But a big yes for still typing code by hand, and not leaving it to the llm. Except it has to be the code generated by your brain.
That is what will create new neurons and new connections, which is what will keep away the cognitive decline.
And the constraint of not having to use llms will enhance creativity.
Actually, the constraints llms add to your code are more in number than the former. llms code in only the specific ways they've been trained on. So you won't ever come across of other ways.
Off the top of my head.. here's RubyQuiz.com [0] which I came across when I was learning ruby more than a decade ago. Looking at the many user-submitted solutions (you have to download the zip file!) you'll see completely different ways the problems were solved.
Sure, many won't be deemed efficient or standard by today's llm or rubocop checks, but looking at their code.. and retyping them and seeing them work.. was crucial in how I was able to think in Ruby for solving coding problems.
I did the same with Go too, with the "learn go with tests" guide [1].
I disagree. Before coding agents really took off, back in like 2022, I was trying to learn how to create grammars in Treesitter. One evening I sat down with ChatGPT and had it generate a grammar for me. But because it was on ChatGPT, I had to manually write everything it spit out.
What ended up happening is that I was typing, I'd notice little weird bits here and there, and I'd ask questions about it, explore alternatives, etc. So the end result was partially generated by ChatGPT, but also partially influenced by me.
But the biggest win was that within 3-4 hours, I was comfortable enough with the syntax that I was writing it entirely by hand, without AI assistance.
Now, if all you're doing is literally typing the characters and not thinking at all about what you're typing, and if you accept the LLMs output verbatim, then yes I'd say that you aren't really learning anything. But this is no different than just copying things from Stack Overflow. That behavior is nothing new.
The core distinction, as always, is whether you are exercising your brain.
> But a big yes for still typing code by hand, and not leaving it to the llm. Except it has to be the code generated by your brain.
> That is what will create new neurons and new connections, which is what will keep away the cognitive decline.
100% agree with this.
The problem is, your employer doesn’t care whether your brain is creating new neurons and connections. They care about productivity and profit.
I feel like the folks that believe we can continue to write code by hand are either in denial that LLMs will eventually (if not already) outperform handwritten code, or are in denial that employers will be ok with lower levels of productivity.
If you have the luxury of working for an employer that either doesn’t care or is oblivious to this, then 100% continue writing code by hand.
Or if you do work on the side (whether for fun or for profit) and are ok with lower productivity, then yes, stick to handwritten code.
But just because handwriting code is better for your brain and cognitive development, doesn’t mean that the industry will be in support of it.
I feel like there is an unspoken assumption of long term maintainability when it comes to LLM generated software. We are still very early in this, so I don't want to make assumptions. In principle, it shouldn't be impossible to both write and maintain stable software, purely with agents.
At least, I'm not aware of any actual reasons, backed by a proper theory.
On the other hand. I've noticed some persistent issues with code generated by agents, especially poorly supervised agents. If engineers become less vigilant, agents never get to the point of not needing supervision and juniors never pick up required skills, this could lead to real trouble.
Did business ever cared about this? Like ever?.. I've seen too many code bases with awful code by humans, now AI simply exacerbated the issue but this is nothing new in our field.
I just don't see how this is going to be a battle engineering departments can win. As long as AI is brining money to the business, they are not going to listen.
LLMs aren't perfect and they have problems, but often they write better code than humans. At least this human.
It seems they run into problems with larger concepts and general organization and need guidance at the moment but for a single code file they often do better then I would have.
If an LLM writes better code than you, that says more about you than anything else (either your ability to write code or your ability to evaluate LLMs). The code they write is garbage.
The complexity of code is combinatorial. Code is harder to understand than it is to create. The LLMs will be creating code that they will not (nor will any human) be able to understand later, or it won't be the same understanding that was used to create it. We'll find that systems that were created with LLM code will be impossible for LLMs to contextualize, or will be cost-prohibitive to do so.
It's a moot point. In Mag7 companies internal code gets thrown away within 2 years on average because of contract negotiations, new opensource projects that obviate need for the internal codebase, license changes, layoffs, etc, etc. We don't need long term maintainability because its already on its way to the garbage bin. Frankly it's always been this way.
Its' the exact opposite ... Google, nVidia, Amazon, Apple, etc. all have a deep bench of code that is a moat. They have lots of throwaway code too, but those companies are precisely the ones that pay attention to code quality, and comprehensibility by experts
A rough proxy for this would be if they say contribute to the C++ standards process, which Google Microsoft nVidia do, and basically zero "normal" companies (say SaaS) do. It means they are investing in multi-decade maintenance of their codebase
"Contribute" is an interesting word, since those companies are so big they can just dictate the standards they prefer. Also they are so big and have so much cash pouring in that they can afford to pay employees with nothing better to do than spend time drafting C++ standards. The average normal SaaS company is hoping they can make payroll next month.
> or are in denial that employers will be ok with lower levels of productivity.
More like sweeping the dust under the rug for later. The initial productivity boost is massive but at the cost of massive upfront tech debt. Given that LLMs aren't as good at removing code as they are at generating it, this is a problem.
this pure speculation. maybe you're right that it's a matter of time but /maybe you're wrong/. neither of us can actually know, because you're making predictions about the future rather than claims about what is true today
Perhaps the thing we’re going to learn to leave behind after all the dust settles … will be shitty management at shitty companies?
Because to be honest, more and more it seems many businesses have no real purpose other than to act as a sort of adult daycare for otherwise useless people.
> I feel like the folks that believe we can continue to write code by hand are either in denial that LLMs will eventually (if not already) outperform handwritten code...
They certainly don't today. Perhaps they will in the future, but based on the lack of improvement thus far it seems unlikely that they will get to this point. But whether or not they do improve to that point, the reality is that today, LLMs do not give you a productivity boost unless you give up on quality and just YOLO stuff the LLM gives you without actually checking it. And nobody should be willing to do the latter, because that is how you get software/infrastructure that doesn't actually work.
> The problem is, your employer doesn’t care whether your brain is creating new neurons and connections. They care about productivity and profit.
Which is why businesses, tech and otherwise, are falling apart constantly unless they achieve gigafuck scale status, at which point they're unkillable no matter how much of this cancer infests their management.
I don't know how we arrived at a social norm where it's just completely fine that leadership in massive companies is just absolutely useless at their jobs, but the firms that last another hundred years are going to be the ones that figure out how to fire them all.
> I feel like the folks that believe we can continue to write code by hand are either in denial that LLMs will eventually (if not already) outperform handwritten code, or are in denial that employers will be ok with lower levels of productivity.
Cooking a steak and then flushing it straight down the toilet outperforms all the slow and tedious business of eating and digesting it, too.
Why should I use an LLM to write code? It cannot do the job I want it to. It cannot type the code I've already written and it cannot generate correct code.
> Cooking a steak and then flushing it straight down the toilet outperforms all the slow and tedious business of eating and digesting it, too.
That would be more equivalent to writing the code and immediately deleting it.
Except this code is being deployed, and it is still (mostly) functional, at least functional enough to satisfy your employer (their “hunger” in your analogy).
> Why should I use an LLM to write code? It cannot do the job I want it to.
Because your employer is convinced that it can do the job they want it to (whether it does the job you want it to do is irrelevant, unless you're the employer, in which case 100% handwrite your code).
> Except this code is being deployed, and it is still (mostly) functional, at least functional enough to satisfy your employer (their “hunger” in your analogy).
Bzzzt. Wrong. I don't give a fuck who's satisfied by my code. I only care about writing it.
> Because your employer is convinced that it can do the job they want it to
Bzzzt. Wrong. The code is incorrect because it is not precisely the code I would have written.
LLMs are useless to me because they don't solve the problem of actually typing the code I've written into a computer.
Thing is, companies don’t care if it’s not your code or your view of it. If it works, that’s what they want. Since I stopped typing code I’ve been using my time to play some video games, read some books etc while the agents do the boring stuff (work itself). I get paid, the company is more productive, I got a bonus and a raise, everyone’s happy.
Again, unless you’re the employer, then you’re getting paid by someone that cares more about productivity than whether you think LLMs produce incorrect code.
All that matters is whether they agree with you.
The only control you have over this is to either a) work for yourself, or b) keep looking until you find an employer that agrees with you.
My argument is that the number of employers that fall under (b) is shrinking.
I mean in your case, you are not able to use LLMs effectively to build features, so you should type the code by hand.
However, upper management is excited about AI because it can push features quickly to production. Granted, they will be giant balls of slop but they don't care. As long as it works in the UI, they are happy.
If no one could get anything done with AI, there wouldn't be billions invested i n it.
