Kinda funny but my current feeling about it is different from a lot of people.
I did a lot of AI assisted coding this week and I felt, if anything, it wasn't faster but it led to higher quality.
I would go through discussions about how to do something, it would give me a code sample, I would change it a bit to "make it mine", ask if I got it right, get feedback, etc. Sometimes it would use features of the language or the libraries I didn't know about before so I learned a lot. With all the rubber ducking I thought through things in a lot of depth and asked a lot of specific questions and usually got good answers -- I checked a lot of things against the docs. It would help a lot if it could give me specific links to the docs and also specific links to code in my IDE.
If there is some library that I'm not sure how to use I will load up the source code into a fresh copy of the IDE and start asking questions in that IDE, not the one with my code. Given that it can take a lot of time to dig through code and understand it, having an unreliable oracle can really speed things up. So I don't see it as a way to gets things done quickly, but like pairing with somebody who has very different strengths and weaknesses from me, and like pair programming, you get better quality. This week I walked away with an implementation that I was really happy with and I learned more than if I'd done all the work myself.
Are you new to it? There's a pretty standard arc that starts with how great it is and ends with all the "giving up on AI" blog posts you see.
I went through it to. I still use a chatbot as a better stack overflow, but I've stopped actually having AI write any code I use - it's not just the quality, it's the impact on my thinking and understanding that ultimately doesn't improve outcomes over just doing it myself.
I've been doing it for a while. I never really liked stack overflow though it always seemed like a waste of time versus learning how to look up the real answers in the documentation. I never really liked agents because they go off for 20 minutes and come back with complete crap But if I can ask a question get an answer in 20 seconds and iterate again I find that's pretty efficient.
I’ve usually been skeptical about people who get unreasonably good results and not surprised when they wake up a few weeks later and are disappointed. One area where I am consistently disappointed is when there are significant changes across versions: I had one argue over where I could write a switch in Java that catches null (you can in JDK 21) and lots of trouble with SQLAlchemy in Python which changed a lot between versions. I shudder to think what would happen if you asked questions about react-router but actually I shudder to think about react-router at all.
When's the last time you "went through the loop" ? I feel like with this stuff I have to update my priors about every three or four months.
I've been using AI regularly since GPT 4 first came out a couple years ago. Over that time, various models from Sonnet to Gemini to 4o have generally been good rubber ducks. Good to talk to and discuss approaches and tradeoffs, and better in general than google + stack overflow + pouring over verbose documentation.
But I couldn't really "hand the models the wheel." They weren't trustworthy enough, easily lost the plot, failed to leverage important context right in front of them in the codebase, etc. You could see that there was potential there, but it felt pretty far away.
Something changed this spring. Gemini 2.5 Pro, Claude 4 models, o3 and o4-mini -- I'm starting to give the models the wheel now. They're good. They understand context. They understand the style of the codebase. And they of course bring the immense knowledge they've always had.
It's eerie to see, and to think about what comes with the next wave of models coming very soon. And if the last time you really gave model-driven programming a go was 6 months or more ago, you probably have no idea what's about to happen.
Just one person's opinion: I can't get into the mode of programming where you "chat" with something and have it build the code. By the time I have visualized in my head and articulated into English what I want to build and the data structures and algorithms I need, I might as well just type the code in myself. That's the only value I've found from AI: It's a great autocomplete as you're typing.
To me, programming is a solo activity. "Chatting" with someone or something as I do it is just a distraction.
A good part of my career has been spent pair programming in XP-style systems, so chatting away with someone about constraints, what we're trying to do, what we need to implement, etc, might come a bit more naturally to me. I understand your perspective though.
That may be one of the reason for the conflict of opinions. I usually build the thing mentally first, then code it, and then verify it. With tools like linters and tests, the shorter feedback make the process faster And editor fluency is a good boost.
By the time, I'm about to prompt, I usually have enough information to just code it away. Coding is like riding a bicycle downhill. You just pay enough focus to ride it. It's like how you don't think about the characters and the words when you're typing. You're mostly thinking about what you want to say.
When there's an issue, I switch from coding to reading and thinking. And while the latter is mentally taxing, it is fast as I don't have to spell it out. And a good helper to that is a repository of information. Bookmarks to docs, documentation browser, code samples,.. By the times the LLM replies with a good enough paragraph, I'm already at the Array page on MDN.
Having it write unit tests has been one place it is reliably useful for me. Easily verifiable that it covers everything I’d thought of, but enough typing involved that it is faster than doing it myself - and sometimes it includes one I hadn’t thought of.
I’ve read somewhere, IIRC, that you moslty need to test three things: Correct input, incorrect input, and input that are on the fence between the two. By doing some intersection stuff (with the set of parameters, behavior that are library dependent), you mostly have a few things left to test. And the actual process of deciding on which case to test is actually important as that is how you highlight edge cases and incorrect assumptions.
Also writing test cases is how you experience the pain of having things that should not be coupled together. So you can go refactor stuff instead of having to initialize the majority of your software.
I'm not chatting with the LLM – I'm giving one LLM in "orchestrator mode" a detailed description of my required change, plus a ton of "memory bank" context about the architecture of the app, the APIs it calls, our coding standards, &c. Then it uses other LLMs in "architect mode" or "ask mode" to break out the task into subtasks and assigns them to still other LLMs in "code mode" and "debug mode".
When they're all done I review the output and either clean it up a little and open a PR, or throw it away and tune my initial prompt and the memory bank and start over. They're just code-generating machines, not real programmers that it's worth iterating with – for one thing, they won't learn anything that way.
That's my basic doubt, too. When developing software, I'm translating the details, nuances and complexities of the requirements into executable code. Adding another stage to this process is just one more opportunity for things to get lost in translation. Also, getting an LLM to generate the right code would require something else than the programming languages we know.
Interesting point, I agree that things change so fast that experience from a few months ago is out of date. I'm sceptical there has been a real step change (especially based on the snippets I see claude 4 writing in answer to questions) but it never hurts to try again.
My most recent stab at this was Claude code with 3.7, circa March this year.
To be fair though, a big part of the issue for me is that having not done the work or properly thought through how a project is structured and how the code works, it comes back to bite later. A better model doesn't change this.
There has been a big change with Claude 4.0 in my opinion. Probably depends on your environment, but it’s the first time I’ve been able to get hundreds of lines of Python that just works when vibe coding a new project.
It’s still slower going as the codebase increases in size, and this is my hypothesis for the huge variance; I was getting giddy at how fast I blew through the first 5 hours of a small project (perhaps in 30 mins with Claude) but quickly lost velocity when I started implementing tests and editing existing code.
If you give it another try, my goto right now is Sonnet 4 Thinking. There's a pretty massive difference in intelligence by switching from just plain 4 to 4 Thinking. It's still pretty fast, and I think hits the right balance between speed and useful intelligence.
However, at least in my experience, nothing beats o3 for raw intelligence. It's a little too slow to use as the daily driver though.
It's kind of fun seeing the models all have their various pros and cons.
> To be fair though, a big part of the issue for me is that having not done the work or properly thought through how a project is structured and how the code works, it comes back to bite later. A better model doesn't change this.
Yes, even as I start to leverage the tools more, I try to double down on my own understanding of the problem being solved, at least at a high level. Need to make sure you don't lose the plot yourself.
Do you get very far at work with contrarian takes backed up without evidence or detail? How does dismissing other people’s experience so condescendingly work out for you in real life?
I’m curious if/how you’ve built a career this way. Or is this account just a stress relief “shit-posting” persona?
