

PC gaming and specifically modding is what I can thank for much of my computer proficiency, but even that is easy these days with Steam and its workshops for many games.

I'm not talking about millenials/gen X with computers -- I'm talking about silent gen/(boomers/greatest gen?) with cars.

In both cases, a new technology dominated the world, but was new and brittle at first. The kids who saw it in their youth were fascinated, but because of its brittleness they had to become at least minor experts in at least minor troubleshooting and mainteanance (like tuning a carburetor or defragging a hard disk) just to access the coolness.

I mean, it really was a pain to be a car owner in 1960. A lot like being a computer owner in 1995. If you wanted to enjoy one, you were going to need to change a spark plug or registry value once in a while. And you had to be ready to recover from an engine overheating or a blue screen of death, because these were not rare events.

Then the future generations increasingly lost touch with those skills because the technology got smoothed out as it developed further.

So I think it's quite plausible that future generations will permanently have less interest in serious skills with computers since the same thing has happened with cars. There is a way smaller perecentage of people my age (millenial) who are "into" cars or have moderate familiarity with car repair than people in their 70s. So it seems plausible the same pattern could continue to play out with computers, rather than a "old man yells at cloud" illusion

this is a goal for maybe 2% of the industry. And those folks know their assembler or they don't get hired. I think we are producing skills in the proportion of its requirement in the industry.

LLMs will not replace us, they just take some of the tedium out of writing code. Yes, they will have to be trained on new frameworks and languages but I don’t see that as a problem. People have to wait for SO questions and blog posts to be written about new frameworks and languages as it stands today.

I’m not sure I agree with your arguments about needing a lot of devs to understand assembly or the like. Or rather I disagree with your prediction of where that leads. We are effectively already there and it hasn’t led to your future. We have an amazing open source ecosystem and that billions of people rely on daily without knowing how every part of it works. Open source has only grown during that time.

I’ve never done the math myself for calculating the distance between 2 points on a globe. I just grab the Haversine formula and keep going. I don’t see that as a problem. Similarly I’ll reach for an open source library to solve a boring problem instead of doing it myself. I quite literally see no difference in that and using an LLM.

The people who really know the inner workings of things are compensated accordingly and there will always be these kind of people.

It sure can when you use multiple agents having discussions and optimizing characteristics with each other. CrewAI and AutoGen come to mind.

At each layer, though, we have a legible formal theory bridging one level to the next, and the abstractions tend to be so reliable the last thing most of us will consider is a hardware/assembler/compiler bug (though they do occasionally happen).

LLMs bridge layers illegibly and probabilistically. I can already see how they're helpful, but they do have some different characteristics that mean reliability and the connection to lower levels of the stack are an issue.

I’m not sure I agree but I want to make sure we aren’t talking past each other.

Using an LLM to write code does not have this problem IMHO.

Using an LLM as part of the code does have this problem.

I’m not writing off the usefulness of calling out to an LLM from code but it does bring “illegibly and probabilistically” into the mix whereas using an LLM to write code that’s human-reviewed/modified does not.

In fact, you should be unable to tell what I wrote vs what the LLM wrote if I’m doing right (IMHO). “Clever” code is always bad no matter who/what wrote it.

The only time I’ve been asked “did you use to write this?” Was due to the speed at which I completed something or because it’s in a language that the person asking the question knew I didn’t know very well.

For somebody new at the language or new to programming in general, that difference can be imperceptible until it gets to a review.

I've also managed to spot people using an LLM to write stuff due to them doing things which won't pass linters or code tests.

As for not passing linters or code tests that’s unacceptable behavior from an employee. If that happened more than a few times I’d be having a talk with the employee followed by showing them the door if they continued to do it. I’d behave the exact same way if no LLM was involved.

I do wish LLMs like Copilot could use the lint rules and/or tests to inform the code they generate but thankfully it’s normally a keystroke away for me to reform a block of code to match the style I prefer so it not the end of the world.

To use LLMs (or any tool) you have to be able to show you can use it correctly, safely, effectively, etc. If you can’t do that then you have no business being in a coding role IMHO.

This is true, but we still do have to have at least some knowledge of what those giants built. Otherwise we're actually just standing on clouds

I agree _someone_ has to have that knowledge, I reject they _everyone_ must have that knowledge.

I have zero desire to learn more about logic gates, they don’t interest me and while they enable me to do the work I do I don’t need to know about them to do the work. The same way that I couldn’t tell you how my car engines works (ok, at a high level I could, probably about the same level as I could talk about logic gates) but I’m able to drive from point A to point B without issue.

There are people who enjoy assembly, let them work on it. I’m not one of those people and I don’t have a problem with those people but I do have a problem with people who think I must know assembly to be an effective developer.

Sure, but you know that they exist, which to me suggests that you learned about them somewhere and probably have at least some foundational understanding of how they work

The problem is when people can skip any of that broad foundation and go straight to specializing somewhere, a lot of general knowledge is lost

Part of learning the broad foundations is also learning that those options exist for a specialization path

Someone who may love working on hardware designs may wind up spending their life as a mediocre web developer because they took an AI assisted web bootcamp that skips learning important fundamentals

It's an engine, and can make all kinds of things move.

If you don't know it works, sure you can drive a car, but a lot of missed opportunities to leverage the same exact tech for other use cases disappear when you don't know fundamentals.

I have to disagree on that one.

Most CS programs will have an assembly class as a mandatory one.

Is it used daily? No. Would they be incredibly rusty and probably require a refresher? Probably, but so would most people on something not used daily.

If I ever needed to write assembly (which probably means something has gone horribly wrong in my life) then I’m sure I could learn it on the fly, just like I’ve learned other things never taught in college on an as-needed basis.

Imagine never having used a compiler. Don't get me wrong, people get stuff done with Python and JavaScript, but it's a crazy time we live in.

It’s quite alarming to think that the most advanced language many new software engineers are familiar with is merely C or C++.

Imagine graduating without ever having programmed in assembly. It’s not that C or C++ aren’t capable languages, but we’re definitely in a strange era when the deeper understanding of machine interaction through lower-level programming is becoming a rarity.

Folks should know their basic C/C++ just like they should know enough x86 to make sense of what godbolt.org tells them. But oh, well, that's probably a lonely hill to die on. Having a rough idea of all these levels of abstraction work and interact is something I profit a lot in my line of work, but that's just a small niche and I get that (profiling and optimizing scientific, signal processing routines).

i got a c in that class (the grade not the language) because i didn't know java and kept having difficulties with the way it forces you to shoehorn OOP into everything.