There are jobs where llms can massively speed up delivery. Think about tiny/one man indie games for example, they can also get (stolen) assets almost for free. Helping with hobby projects, a subtype of that one man show.
Then there are sluggish corporate jobs. Here, even 10x speed up of development won't change delivery dates significantly or at all. I am in one such job, team leader has claude code higher tier and basically uses it for some more complex bash scripts and thats mostly it. Given this topic, I dont complain much, I value my long term senior skills way more than those new agentic ones.
This will cause cognitive debt anyway. As mentioned in https://arxiv.org/pdf/2509.21972v1: "When students rely
on these outputs as a substitute for their own reasoning or critical engagement, the learning process is fundamentally
compromised. Genuine learning requires the active construction of meaning, integration of knowledge, and reflective
engagement with content. These processes cannot occur through passive consumption of syntactically correct but
semantically hollow responses. Without this deeper cognitive work, learners risk mistaking linguistic fluency for
understanding, thereby undermining the very goals of education".
Personally, I don't think we will ever be able to reconcile using LLMs and cognitive debt. Even before LLMs we were aware if it: we knew people moving to managerial/PM roles eventually get their coding skills rusted. Well, now we are all in those managerial roles...
What do you think would be the effect on people that already have the skills and abilities. The LLM would build these systems using the skills that are largely understood by the programmer but piece them in new ways suitable for the system. This could lead to new enmergent behaviors that are not understood by the programmer.
I still think that you can build this model in your head even with LLM's but I'm not sure neither one way or the other.
> Even before LLMs we were aware if it: we knew people moving to managerial/PM roles eventually get their coding skills rusted. Well, now we are all in those managerial roles...
There's always some component of a skill that is not merely knowing something but practising something seemingly procedurally. Even if I understand integration well and have done it thousands of times, if I don't continually practise it from scratch, I begin to forget tiny bits and pieces and that accumulates to an eventually lack of understanding. So it is true for all mental tasks. There really isn't such a thing as understanding something completely without working it out by hand, from scratch. AI exacerbates a lack of understanding in this regard, although admittedly layers of abstraction in the coding realm already do that to some extent...
Lots of reactions here, but if it works for you then that's great.
For me I feel that LLMs have exploded (in a good way) my cognitive capabilities. I'm now the general of an army, rather than playing the role of a soldier. Of course that means that I lose the experience of being the lonely soldier, but it is a no-brainer tradeoff for me.
Anyway, I have to go now so I can push my car to the grocery store (so I don't forget how to walk), me and my giant calves will be back in a few hours.
All meant in good spirit. Keep doing what you're doing, thanks for sharing, and hope people are kind and only give good natured ribbings.
I feel like you think the car analogy was a clever parallel to draw here but looking at a car centric north american population and the dimensions of a non trivial slice of them that are unable to walk a 5k, id say you made the opposite point.
You have a point based on the way I wrote it. What I was trying to get at:
If you're going to walk to the store, don't push you car there! Just walk. So if you want to write code, don't retype what the LLM said, create your code from your own brain.
Don’t good generals know how everyone else in their army does their jobs? At the very least to the extent they can see through the bullshit.
For example, I don’t handwrite the code that the compiler produces, but I know how to troubleshoot the IR/assembly and fix optimization issues that arise occasionally. That makes me better at my job of directing the compiler.
This does not sound fun. It's better to work on your side projects with manual coding. You will learn more.
Retyping things is inefficient for learning. It's like trying to retype calculus solutions — you don't learn from it. Even if there is an explanation of why the code is written in such a way, you did not come up with it, and you don't know alternative solutions. It is a practice for memorizing, not for building your intuition.
A better option is to write it yourself first and ask LLMs for better options. They are pretty good at it, especially when you need to optimize hot loops.
When I was learning to code at college, by myself (I did a Business degree), I bought a book on iOS development[0]. This book mandated that you type all the examples out line-by-line. I thought the idea was a pretty silly one, but I stuck with it because I didn't know what I was doing and wanted to learn.
14 years later, as a software engineer, I still think about that book and the way I can trace back a lot of my initial improvements to its requirement that code got written line-by-line. I still maintain the habit of rote text copying as a way to pick up new tools and commands (i.e. copying documentation examples exactly into my editor), and also when people make an assertion like:
> It's like trying to retype calculus solutions — you don't learn from it.
Maybe not everyone learns that way, but I sure did!
Very much similar experience to yourself, when I was learning from YouTube tutorials I refused to copy/paste from their repos and instead typed everything out. Maybe changing variable names or structure.
I can definitely say it helped me learn a lot more than just blindly copy/pasting everything over.
Except that you do. Otherwise you could just sit in school or university and just listen and do nothing, but that way you just learn A LOT less, instead of taking down the lecture/lesson.
Of course you learn more if you retype material than if you do nothing, but it's still a waste of time, comparatively speaking, because there are much better ways of engaging with the material like solving practice problems.
Having to spend 95% of the time taking notes of things that could be distributed in a PDF has only ever hindered me academically. It's mechanical and boring, my mind tunes out and I get nothing out of it.
Taking down the lesson/lecture does relatively little, you can do it on autopilot. If anything, it distracts from listening to the content. When I took computer science at university (mid naughties) we were given digital lecture notes anyway. (Mathematics lectures did have to be written down but there were no typed notes to start with so it was just out of necessity.) It's the exercises afterwards that are useful for learning. Or summarising notes in your words.
When I did a CS degree in the 1980s we had one lecturer who handed out copies of hand-written notes but then would talk about stuff that wasn't really in the notes - sort of verbal annotations.
Yes some people teach C this way, for a little while, I don’t forget semicolons as much as people around me later seemed too, may or may not be related
I'm not sure I agree about retyping calculus solutions. I often find that writing out a proof or derivation forces me to engage with some minor detail that I hadn't fully appreciated beforehand. That usually raises productive questions.
I think the disconnect here is that you can't only retype the solutions. You have to already sort-of know what's going on and you have to also care to understand the gaps.
So transcription can definitely be helpful when part of a broader, intentional process, while also being insufficient to do much on its own.
And the next logical question to ask is whether there's a better form of intentional behavior that might be more effective.
In which case it would be the engagement with minor details that does the productive work. Retyping is merely a gateway to (sometimes) trigger the engagement.
The solution in TFA feels like an on-ramp to cargo culting somehow; observing that manual typing and good results often go together, but then thinking that it's the typing that directly causes the good results rather than the thought process that accompanied the typing.
There's a much better article hiding inside the current one that's titled "Prevent cognitive debt by understanding deeply the code that your LLM spits out", but that sounds like hard work and would probably not be very popular. It's much better for audience engagement to provide a simple solution that anyone can do and does not require a lot of deep thought like "manually retype everything the LLM generated", even if it doesn't actually work.
Writing reinforces. You won't learn from blind uncomprehending rewriting, sure, but when you already know the field, writing gives you the space to comprehend and digest. Certainly more than copying and pasting or blind acceptance of generated code. It doesn't have to be fun to be better.
At the beginning of my professional journey I was coding along youtube tutorials to learn, and I can tell you for a fact that all writing reinforces is syntax, which is reinforced regardless when you build your own projects while you develop your decision making skills at the same time.
Writing certainly reinforces more than that when you're not just starting out. It's a poor learning tool. When you have no grasp at all of the meaning, yes, it'll only enforce the syntax you can grasp at that point. I'll maintain that it's a good reinforcement tool, especially to the ends mentioned in the article.
You have implicit assumption that the person rewriting does nothing else. Understanding intention and solution is in there too for most people.
A lot of calculus is rewriting blocks of solutions and applying them to problems. There is a huge chasm between how calculus and real analysis are taught. By your logic calculus should be of very low value. Yet somehow it opens a lot of doors for people to learn other things based on it.
I had fun and also learned a lot when I retyped programs from magazines back in the day. I am not sure if it's suitable now but there is certainly some merit to the idea.
I think it depends on what type of cognition you want to stimulate. It's probably useful to familiarize yourself with a suite of API calls, or get a big picture view of how an algorithm runs.
I'll note this one down. I was under the impression that yes, retyping helps learning something about a language and architecture, but I found myself forgetting it after a while...
Trying it first sounds slower, but definitely better for cognitive training :)
One thing it taught me was that sometimes the corrections would be in next month's edition, so I had that long to figure out for myself why the program didn't work as stated.
> Retyping things is inefficient for learning. It's like trying to retype calculus solutions
Says who? You're saying this unequivocally like you have research that supports this.
I used to re-write the notes I took for studying and it was like night and day for how well I did on tests. IT also gave me a chance to tighten the information I was receiving. And it's exactly what's happening here.