I'm hanging in there with it. The article was remarkably dispassionate about something that gets everyone all hot around here: easy stuff benefits from it a lot more than hard stuff. If your whole world is Node.js? Yeah, not a lot of deep water energy discovery geology data mining at exabyte scale or whatever. It pushed the barrier to entry on making a website with a Supabase backend from whatever it was to nearly zero. And I want to be really clear that frontend work is some of my favorite work: I love doing the web interface, it's visual and interactive and there's an immediacy that's addictive, the compiler toolchains are super cool, it's great stuff. I worked on a web browser, got some patents for it. I like this stuff.
But getting extreme concurrency outcomes or exotic hardware outcomes or low latency stuff or heavy numerics or a million other things? It's harder, the bots aren't as good. This is the divide: it can one shot a web page, even the older ones can just smoke a tailwind theme, headshot. A kernel patch to speed up a path on my bare metal box in a latency contest? Nah, not really.
But I see a lot of promise in the technology even if the sort of hype narrative around it seems pretty much false at this point: I still use the LLMs a lot. Part of that is that search just doesn't work anymore (although... Yandex got a lot better recently out of the blue of all things), and part of it is that I see enough exciting glimpses of like, if I got it hooked up to the right programming language and loaded the context right, wow, once in a while it just slams and it's kinda rare but frequent enough that I'm really interested in figuring out how to reproduce it reliably. And I think I'm getting better outcomes a little bit at a time, getting it dialed in. Two or three months ago an even with claude code would have me yelling at the monitor, now it's like, haha, i see you.
I've been back and forth, and currently heavily relying on AI-written code. It all depends on knowing what the AI can and can't do ahead of time. And what it can do often overlaps with grunt work that I don't enjoy doing.
> There's a pretty standard arc that starts with how great it is and ends with all the "giving up on AI" blog posts you see.
Wouldn't be shocked if this is related to getting ramped up.
Switch to a language you don't know. How will that affect your AI usage? Will it go up or down over time?
Had similar experiences during the SO early days or when diving into open source projects. Suddenly you go from being stuck in your bubble to learning all kinds of things. Your code gets good. The returns diminish and you no longer curiously read unless the library is doing something you can't imagine.
"I would go through discussions about how to do something"
Have you compared that to your normal debugging thought processes? I get that you might be given another way to think about the problem but another human might be best for that, rather than a next token guesser.
I have a devil of a time with my team and wider, the younger ones mainly, getting them to pick up a phone instead of sending emails or chats or whatever. A voice chat can solve a problem within minutes or even seconds instead of the rather childish game of email ping pong. I do it myself too (email etc) and I even encourage it, despite what I said earlier - effective use of comms is a skill but you do need to understand when to use each variety.
This was a really fascinating project to work on because of the breadth of experiences and perspectives people have on LLMs, even when those people all otherwise have a lot in common (in this case, experienced programmers, all Recurse Center alums, all professional programmers in some capacity, almost all in the US, etc). I can't think of another area in programming where opinions differ this much.
As a recurse alum (s14 batch 2) I loved reading this. I loved my time at recurse and learned lots.
This highlight from the post really resonates:
“ Real growth happens at the boundary of what you can do and what you can almost do. Used well, LLMs can help you more quickly find or even expand your edge, but they risk creating a gap between the edge of what you can produce and what you can understand.
RC is a place for rigor. You should strive to be more rigorous, not less, when using AI-powered tools to learn, though exactly what you need to be rigorous about is likely different when using them.”
> RC is a place for rigor. You should strive to be more rigorous, not less, when using AI-powered tools to learn, though exactly what you need to be rigorous about is likely different when using them.
This brings about an important point for a LOT of tools, which many people don't talk about: namely, with a tool as powerful as AI, there will always be minority of people with healthy and thoughtful attitude towards its use, but a majority who use it improperly because its power is too seductive and human beings on average are lazy.
Therefore, even if you "strive to be more rigorous", you WILL be a minority helping to drive a technology that is just too powerful to make any positive impact on the majority. The majority will suffer because they need to have an environment where they are forced not to cheat in order to learn and have basic competence, which I'd argue is far more crucial to a society that the top few having a lot of competence.
The individualistic will say that this is an inevitable price for freedom, but in practice, I think it's misguided. Universities, for example, NEED to monitor the exam room, because otherwise cheating would be rampant, even if there is a decent minority of students who would NOT cheat, simply because they want to maximize their learning.
With such powerful tools as AI, we need to think beyond our individualistic tendencies. The disciplined will often tout their balanced philosophy as justification for that tool use, such as this Recurse post is doing here, but what they are forgetting is that by promoting such a philosophy, it brings more legitimacy into the use of AI, for which the general world is not capable of handling.
In a fragile world, we must take responsibility beyond ourselves, and not promote dangerous tools even if a minority can use them properly.
Wait, you're literally advocating for handicapping everyone because some people can't handle the tools as well as others.
"The disciplined minority can use AI well, but the lazy majority can't, so nobody gets to use it" I feel like I read this somewhere. Maybe a short story?
Should we ban calculators because some students become dependent on them? Ban the internet because people use it to watch cat videos instead of learning?
You've dressed up "hold everyone back to protect the incompetent" as social responsibility.
I never actually thought I would find someone who read Harrison Bergeron and said "you know what? let's do that!"
But the Internet truly is a vast and terrifying place.
A rather shallow reply, because I never implied that there should be enforced equality. For some reason, I get these sorts of "false dichotomy" replies constantly here, where the dichotomy is very strong exaggerated. Maybe it's due to the computer scientist's constant use of binary, who knows.
Regardless, I only advocate for restricting technologies that are too dangerous, much in the same way as atomic weapons are highly restricted by people can still own knives and even use guns in some circumstances.
I have nothing against the most intelligent using their intelligence wisely and doing more than the less intelligent, if only wise use is even possible. In the case of AI, I submit that it is not.
Why are you putting down a well reasoned reply as being shallow? Isn't that... shallow? Is it because you don't want people to disagree with you or point out flaws in your arguments? Because you seem to take an absolutist black/white approach and disregard any sense of nuanced approach.
I don't have a dog in this fight but I think the counter argument was a terrible straw man. Op said it's too dangerous to put in general hands. Treating that like "protect the incompetent from themselves and punish everyone in the process" is badly twisting the point. A closer oversimplification is "protect the public from the incompetents".
In my mind a direct, good faith rebuttal would address the actual points - either disagree that the worst usage would lead to harm of the public or make a point (like the op tees up) that risking the public is one of worthy tradeoffs of freedom.
The rebuttal is very simple. I'll try and make it a bit less emotionally charged and clear even if your original opinion did not appear to me to go through the same process:
"While some may use the tool irresponsibly, others will not, and therefore there's no need to restrict the tool. Society shouldn't handicap the majority to accommodate the minority."
You can choose to not engage with this critique but calling it a "false dichotomy" is in poor form. If anything, it makes me feel like you're not willing to entertain disagreement. You state that you want to start a discussion by expressing your opinion but I don't see a discussion here. I observe you expressing your opinion and dismissing criticism of that opinion as false.
Some students treat AI like those things. Others are effectively a meat proxy for AI. Both ends of the spectrum would call themselves "AI fluent."
I don't think the existence of the latter should mean we restrict access to AI for everyone, but I also don't think it's helpful to pretend AI is just this generation's TI-83.
Why is "AI" (current LLM based systems) a danger on the level comparable to nukes?
Not saying that it is not, just would like to understand your reasoning.
Who decides what technologies are too dangerous? You, apparently.