However, I didn't get a degree so maybe it was shoehorned into some high level course. I had a job writing software while I was in college and dropped out to do that full time once I felt I wasn't getting any value from continuing my "education". It's never been an issue in my career and I've done well by my own metrics at least.

Yeah my husbands college (I'm a SW eng, he wants to be one) teaches C++ as the base...and this is a "just" a state school in a Arkansas.

Also gotta correct myself, after asking him it looks like I was wrong. Assembly isn't a direct requirement but it's one of three electives you have to chose from.

You have to choose between either assembly, cybersecurity or data science and assembly is considered the easiest apparently.

I’d argue that there are more self-taught kids who know low level programming than 20 years ago.

The information to do this is so much more accessible now and LLMs are a very powerful tool in the hands of someone who is curious and motivated.

Outside of a small set of folks most devs need NOT know assembly due to compilers being very good at converting to assembly.

Not the same Subnet/Gateway/NAT. These are first class constructs in most cloud developments and has huge implications on security, performance and costs. Unless you have some kind of automated way that abstracts away these issues (akin to compilers) most devops folks would need to learn about these concepts while working in a cloud environment.

Those are robust constructs coming out of fundamental thinking and design.

AI-based copilots, on the other hand, look like unstable libraries sitting on top of those languages.

To use an LLM, you still need to know the language you are using it to write. You don't need to know C or assembly as a Python/JS programmer today, but you do need to know JS to be an LLM-heavy JS programmer.

I agree, not everyone does. Maybe we have progressed to the point we can all say those people are wrong and their arguments were always wrong, maybe not.

> but you do need to know JS to be an LLM-heavy JS programmer.

I also agree. That's my whole point, you do need to know the language to be able to effectively use an LLM.

My issue is with people who immediately reject LLMs as a net-bad because they do part of the job for you. I reeks of "well I had to do it the hard way so you do too" which I cannot abide and immediately flips the bozo bit on them for me.

CoPilot is just blundering along using what it has learned from elsewhere - which is not always correct.

I'm very impressed with the best models — but, because I remember how awful NLP used to be, "very impressed" still means I rate the best as being around the level of an intern/work placement student most of the time, and even at their best still only a junior.

It's great, if you're OK with that. I've used GPT-3.5 to make my own personal pay-as-you-go web interface for any LLM that's API-compatible with OpenAI despite not being a professional Web Developer.

It's fairly fragile because it's doing stupid things to get the "good enough" result.

Bu that's OK, because it's for me, I'm not selling it.

(As for the lesser models… I asked one for a single page web app version of Tetris; it started off lazy, then suddenly switched from writing a game in JavaScript into writing a machine learning script in python!)

Everything else being exactly equal would I like someone I work with to know c over not? Sure.

But would I take a world with many fewer programmers that all knew c over the one we have today—definitely not. It would be a significantly poorer world.

In a way, ChatGPT both replaces the need for junior developers and creates a situation where you'll end up with a developer shortage because not everyone can start right away as a mid-senior level.

Well, they can, but the "no filter" option does not necessarily generate satisfactory results either. I find it hard to blame Google for not wanting to put their brand on an AI providing a ranking list of races by intelligence or whatever kind of offensive thing people are going to try to make it produce.

He's had to completely rethink how they approach their industrial designs and build machines and transition to much more electronic based things with fewer moving parts, and build things they can just adjust with computers and point-and-click GUIs, because people just don't know how these old things work from a physical reality perspective. My dad was very distraught by the caliber of engineers he's seeing in the "next generation".

But that's not the only thing.

In Canada, our aging population of tradespeople are retiring in droves and we have a dearth of younger people to replace them. It's so bad, that in my province they've completely redesigned the Grade 11-12 highschool curriculum to have "fast track" paths into trades where 80% of their time is spent in coop/apprenticeship (good thing!). We have a housing crisis and we have nobody that actually knows how to build houses in our country while the federal government imports 1 million people per year with nowhere for them to live. Most kids these days just want to be "influencers".

Spelling and grammar in young people is absolutely atrocious because they are totally dependent on autocorrect; and they can't type using physical keyboards because they only ever used a touchscreen keyboard on phones and tablets.

It's not just programming being destroyed by copilots. It's an overall dumbing down of our entire civilization.

Another example that I see all the time: I recently had to review a pull request from a junior. 200 lines of very simple C++ doing almost nothing, you can't fail that. I had to write more than 50 suggestions and comments on very basic stuff that the guy should have learned when he started coding. Yes, they must learn the ropes, but it seems like the passion is not there. We used to be passionate about coding, and all I see is people who went to private college to make money but don't enjoy what they do.

Heck even folks coming in with experience, but from a company with a very different coding culture or simply a different primary language can result in the same situation.

I work with seniors that are scared of the command line. I can somewhat understand why Microsoft might be trying to turn everything into AI Copilots because when I'm trying to explain to someone how to revert changes in a file with `git reset/checkout` they practically recoil in terror at the suggestion of using the terminal. They are married to their git guis and have no idea how it works. Best AI-ify as much as possible to keep people in their ecosystems.

But are they any less productive? My experience is no. I was a command line aficionado when I entered the workforce, but I saw several people get the same results as me in a purely GUI environment. I see the same today. They get shit done and that is what is required. I feel like older devs romanticize certain parts of their workflow too much.

What is sad is that this job is left to influencers. Not the kid's fault if our education system, parenting and attention economy are completely f'd up.

Tangent, but it used different fonts? I can’t imagine how this is possible given how LLMs work under the hood (sampling likely tokens which represent Unicode character sequences - it doesn’t operate on rich text so afaik has no way of seeing or changing font information). Is there something weird happing with the frontend breaking the code/not-code formatting they do (as a cosmetic thing rendering the output)?

As a Polish person, I don’t mind you changing the constitution, but for example with our national anthemn we are way more attached to changing how it sounds than Americans are to theirs. Ditto where our flag and our symbol can be used.

As for Bard - did you try arguing with it that constitution guarantees you the freedom to play with it? I managed to convince gpt to do some weird stuff by arguing for cultural sensitivity.

I will not argue with Bard or any other tool of that sort. I have better things to do. They are supposed to be good at translation, so I thought ok, let's try giving it a piece of my own writing in English and ask it to translate it into French (a gendered language). It could not infer from the text that the internal monologue that the protagonist was a female, so it translated it all using male voice. I asked it to switch gender to female voice but it stopped doing so after translating about two pages of the text and even on those two pages it kept switching to male voice. Then, after translating three pages it replied "I don't know how to do it" when I asked it translate another page, even though I used the same prompt I was using to ask it to translate previous pages. These are toys not tools. They are shit at their job and a waste of time and electricity.