It’s very common here on HN from the threads I’ve engaged in - everybody has an idea how learning works but it’s usually based on perceived personal experience and not actual research. I.e. you’re completely right with calling it vibe learning.
I had to re-read it a few times to make sure I was actually interpreting it correctly because I couldn't believe someone would make a claim like that for everyone.
Good advice yesterday, good advice today, and good advice tomorrow.
I don't remember if I read this advice or just intuited it myself (perhaps after some hard lessons), but it's a programming habit I've kept for as long as I can remember (I started coding in the 90s). If I feel rushed, e.g. someone looking over my shoulder, and I copy+paste something, it always leaves me with a sense of unease. It creates a memory & comprehension hole that sticks out like a sore thumb, even for seemingly simple snippets. You can't really be sure it's simple without stepping through it carefully, and simple can be deceptive because it's usually the interactions and assumptions wrt surrounding code that lead to surprises. Typing out code manually gives you time and space to consider the broader picture.
I got made redundant shortly before the release of LLM code generators, so I've never used one, but I did occasionally do this for short snippets from tutorials and suchlike.
> As I manually type every single line of LLM generated code into my editor, I build up a mental model of how it works and fits into my existing codebase. If I don't understand an API or algorithm, I can stop to look it up, or just ask the LLM to explain it.
This is part of how I learned how to code! Well, sans LLMs. Instead of copypasta-ing code blocks from books, Expert Sexchange and Stack Overflow, I manually retyped everything, looked up what I didn't understand and changed what I could.
In my love(?)-hate relationship with "vibecoding", even I tried this approach. For a while it worked, though that "while" didn't last as long as the months OOP has been riding this wave. Though, the vibes have usually been off, so I wish I could keep both thinking of and writing that code which adds negligibly to shareholder value. I say "wish" because a part of me has definitely been hijacked, in much the same way as the addictive type of social media. Sometimes I feel like I need a serious intervention.
Do mathematicians and physicists put away the calculator and computer (this always reminds me of the last scene from Star Wars) and do the computation by hand? A physicist isn't going to manually invert a 10,000x10,000 matrix.
Exactly as we don't write machine code letting the compiler do that, now and in the near future we won't be writing high level programming language code. We are moving towards working on a higher level of abstraction.
When I ask a frontier model to write a loop 10 different ways in Python and TypeScript and test the performance of each using a 1,000,000 iterations, it isn't creating cognitive debt. For the time being, I'm still racking my brain asking the question, how does garbage collection affect the performance.
I feel like we're doomed to respond to these lazy analogies ad infinitum.
Knowledge abstractions have historically been built by people with a detailed understanding of a problem domain and with a period of vetting the abstraction by many people. That is simply not true with LLM-generated code. We do not really understand what techniques LLMs are using to understand directions, value aesthetic/legibility characteristics, assess tradeoffs, retrieve contextual information, generate output, etc. Embedded in everything LLMs do are small decisions.
If you reach for formulas that you don't understand the applicability of, you will similarly get bad outcomes! But a calculator has almost no resemblance to what happens when you give general instructions to an LLM and get a generated code back, because in between that prompt and the result are many layers of decisions you the developer did not make and likely will not understand without carefully assessing the output!
If you have no discomfort with being culpable for something and doing none of the work to understand it, by all means, don't do anything to understand the output! Don't read the code, don't re-type the result, etc. I have concerns that you'll have a job in a year, but that's your decision to make!
What I did is kinda similar, I downgraded to $20 plan and just ask questions and almost never let it write the code, and if I can I use the web ui like the good old days and not spend my CLI tokens.
This is really the distinction between the mentality of a technical manager and an individual contributor.
Managers never got to write and internalize every line of code anyway. Arguably their essential skill is producing useful stuff without needing to do so. So is that 'cognitive debt'? When I use an agent to code, I do the same things: I validate the direction, organization, and core decisions, but delegate the details. It's an intentional calculation of risk, managing the scope of future problems if there's a failure of implementation somewhere.
It's a question of where you put the value of cognitive focus. I don't understand 'line by line' how my car's ICE works, even though it will likely impact me at some point, because while I might be able to fix it myself if it breaks, the likely applied solution will be to delegate that work anyway.
As an aside, back in the days of Stack Exchange I would always type out manually whatever answer I found to make sure I understood WTF I was adding to the system.
I did this too. Almost always I'd rename variables, change formatting, add or remove comments, etc..
Unfortunately this feels less easy to do with modern tools. For example, Claude Code expects to edit your actual source files, and the Claude chat is much worse at writing code.
As someone who, at a point, would copy homework from someone else, copy book reports from online, and use the answer sheets to complete assignments, I can tell you this strategy is long known to accumulate and not prevent cognitive debt
Retyping LLM code sounds absolutely miserable and soul crushing. Like hand copying the HTML produced by page generators... why would you do that to yourself.
This is just a miserable career of "paint-by-number" because people can't be bothered to have a creative thought about their professional work or programming hobbies.
Software developers think that they are being clever with these kinds of strategies to "keep their skills sharp", but unfortunately the entire industry knows about this, and especially the upper management who are already eliminating these assembly line, JIRA-ticket-taker software jobs en masse.
Right, it's just pretending to be able to delay the inevitable. It's like the assembly programmers of the 70s and 80s keeping their assembly-fu sharp. Yes it might come handy, and it's good to have a grasp of the concepts, but most careers have shifted to not needing to use assembly. Yes, I know that better knowledge of the low level would improve performance and efficiency. But people don't work with this any more, and the goal back then also wasn't to keep retyping a GCC output to keep the skill fresh. It was to get to a higher level of control and think about the organization of structured code, code maintenance issues, thinking at the level of how to make the C++ implementation.
With AI, our role also shifts. It's mainly to know what to spend effort on, to set priorities and, to be able to verbalize requirements, missing social context and unwritten rules, to anticipate what additional documents the agent needs, to prioritize deadlines, feature necessity, and other judgment calls.
We are right at the stage where our coding ability and review ability is still needed though, but this stage won't last long. Soon there will be as little point to a human diving into the code as to trying to beat a chess engine, or humans constructing buildings by hand. Of course the discussion and prioritization may involve looking at the code itself, to get a better idea of why the agent says that a certain feature would be tedious to implement in the current architecture, but then most people will just learn to take its word for it, just as you may want to understand a chess engine's step, but you typically wouldn't want to override it.
> With AI, our role also shifts. It's mainly to know what to spend effort on, to set priorities and, to be able to verbalize requirements, missing social context and unwritten rules, to anticipate what additional documents the agent needs, to prioritize deadlines, feature necessity, and other judgment calls.
Did you not do that before AI? It’s so strange to me when people are calling out these kind of tasks like they were not already a requirement for the job. What were you doing before?
> Soon there will be as little point to a human diving into the code as to trying to beat a chess engine, or humans constructing buildings by hand
Chess is way less complex than coding. The rules are like a few pages. While the specs for an 8 bit chip like the AVR is in the hundreds of pages. Books like “The Linux Programming Interface” are thousand pages long.
Also humans are using tools for building. Tools that do exactly what you control them to do. When you use a drill for a hole, you don’t have to worry that pressing the trigger have a good chance of sending the bit in your guts.
> Did you not do that before AI? It’s so strange to me when people are calling out these kind of tasks like they were not already a requirement for the job. What were you doing before?
No, you didn't have to explicitly say it in words. My mind doesn't run on internal monologue. Many people can just do their work without ever having reflected on it in words. Tacit knowledge, routines, shared assumptions and culture in a team, common knowledge etc. People have a hard time using AI because they are bad at modeling the knowledge state / information context from the AI POV. You need good theory of mind for this, and being a good programmer is distinct from that soft skill. Yes yes blabla soft skills are more important than hard skills blabla, I don't buy it. It used to be valuable to be great at the hard skills even with mid-tier soft skills. You can have a ton of smooth talkers who are attuned to feel each others emotion and desires super well, but the thing has to actually work too.
> What were you doing before?
Wrote code. Yes, you have to explain the outcome to your boss or your team at some point, but people generally have better developed theories of mind for people than for AI.
> Also humans are using tools for building. Tools that do exactly what you control them to do. When you use a drill for a hole, you don’t have to worry that pressing the trigger have a good chance of sending the bit in your guts.
Right. I'm not sure how to reconcile the two though. A tool whose job is to do some of the thinking part seems to be a contradiction to me. If I so much know what there is to do that it's pure execution and can reliably be executed in a way that basically ensures no potential surprises to me, then I wouldn't need more thinking. But I agree, it would be better to somehow find a hybrid that is both doing thinking and feels more like a tool also while using it.