AI isn't nukes - anyone can train a model at home. There's no centralized thing to restrict. So what's your actual ask? That nobody ever trains a model? That we collectively pretend transformers don't exist?
You're dressing up bog-standard tech panic as social responsibility. Same reaction to every new technology: "This tool might be misused so nobody should have it."
If you can't see the connection between that and Harrison Bergeron's "some people excel so we must handicap everyone," then you've missed Vonnegut's entire point. You're not protecting the weak - you're enforcing mediocrity and calling it virtue.
> Who decides what technologies are too dangerous? You, apparently.
I see takes like this from time to time about everything.
They didn't say that.
As with all similar cases, they're allowed to advocate for whatever being dangerous, and you're allowed to say it isn't, the people who decide is all of us collectively and when we're at our best we do so on the basis of the actual arguments.
> AI isn't nukes - anyone can train a model at home.
(1) They were using an extreme to illustrate the point.
(2) Anyone can make a lot of things at home. I know two distinct ways to make a chemical weapon using only things I can find in a normal kitchen. That people can do a thing at home doesn't make the thing "not prohibited".
> Who decides what technologies are too dangerous? You, apparently.
Again, a rather knee-jerk reply. I am opening up the discussion, and putting out my opinion. I never said I should be God and arbiter, but I do think people in general should have a discussion about it, and general discussion starts with opinion.
> AI isn't nukes - anyone can train a model at home. There's no centralized thing to restrict. So what's your actual ask? That nobody ever trains a model? That we collectively pretend transformers don't exist?
It should be something to consider. We could stop it by spreading a social taboo about it, denigrate the use of it, etc. It's possible. Many non techies already hate AI, and mob force is not out of the question.
> You're dressing up bog-standard tech panic as social responsibility. Same reaction to every new technology: "This tool might be misused so nobody should have it."
I don't have that reaction to every new technology personally. But I think we should ask the question of every new technology, and especially onces that are already disrupting the labor market.
> If you can't see the connection between that and Harrison Bergeron's "some people excel so we must handicap everyone," then you've missed Vonnegut's entire point. You're not protecting the weak - you're enforcing mediocrity and calling it virtue.
What people call excellent and mediocre these days is often just the capacity to be economically over-ruthless, rather than contribute any good to society. We already have a wealth of ways that people can excel, even if we eradicated AI. So there's definitely no limitation on intelligent individuals to be excellent, even if we destroyed AI. So your argument really doesn't hold.
Edit: my goal isn't to protect the weak. I'd rather have everyone protected, including the very intelligent who still want to have a place to use their intelligence on their own and not be forced to use AI to keep up.
> Wait, you're literally advocating for handicapping everyone because some people can't handle the tools as well as others.
No, they're arguing on the grounds that the tools are detrimental to the overwhelming majority in a way that also ends up being detrimental to the disciplined minority!
I'm not sure I agree, but either way you aren't properly engaging their actual argument.
Second reply to your expanded comment: I think in some cases, some technologies are just versions of the prisoner's dilemma where no one is really better off with the technology. And one must decide on a case by case basis, similar to how the Amish decide what is best for their society on a case by case basis.
Again, even your expanded reply shrieks with false dichotomy. I never said ban every possible technology, only ones that are sufficiently dangerous.
I agree with your reasoning. But the conclusion seems to be throwing the baby out with the bathwater?
The same line of thought can be used for any (new) tool, say a calculator, a computer or the internet. Shouldn't we try to find responsible ways of adopting LLMs, that empower the majority?
> The same line of thought can be used for any (new) tool, say a calculator, a computer or the internet.
Yes, the same line of thought can. But we must also take power into account. The severity of the negative effects of a technology is proportional to its power and a calculator is relatively week.
> Shouldn't we try to find responsible ways of adopting LLMs, that empower the majority?
Not if there is no responsible way to adopt them because they are fundamentally against a happy existence by their very nature. Not all technology empowers, even when used completely fairly. Some technology approaches a pure arms race scenario, especially when the proportion of its effect is mainly economic efficiency without true life improvement, at least for the majority.
Of course, one can point to some benefits of LLMs, but my thesis is that the benefit/cost quantity approaches zero and thus crosses the point of diminishing returns to give us only a net negative in all possible worlds where the basic assumptions of human nature hold.
I won't recommend anything. Every situation is different and you are arbitrarily transposing my argument rudely into another without much real thought, which is a shame. For instance, one thing you are ignoring is that we are evolutionary geared to handle situations of varying beauty.
I could point out many more differences between the two situations but I won't because your lack of any intellectual effort doesn't even deserve a reply.
> One particularly enthusiastic user of LLMs described having two modes: “shipping mode” and “learning mode,” with the former relying heavily on models and the latter involving no LLMs, at least for code generation.
Crazy that I agreed with the first half of the sentence and was totally thrown off by the end. To me, “learning mode” is when I want the LLM. I’m in a new domain and I might not even know what to google yet, what libraries exist, what key words or concepts are relevant. That’s where an LLM shines. I can see basic generic code that’s well explained and quickly get the gist of something new. Then there’s “shipping mode” where quality is my priority, and subtle sneaky bugs really ought to be avoided—the kind I encounter so often with ai written code.
The e-bike analogy in the article is a good one. Paraphrasing: Use it if you want to cover distance with low effort. But if your goal is fitness then the e-bike is not the way to go.
>But if your goal is fitness then the e-bike is not the way to go.
If the e-bike is an alternative to a road bike then yes. I'd argue that is almost never the case. The people I've spoken to are using them as an alternative to driving which is clearly beneficial to their fitness.
It is a good one. I'm going to keep it in my pocket for future discussions about AI in education, as I might have some say in how a local college builds policy around AI use. My attitude has always been that it should be proscribed in any situation in which the course is teaching what the AI is doing (Freshman writing courses, intro to programming courses, etc.) and that it should be used as little as possible for later courses in which it isn't as clearly "cheating". My rationale is that, for both examples of writing and coding, one of the most useful aspects of a four year degree is that you gain a lot from constantly exercising these rudimentary skills.
But there’s also something in between, an e-assisted bike, which covers a lot of distance, but you still have to put some extra effort to it. And helps a bit with fitness so. That’s how I would categorize AI-assisted coding right now.
I ride about twice as much distance (mountain biking) after I got an ebike (per Strava). It's still a great workout.
Sample size one disclaimer...
A better biking analogy that I've used in the past is that it I wanted to go ride slick rock and have never ridden before, an ebike is not going to prevent me from endoing.
It is a good analogy, also in the sense that some areas are not reachable without an e-bike and that you'll need to be prepared differently as you have to plan with charging and bigger weight etc.
The analogy doesn’t work too well, in my opinion. An e-bike can basically get you with low effort anywhere a regular bike can. The same is not true for AI vs. non-AI, in its current state. AI is limited in which goals you can reach with it with low effort, and using AI will steer you towards those goals if you don’t want to expend much effort. There’s a quality gradient with AI dependent on how much extra effort you want to spend, that isn’t there in the e-bike analogy of getting from A to B.
It’s a thin line to walk for me, but I feel that the whole “skill atrophy” aspect of it is the hardest to not slip into.
What I’ve personally liked about these tools is that they give me ample room to explore and experiment with different approaches to a particular problem because then translating a valid one into “the official implementation” is very easy.
I’m a guy who likes to DO to validate assumptions: if there’s some task about how something should be written concurrently to be efficient and then we need some post processing to combine the results, etc, etc, well, before Claude Code, I’d write a scrappy prototype (think like a single MVC “slice” of all the distinct layers but all in a single Java file) to experiment, validate assumptions and uncover the unknown unknowns.