Since you are Polish, I was told by my Polish colleagues that onet.pl, a large Polish news site uses AI to auto-translate articles from foreign media sources and that all of those translations are done in the male voice, even when the original text clearly describes what a woman did or said. My colleagues say it's been going on for a while and nobody bothers to fix these translations. If true, the future is here, it is shit, and AI is good at one thing that VCs love to fund, i.e. extraction of value through destruction of value.

The consequences are that developers can tackle basic tasks which are supported by the frameworks they use, but once something is not supported or straightforward they don’t know what to do and get completely stuck.

From society’s point of view, the usefulness and value of the task force decreases and important problems are not solved or aren’t efficiently solved.

That’s CTO level coding right there, gotta tell bard about my great app idea.

If you ask me, they shouldn't have to, in the same way that you probably don't know how to diagnose a coax cable problem.

The major public clouds all have completely software-defined networks (SDNs), and concepts like subnets are basically emulated for the sake of legacy systems.

Why would we want subnets, like... at all? Why can't all systems just have an IP address and use point-to-point communications? That's literally what the underlying SDN does with the packets anyway! The subnets and routes you see don't map 1:1 to the underlying network. The packets are stuffed inside a VXLAN packet and routed between random hypervisors scattered randomly across multiple buildings. Just give up the pretence, give everything an system-assigned public-routable IPv6 address and be done with it. Better yet, auto-register everything in a private DNS zone and auto-generate internal use SSL certs too.

The amount of wiring we do manually is just insane, just so that Azure and Amazon can help greybeards pretend that they're operating a Cisco router and need to stand in line to file the paperwork required to get an SSL certificate.

No, that was definitely part of the sysadmin job description ~25 years ago, both line discipline and physical medium problems (I had a TDR and a fiberglass fish at my desk for my first sysadmin job and was kind of bummed to find out that isn't remotely normal anymore)

Until you scale up a Kubernetes Cluster so far that the abstractions begin to leak.

Congratulations folks! We undid several decades of progress and went back to the 1960s and 1970s when software can only run on the specific mainframe for which it was written.

I'd prefer switching those windows servers to Linux, but we're too integrated with Microsoft at this point.

The cloud is the most closed, most locked in, most rent extraction oriented computing paradigm since the pre-minicomputer mainframe era. It’s really a return to that era.

Computing tends to cyclically reinvent wheels. I suppose next we will reinvent minis, micros, LANs, etc.

Jesus, they have coded pearl-clutching and finger-wagging lectures in my computers. You know, those machines that used to be brutally efficient at doing what you asked of them.

In a way, that's a natural thing to happen. Computer systems are weird in that we have an extremely good model of how they work at a certain level: "processor will (almost) always process assembly exactly as it should, etc."

When computers first came around, this "computer does exactly as you say" thing was weird to people because there weren't really any such things before computers. People would make small mistakes in their code and wonder why the computer can't "just do the obvious correct thing like any sensible person".

However, as multiple complex software systems interact and build on top of each other, this ability to "understand everything" at one of these "perfect model" levels (e.g assembly, logic gates, state machines...) becomes useless. To work with them, fuzzy models (like those we use for other humans or animals) are more appropriate. Complex software systems will routinely not do the same thing when given the same command. They will change over time, break in incomprehensible und unpredictable ways, and, as the newest addition, argue with you instead of doing what you say. That development makes total sense. At least those systems can now "take a hint".

Something like this was happening with coding bootcamps as well. Lots of people who can code what they were directly taught but don’t have the basics to go outside of that.

That said, I don't know that I would allow AI for learning to code in the class. I do not consider "prompt engineering" to be a substitute for programming, and it's very easy to get AI to write some code and copypaste it, not understand anything it said, and be done.

Last year, when I was administering a test, I said that they were allowed to use any IDE they wanted, but they could not use any AI assistance. I am pretty convinced one student just copypasted my test questions into ChatGPT or BingAI and then copied the answers. I didn't have a way to prove it so I had to give him the benefit of the doubt and grade it assuming he did it honestly.

Before someone says "YOU SHOULD TEACH THEM TO USE THE AI TOOLS LOL THAT'S THE FUTURE!!!", stop. That's not true, not yet at least. Just because I can call an Uber doesn't mean I get to say I know how to drive. Just because I can go to McDonalds doesn't mean I get to say I know how to cook. I was teaching them how to write Java and Python, the goal of the class was for them to learn Java and Python, it wasn't learning how to copypaste into OpenAI.

You can only learn to play the guitar by picking it up and spending a lot of time mucking about with it. There's a bit more to it, but this is really the core: you need to play and get that muscle memory and "feeling" for it. You need to "rewire your brain".

Coding, or any other skill for that matter, is no different. The only way to learn to code is to actually write code. Even if you fully understand anything ChatGPT gives you (which most students probably don't), that's no substitute for actually writing the code yourself.

I don't see how it's not hugely harmful for the development of students and junior programmers to use AI. Even if these tools were perfect (which they're not), you need to develop these basic skills of learning to read the code. You need to make mistakes and end up with crummy unmaintainable code, and realize what you did wrong. Etc. etc.

Even for senior programmers I'm not so convinced AI tools are actually all that beneficial in the long run.

Only when AI systems will be able to fully understand entire systems and full context and can completely replace human programmers will that change. I'd estimate that's at least 50 years off, if not (significantly) longer. Anything before that: you need someone who fully understands the code and context, in depth.

What worries me is that AI code is getting complicated and harder to correct. I recently noticed that after my IDE generated some code I felt a palpable sense of dread.

I often have to go over it in detail because it makes subtle, hard to catch errors. It pulls me out of my flow. I am starting to dislike that effect and I am certain huge swaths of the (new) coding population will lack the skill and motivation to correct these things (as various earlier comments here show).

Power tools don’t make you a master carpenter.

I’ve also found it makes otherwise knowledgeable and experienced people think they can do stuff without learning it. I’ve had several people on my team tell me they think Copilot will help them get up to speed and help out with some of the stuff I’m working on. So far none of them have done anything and I’m not sure how Copilot explaining a block of code is going to do anything for them. It’s already a very easy syntax to read, and they all already have coding experience in other languages. It’s a tool to let them think helping will be easy, so they volunteer, then do nothing because it isn’t the reality of the situation.

Back in the early days of computing people wrote software by working it out on paper, encoding it on to punch cards, and then giving that program (deck of cards) to an operator who loaded them and ran the code. If it didn't work properly you would get back a print out of the 'debug' which amounted to a memory dump. You'd then patch your punch cards based on working out where you'd screwed up, and try again.