The point is that you have to have an idea of what it has access to in its context. Many expect it to work like an omniscient genie and then give up once it turns out that it can't read your mind and you have to be able to halfway-coherently state what you want.
> No, you didn't have to explicitly say it in words. My mind doesn't run on internal monologue. Many people can just do their work without ever having reflected on it in words.
Were you a solo developer? I think the amount of reports and explanations (either written or verbal) dwarfs by large the amount of code I’ve written. From training juniors to drafting a design specs for a feature. That is why people say code is the trivial part of the job because it’s easy. I’m pretty sure the mailing list of the Linux kernel is bigger than the code itself.
I'm in academic ML research. Coding is mostly scoped to myself. And it's often nontrivial. My communication is more in papers, reports on experimental results, deciding what to try next, but how the code is organized is rarely the focus of discussion. Of course this may be entirely different in a software shop building routine features where the difficulty is indeed the social part and the software itself is more clearly understood. But I'd say that's when AI can be even more effective since it can do mundane coding even better proportionally than research code. Of course Amdahl's law kicks in and even if you reduce that drudgery to 0, the proportion is small when taking total work effort into context.
> Of course this may be entirely different in a software shop building routine features where the difficulty is indeed the social part and the software itself is more clearly understood. But I'd say that's when AI can be even more effective since it can do mundane coding even better proportionally than research code
And here’s another fallacy that is always thrown around. Always underestimating the other’s tasks when you are not an expert in it and don’t know intimately what’s involved.
So which is it? Is the coding part easy or hard? If it's easy and the task is mainly all the song and dance with the client, the stakeholders, getting approval and whatnot, then AI can take care of that easy coding and at least save that time. But then that is proportionally not much. I do have friends in industry, and they do sit quite a lot of hours in front of an IDE typing and fighting compilation issues, etc. It's not like they are always in meetings. I know people who work in the computer vision industry, 3D perception, and they have relatively few meetings and report to their boss occasionally, are not micromanaged, there is little red tape. They are getting real benefits from AI. It has eliminated several efficiency problems in their code that nobody took the time to fix, like consolidating repeated computations to just being done once, or similar things. They have solved a lot of CI pain with it, where they used to have an entire employee just taking care of keeping the CI in shape and updating it and remembering all the quirks, it's no longer a full time job, though also not fully automated. The CI person of course doesn't like the fact that his arcane knowledge has become un-moated. (I've worked on maintaining servers myself and I was the only one who really knew how it worked or how to fix it when it was broken, how to use it in specific special contexts, e.g. in context of a Slurm cluster. Today a lot of that hard-earned detailed knowledge is obviated by Claude, because it can answer similarly well to how I could, or better. So I have experienced such things myself too.)
I don't think we should be making sweeping claims. But everyone generalizes. The person on top claimed that productivity doesn't increase for competent people and that coding is anyway very easy. That may be true in some contexts but not in others. Some software jobs require a lot of face-to-face, others less. Some managers micromanage, others just care about results on a longer timeframe. Some people build technically difficult, computationally heavy code, deployed on special hardware with efficiency requirements, others deploy website-like iPhone apps and CRUD webapps all day. There's no single shape for a software-related job.
I empathise with this. I run a little open source project called SmallDocs [1][2] which Claude/Coding Agent invokes to generate easily human readable Markdown documents (and render code files).
You can ask Claude to "sdoc me an annotated code walkthrough of this MR" and it will produce something like this:
I don’t think so, because it’s being used as a way to try to remedy one of the new problems brought in by agentic coding - loss of context of what code does.
I'm taking a slightly different approach. I've started a project where I intentionally don't use agentic coding. I use LLMs for researcher and to learn, but write all the code by hand.
The goal is to maintain the taste, for lack of a better word, that I've developed over decades of programming.
Claude put me on to the concept "Étude", so I've taken to calling it my Étude project.
Generate code using LLM on a small project, then copy manually toward your big project. This reduces the context (and the cost) for the LLM and gives you many small projects where you can experiment ideas with more agility.
This is not slightly comical, it is very comical. Side projects are not mandatory, if you are using them to learn something, asking LLMs to generate and for you to type it makes no sense - just do it on your own. If you want to offload tedious boilerplate part to LLM, then by definition no need to learn it as it is tedious, so typing it out is useless.
Ask your AI to chart the data flow through the program. Not that that's a magic solution but it's a pretty good start.
By default, if you ask an AI to "generate documentation for this code" it generates the same broken documentation all the humans do too; an enumeration of all the modules in the code and what their API is. I'm not surprised, the training data is biased probably at least 25:1 in favor of this rather than the useful data flow documentation. Fred Brooks was complaining about this over 50 years ago and the discipline as a whole still gets this wrong.
I'm not saying this is a future solution to all problems, but it is a now solution to some problems.
3D doesn't help. We live in a 3D world but our vision is 2D with a bit of augmentation from a second view point just a bit away. We derive some depth information from that, but we don't really "see in 3D". To do that we'd need to be 4D beings. There's a lot less juice in the 3D squeeze than meets the eye.
I am also worried about "cognitive debt". I hardly remember what I had Claude do, even hours later. Back in May, I advised of a similar mitigation, citing the "generation effect" as the reason typing the code would make you remember it better:
> For your next ticket or feature, engage with your LLM as you normally would to produce a design and implementation plan, but with that plan in hand, make the mechanical edits yourself...You can expect this to improve your knowledge retention compared to merely reading a diff. You want to go slower now so that later you can go at all.
This is what I'm doing right now to learn Electron, I essentially had Opus write me a tutorial to write the application I want to have, and I'm modifying bits and pieces as I go. It's been enlightening thus far, and the bot isn't always right so I still need to look up documentation on occasion.
Frontier LLMs write better code at CRUD tasks than 95% of developers today. They’ll get to 95% of most niche coding domains by December and likely all coding tasks sometime next year; 99% better at all tasks by December 2028.
You may be correct now and it doesn’t matter one bit.
I'd say as long as we can clearly see the 5% are working better than an LLM, why not strive for it? Shows what humans are capable of, and it's probably achievable to most.
I'm not hanging my hat yet and can still see where LLMs struggle. I think it's the best bet you can make: keep working for the future where you'll still be needed, because you can't prepare for the future where you're not.
I agree about your general assessment, but I don't see my coding skills of, depending on how you count them two or three decades, being needed in 36 months - so I'm trying to upskill LLM piloting, but that also seems a bit of a dead end since LLMs will be perfectly capable of piloting themselves in approximately the same timeframe - we'll see what needs upskilling in by winter...
I think there are far more people who can't write better code than an LLM. Of course, there are a few exceptions, but it's a fact that LLMs are already handling PhD-level mathematics and papers.
I also think I write better code than an LLM in certain areas, but in most programming domains, the LLM knows more than I do across many dimensions. As prompts get deeper, LLMs are already producing PhD-level code—and that's been shown in research. The vast majority of people don't have that level of education. Of course, having a PhD doesn't guarantee good coding, but at least it's clear that LLMs can handle that level of capability.
People might disagree, but my view is firm on this.
> I also think I write better code than an LLM in certain areas, but in most programming domains, the LLM knows more than I do across many dimensions.
Remember that the quality of the LLMs code in the areas you don’t know is as good as the quality as the area you do know. You’re only able to gauge the quality of what you do know.
Your main target seems to be the view that software quality is a matter of 'aesthetic intuition,' but I see it differently. I think because it's 'engineering,' there must be measurable indicators.
Executable specifications
Unit, integration, and property tests
Official API documentation
All of these provide ways to judge quality. There are so many metrics that the problem is actually choosing which ones to use.
1.Does it produce output A for input B?
2.Can it process 100,000 records within 5ms?
3.Is memory usage within the defined limit?
4.Does the protocol handle error conditions properly?
You don't need to be an expert to test these. People forget that programming is one of the few fields where judgment criteria can be easily translated into machine executable tests.
The biggest problem with epistemological objections is that they often assume a binary: 'experts can judge, non-experts cannot.' But in reality, it's a continuous process.
You run the code, notice something odd, look up the terminology, compare it with official documentation and reference implementations, add tests, and gradually build up judgment in that area. Rewriting LLM generated code compresses that entire learning curve.
In the past, entering an unfamiliar domain meant starting from a blank file and reading documentation. Now, you start with a working hypothesis and modify it. That's far cheaper than starting from scratch.
If you treat LLM generated code as executable teaching material and a falsifiable hypothesis, it's a very powerful resource.
I feel uncomfortable when people reduce programming to aesthetic quality alone. We were all trained to measure things.