It’s how I approach programming and always will. I think writing a spec as an issue or ticket about something without getting your hands dirty will always be incomplete and at odds with reality. So I write, prototype and build.
With a “validated experiment” I’d still need a lot of cleaning up and post processing in a way to make it production ready. Now it’s a prompt!
The learning is still the process of figuring things out and validating assumptions. But the “translation to formal code” part is basically solved.
Obviously, it’s also a great unblocking mechanism when I’m stuck on something be it a complex query or me FEELING an abstraction is wrong but not seeing a good one etc.
(Full disclosure: I have a lot of respect for RC and have thought about applying to attend myself. This will color my opinion.)
I really enjoyed this article. The numerous anecdotes from folks at RC was great. In particular thanks for sharing this video of voice coding [1].
This line in particular stood out to me that I use to think about LLMs myself:
"One particularly enthusiastic user of LLMs described having two modes: “shipping mode” and “learning mode,” with the former relying heavily on models and the latter involving no LLMs, at least for code generation."
Sometimes when I use Claude Code I either put it in Plan Mode or tell it to not write any code and just rubber duck with it until I come up with an approach I like and then just write the code myself. It's not as fast as writing the plan with Claude and asking it to write the code, but offers me more learning.
Such a thoughtful and well-written article. One of my biggest worries about AI is its impact on the learning process of future professionals, and this feels like a window into the future, hinting at the effect on unusually motivated learners (a tiny subset of people overall, of course). I appreciated the even-handed, inquisitive tone.
right now, the opportunity cost is probably as high as it's ever been (unrelated, but same also applies to people considering business school etc). What got you looking into it?
Whoops I missed following up here. It's been on my mind for a few years, after seeing someone talk about it (probably here or linked from here). Maybe from Julia Evans?
In the sense that you have to take a month off the rest of your life to go there. What about my job and my friends and my family and my house? Those don't stop existing and happening, and leaving them for a month is too difficult for people who just aren't committed enough. It's a decent filter/gatekeep for "who actually cares enough to do this?"
I feel like John Holt, author of Unschooling, who is quoted numerous times in the article, would not be too keen on seeing his name in a post legitimizes a technology that uses inevitabilism to insert itself in all domains of life.
--
"Technology Review," the magazine of MIT, ran a short article in January called
"Housebreaking the Software" by Robert Cowen, science editor of the "Christian
Science Monitor," in which he very sensibly said: "The general-purpose home
computer for the average user has not yet arrived.
Neither the software nor the information services accessible via telephone are
yet good enough to justify such a purchase unless there is a specialized need.
Thus, if you have the cash for a home computer but no clear need for one yet,
you would be better advised to put it in liquid investment for two or three
more years." But in the next paragraph he says "Those who would stand aside
from this revolution will, by this decade's end, find themselves as much of an
anachronism as those who yearn for the good old one-horse shay." This is mostly
just hot air.
What does it mean to be an anachronism? Am I one because I don't own a car or a
TV? Is something bad supposed to happen to me because of that? What about the
horse and buggy Amish? They are, as a group, the most successful farmers in the
country, everywhere buying up farms that up-to-date high-tech farmers have had
to sell because they couldn't pay the interest on the money they had to borrow
to buy the fancy equipment.
Perhaps what Mr. Cowen is trying to say is that if I don't learn how to run the
computers of 1982, I won't be able later, even if I want to, to learn to run
the computers of 1990. Nonsense! Knowing how to run a 1982 computer will have
little or nothing to do with knowing how to run a 1990 computer. And what about
the children now being born and yet to be born? When they get old enough, they
will, if they feel like it, learn to run the computers of the 1990s.
Well, if they can, then if I want to, I can. From being mostly meaningless, or,
where meaningful, mostly wrong, these very typical words by Mr. Cowen are in
method and intent exactly like all those ads that tell us that if we don't buy
this deodorant or detergent or gadget or whatever, everyone else, even our
friends, will despise, mock, and shun us the advertising industry's attack on
the fragile self-esteem of millions of people. This using of people's fear to
sell them things is destructive and morally disgusting.
The fact that the computer industry and its salesmen and prophets have taken
this approach is the best reason in the world for being very skeptical of
anything they say. Clever they may be, but they are mostly not to be trusted.
What they want above all is not to make a better world, but to join the big
list of computer millionaires.
A computer is, after all, not a revolution or a way of life but a tool, like a
pen or wrench or typewriter or car. A good reason for buying and using a tool
is that with it we can do something that we want or need to do better than we
used to do it. A bad reason for buying a tool is just to have it, in which case
it becomes, not a tool, but a toy.
On Computers
Growing Without Schooling #29
September 1982
I don't agree with your characterization of my post, but I do appreciate your sharing this piece (and the fun flashback to old, oversized issues of GWS). Thanks for sharing it! Such a tragedy that Holt died shortly after he wrote that, I would have loved to hear what he thought of the last few decades of computing.
Same, after reading your post, it sent me down reading all sorts of guest articles he did left and right, and it really made me wonder what he'd think of all this. I feel like his views on technology changed over his lifetime. He got more.. I dunno, cynical over time?
You say this like it should give him more credibility. He created a homeschooling methodology that scores well below structured homeschooling in academic evaluations. And that's generously assuming it's being practiced in earnest rather than my experience with people doing it (effectively just child neglect with high minded justification)
I have absolutely no doubt that a quack like John Holt would love AI as a virtual babysitter for children.
It was insightful of him and there absolutely are similar strains of hype today!
He also both points out the silliness of the pitch for the hype-y tech of the day and calls that same tech a tool some might use with good reason.
That doesn't read to me like "tech hyped as inevitable is never okay" but more like "ignore sales pitches and evaluate the hyped tech for yourself." Your own judgment of today's hyped tech might be "kill it with fire." Still, it seems fully in sync with the spirit of that passage for Recurse to ask for a bunch of smart people's varied opinions on it and share them with the world.
Kinda funny but my current feeling about it is different from a lot of people.
I did a lot of AI assisted coding this week and I felt, if anything, it wasn't faster but it led to higher quality.
I would go through discussions about how to do something, it would give me a code sample, I would change it a bit to "make it mine", ask if I got it right, get feedback, etc. Sometimes it would use features of the language or the libraries I didn't know about before so I learned a lot. With all the rubber ducking I thought through things in a lot of depth and asked a lot of specific questions and usually got good answers -- I checked a lot of things against the docs. It would help a lot if it could give me specific links to the docs and also specific links to code in my IDE.
If there is some library that I'm not sure how to use I will load up the source code into a fresh copy of the IDE and start asking questions in that IDE, not the one with my code. Given that it can take a lot of time to dig through code and understand it, having an unreliable oracle can really speed things up. So I don't see it as a way to gets things done quickly, but like pairing with somebody who has very different strengths and weaknesses from me, and like pair programming, you get better quality. This week I walked away with an implementation that I was really happy with and I learned more than if I'd done all the work myself.
> I did a lot of AI assisted coding this week
Are you new to it? There's a pretty standard arc that starts with how great it is and ends with all the "giving up on AI" blog posts you see.
I went through it to. I still use a chatbot as a better stack overflow, but I've stopped actually having AI write any code I use - it's not just the quality, it's the impact on my thinking and understanding that ultimately doesn't improve outcomes over just doing it myself.
I've been doing it for a while. I never really liked stack overflow though it always seemed like a waste of time versus learning how to look up the real answers in the documentation. I never really liked agents because they go off for 20 minutes and come back with complete crap But if I can ask a question get an answer in 20 seconds and iterate again I find that's pretty efficient.