That was the computer software industry for about a decade before time-sharing, VDUs, etc.

People absolutely did learn to code by reading books and nothing much else. Access to computers was so restricted (because time to use a computer was shared between lots of people) there weren't any other options.

Heck, I learned a lot of early web stuff like TCP, HTML, etc reading books at my parents house when I was at home from uni and didn't have my computer with me, and that was the late 90s. Of course you can learn coding by studying the theory without practicing. It's just a lot less fun.

Dijksta's "I had everything worked out before I wrote the code, because the computer didn't exist yet" is the same. It's like working mathematics out: that's not "just reading", it's actively engaging. Actively writing. Completely different from the passive consumption of AI output.

> Of course you can learn coding by studying the theory without practising. It's just a lot less fun.

No you can't. When you first started an editor or IDE you wrote some vague code-shaped junk that probably wasn't even syntactically correct, and didn't learn programming until you practised. Of course you need to learn some some theory from a book or instructor, but that's not the same as actually learning something.

Being even 80% (or 90 or even 95) there isn't enough - something will always be missed because it's only able to "reason" probabilistically within a narrow area not far away from the training data.

I recently learned Swift & Metal this way - knowing Python and other languages, I kept pasting my pseudo-code, asking GPT to convert it into Metal/Swift, and then iterating on that. Took me a week or two to get the language without reaching for any sort of manual/tutorial. Speaking from experience - it would take me 2-4x as much time to learn the same thing otherwise.

If GPT was any better, I'd have no need to fix the code, and I wouldn't learn as much.

Significant here is that you knew other languages already. I've had the same experience with GitHub Copilot, but I'm cautious about recommending it to new learners who don't yet know the fundamentals.

All indications I've seen indicate that it's easy for people who already know the foundations of programming to use AI tools to learn new tech, and that can actually be the most effective way for them, but that it doesn't work nearly as well for people who don't have knowledge of other languages and frameworks to lean on.

A lot of it is just a general lack of learning material for that demographic. For most languages, you get to choose between an introductory text that assumes you've never even heard of a "pointer" or an advanced text that is meant as a reference for an experienced developer of the language.

There's not really a lot of stuff out there to teach someone who already knows another language, who just wants to know the syntax and common idioms in that language. For example, a C programmer can easily pick up Python, learn some syntax, and go nuts -- but they just won't know about stuff like list comprehensions or dataclasses unless someone points it out. They'll write Python code that reads like C, not like Python.

I use ChatGPT all the time for learning new stuff, but a) I already know how to program well enough to teach a class on it, and b) it's a supplement to doing stuff on my own, without AI help. I don't feel I learn that much from just copying and pasting.

Totally agree with your process though; correcting and arguing with ChatGPT is an insanely good way to learn stuff. It's really helped me get better at TLA+ and Isabelle proofs.

I think you can upgrade to Ultimate at a per-account level for another $15/month.

EDIT: Accidentally wrote "pro" when I meant Ultimate.

That is a cute thought. Instead, they do submit the LLM generated code as is, messed up or not, and still expect to get full grade.

I’ve never met anyone who prefers debugging over writing fresh code. Did LLMs just automate away the fun part and leave us with the drudgery? That sounds like a horrible way to learn. All work and no play…

It also would mainly teach how to get good at spotting those little mistakes. But without context from writing code, it seems like it would be harder to pickup on what doesn’t look right. They’d also miss out on learning how to take a problem and break it down so it can be done with code. That’s a foundational skill that takes time and effort to build, which is being farmed out to the LLM.

Algorithmic code aside, I’m learning cuda now, and I just told gpt to write me some code and I debugged it then (with gpt’s assistance as well). It took me an hour to produce something that I expected it would take 2-3 days otherwise.

As for picking up what doesn’t look right - if it doesn’t work, you can pinpoint place where sth is messed up using a debugger/prints, and then you learn how the code works on the way.

I remember that when I was learning to program 30 years ago was the same - I rewrote pong game from a magazine, then kept changing and messing up things until I learned how it should work.

I have no idea how an LLM could help with that process.

[1] I successfully found a bug in a CFD solver (not a device driver) in the early days of templated g++ only after 2 weeks of fairly grueling half time work. Missing ampersand in an argument list. Believe it or not I was very happy and doubled down on the code. However! I once fucked up a job situation where my MPI-IO driver failed because I had a very subtle linux RAID hardware bug that I could not find after weeks of effort. I found it later, too late. That truly sucked. I really don't know how LLMs could possibly help with any of this.

I am very curious how an LLM is supposed to be trained on situations whose context does not exist on the open internet.

I recently learned javascript this way - knowing English and other languages (Thai, Mandarin), I kept pasting my pseudo-code, asking GPT to convert it into javascript, and then iterating on that. Took me a week or two to get the language without reaching for any sort of manual/tutorial. Speaking without experience - it would take me 2-4x as much time to learn the same thing otherwise.

:) My story is not entirely true, but close. My point being llms are learning language and logic (mostly English currently). Programming languages are just languages with logic (hopefully).

And if you think the ability to shorten meaning of a complex idea is exclusively the purview of code, think of a word like "tacky" or "verisimilitude"- complex ideas expressed in a shorter format, often with intended context with significant impact on the operations in the sentence around them.

I guess what you really learned is that the Apple-specific ecosystem technologies are so crappy, you always use a middleware to defeat them. Either Unity, React Native, or in this case, a 170b parameter super advanced LLM.

There's just no getting through to those Apple guys to stop making their own thing and being a huge PITA.

Imho Swift/SwiftUI is way more of a joy to work with. In particular, SwiftUI is much easier to visually parse whereas JSX is noisy and IMO hideous. I also ended up needing to bring in way more third-party dependencies for JS/React, something I hate doing if avoidable**.

* It is my understanding that many iOS professionals prefer UIKit bc SwiftUI is still missing stuff. I only ended up needing to fall back to it once or twice.

** ClojureScript/Reagent do solve most of my issues with JS/React (by essentially replacing them). Clojure is well-designed with a great standard library (so less need for dependencies or implementing stuff that should be in a standard library). Hiccup is way preferable to JSX.

Ditto Xcode.

The biggest issue is the lack of documentation for Metal.

If anything, it's like cooking with kids, not like going to McDonalds. I'd give extra points if they can solve the task with LLM in time. The hardest parts in programming is finding subtle bugs and reviewing code written by others - two tasks that LLMs can't help us with.

If they can easily solve the tasks with LLMs then it is a legitimate question to ask if you should be teaching that skill. Only common ones can be solved that way though. Why don't you give them a bugged code to fix, that way LLM inspiration is not going to work, if you check first to make sure LLMs can't fix the bug?