My "aesthetic" sense is more about how modular the code is, how DRY (which is also an aesthetic balancing act), how clean the abstractions are and how well they fit the domain and the workflow.
Also, error handling.
Tests, specs, and docs are are all downstream of that.
So far I've found that AI does an adequate-to-very-good job up to a certain amount of code, then things tend to fall apart. The solution is modularity and clean interfaces - as it always was.
I'm not really sure. I'm mostly thrown into existing codebases and just modify things to fit the existing style, so I'm almost always evaluated purely by metrics. I do think your (TheOtherHobbes) standards are really good, though.
>You’re only able to gauge the quality of what you do know.
That's not true. You can follow the logic step by step, search for similar domains, and form a comparison group.
Realistically, if we take this logic to its conclusion, it's like asking: 'You're just a spectator, so can you really judge whether a football player is good at football?
Realistically, LLMs write code well. I'm a C# programmer, and as you know, the current trend in modern programming is to refine error handling—using try/catch at higher layers, working with monads, preserving computable contexts, and branching accordingly. That kind of code typically takes 1 to 2 hours of modeling before you even start writing.
But an LLM can do it in just 5 minutes.
So I think the opposite way: if LLMs can do this well in a domain I'm familiar with, they're probably just as capable in other domains too. And that makes them feel trustworthy.
And more importantly, no matter how great a human programmer is, once you step outside your domain, you're in unknown territory. In those areas, LLMs are surprisingly helpful.
Epistemologically, it might be true that you can't evaluate code in a domain you don't know. But people keep forgetting the fact that you can learn through that code. This very post is itself an example of learning through LLM-typed code.
And programming actually provides clearer results than many other domains. It's not based on subjective UX like UI design. You have:
1.Executable specifications
2.Performance measurements
3.Comparisons with official documentation
These are clear engineering outcomes that allow for objective comparison. For example, you can verify whether a program produces results within 5ms when executed.
Many epistemological arguments tend to overlook this.
A lot of code is open source. You can find mature implementations anywhere. Finding a repo from someone claiming 10 or 20 years of experience and running a differential comparison is not difficult at all."
I've had opportunities to look at code from Korean and Chinese companies, not just academia. From what I've seen, AI generated code is often better than the average code in those codebases.
When I talk about PhD level code, I'm not referring to research code quality. I'm talking about research level algorithms.
Production code is built around reusability, policies, monadic chaining, and various code hygiene practices. Research code isn't. So what I'm saying is: 'Yes, I've collaborated with professors from top Korean universities.'
That said, I'm not writing PhD level code myself. The reason is that I take existing algorithms and apply code hygiene to them. I'm not actually developing new algorithms at a PhD level of understanding
So from what you're saying, it sounds like:
'Are you talking about those horrible research paper codes?'
But what exists in those research papers is:
'The domain modeling itself is difficult.'
I learned about code hygiene in production code—things like using functional approaches, and so on. Research code often lacks those things, so it might look bad. But that means the code quality is bad—not that the modeling itself is bad. And in fact, the core value is in being able to produce modeling at that level of difficulty.
In fact, most programmers, if trained like me, can do these things well enough. But domain modeling is different.
This touches on the distinction Frederick Brooks made in The Mythical Man-Month between 'Essential Complexity' and 'Accidental Complexity.' Code hygiene, functional chaining, monadic structures—these are techniques for reducing accidental complexity. With enough training, you can learn them. But domain modeling deals with essential complexity. It's about how difficult the problem itself is, and it's not easily solved through training alone. That's why the depth of modeling that researchers produce should be evaluated on a different axis from code quality.
This is what we call 'Essential Complexity.' From that perspective, saying code is 'good' usually means that the essential complexity is handled well on average. What I'm calling 'PhD-level' usually refers to modeling problems that are commonly difficult to solve.
I see it 'very often.' Most research paper code has no reusability—they just implement the algorithm.
So I can see why it might be considered low-quality. They don't use things like Result or policy types like we do in production code. But they're modifying or creating new algorithms, right?
The thing is, they don't need to do those things, so it looks lower-level—but the algorithm implementations themselves are actually pretty good, aren't they?
PhD thesis code often looks low-quality simply because there's no reason to reuse it like production code. The tensor computations inside are things that typical programmers can't easily do.
The point I'm making is that the core algorithmic work is something most programmers can't handle. And that's natural—highly skilled programmers have already built deep libraries for that.
When I talk about PhD-level code, I'm not referring to overall code quality. I'm talking about the level of the algorithms themselves.
> there's no reason to reuse it like production code.
This strikes me as a self-fulfilling prophecy. There are probably many people who would like to use the code published with PhD level research, but they don't because the code is not easy to adapt (or sometimes even to get running).
Then a decade later someone implements that same algorithm in a library with a well-designed interface and it suddenly becomes a useful tool for others. So ultimately there was a reason to reuse it, it was the quality of some aspects of the code that held it back.
Related, a writing advice I stole from Neal Stephenson is to write the first draft by hand. The thing is, there are a lot of small corrections where you kinda should change the text but nah, and if you already committed to copy the entire thing than you are already working at that sentence anyhow.
When I was younger and took over a codebase I open it up in one window and then type it back into another window. Not only did I catch/fix a crazy number of bugs, but I became a near expert overnight. Doing the typing would cause me to question everything, ask why we import something, why x is used and not y, etc.
I also tried where I would print out the code (with color) and then go read it with a red pen. Faster and similar results of forcing me to be able to read it enough to be "an editor" to the code.
And I have done the same for LLM-generated code and text, back in '23 this meant more taking their so-so output and then turning it into my own, but between now and then come up with a number of techniques to improve the AI output to more acceptable out of the gate so more learning than being inspired.
These are good techniques as it causes your own brain to rapidly learn the material, but no matter how good it is I have never met anyone else that does it so the real ponderable is assuming that everyone else doesn't do this and what does that mean?
I don't manually retype the code from pull requests of other engineers.
It's important to retain the what why and how as a team to the degree that development can be efficient, extensible, and maintainable. (And ideally a good devx)
Needing to manually touch every line of code is not scalable.
This was true 10 years ago. It's still true today.
I never had so little free time as I have these days. Most of my time is spent at work or with my lovely family.Love my family, and work is great.
The thing is that, probably like many of you, I love going deep in a side project, even if it lands nowhere. With so little time, working on them has become a very frustrating activity.
This is where I found my trap... do more with very little time by delegating to an LLM. You get dopamine shots, the feeling of achieving something but the cognitive dept is just crazy. So much that the activity becomes almost meaning less. After couple of months doing this, I'm not even sure it's a good use of this time. I get very little satisfaction on the long run.
I don't have a solution to this problem, not even sure there one. I think I have to accept that this is an activity that takes time, and only time gives the real gratification.
This resonates with me. The concept of cig it I've debt was something I've been experiencing but didn't have a name for it.
I think it is worth noting that not all code is equal... One could argue that adding a library is in some ways similar to copy/pasting code in as much as, one doesn't know what the code is doing, and yet that doesn't leave me with a sense of unease!
So it might be that as I'm working with an LLM there are parts (boring, as the author calls them) that are not worth "knowing" how they work, something uninteresting or that a correct output is all that us needed, I'm totally fine having the agent write that code, but the sections I need to know how they work, I think it might make sense to write those by hand!
> One could argue that adding a library is in some ways similar to copy/pasting code in as much as, one doesn't know what the code is doing, and yet that doesn't leave me with a sense of unease!
It does, to me. Been burned enough times that I now, at minimum, audit the source code of third-party libraries before I use them.
I've used LLMs in a similar way and I'm reminded of learning to draw / paint.
At the very beginning stages you might be tempted to trace over an image but this builds a very shallow understanding. Instead you should quickly move onto replicating work you admire by sight. Consider the form, volume and values, conceptualise them in your mind and try to apply them in whatever medium you are using. This helps form your own mental model of the process. Eventually you can start using those techniques on original works.
I'm currently rethinking language learning too in a similar manner. Comprehensible input, shadowing. That sort of thing.
Alternatively, what I tend to do after receiving generated code is a lot of asking "why?".
I've learned things I wouldn't otherwise have learned because I hadn't considered using the tools the LLM recommends. It's also a way to eliminate some hallucinating, given that critical questions are posed as unbiased as possible. For that, I also like to open a new chat with a different model and asking open-ended questions about a recommended tool I don't know much about, to double-check that the original LLM was likely correct in its recommendation in the first place.
I don't know if retyping is the solution, but for me is clear that we need something additional to a terminal and a code editor. I envision a software that an agent can use and showcase to you how it did implement the changes and why.
The same way a colleague would do, focusing on the important bits, then ending with the trivial stuff. Something in between pair programing and code reviews.