I’ve usually been skeptical about people who get unreasonably good results and not surprised when they wake up a few weeks later and are disappointed. One area where I am consistently disappointed is when there are significant changes across versions: I had one argue over where I could write a switch in Java that catches null (you can in JDK 21) and lots of trouble with SQLAlchemy in Python which changed a lot between versions. I shudder to think what would happen if you asked questions about react-router but actually I shudder to think about react-router at all.
When's the last time you "went through the loop" ? I feel like with this stuff I have to update my priors about every three or four months.
I've been using AI regularly since GPT 4 first came out a couple years ago. Over that time, various models from Sonnet to Gemini to 4o have generally been good rubber ducks. Good to talk to and discuss approaches and tradeoffs, and better in general than google + stack overflow + pouring over verbose documentation.
But I couldn't really "hand the models the wheel." They weren't trustworthy enough, easily lost the plot, failed to leverage important context right in front of them in the codebase, etc. You could see that there was potential there, but it felt pretty far away.
Something changed this spring. Gemini 2.5 Pro, Claude 4 models, o3 and o4-mini -- I'm starting to give the models the wheel now. They're good. They understand context. They understand the style of the codebase. And they of course bring the immense knowledge they've always had.
It's eerie to see, and to think about what comes with the next wave of models coming very soon. And if the last time you really gave model-driven programming a go was 6 months or more ago, you probably have no idea what's about to happen.
Just one person's opinion: I can't get into the mode of programming where you "chat" with something and have it build the code. By the time I have visualized in my head and articulated into English what I want to build and the data structures and algorithms I need, I might as well just type the code in myself. That's the only value I've found from AI: It's a great autocomplete as you're typing.
To me, programming is a solo activity. "Chatting" with someone or something as I do it is just a distraction.
A good part of my career has been spent pair programming in XP-style systems, so chatting away with someone about constraints, what we're trying to do, what we need to implement, etc, might come a bit more naturally to me. I understand your perspective though.
That may be one of the reason for the conflict of opinions. I usually build the thing mentally first, then code it, and then verify it. With tools like linters and tests, the shorter feedback make the process faster And editor fluency is a good boost.
By the time, I'm about to prompt, I usually have enough information to just code it away. Coding is like riding a bicycle downhill. You just pay enough focus to ride it. It's like how you don't think about the characters and the words when you're typing. You're mostly thinking about what you want to say.
When there's an issue, I switch from coding to reading and thinking. And while the latter is mentally taxing, it is fast as I don't have to spell it out. And a good helper to that is a repository of information. Bookmarks to docs, documentation browser, code samples,.. By the times the LLM replies with a good enough paragraph, I'm already at the Array page on MDN.
Having it write unit tests has been one place it is reliably useful for me. Easily verifiable that it covers everything I’d thought of, but enough typing involved that it is faster than doing it myself - and sometimes it includes one I hadn’t thought of.
I’ve read somewhere, IIRC, that you moslty need to test three things: Correct input, incorrect input, and input that are on the fence between the two. By doing some intersection stuff (with the set of parameters, behavior that are library dependent), you mostly have a few things left to test. And the actual process of deciding on which case to test is actually important as that is how you highlight edge cases and incorrect assumptions.
Also writing test cases is how you experience the pain of having things that should not be coupled together. So you can go refactor stuff instead of having to initialize the majority of your software.
If you need to hand write tests to think through all that then sure, don’t let an AI do it for you.
I'm not chatting with the LLM – I'm giving one LLM in "orchestrator mode" a detailed description of my required change, plus a ton of "memory bank" context about the architecture of the app, the APIs it calls, our coding standards, &c. Then it uses other LLMs in "architect mode" or "ask mode" to break out the task into subtasks and assigns them to still other LLMs in "code mode" and "debug mode".
When they're all done I review the output and either clean it up a little and open a PR, or throw it away and tune my initial prompt and the memory bank and start over. They're just code-generating machines, not real programmers that it's worth iterating with – for one thing, they won't learn anything that way.
That's my basic doubt, too. When developing software, I'm translating the details, nuances and complexities of the requirements into executable code. Adding another stage to this process is just one more opportunity for things to get lost in translation. Also, getting an LLM to generate the right code would require something else than the programming languages we know.
Interesting point, I agree that things change so fast that experience from a few months ago is out of date. I'm sceptical there has been a real step change (especially based on the snippets I see claude 4 writing in answer to questions) but it never hurts to try again.
My most recent stab at this was Claude code with 3.7, circa March this year.
To be fair though, a big part of the issue for me is that having not done the work or properly thought through how a project is structured and how the code works, it comes back to bite later. A better model doesn't change this.
There has been a big change with Claude 4.0 in my opinion. Probably depends on your environment, but it’s the first time I’ve been able to get hundreds of lines of Python that just works when vibe coding a new project.
It’s still slower going as the codebase increases in size, and this is my hypothesis for the huge variance; I was getting giddy at how fast I blew through the first 5 hours of a small project (perhaps in 30 mins with Claude) but quickly lost velocity when I started implementing tests and editing existing code.
If you give it another try, my goto right now is Sonnet 4 Thinking. There's a pretty massive difference in intelligence by switching from just plain 4 to 4 Thinking. It's still pretty fast, and I think hits the right balance between speed and useful intelligence.
However, at least in my experience, nothing beats o3 for raw intelligence. It's a little too slow to use as the daily driver though.
It's kind of fun seeing the models all have their various pros and cons.
> To be fair though, a big part of the issue for me is that having not done the work or properly thought through how a project is structured and how the code works, it comes back to bite later. A better model doesn't change this.
Yes, even as I start to leverage the tools more, I try to double down on my own understanding of the problem being solved, at least at a high level. Need to make sure you don't lose the plot yourself.
> just try a newer model bro
Fundamentally none of the issues inherent in LLMs will be fixed by increasing parameter count or better weights.
Shilling for the newest model is a dead end, better to figure out how to best put LLMs to use despite their limitations.
> Fundamentally none of the issues inherent in LLMs will be fixed by increasing parameter count or better weights.
Um, ok just disregard my entire post where I talk about how my issues with using LLMs for programming were literally solved by better models.
They weren't. And if you think they were, you're simply wrong.
Sorry but it is what it is.
Do you get very far at work with contrarian takes backed up without evidence or detail? How does dismissing other people’s experience so condescendingly work out for you in real life?
I’m curious if/how you’ve built a career this way. Or is this account just a stress relief “shit-posting” persona?
I'm hanging in there with it. The article was remarkably dispassionate about something that gets everyone all hot around here: easy stuff benefits from it a lot more than hard stuff. If your whole world is Node.js? Yeah, not a lot of deep water energy discovery geology data mining at exabyte scale or whatever. It pushed the barrier to entry on making a website with a Supabase backend from whatever it was to nearly zero. And I want to be really clear that frontend work is some of my favorite work: I love doing the web interface, it's visual and interactive and there's an immediacy that's addictive, the compiler toolchains are super cool, it's great stuff. I worked on a web browser, got some patents for it. I like this stuff.
But getting extreme concurrency outcomes or exotic hardware outcomes or low latency stuff or heavy numerics or a million other things? It's harder, the bots aren't as good. This is the divide: it can one shot a web page, even the older ones can just smoke a tailwind theme, headshot. A kernel patch to speed up a path on my bare metal box in a latency contest? Nah, not really.