I said in sibling thread that I'd be fine enough with having a class like "Software Engineering Using AI" or something, but when the class is specifically about learning Object Oriented programming and Java and Python, I do not think having heavy use of ChatGPT is a good idea.

Also, not all the questions were pure coding, I had some more conceptual questions on there, and ChatGPT is really good at answering those.

I like to imagine LLM assistance as over-enthusiastic interns, except they don't actually improve with mentoring.

The trick becomes knowing which tasks will be improved by their participation... and which tasks will become even harder.

I have worked exclusively with React/Next, Node, and Mongo for years until I took over a legacy code base in October.

I had to teach myself Python/Flask, Vue, and Laravel without any kind of documentation or guidance from the previous developers or anyone else in my company.

AI has been able to help me get a bit further down the road each time I run into an issue. But AI hasn't been able to hand me perfect solutions for any of my problems. It helps me better understand the existing code base, but thus far none of the answer provided have been 100% accurate.

It's also not nearly mature enough for learning it be a good ROI in a degree program. Community college or adult education class? Maybe. Bootcamp track? Sure, I guess. Novelty elective for a few easy credits and some fun? Totally.

But if I'm a student in the midst of a prolonged, expensive program spanning years, learning how to coax results out of today's new generative AI tooling is not preparing me very well at all for what I can expect when I try to enter the workforce 2 or 4 or 8 more years. The tools and the ways to interface them, "Prompt Engineering" or whatever else it's called will inevitably evolve dramatically between now and then. So why am I learning it while I'm still deep in my academic bubbles? And what are prospective employers getting from a degree that focused heavily on some now-defunct and dated techniques? My degree is supposed to mean that I've learned foundational material and am ready to be productive on something, but what that mean when too much of what I've learned is outdated?

What bothers me is that people have told me that I should just allow AI for literally everything because it's the future and you should be teaching them the future or something, but I think that's kind of dumb. In those classes, the goal was for them to leave having some competence in with Python, Java, and Object Oriented programming, and I firmly do not believe you can get an understanding of that just by copying and pasting from ChatGPT, and I think even Copilot might hinder the process a bit.

To be clear, I love ChatGPT, I use it every day, it's a very valuable tool that people probably should learn how to use, I just don't feel that it's a substitute for actually learning new stuff.

I wonder what "people" told you that. My personal experience is that such advice usually comes from people who understand neither AI nor what I teach. Most of them are university administrators of sorts, and AI is a problem for them more than for me.

Introductory classes teach skills that can be performed reasonably well by AI. Those skills are the foundation you need to build higher level skills. Just like kids need to know how to read to be functional in society and in their later classes, despite screen readers doing an excellent job.

When I teach a foundations class this is my focus, and I don't fool myself or my students into thinking that they will be using those skills directly, but I try to convey the idea that the skills pervade through much of what they will later learn and do.

However that means that I cannot force students to learn. They can cheat, and it's easy, and I prefer spending my efforts on helping the learners than catching the cheaters.

The university administrators, however, are in the business of selling diplomas, which are only worth what the lowest common deminator is worth. So cheaters are a big problem for them. Typically for such people, they just bury their heads in the sand and prefer to claim that teaching students to use chatGPT (that lowest common denominator) is where the value is.

Otherwise it's been with in-person conversations and I didn't record them, there's a spectrum to how completely they suggest I allow AI.

Everything else you said I more or less agree with. Obviously if someone wants to cheat they're going to do it, but I feel that until we restructure the job market to not take GPAs as seriously (which I think I'd probably be onboard with), we should at least have cursory efforts to try and minimize cheating. I'm not saying we have to have Fort Knox, just basic deterrence.

I'm not an adjunct anymore, partly because I took stuff way too personally and it was kind of depressing me, partly because it was time consuming without much pay, but largely because I realized that most universities are kind of a racket (particularly the textbook corporations are a special kind of evil).

[1] https://news.ycombinator.com/item?id=36089826

[2] https://news.ycombinator.com/item?id=36087840

[3] https://news.ycombinator.com/item?id=36087501

With respect to deterring teaching I totally agree that we should go for it. There are ways to mitigate the value of cheating and ways to promote the value of learning, both of which are deterrents. Personally I like having lots of small tasks that build and follow on one another. If the student is working and trying to learn it makes sense and we get to reinforce the high level skills that matter. If the student is cheating it should become increasingly harder to keep a consistent story.

However if we turn this into a cop and robbers game that's what we're going to get.

As for the focus on GPA I think that the tide is turning. Employers need to find an alternative that doesn't eat their time.

And yes universities are rackets and aren't good value employers. They don't even offer job security anymore.

This is why I think that the old way of having tutorials instead of tests is vastly superior. When you are in a tutorial group (typically five or fewer students) and your tutor asks you to explain something to the other members of the group you can't hide behind an AI, a textbook, or even your own notes. Your lack of preparedness and understanding is made abundantly clear.

I found that having a solid foundation was critical for knowing if something would work, and even just to write a prompt that was semi-decent.

At one point I asked for what I wanted, but it kept only doing half of it and I could tell just by glancing at the code it was wrong. I then had to get very, very specific about what I wanted. It eventually gave a correct answer, but it was the long annoying option I was trying to avoid, so it didn’t change my end result, and I don’t even think it saved me any typing due to all the prompts to get there. It just gave me some level of confirmation that there wasn’t an obvious better way that I’d be able to find quickly.

To me, exams are taken in halls, written on paper, proctored, under deadline. Points may be deducted for syntax mistakes or unclarity as the examiner wishes.

Separately, home work is graded in ways that already makes cheating pointless; usually for 1) ambition/difficulty in the chosen problem 2) clarity in presentation and proofs, argued in person to TAs.

LLM should have no bearing on any part of education (CS or otherwise) unless the school was already a mess.

Perhaps the lesson is about how to read and evaluate code and how to test code. If students get good at how and when to spot errors and how to construct test scenarios that ensure the code is doing what it should then perhaps that will lead to even higher quality code than if they were learning, producing bugs, and then learning how to evaluate the code and test what they had written.

They lack the skills required to determine that, to fix those basic errors. But they'll still submit the code.

If I were a teacher, I would have asked the popular bullshit generators to generate solutions for the test questions, and if a student’s solutions were very similar, I’d have them do some 1:1 live-coding to prove their innocence.

The issue is that the thing he submitted was correct, so I was going completely off “vibes”; I never got it dead-to-rights with AI generating a one-for-one match. It got pretty similar, and ChatGPT text does have kind of a recognizable style to it, but I didn’t feel comfortable reporting a student for cheating and risking them getting expelled if I wasn’t 100% sure.