Right. I want an AI that sees my screen, sees my mouse cursor, has my audio transcript synced to the mouse movements, and it can similarly do TTS and and pointer movements or put things like circles and rectangles and background highlights on things while it talks, and slows down if I say so etc, like a human discussion partner.
Diffity has a “tour” feature that can be directed to explain a local code change. Walks you through the important bits of the change with explanations.
As others mentioned retyping is not fun. My approach is to let it write the code, but only in small portion. Not "implement this feature". But "open this file and make these changes". Each small change is easily reviewable and often times I end up asking it about better options and iterate a bit. Still feels like I'm in charge. Still feels like I'm learning stuff.
Manually typing in code is an underappreciated trick in a lot of circumstances. It's one of the fastest ways to get to grips with a certain piece of code, a new library, or some methodology.
This was true when I first learned to program, and is still true today. And I do find myself manually typing in really critical code. In those cases even if I do have an LLM alongside these days, LLM suggestions also then get manually typed.
This is the workflow that Vs Code Copilot does. All the AI generated code changes are in a git worktree and you can step through them all. This is what I missed after Claude forced third parties to start charging API pricing and now I have to use Claude directly and I have to do this same review process in a clunkier way via my git client.
I already wrote my opinion on this, which I don't think anyone read, but my idea is to let AI code the working system, and then prompt it to teach you, give you challenges, and grade your work.
If you write, you should write in your own words, to demonstrate your own understanding - the so-called Feynman technique. Never verbatim. That's as true for coding as it is for study notes.
My counterpoint to that is, you never know when the factuality of its analysis is mistaken because you're making it the point of authority over knowledge you should be working to acquiring.
In math classes back at school, it didn't matter how much the math professor explained how the formula works. What mattered is me putting in the effort to understand it. The implication to your example is, I should already be familiar enough to understand the generated code to the point where all the explanation that it's doing is effectively a "Quality of Life feature".
I ask the LLM to generate the code but mask the last token. Then I do softmax and give it an answer. Pretty soon my perceptrons are more connected than ever.
I wonder how effective it finally will be. At first glance it reminds me painting by numbers a d I'm not sure if that will help the real painter to keep his skills and surely won't teach aspiring painter much about the craft.
This is not about an aspiring painter though. This method is intended for an already accomplished painter. Not saying how effective it is but your comparison is not relevant.
Pair programming with the LLM is a better approach. You can take either role and even take turns. It’s slower but gives ample time to read everything and push back on decisions or receive feedback and review on your own decisions.
If you can afford it, why not. For certain phases of projects like a proof-of-concept, you need to move fast and validate several ideas. Once it's locked down, rewrite from scratch, and here, if you can afford it, type or write the code manually.
It's better than nothing perhaps, but reminds me of UK highschool in the 80's (is it any different now?) where we had to manually copy everything down that the teacher was writing on the blackboard rather than the teacher giving handouts so you could pay attention to the teaching. The act of copying everything down was a negative rather than a positive.
Of course agentic coding tools are not trying to peer code or teach/inform you what they are doing, so being present in the moment doesn't help, but I suspect that copying it all down later doesn't help much either.
When you are/were developing software without AI, even for pretty large projects you do end up internalizing (memorizing, but not deliberately so) a lot of detail, but from my own experience I'd say it's more the design than the code. The design is what you put effort into, thought about, etc, so is both what you naturally end up memorizing, and is what you need to know to have a mental map of the project and therefore understand how best to modify it. The code itself was naturally always the last thing you did, and followed automatically from the design and module/component interfaces - not something you typically think much about other than while in the flow of just "coding it up".
By retyping LLM-generated code, it seems you are mostly going to be gaining familiarity with the wrong thing - the code and not the design. Memorizing the code is not going to help much in grokking the design.
Nice workflow! I'll give it a try.
I'm struggling with building mental model of AI-generated code. And code review fatigue is real. This may be the way.
I like the "cognitive debt" term. With the latest models, what I've observed is that they are really good, but I don't use them to write main code because I need to know what I'm doing.
The article is not wrong though that it pays off to have some imagination on how to use the models. For example, I want to use SIMD instructions in an ESP32-P4 CPU. Those instructions are undocumented for the most part, with just a couple of handwavey blog posts and some infuriatingly vague marketing material. So I just asked an LLM to create a `SIMD_P4.md` document with all the details. Lo and behold, it practically reverse-engineered the ISA. Now I can program in assembler by hand all I want and build that skill in my own brain, and whenever I find a slightly unclear op in the document, I ask the LLM to refine the documentation in that op.
I don't understand the concern about "cognitive debt". I frequently have to maintain code I wrote, or someone else wrote, weeks/months/years ago and I have NFI what's going on. Now I say to the LLM "tell me what's going on" and it tells me. I can ask it some follow up questions, and build up my understanding. It's SO much faster than grepping through the source myself and I can do it for as multiple issues in parallel. The notion of reading every line of code is absurd to me, the notion of RETYPING it beggars belief. Surely this is satire.
My rule is: I only let AI code for me, I don’t let it think for me.
Since writing is thinking, coding is thinking since coding is writing. That means any time I am not certain how I’m gonna implement some feature or bug fix, I have to code it myself because that’s the only way I can force myself to think through it. Only when I get to a point where I’m line “ok I know exactly what to do now- all that’s left to do is type it out” that’s when AI can be employed - essentially as a autocomplete.
This is only for projects where I will be held responsible for outcomes and must understand how it works. For hackathon / personal projects, I vibe away.
I also use AI to brainstorm at the outset of the task when I don’t know where to start at all.
Is this inefficient? My take: no. It’s maximally efficient. Over the long term it gives me an edge over any teammates who just vibe code everything because I actually, you know, understand how stuff works.
I become the guy who can save the day at 3am when the team’s business critical app goes down. I become the guy that gets pulled into meetings so the suits can ask “is this possible?”. I see opportunities and problems before my teammates because I have a relationship with our code and system that they never took the time to develop or think about.
Would one retype assembly language for C generate code?
Having LLMs write out their design and reviewing it seems more efficient. Have LLMs, maybe with a different model, check that the implementation meets the design.
For learning? You can use llms to help you with stuff and still learn new things in the process. Its really surprising that so many people don't understand this.
Are people really out there just mass copy pasting llm code without even trying to understand it! lol
But this way you move way slowly even on personal projects, like you will not even get the basic UI for the app done in a few days? Is that OK for you?
No you misunderstand me. I support such a view but cannot hold it because my pace at work is so much fast. And hand coding like this will make personal projects s slow and choreful with no visible progress. Like where is the joy in that?
Without an active harness (eg. Appium) that can end-to-end deterministically verify the changes you make continue to work correctly it is almost impossible to continue to keep the same pace on the app.
Unsupervised LLMs (even fabel) are categorically
incapable of running parallel unsupervised mobile app feature development.
That is my personal, first hand experience working in a team in this space.
What you are (I guess?) experiencing is user-in-the-loop light touch LLM development where you can 80% most tasks quite quickly (much faster than without assistance!) with a small number of human developers working on largely unrelated features and manually verifying they are correct and manually fixing the platform specific issues you encounter.
Maintaining a strong appium end-to-end test suite is still extremely challenging with notifications and maps.
Honestly, it blows my mind you could even being to claim that of all things, native apps using obscure languages like swift are suitable for this, compared to the much much easier path of web + react.
You might say “yeah yeah, but one month? Come on!”
…but have you actually seen how much code fabel can write in a month?
Its a lot.
So sure, you say, work at a slower pace. Don't just endlessly run a frontier model in unsupervised feature development mode.
Yes… you see, thats the point. Thats what the op is saying.
Move more slowly, and you can avoid building a spaghetti castle (ok sure! If you dont wanna, maybe don't retype every character by hand, but the point of that practice is not upping your wpm typing speed. :p It is to take the time to think, design and collaborate, not rush rush rush)
Depends what they're doing... I can crack out a basic UI in a few hours at my job, and I don't use LLMs at all, and I wouldn't class myself as an expert developer or anything
Do you really find your typing speed to be the bottleneck in getting things done? I suppose that's pretty easily fixed, at least.
Anyway the author did address that
> Using LLMs this way allows me to work faster than not using LLMs at all, but I'm still slower than those who are willing to allow the machine to think for them. Instead of being 10x faster, I'm probably only 2x faster. But what I lose out on in terms of speed, I gain in terms of a deeper understanding of my code.
When I got my first corporate job, I was placed in a group of 20 trainees in a rigorous COBOL course. We were given assignments and a schedule to complete them.