But I see a lot of promise in the technology even if the sort of hype narrative around it seems pretty much false at this point: I still use the LLMs a lot. Part of that is that search just doesn't work anymore (although... Yandex got a lot better recently out of the blue of all things), and part of it is that I see enough exciting glimpses of like, if I got it hooked up to the right programming language and loaded the context right, wow, once in a while it just slams and it's kinda rare but frequent enough that I'm really interested in figuring out how to reproduce it reliably. And I think I'm getting better outcomes a little bit at a time, getting it dialed in. Two or three months ago an even with claude code would have me yelling at the monitor, now it's like, haha, i see you.
I've been back and forth, and currently heavily relying on AI-written code. It all depends on knowing what the AI can and can't do ahead of time. And what it can do often overlaps with grunt work that I don't enjoy doing.
> There's a pretty standard arc that starts with how great it is and ends with all the "giving up on AI" blog posts you see.
Wouldn't be shocked if this is related to getting ramped up.
Switch to a language you don't know. How will that affect your AI usage? Will it go up or down over time?
Had similar experiences during the SO early days or when diving into open source projects. Suddenly you go from being stuck in your bubble to learning all kinds of things. Your code gets good. The returns diminish and you no longer curiously read unless the library is doing something you can't imagine.
"I would go through discussions about how to do something"
Have you compared that to your normal debugging thought processes? I get that you might be given another way to think about the problem but another human might be best for that, rather than a next token guesser.
I have a devil of a time with my team and wider, the younger ones mainly, getting them to pick up a phone instead of sending emails or chats or whatever. A voice chat can solve a problem within minutes or even seconds instead of the rather childish game of email ping pong. I do it myself too (email etc) and I even encourage it, despite what I said earlier - effective use of comms is a skill but you do need to understand when to use each variety.
This is great, it's so easy to get into the "go fast" mode that this potential gets overlooked a lot.
> It would help a lot if it could give me specific links to the docs
Just a super quick test: "what are 3 obscure but useful features in python functools. Link to doc for each."
GPT 4o gave good links with each example.
(its choices were functools.singledispatch, functools.total_ordering, functools.cached_property)
Not sure about local code links.
I've had this return great results, and I've also had this return hallucinated ones.
This is one area where MCPs might actually be useful, https://context7.com/ being one of them. I haven't given it enough of a shot yet, though.
viewing it as an assistant is the way to go. it's there to help you - like an overpowered autocomplete - but not there to think for you.
Some people copy and paste snippets of code without knowing what it does, and in a sense, they spread technical debt around.
LLMs lower the technical debt spread by the clueless, to a lower baseline.
You were part of the clueless, so an LLM improved your code and lowered the technical debt you would have spread.
(Author here.)
This was a really fascinating project to work on because of the breadth of experiences and perspectives people have on LLMs, even when those people all otherwise have a lot in common (in this case, experienced programmers, all Recurse Center alums, all professional programmers in some capacity, almost all in the US, etc). I can't think of another area in programming where opinions differ this much.
Thank you Nick.
As a recurse alum (s14 batch 2) I loved reading this. I loved my time at recurse and learned lots. This highlight from the post really resonates:
“ Real growth happens at the boundary of what you can do and what you can almost do. Used well, LLMs can help you more quickly find or even expand your edge, but they risk creating a gap between the edge of what you can produce and what you can understand.
RC is a place for rigor. You should strive to be more rigorous, not less, when using AI-powered tools to learn, though exactly what you need to be rigorous about is likely different when using them.”
> RC is a place for rigor. You should strive to be more rigorous, not less, when using AI-powered tools to learn, though exactly what you need to be rigorous about is likely different when using them.
This brings about an important point for a LOT of tools, which many people don't talk about: namely, with a tool as powerful as AI, there will always be minority of people with healthy and thoughtful attitude towards its use, but a majority who use it improperly because its power is too seductive and human beings on average are lazy.
Therefore, even if you "strive to be more rigorous", you WILL be a minority helping to drive a technology that is just too powerful to make any positive impact on the majority. The majority will suffer because they need to have an environment where they are forced not to cheat in order to learn and have basic competence, which I'd argue is far more crucial to a society that the top few having a lot of competence.
The individualistic will say that this is an inevitable price for freedom, but in practice, I think it's misguided. Universities, for example, NEED to monitor the exam room, because otherwise cheating would be rampant, even if there is a decent minority of students who would NOT cheat, simply because they want to maximize their learning.
With such powerful tools as AI, we need to think beyond our individualistic tendencies. The disciplined will often tout their balanced philosophy as justification for that tool use, such as this Recurse post is doing here, but what they are forgetting is that by promoting such a philosophy, it brings more legitimacy into the use of AI, for which the general world is not capable of handling.
In a fragile world, we must take responsibility beyond ourselves, and not promote dangerous tools even if a minority can use them properly.
This is why I am 100% against AI – no compromise.
Wait, you're literally advocating for handicapping everyone because some people can't handle the tools as well as others.
"The disciplined minority can use AI well, but the lazy majority can't, so nobody gets to use it" I feel like I read this somewhere. Maybe a short story?
Should we ban calculators because some students become dependent on them? Ban the internet because people use it to watch cat videos instead of learning?
You've dressed up "hold everyone back to protect the incompetent" as social responsibility.
I never actually thought I would find someone who read Harrison Bergeron and said "you know what? let's do that!" But the Internet truly is a vast and terrifying place.
A rather shallow reply, because I never implied that there should be enforced equality. For some reason, I get these sorts of "false dichotomy" replies constantly here, where the dichotomy is very strong exaggerated. Maybe it's due to the computer scientist's constant use of binary, who knows.
Regardless, I only advocate for restricting technologies that are too dangerous, much in the same way as atomic weapons are highly restricted by people can still own knives and even use guns in some circumstances.
I have nothing against the most intelligent using their intelligence wisely and doing more than the less intelligent, if only wise use is even possible. In the case of AI, I submit that it is not.
Why are you putting down a well reasoned reply as being shallow? Isn't that... shallow? Is it because you don't want people to disagree with you or point out flaws in your arguments? Because you seem to take an absolutist black/white approach and disregard any sense of nuanced approach.
I don't have a dog in this fight but I think the counter argument was a terrible straw man. Op said it's too dangerous to put in general hands. Treating that like "protect the incompetent from themselves and punish everyone in the process" is badly twisting the point. A closer oversimplification is "protect the public from the incompetents".
In my mind a direct, good faith rebuttal would address the actual points - either disagree that the worst usage would lead to harm of the public or make a point (like the op tees up) that risking the public is one of worthy tradeoffs of freedom.
The original post concluded with the sentence "This is why I am 100% against AI – no compromise." Not "AI is too dangerous for general hands".
My arguments are nuanced, but there's nothing saying a final position has to be. Nuanced arguments can lead to a true unilateral position.
I do want people to argue or point out flaws. But presenting a false dichotomy is not a well-reasoned reply.
The rebuttal is very simple. I'll try and make it a bit less emotionally charged and clear even if your original opinion did not appear to me to go through the same process:
"While some may use the tool irresponsibly, others will not, and therefore there's no need to restrict the tool. Society shouldn't handicap the majority to accommodate the minority."
You can choose to not engage with this critique but calling it a "false dichotomy" is in poor form. If anything, it makes me feel like you're not willing to entertain disagreement. You state that you want to start a discussion by expressing your opinion but I don't see a discussion here. I observe you expressing your opinion and dismissing criticism of that opinion as false.
> even if a minority can use them properly.
Most students today are AI fluent. Most teachers aren't. Students treat AI like Google Search, StackOverflow, GitHub, and every other dev tool.