I might have tried to get him to do a one on one coding session, but this was the final exam and literally three hours after it I had to fly to the UK for an unrelated trip for three weeks. Grades were due in one week, so I didn’t really have a means of testing him.

A C developer working in notepad.exe is a real developer, as is a Python developer working in PyCharm and using the standard IDE features to improve their productivity. Someone blindly copy-pasting output from a LLM is not a developer.

Good, I’m glad you grasped the point of my comment. I was talking about the absurdity of people “gatekeeping” programming. Those arguments are just as silly as people saying using an LLM (in any capacity) is wrong and means you aren’t programming anymore.

Yes, blinding pasting code from an LLM does not a developer make. However that’s not what I suggested. I believe LLMs can be useful but you need to understand what it’s generating. The same way that SO is useful as long as you understand the code you are reusing (ideally modifying and reusing instead of a straight copy/paste).

That was never in dispute and not disagreed with in what I wrote. I didn’t say “using an LLM is wrong in any capacity” nor is that implied by anything I wrote. I use ChatGPT daily, I even told students they should use it if they needed help with understanding concepts outside of class”. I didn’t “gatekeep” programming, I just said they couldn’t use AI during an exam.

> I believe LLMs can be useful but you need to understand what it’s generating.

Yeah, if only we had some way of EXAMining if the students understand what they were generating. Like, crazy idea, maybe we could have some kind of crazy test where they aren’t allowed to use ChatGPT or Copilot to make sure they understand the concepts first before we let them have a hand-holding world.

I never said you said that, I said some people. I never said you should or shouldn’t use LLMs on exams, I have no idea why that’s being brought into this conversation.

I can only assume you’ve lost the thread and/or think you’re replying to someone else. This will be my last reply.

Maybe I misread your intent there, I just got a vibe that you were defending the use of LLMs during exams since that was the thing you were responding to. Apologies if I misread.

I love LLMs for coding but only because I have the experience to instantly evaluate every suggestion and discard a double-digit percentage of them. If an inexperienced programmer uses Copilot they aren’t going to have the confidence to disagree and it will stunt their development.

Did we just stop teaching kids basic spelling because spell check was built into MS Word? No, of course not, because even though spelling has been a more-or-less solved problem for people who already know how to read for the last thirty years, having kids learn to spell helps with their actual comprehension of a subject.

Also, if they do not learn the fundamental concepts then they will be able to differentiate a good solution from a bad solution. There's a lot of really shitty code that does technically accomplish the goal that it sets out for, and until you learn the fundamentals that fact won't be clear.

An year into the project I am forced to revise my opinion. When browsing my code-base I often stumble in abstruse niche solutions for problems that should not have existed. It was clearly the work of someone inexperienced walking through walls in an AI-fuelled coding frenzy.

Having an oracle that knows all answers is useless if you don't know what to ask.

Isnt this what code reviews are for? I catch a decent amount of code that looks AI generated. Typically, some very foreign pattern or syntax that this engineers never used nor is common in the codebase. Or something weirdly obtuse that could be refactored and shows a lack of understanding.

Normally I ask something like, "Interesting approach! Is there a reason to do it this way over (mention a similar pattern in our codebase)?" or if it's egregious, I might ask, "Can you explain this to me?".

This feel similar to early career engineers copy pasting stack overflow code. Now its just faster and easier for them to do. It's still fairly easy to spot though.

There is no substitute to doing something correctly in the first place. The problem is that in the real world, deadlines and lack of time will always cause the default solution to be accepted a small percentage of time even when it is not ideal. The increasing creep of AI will only exacerbate that and most technophiles will default into thinking of a new and improved AI tool to help with the problem, until it will be AI tools all the way down.

No thanks.

If you have an engineering culture that doesn't emphasize thorough code review (at least of juniors, leads and architects emergency-pushing is a different story) that's a problem. In addition to catching bugs, that's a major vector for passing on knowledge.

I tell you, it's spiralling out of control. Besides, even if you're doing the hiring, you may not have control if there is a profit motive. Us technical people cannot control the race to the bottom line, especially over the period of decades.

Listening to people talk in Japanese, I picked up enough to have some idea what they're talking about. I can't speak the language beyond some canned phrases. I certainly wouldn't claim to know Japanese. And I definitely wouldn't get a job writing books in Japanese, armed with Google Translate.

For programming, my solution to this right now is lots of pair programming. Really gives you a good idea of where somebody is at, what they still need to learn, and lots of teaching opportunities. I just hired a junior and we spend about 8 hours a week pairing.

A foundational concept of quality control is to not rely on inspection to catch production defects. Why not? It diffuses responsibility, lets more problems get to the customer and is less efficient than doing it correctly to start with.

The point being I think it depends on the project, it's size, audience, lifetime, etc whether or not code review is a win. my gut says it usually is a win but not always

At the one company I was where we did code reviews, they took at most one hour per day. The reviewer would go through the changes, ask for explanations when they didn't understand something, suggest small modifications to fit the codebase's style/structure/vibe, and move on.

They definitely didn't take 50% of our time. Half my time wasn't even spent coding, it was spent thinking about problems (and it was a workplace with an unusually high coding-over-thinking ratio, because of its good practices).

Imo introducing code reviews might very well decrease the velocity by 50% even if the actual time spent on code reviews is much less.

I see at least two reasons for this: 1) increased need for synchronization/communication 2) increased subjective frictions and mental overhead (if only for task switching).

Code reviews are not special in this respect, similar effects can be caused by any changes to the process.

Less time may be required if you have good rapport and long work relationship with the person, but I would say halving productivity sounds about right if we are talking mandatory reviews.

Using an LLM to generate code means having to do the same. The longer the autocompleted text, the more careful you have to be vetting it. Lower predictability, compared to a person and especially one you know well, means that time requirement would not go down too much.

Agree 100%, even when LLMs aren’t involved.

Certainly not all code reviews are like this—in many cases the approach is fundamentally sound, but you might have some suggestions to improve clarity or performance.

But where I work it’s not all that rare to open up a PR and find that the person has gone full speed in the wrong direction. In those cases you’re losing at least a morning to do a lot of the same leg work they did to find a solution for the problem, to then get them set off again down a better path.

Now in the ideal case they understood the problem they needed to solve and perhaps just didn’t have the knowledge or experience to know why their initial solution wasn’t good; in such a scenario the second attempt is usually much quicker and much higher quality. But introduce LLMs into the mix and you can make no guarantees about what, if anything, was understood. That’s where things really start to go off the rails imo.