Most of us read the specs, then raced into the coding phase, hands to IBM mechanical keyboards. One guy took a different approach. He took a legal pad and pencil, and wrote his whole program on paper before he ever approached a terminal. He’d do his own bug checking and syntax checking, instead of having the compiler do it ( compiles took longer in those days, and required JCL ). He avoided the entire compile/wait/read-with-dismay/quickly-try-again loop.
He was one of the top students, of course. And a lot less stressed, as I recall.
Mindlessly typing something is not much better than copy and pasting?
I could maybe see it if you asked it to spit out pseudocode you had to rewrite. At least there’s some translation there…
But this is bizarre. Write it yourself at that point. Is it any faster (or faster at all frankly) to prompt what you want, manually write it out, and maybe even make adjustments as you go? I’d argue not.
The way I wrote code in the past was to just first comment out what I wanted to do, and then underneath write the syntax for it. You could maybe do this too? Take the LLM code, and go through commenting what each section does to be able to effectively break it up? It still seems dumb.
Funny that there's another trending post titled, "Don't be a meat proxy," just above this proposal that we literally meat-proxy all the code.
Whenever I encounter an especially preposterous proposal like this one, I like to imagine a USMC Drill Instructor wandering into the open plan office and having an interaction something like this:
USMC Drill Instructor: "What the actual fuck are you doing?!"
Smelly Recruit: "Sir, I'm hand typing the LLM output. Sir!"
USMC Drill Instructor: "Are you fucking with me recruit?! I said I wanted a SASS App, not a typing tutor! Drop and give me 20!"
I don’t disagree with this if you code for a hobby.
Buy if you code for a job, good luck justifying this to management. “Yeah Claude already gave me the solution, I’ll take the rest of the week to type it out”
I use LLM code for hobby/fun projects only (I also don't code for a living) and I still wouldn't want to type up the code it gives me. Instead what I dream of doing is one day to rewrite all the little things from scratch (well, with a blueprint of a working result). Your management wouldn't even talk to me, rightly so :P
I don't think it's bad to manually retype code as a way of learning.
Isn't a working program itself the best textbook? It's just a difference in learning methods. Depending on Stack Overflow is also a dependency, and searching for code on GitHub is also a dependency. How much dependency you allow is purely a personal difference, and it varies depending on your own study habits and learning style. Whether your learning method is superior or not likely depends on how your brain works.
People tend to think that the more painful something is, the better it is.
I don't deny that there are talented people who can read the manual and build everything from scratch. But I think that analyzing and rebuilding a working template step by step is also valuable.
I agree with the view that LLMs may cause cognitive decline. But if you go down that path, Socrates already criticized writing for weakening human memory. And how did that turn out? Books became a universal medium for knowledge. Then the internet came along. When Stack Overflow appeared, there was opposition, but it also had explosive adoption. LLMs are just the next step in that sequence.
If there is cognitive decline, I think there's also compensation in other areas. Using LLMs clearly causes some cognitive decline. And I think there are areas that need to be reinforced to compensate.
But having a baseline to work from—modifying already-working code—is genuinely helpful. I don't see what's wrong with using that as a way to learn.
In my current workflow, I've settled into a three tier system when coding:
1. HIGH-VALUE CODE:
I write it all myself. I will occasionally use AI for mostly mechanical changes, like cleaning up variable names or mass-changes when a function signature has changed. Either way, every line is read carefully. Sometimes this means isolating my high-value code as a library in a separate repo. Usually it's just a note in AGENTS.md, or even a well-written comment at the top of certain files. I'm not obsessive about it, though, as it can't hide from git. And learning what it's trying to change is sometimes a useful insight.
That doesn't stop me from using AI as a consultant. This is the one time I'll use a beast like Fable. Ask it to write a technical/security analysis on a section of code and damn it can pull out some impressive insights. It can't write new code particularly well, but it can inspect code like a boss. But that all stays in the chat window. (And despite being so infrequent, they ends up costing significantly more than all my other AI costs combined!)
2. BOILERPLATE/PROCEDURAL CODE:
I'll write the first draft, but once I've set the tone, I'll allow AI to build and maintain it. I keep on top of things like a senior manager, just to make sure it's not doing stupid things. Every few days I tell it to mow its own grass: AI is good at recognising its own stupidity, you just need to give it an opportunity to look.
3. TEST/HARNESS CODE:
Bring on the slop. If I get nothing else from the AI revolution, it's not having to write another stupid test unit. Nothing makes me happier than setting the AI to work writing every permutation of test I can think of. I will slop this code all day, and I won't read a single line of it. Why should I? If I ever doubt whether a particular test is correct, I'll test the test by breaking the code, not by reading the test. But I almost never catch it out. In my experience, AI is especially good at writing tests. Perhaps more than anything else.
Tests don't just take the form of a few mocks and props in a test harness. In one recent case, my project involved writing a library for the API of an obscure commercial microcontroller-powered device. I took the API documentation and made AI build me a complete simulator. I then made it write a full suite of tests using my client library within the test code. I then got it to run that test suite against real hardware and identify any inconsistencies. From there it could recursively modify the simulator until it became unreasonably good at mimicking the real hardware. I haven't read a single line of its code. But it's now core to the library's CI.
I can't reliably confirm that you read my post all the way to the end. I pointed out multiple ways where tests are proven. One is to verify the test by breaking the code under test. Another way is to build a fully independent, highly complex test rig that would never be (commercially) feasible without AI.
Realistically, LLMs write code much better than most people. In my domain, there are areas where I still write better code than an LLM, especially when it comes to physical constraints it might not understand, but there are far more domains where the LLM writes much better code than I do. In that sense, writing code with an LLM and keeping track of it feels more helpful than I expected.
Practicing solo coding for an hour a day often ends up being mechanical and not very useful. This might actually be more helpful.
Big no for retyping llm generated code by hand.
But a big yes for still typing code by hand, and not leaving it to the llm. Except it has to be the code generated by your brain.
That is what will create new neurons and new connections, which is what will keep away the cognitive decline.
And the constraint of not having to use llms will enhance creativity.
Actually, the constraints llms add to your code are more in number than the former. llms code in only the specific ways they've been trained on. So you won't ever come across of other ways.
Off the top of my head.. here's RubyQuiz.com [0] which I came across when I was learning ruby more than a decade ago. Looking at the many user-submitted solutions (you have to download the zip file!) you'll see completely different ways the problems were solved.
Sure, many won't be deemed efficient or standard by today's llm or rubocop checks, but looking at their code.. and retyping them and seeing them work.. was crucial in how I was able to think in Ruby for solving coding problems.
I did the same with Go too, with the "learn go with tests" guide [1].
[0] - http://rubyquiz.com/
[1] - https://quii.gitbook.io/learn-go-with-tests
I disagree. Before coding agents really took off, back in like 2022, I was trying to learn how to create grammars in Treesitter. One evening I sat down with ChatGPT and had it generate a grammar for me. But because it was on ChatGPT, I had to manually write everything it spit out.
What ended up happening is that I was typing, I'd notice little weird bits here and there, and I'd ask questions about it, explore alternatives, etc. So the end result was partially generated by ChatGPT, but also partially influenced by me.
But the biggest win was that within 3-4 hours, I was comfortable enough with the syntax that I was writing it entirely by hand, without AI assistance.
Now, if all you're doing is literally typing the characters and not thinking at all about what you're typing, and if you accept the LLMs output verbatim, then yes I'd say that you aren't really learning anything. But this is no different than just copying things from Stack Overflow. That behavior is nothing new.
The core distinction, as always, is whether you are exercising your brain.
> But a big yes for still typing code by hand, and not leaving it to the llm. Except it has to be the code generated by your brain.
> That is what will create new neurons and new connections, which is what will keep away the cognitive decline.
100% agree with this.
The problem is, your employer doesn’t care whether your brain is creating new neurons and connections. They care about productivity and profit.
I feel like the folks that believe we can continue to write code by hand are either in denial that LLMs will eventually (if not already) outperform handwritten code, or are in denial that employers will be ok with lower levels of productivity.
If you have the luxury of working for an employer that either doesn’t care or is oblivious to this, then 100% continue writing code by hand.
Or if you do work on the side (whether for fun or for profit) and are ok with lower productivity, then yes, stick to handwritten code.
But just because handwriting code is better for your brain and cognitive development, doesn’t mean that the industry will be in support of it.
I feel like there is an unspoken assumption of long term maintainability when it comes to LLM generated software. We are still very early in this, so I don't want to make assumptions. In principle, it shouldn't be impossible to both write and maintain stable software, purely with agents.
At least, I'm not aware of any actual reasons, backed by a proper theory.