Some students treat AI like those things. Others are effectively a meat proxy for AI. Both ends of the spectrum would call themselves "AI fluent."
I don't think the existence of the latter should mean we restrict access to AI for everyone, but I also don't think it's helpful to pretend AI is just this generation's TI-83.
Why is "AI" (current LLM based systems) a danger on the level comparable to nukes? Not saying that it is not, just would like to understand your reasoning.
Who decides what technologies are too dangerous? You, apparently.
AI isn't nukes - anyone can train a model at home. There's no centralized thing to restrict. So what's your actual ask? That nobody ever trains a model? That we collectively pretend transformers don't exist?
You're dressing up bog-standard tech panic as social responsibility. Same reaction to every new technology: "This tool might be misused so nobody should have it."
If you can't see the connection between that and Harrison Bergeron's "some people excel so we must handicap everyone," then you've missed Vonnegut's entire point. You're not protecting the weak - you're enforcing mediocrity and calling it virtue.
> Who decides what technologies are too dangerous? You, apparently.
I see takes like this from time to time about everything.
They didn't say that.
As with all similar cases, they're allowed to advocate for whatever being dangerous, and you're allowed to say it isn't, the people who decide is all of us collectively and when we're at our best we do so on the basis of the actual arguments.
> AI isn't nukes - anyone can train a model at home.
(1) They were using an extreme to illustrate the point.
(2) Anyone can make a lot of things at home. I know two distinct ways to make a chemical weapon using only things I can find in a normal kitchen. That people can do a thing at home doesn't make the thing "not prohibited".
> Who decides what technologies are too dangerous? You, apparently.
Again, a rather knee-jerk reply. I am opening up the discussion, and putting out my opinion. I never said I should be God and arbiter, but I do think people in general should have a discussion about it, and general discussion starts with opinion.
> AI isn't nukes - anyone can train a model at home. There's no centralized thing to restrict. So what's your actual ask? That nobody ever trains a model? That we collectively pretend transformers don't exist?
It should be something to consider. We could stop it by spreading a social taboo about it, denigrate the use of it, etc. It's possible. Many non techies already hate AI, and mob force is not out of the question.
> You're dressing up bog-standard tech panic as social responsibility. Same reaction to every new technology: "This tool might be misused so nobody should have it."
I don't have that reaction to every new technology personally. But I think we should ask the question of every new technology, and especially onces that are already disrupting the labor market.
> If you can't see the connection between that and Harrison Bergeron's "some people excel so we must handicap everyone," then you've missed Vonnegut's entire point. You're not protecting the weak - you're enforcing mediocrity and calling it virtue.
What people call excellent and mediocre these days is often just the capacity to be economically over-ruthless, rather than contribute any good to society. We already have a wealth of ways that people can excel, even if we eradicated AI. So there's definitely no limitation on intelligent individuals to be excellent, even if we destroyed AI. So your argument really doesn't hold.
Edit: my goal isn't to protect the weak. I'd rather have everyone protected, including the very intelligent who still want to have a place to use their intelligence on their own and not be forced to use AI to keep up.
Hyphenatic phrasing detected. Deploying LLM snoopers.
> Wait, you're literally advocating for handicapping everyone because some people can't handle the tools as well as others.
No, they're arguing on the grounds that the tools are detrimental to the overwhelming majority in a way that also ends up being detrimental to the disciplined minority!
I'm not sure I agree, but either way you aren't properly engaging their actual argument.
Second reply to your expanded comment: I think in some cases, some technologies are just versions of the prisoner's dilemma where no one is really better off with the technology. And one must decide on a case by case basis, similar to how the Amish decide what is best for their society on a case by case basis.
Again, even your expanded reply shrieks with false dichotomy. I never said ban every possible technology, only ones that are sufficiently dangerous.
[dead]
I agree with your reasoning. But the conclusion seems to be throwing the baby out with the bathwater?
The same line of thought can be used for any (new) tool, say a calculator, a computer or the internet. Shouldn't we try to find responsible ways of adopting LLMs, that empower the majority?
> The same line of thought can be used for any (new) tool, say a calculator, a computer or the internet.
Yes, the same line of thought can. But we must also take power into account. The severity of the negative effects of a technology is proportional to its power and a calculator is relatively week.
> Shouldn't we try to find responsible ways of adopting LLMs, that empower the majority?
Not if there is no responsible way to adopt them because they are fundamentally against a happy existence by their very nature. Not all technology empowers, even when used completely fairly. Some technology approaches a pure arms race scenario, especially when the proportion of its effect is mainly economic efficiency without true life improvement, at least for the majority.
Of course, one can point to some benefits of LLMs, but my thesis is that the benefit/cost quantity approaches zero and thus crosses the point of diminishing returns to give us only a net negative in all possible worlds where the basic assumptions of human nature hold.
Wait till you learn a minoity of prettier people end up having easier lives than the 90% majority. What will you recommend I wonder?
I won't recommend anything. Every situation is different and you are arbitrarily transposing my argument rudely into another without much real thought, which is a shame. For instance, one thing you are ignoring is that we are evolutionary geared to handle situations of varying beauty.
I could point out many more differences between the two situations but I won't because your lack of any intellectual effort doesn't even deserve a reply.
Sure, I guess we bow before your explosive arguments of intellectual devastation.
> One particularly enthusiastic user of LLMs described having two modes: “shipping mode” and “learning mode,” with the former relying heavily on models and the latter involving no LLMs, at least for code generation.
Crazy that I agreed with the first half of the sentence and was totally thrown off by the end. To me, “learning mode” is when I want the LLM. I’m in a new domain and I might not even know what to google yet, what libraries exist, what key words or concepts are relevant. That’s where an LLM shines. I can see basic generic code that’s well explained and quickly get the gist of something new. Then there’s “shipping mode” where quality is my priority, and subtle sneaky bugs really ought to be avoided—the kind I encounter so often with ai written code.
The e-bike analogy in the article is a good one. Paraphrasing: Use it if you want to cover distance with low effort. But if your goal is fitness then the e-bike is not the way to go.
>But if your goal is fitness then the e-bike is not the way to go.
If the e-bike is an alternative to a road bike then yes. I'd argue that is almost never the case. The people I've spoken to are using them as an alternative to driving which is clearly beneficial to their fitness.
It is a good one. I'm going to keep it in my pocket for future discussions about AI in education, as I might have some say in how a local college builds policy around AI use. My attitude has always been that it should be proscribed in any situation in which the course is teaching what the AI is doing (Freshman writing courses, intro to programming courses, etc.) and that it should be used as little as possible for later courses in which it isn't as clearly "cheating". My rationale is that, for both examples of writing and coding, one of the most useful aspects of a four year degree is that you gain a lot from constantly exercising these rudimentary skills.
But there’s also something in between, an e-assisted bike, which covers a lot of distance, but you still have to put some extra effort to it. And helps a bit with fitness so. That’s how I would categorize AI-assisted coding right now.
That's what an E-Bike is. If the motor is doing all of the work it's called a motor cycle.
There are some that can switch now: pedal and it will e-assist you, or just hold the lever and it will run without pedaling.
Bad analogy.
I ride about twice as much distance (mountain biking) after I got an ebike (per Strava). It's still a great workout.
Sample size one disclaimer...
A better biking analogy that I've used in the past is that it I wanted to go ride slick rock and have never ridden before, an ebike is not going to prevent me from endoing.
To go with fitness analogies, I think it is like when lifting weights was something new but the old guard thought it would make you slow for sports.