It really doesn't need to be a massive amount of task-switching. And the benefits were obvious.

It probably also depends on the people that you're working with. I can easily imagine the velocity plummeting when the person reviewing your code loves to nitpick and bicker and ask for endless further changes with every next round of alterations that you do.

Doubly so if they want to do synchronous calls to go over everything and essentially create visible work out of code that already worked and was good enough in the first place.

I'm not saying that there aren't times like that for everyone occasionally, but there are people for whom that behaviour is a consistent pattern.

"One and done" products like games where you're mostly fixing bugs and releasing new content without major overhauls to the underlying code probably only need code reviews for code that is complex/clever where bugs are likely to be hiding, beyond that QA is fine.

reviews don't have to be formal to be reviews

I would have preferred to have someone to collaborate with at each of those places.

When I asked him what prompted him to do that, he said copilot suggest it so I just followed. I wonder if you could hijack copilot's results and inject malicious code as many end users does not understand lot of the niche code it generates sometimes, you could manipulate them to add the malicious code to the org's codebase.

It might even take the context of the typos in your code comments, and conclude "yeah, this easy to miss subtle error feels right about here".

Now I'm wondering, can you put in a comment which the LLM will pay attention to such that it generates subtle back-doors? And can this comment be such that humans looking at the code don't realise this behaviour will be due to the comment?

Apparently, if you tried to access a class member without specifying a class instance, one of Eclipse's "auto-fix-it" suggestions was to make all members of that class static, and he just followed that suggestion blindly.

This is a widespread problem regardless of AI. Hence the myriad Stack Overflow users who are frustrated after asking insane questions and getting pushback, who then dig their heels in after being told the entire approach they’re using to solve a problem is bonkers and they’re going to run into endless problems continuing down the path their on.

Not that people aren’t on too fine a hair trigger for that kind of response. But the sensitivity of that reaction is a learned defense mechanism for the sheer volume of it.

The problem is, SO can't tell someone who asks an insane question from someone who asks the same question but has constraints that make it sane. *

So in time, sane people during unusual stuff stop asking questions and you're left with homework.

* For example, "we can't afford to refactor the whole codebase because some architecture astronaut on SO says so" is a constraint.

Or another nice one is "this is not and will never be a project that will handle google-like volumes of data".

Stack Overflow is not there to help you solve your use-case. It's there to create a body of knowledge that everyone can refer to. You need to spell out your specific reasons so that the question and answers become useful to others.

A lot of the friction on Stack Overflow comes from people thinking it's a free help website rather than an attempt to create a collaborative knowledgebase.

It was nice to use early in its life when every answer was fresh. These days, the stale knowledge outweighs the fresh and the top answer is usually no longer correct.

If SO wants to be a knowledgebase, it needs to to be redesigned as it's current structure is not well suited to it.

In the same way I can imagine that by 2030 LLMs will still have memory problems and hallucinations. Although I’m sure by then we’ll have something better than pure LLMs.

I would pay for a pre-packaged system which could _locally_ and _privately_ make sense of all the emails, PDFs, slack messages, web pages I saw, other documents shared with me, code, etc etc, and make it all easily queryable with natural language, with references back to the sources. Sooner or later someone will make something like that.

[1] Technically LLMs do have a forget that last toke and go back so I can try again operation so this is only 99% true.

People have been committing terrible code to projects for decades now, long before AI.

The solution is a code review process that works, and accountability if experienced employees are approving commits without properly reviewing them.

AI shouldn't have anything to do with it. Bad code shouldn't be passing review period, no matter if it was AI-assisted or not. And if your org doesn't do code review, then that's the actual problem.

AI makes it much easier to push out bad code, fast...in a "frenzied" way one could say.

The answer is also the same.

Volume. AI makes it trivially easy to generate vast amounts of it that don’t betray their lack of coherence easily. As with much AI content, it creates arbitrary amounts of work for humans to have to sift through in order to know it’s right. And it gives confidence to those who don’t know very much to then start polluting the informationsphere with endless amounts of codswallop.

You’re putting the entire responsibility on senior employees. So we need much more of them. In fact, we don’t need juniors, because we can generate all possible code combinations. After all, it’s the responsibility of the seniors to select which one is correct.

It’s like how hiring was made crap by the “One-click apply” on LinkedIn and all other platforms. Sure it’s easy for thousands of people to apply. Fact is, we offer quite a good job with high salary, and were looking for 5 people. We’ve spent a full year selecting them, because we’ve receive hundreds of irrelevant applications, probably some AI-generated.

It’s no use to flood a filter with crap, hoping that the filter will do better work because it has a lot of input.

Having an oracle that knows how to put a framework of events together (even wit errors) is much better than asking a human to do it from scratch.

I wouldn't be comfortable with an accountant who couldn't do practical arithmetic in their head, or a surveyor who didn't have a fluent grasp of trigonometry and the ratios or function values for common angles.

Of course -- I don't care about the people working for them as a bookkeeper or assistent. They can be button monkeys as much as their boss lets them, but I also wouldn't expect those folk to reach very high in their career. Not everyone's going to, and a disinterest in and lack of technical fluency is a darn good predictor.

Your teacher was giving good advice about building skills and internalizing knowledge, because those are what contribute to mastery of a craft; maybe you were just being too pedantic or cynical to hear it?

The core idea is no, calculating in your head doesn’t turn you into a pro. Being a pro makes calculating in your head safe enough to rely on until you get to your desk and double-check.

That said, I was an integrator half my life and seen some accountants, big and small. Everyone used a calculator, it’s right next to their mouse and there’s another one elsewhere. And no one ever tried to talk about numbers afk, except for ballparks. Most of the times they suggest to go and query a db together. I find it very professional.

I have no interest interacting with someone who is going to get into how great the slide ruler is and how kids these days need to learn the slide ruler. When I was younger I thought this type of person was highly admirable but now older and wiser I see how full of shit they are.

> Does it really matter though? It sounds awfully like when a school teacher said you're not going to have an calculator in your pocket.

Yes. A younger me had teachers say that to me, and that younger me thought they were wrong.

But it turns out they were right, and younger me was wrong. Calculator dependence created a ceiling built from "educational debt" that many years later severely limited my mathematical ability.

The problem is focusing too much on getting the "answer," and losing sight of building the abilities that allow you to get there with your own mind. Eventually the need to manipulate your automated crutch turns into a bottleneck.

I wonder if that would be an issue if nobody had any.

Can you explain how your problems with arithmetic affected your ability to reason and prove things? I struggle to see how the two are connected.

Forbidding calculators and requiring students to do mental math for absolutely everything is unnecessary. But requiring students to solve integrals by hand when they're learning about integrals? Entirely reasonable.

If your goal when teaching coding is to teach the mechanical process of writing code: sure, go ahead and use LLMs for that process. But if your goal is to develop a deeper understanding of how to code, then LLMs can very easily obscure that. The end goal is not always just the answer.

I remember having to write code on a piece of paper in university.

Curiously, this lead to a lot of other students not really learning or caring about indentations and attempting to make code formatted in a generally readable way once they actually got access to IDEs and such.

They'd write it much like you would regular text on a piece of paper and would get absolutely stumped with unbalanced brackets whenever some nested structure would be needed.

Not only that, but somehow a lot of them didn't quite get around to using the various refactoring features of the tools, since they treated them like editors for writing text.

Did it help me memorize the basic constructs of the languages in question? Absolutely not, at least in the long term, since nowadays I have to jump between a lot of different languages and none of the syntax quite sticks, rather a more high level approach to problem solving.

LLMs just make that easier for me, letting me offload more work around language constructs and boilerplate, albeit requiring occasional intervention.

Maybe my memory just sucks.

Using AI to “help” learn to program is replacing the person’s effort thinking through the problems fully and is ultimately going to stunt their learning.

The problem with AI-assisted coding is that, applied uncritically, it circumvents this understanding. And without it, you won't even be able to judge whether the code that CoPilot etc spit out is correct for the task.

imagine if the calculator in your pocket gave you subtly wrong answers most of the time

Would it be better code if someone with 3 years of university and 5 years of coding practice did it? Yes, very probably, but the gap seems to be narrowing. Humorously I don't know enough about good code to tell you whether what I build with llms is good code. Sometime I build a function that feels magical, other times it seems like a fragile mess. But I don't know.

Do I know "javascript" or "python" or the theory of good coding practice? No, not currently. But I am building things. Things that I have personal, very specific requirements for. Where I don't have to liaise or berate someone else. Where I don't have to pay someone else. Where I don't share the recognition (if there is any ever) of the thing, I and only I have produced- (with chatGPT, Gemini and most recently llama3).

Folks have been feeling superior for 70 years and earning a good living because they spoke the intermediary language of compute engines. What makes them actually special NOW is computer science, the theory- the languages, we have very cheap (and in the case of open source local models free) translators for those now. And they can teach you some computer science as well, but that is still time and practice.

I'm the muggle. The blunt. And I'm loving this glowy lantern, this psi-rig.

When it comes to production quality code that should capture complex and/or business-critical functionality, you do want an experienced person to have architected the solution and to have written the code and for that code to have been tested and reviewed.

The risk right now is of many IT companies trying to win bids by throwing inexperienced devs at complex problems and committing to lower prices and timelines by procuring a USD 20 per month Github Co-Pilot subscription.

You individually may enjoy being able to put together solutions as a non-programmer. Good for you. I myself recently used ChatGPT to understand how to write a web-app using Rust and I was able to get things working with some trial and error so I understand your feeling of liberation and of accomplishment.

Many of us on this discussion thread work in teams and on projects where the code is written for professional reasons and for business outcomes. The discussion is therefore focused on the reliability of and the readability of AI-assisted coding.

What I really need is things like "show me the complete chain of code for this particular user activity in the app and highlight tokens used in authentication" ... - something senior engineers struggle to pull from our hundreds of services and huge pile of code. And so far sourcegraph and lightstep are incapable of doing that job. Maybe with better RAG or infinite context length or some other improvement there will be that tool. But currently the combined output of 1000's of engineers over years almost un-navigable. Some of that code might be crisp, some of it is definitely of llm-like quality (in a bad way)- I know this because I hear people's explanation of said code and how they misremembered it's function during post mortems. Folks copy and pasting outdated example code from the wiki etc. ie making things they don't understand. I presume that used to happen from stackoverflow too. Engineers moving to llm won't make too much difference IMO.

I agree, your points are valid, but I see "prompt engineering" as democratization of the ability to code. Previously this was all out of reach for me, behind a wall of memorization of language and syntax that I touched in the Pascal era and never crossed. 12 hours to build my first node.js app that did something in exactly the way I had wanted for 30 years. (including installing git and vscode on windows- see, now I am truly one to be reviled)

Copilot may work for simple scripts, but even for that where it mostly for things right, in my experience it still introduced subtle bugs and incorrect results more often than not.

For me personally, it’s a fantastic way to learn. I could definitely see people just using it and not actually learning, but to each their own.

That is a great point. The issue of not asking the right questions has been around as far as I can remember but I guess it wasn't seen as the bottleneck because people were so focused on solving problems by any means possible that they never had to think about solving problems in a simple way. We're still very far from that though and in some ways we have taken steps back. I hope AI will help to shift human focus towards code architecture because that's something that has been severely neglected. Most complex projects I've seen are severely over-engineered... They are complex but they should not have grown to hundreds of thousands of lines of code; had people asked the right questions, focused on the right problems and chosen the right trade-offs, they would have been under 10K lines and way more efficient, interoperable and reliable.

I should note though, that my experience with coding with AI is that it often makes mistakes for complex algorithms, or it implements them in an inefficient way and I almost always have to change them. I get a lot of benefit from asking questions about APIs and to verify my assumptions or if I need a suggestion about possible approaches to do something.

In my code reviews the person who wrote the code needs to explain to me what they changed and why. If they can’t then we are going to have a problem. If you don’t understand the code that an LLM spits out you don’t use it, it’s that simple. If you use it and can’t explain it, well… we are going to have to have some discussions and if it keeps happening you’re going to need to find other employment.

The exact same thing has been happening for pretty much the entire time we’ve had internet. Stack Overflow being the primary example now but there were plenty of other resources before SO. People have always been able to copy/paste code they don’t understand and shove it into a codebase. LLMs make that easier, no doubt, but the core issue has always been there and we, as an industry, have had decades to come up with defenses to this. Code review being the best tool in our toolbox IMHO.

But that's not what these LLM systems are. https://hachyderm.io/@inthehands/112006855076082650

> You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to.

> Alas, that does not remotely resemble how people are pitching this technology.

It is exactly what happened to you: it wrote bullshit. Plausible bullshit but bullshit nonetheless.

Honestly I find this to be the biggest advantage to using a coding LLM. It's like a more interactive debugging duck. By the time I've described my problem in sufficient detail for the LLM to generate a useful answer, I've solved it.

Language models can copy the top answers from SO, ingest docs and specs etc. And then the information is never updated? Or are they going to train it from scratch? On what? Outdated github saved games?