A ridiculous sentimental idea based on limited observation and bias against change that won't age well.
It is a good analogy, also in the sense that some areas are not reachable without an e-bike and that you'll need to be prepared differently as you have to plan with charging and bigger weight etc.
The analogy doesn’t work too well, in my opinion. An e-bike can basically get you with low effort anywhere a regular bike can. The same is not true for AI vs. non-AI, in its current state. AI is limited in which goals you can reach with it with low effort, and using AI will steer you towards those goals if you don’t want to expend much effort. There’s a quality gradient with AI dependent on how much extra effort you want to spend, that isn’t there in the e-bike analogy of getting from A to B.
It’s a thin line to walk for me, but I feel that the whole “skill atrophy” aspect of it is the hardest to not slip into. What I’ve personally liked about these tools is that they give me ample room to explore and experiment with different approaches to a particular problem because then translating a valid one into “the official implementation” is very easy.
I’m a guy who likes to DO to validate assumptions: if there’s some task about how something should be written concurrently to be efficient and then we need some post processing to combine the results, etc, etc, well, before Claude Code, I’d write a scrappy prototype (think like a single MVC “slice” of all the distinct layers but all in a single Java file) to experiment, validate assumptions and uncover the unknown unknowns.
It’s how I approach programming and always will. I think writing a spec as an issue or ticket about something without getting your hands dirty will always be incomplete and at odds with reality. So I write, prototype and build.
With a “validated experiment” I’d still need a lot of cleaning up and post processing in a way to make it production ready. Now it’s a prompt! The learning is still the process of figuring things out and validating assumptions. But the “translation to formal code” part is basically solved.
Obviously, it’s also a great unblocking mechanism when I’m stuck on something be it a complex query or me FEELING an abstraction is wrong but not seeing a good one etc.
(Full disclosure: I have a lot of respect for RC and have thought about applying to attend myself. This will color my opinion.)
I really enjoyed this article. The numerous anecdotes from folks at RC was great. In particular thanks for sharing this video of voice coding [1].
This line in particular stood out to me that I use to think about LLMs myself:
"One particularly enthusiastic user of LLMs described having two modes: “shipping mode” and “learning mode,” with the former relying heavily on models and the latter involving no LLMs, at least for code generation."
Sometimes when I use Claude Code I either put it in Plan Mode or tell it to not write any code and just rubber duck with it until I come up with an approach I like and then just write the code myself. It's not as fast as writing the plan with Claude and asking it to write the code, but offers me more learning.
[1]: https://www.youtube.com/watch?v=WcpfyZ1yQRA
> Thoughtful, extremely capable programmers disagree on what models can do today, and whether or not they’re currently useful.
Is anyone John Henry-ing this question and having parallel teams build the same product at the same time?
Such a thoughtful and well-written article. One of my biggest worries about AI is its impact on the learning process of future professionals, and this feels like a window into the future, hinting at the effect on unusually motivated learners (a tiny subset of people overall, of course). I appreciated the even-handed, inquisitive tone.
I really want to spend some time at the Recurse Center, but the opportunity cost feels so high
The problem is that in order to spend time at the Recurse Center, you first have to spend time at the Recurse Center.
What do you mean?
It’s a joke about recursion.
right now, the opportunity cost is probably as high as it's ever been (unrelated, but same also applies to people considering business school etc). What got you looking into it?
Whoops I missed following up here. It's been on my mind for a few years, after seeing someone talk about it (probably here or linked from here). Maybe from Julia Evans?
In what sense?
In the sense that you have to take a month off the rest of your life to go there. What about my job and my friends and my family and my house? Those don't stop existing and happening, and leaving them for a month is too difficult for people who just aren't committed enough. It's a decent filter/gatekeep for "who actually cares enough to do this?"
I feel like John Holt, author of Unschooling, who is quoted numerous times in the article, would not be too keen on seeing his name in a post legitimizes a technology that uses inevitabilism to insert itself in all domains of life.
--
"Technology Review," the magazine of MIT, ran a short article in January called "Housebreaking the Software" by Robert Cowen, science editor of the "Christian Science Monitor," in which he very sensibly said: "The general-purpose home computer for the average user has not yet arrived.
Neither the software nor the information services accessible via telephone are yet good enough to justify such a purchase unless there is a specialized need. Thus, if you have the cash for a home computer but no clear need for one yet, you would be better advised to put it in liquid investment for two or three more years." But in the next paragraph he says "Those who would stand aside from this revolution will, by this decade's end, find themselves as much of an anachronism as those who yearn for the good old one-horse shay." This is mostly just hot air.
What does it mean to be an anachronism? Am I one because I don't own a car or a TV? Is something bad supposed to happen to me because of that? What about the horse and buggy Amish? They are, as a group, the most successful farmers in the country, everywhere buying up farms that up-to-date high-tech farmers have had to sell because they couldn't pay the interest on the money they had to borrow to buy the fancy equipment.
Perhaps what Mr. Cowen is trying to say is that if I don't learn how to run the computers of 1982, I won't be able later, even if I want to, to learn to run the computers of 1990. Nonsense! Knowing how to run a 1982 computer will have little or nothing to do with knowing how to run a 1990 computer. And what about the children now being born and yet to be born? When they get old enough, they will, if they feel like it, learn to run the computers of the 1990s.
Well, if they can, then if I want to, I can. From being mostly meaningless, or, where meaningful, mostly wrong, these very typical words by Mr. Cowen are in method and intent exactly like all those ads that tell us that if we don't buy this deodorant or detergent or gadget or whatever, everyone else, even our friends, will despise, mock, and shun us the advertising industry's attack on the fragile self-esteem of millions of people. This using of people's fear to sell them things is destructive and morally disgusting.
The fact that the computer industry and its salesmen and prophets have taken this approach is the best reason in the world for being very skeptical of anything they say. Clever they may be, but they are mostly not to be trusted. What they want above all is not to make a better world, but to join the big list of computer millionaires.
A computer is, after all, not a revolution or a way of life but a tool, like a pen or wrench or typewriter or car. A good reason for buying and using a tool is that with it we can do something that we want or need to do better than we used to do it. A bad reason for buying a tool is just to have it, in which case it becomes, not a tool, but a toy.
On Computers Growing Without Schooling #29 September 1982
by John Holt.
I don't agree with your characterization of my post, but I do appreciate your sharing this piece (and the fun flashback to old, oversized issues of GWS). Thanks for sharing it! Such a tragedy that Holt died shortly after he wrote that, I would have loved to hear what he thought of the last few decades of computing.
Same, after reading your post, it sent me down reading all sorts of guest articles he did left and right, and it really made me wonder what he'd think of all this. I feel like his views on technology changed over his lifetime. He got more.. I dunno, cynical over time?
>author of Unschooling
You say this like it should give him more credibility. He created a homeschooling methodology that scores well below structured homeschooling in academic evaluations. And that's generously assuming it's being practiced in earnest rather than my experience with people doing it (effectively just child neglect with high minded justification)
I have absolutely no doubt that a quack like John Holt would love AI as a virtual babysitter for children.
It was insightful of him and there absolutely are similar strains of hype today!
He also both points out the silliness of the pitch for the hype-y tech of the day and calls that same tech a tool some might use with good reason.
That doesn't read to me like "tech hyped as inevitable is never okay" but more like "ignore sales pitches and evaluate the hyped tech for yourself." Your own judgment of today's hyped tech might be "kill it with fire." Still, it seems fully in sync with the spirit of that passage for Recurse to ask for a bunch of smart people's varied opinions on it and share them with the world.