On the other hand. I've noticed some persistent issues with code generated by agents, especially poorly supervised agents. If engineers become less vigilant, agents never get to the point of not needing supervision and juniors never pick up required skills, this could lead to real trouble.
> long term maintainability
Did business ever cared about this? Like ever?.. I've seen too many code bases with awful code by humans, now AI simply exacerbated the issue but this is nothing new in our field.
I just don't see how this is going to be a battle engineering departments can win. As long as AI is brining money to the business, they are not going to listen.
LLMs aren't perfect and they have problems, but often they write better code than humans. At least this human.
It seems they run into problems with larger concepts and general organization and need guidance at the moment but for a single code file they often do better then I would have.
If an LLM writes better code than you, that says more about you than anything else (either your ability to write code or your ability to evaluate LLMs). The code they write is garbage.
I agree with everything you said.
I’m simply playing devil’s advocate, because engineers can believe one thing, but until employers believe it, it doesn’t matter much.
I’m also not convinced that the reality of LLMs will never catch up with what employers think they can do.
It may never happen, but it very well could.
Either way, I feel the days of employers being ok with handwriting code are limited.
The complexity of code is combinatorial. Code is harder to understand than it is to create. The LLMs will be creating code that they will not (nor will any human) be able to understand later, or it won't be the same understanding that was used to create it. We'll find that systems that were created with LLM code will be impossible for LLMs to contextualize, or will be cost-prohibitive to do so.
It's a moot point. In Mag7 companies internal code gets thrown away within 2 years on average because of contract negotiations, new opensource projects that obviate need for the internal codebase, license changes, layoffs, etc, etc. We don't need long term maintainability because its already on its way to the garbage bin. Frankly it's always been this way.
Its' the exact opposite ... Google, nVidia, Amazon, Apple, etc. all have a deep bench of code that is a moat. They have lots of throwaway code too, but those companies are precisely the ones that pay attention to code quality, and comprehensibility by experts
A rough proxy for this would be if they say contribute to the C++ standards process, which Google Microsoft nVidia do, and basically zero "normal" companies (say SaaS) do. It means they are investing in multi-decade maintenance of their codebase
"Contribute" is an interesting word, since those companies are so big they can just dictate the standards they prefer. Also they are so big and have so much cash pouring in that they can afford to pay employees with nothing better to do than spend time drafting C++ standards. The average normal SaaS company is hoping they can make payroll next month.
> or are in denial that employers will be ok with lower levels of productivity.
More like sweeping the dust under the rug for later. The initial productivity boost is massive but at the cost of massive upfront tech debt. Given that LLMs aren't as good at removing code as they are at generating it, this is a problem.
Well there’s a lot of middle ground between “don’t use AI” and “generate everything, exclusively.”
When the bill for the latter lands with a heavy thud, moderation and common sense start to look like a pretty good idea.
> Well there’s a lot of middle ground between “don’t use AI” and “generate everything.”
Sure, but again, this assumes both that handwriting code sometimes outperforms LLMs, and also that your employer agrees with this.
I think it’s only a matter of time (again, if we’re not already there) before LLMs outperform handwritten code nearly all of the time.
And, even if that’s not the case, I’m pretty convinced nearly all employers believe this is true, whether it is or not.
So your employer only sees a “middle ground” as room for higher productivity.
this pure speculation. maybe you're right that it's a matter of time but /maybe you're wrong/. neither of us can actually know, because you're making predictions about the future rather than claims about what is true today
Perhaps the thing we’re going to learn to leave behind after all the dust settles … will be shitty management at shitty companies?
Because to be honest, more and more it seems many businesses have no real purpose other than to act as a sort of adult daycare for otherwise useless people.
> I feel like the folks that believe we can continue to write code by hand are either in denial that LLMs will eventually (if not already) outperform handwritten code...
They certainly don't today. Perhaps they will in the future, but based on the lack of improvement thus far it seems unlikely that they will get to this point. But whether or not they do improve to that point, the reality is that today, LLMs do not give you a productivity boost unless you give up on quality and just YOLO stuff the LLM gives you without actually checking it. And nobody should be willing to do the latter, because that is how you get software/infrastructure that doesn't actually work.
> The problem is, your employer doesn’t care whether your brain is creating new neurons and connections. They care about productivity and profit.
Which is why businesses, tech and otherwise, are falling apart constantly unless they achieve gigafuck scale status, at which point they're unkillable no matter how much of this cancer infests their management.
I don't know how we arrived at a social norm where it's just completely fine that leadership in massive companies is just absolutely useless at their jobs, but the firms that last another hundred years are going to be the ones that figure out how to fire them all.
> I feel like the folks that believe we can continue to write code by hand are either in denial that LLMs will eventually (if not already) outperform handwritten code, or are in denial that employers will be ok with lower levels of productivity.
Cooking a steak and then flushing it straight down the toilet outperforms all the slow and tedious business of eating and digesting it, too.
Why should I use an LLM to write code? It cannot do the job I want it to. It cannot type the code I've already written and it cannot generate correct code.
> Cooking a steak and then flushing it straight down the toilet outperforms all the slow and tedious business of eating and digesting it, too.
That would be more equivalent to writing the code and immediately deleting it.
Except this code is being deployed, and it is still (mostly) functional, at least functional enough to satisfy your employer (their “hunger” in your analogy).
> Why should I use an LLM to write code? It cannot do the job I want it to.
Because your employer is convinced that it can do the job they want it to (whether it does the job you want it to do is irrelevant, unless you're the employer, in which case 100% handwrite your code).
> Except this code is being deployed, and it is still (mostly) functional, at least functional enough to satisfy your employer (their “hunger” in your analogy).
Bzzzt. Wrong. I don't give a fuck who's satisfied by my code. I only care about writing it.
> Because your employer is convinced that it can do the job they want it to
Bzzzt. Wrong. The code is incorrect because it is not precisely the code I would have written.
LLMs are useless to me because they don't solve the problem of actually typing the code I've written into a computer.
Thing is, companies don’t care if it’s not your code or your view of it. If it works, that’s what they want. Since I stopped typing code I’ve been using my time to play some video games, read some books etc while the agents do the boring stuff (work itself). I get paid, the company is more productive, I got a bonus and a raise, everyone’s happy.
With the rise and rise of LLM slop in code, I've just had to jack my prices through the roof.
The first thing I do when I'm asked to clean up the LLM slop is "rm -rf ." and start from scratch.
You will pay a fortune for this.
This is cope.
Again, unless you’re the employer, then you’re getting paid by someone that cares more about productivity than whether you think LLMs produce incorrect code.
All that matters is whether they agree with you.
The only control you have over this is to either a) work for yourself, or b) keep looking until you find an employer that agrees with you.
My argument is that the number of employers that fall under (b) is shrinking.
> My argument is that the number of employers that fall under (b) is shrinking.
That's great. Sucks to be them. I've doubled my prices and doubled my time estimates and I'm still turning work away.
"I am the world hence everything i say must be correct and there can be no counter opinions because i know everything. bzzzt."
> It cannot do the job I want it to.
I mean in your case, you are not able to use LLMs effectively to build features, so you should type the code by hand.
However, upper management is excited about AI because it can push features quickly to production. Granted, they will be giant balls of slop but they don't care. As long as it works in the UI, they are happy.
If no one could get anything done with AI, there wouldn't be billions invested i n it.
There are jobs where llms can massively speed up delivery. Think about tiny/one man indie games for example, they can also get (stolen) assets almost for free. Helping with hobby projects, a subtype of that one man show.
Then there are sluggish corporate jobs. Here, even 10x speed up of development won't change delivery dates significantly or at all. I am in one such job, team leader has claude code higher tier and basically uses it for some more complex bash scripts and thats mostly it. Given this topic, I dont complain much, I value my long term senior skills way more than those new agentic ones.
The middle is... well somewhere middle.
Copying is not theft.
This will cause cognitive debt anyway. As mentioned in https://arxiv.org/pdf/2509.21972v1: "When students rely on these outputs as a substitute for their own reasoning or critical engagement, the learning process is fundamentally compromised. Genuine learning requires the active construction of meaning, integration of knowledge, and reflective engagement with content. These processes cannot occur through passive consumption of syntactically correct but semantically hollow responses. Without this deeper cognitive work, learners risk mistaking linguistic fluency for understanding, thereby undermining the very goals of education".
Personally, I don't think we will ever be able to reconcile using LLMs and cognitive debt. Even before LLMs we were aware if it: we knew people moving to managerial/PM roles eventually get their coding skills rusted. Well, now we are all in those managerial roles...
What do you think would be the effect on people that already have the skills and abilities. The LLM would build these systems using the skills that are largely understood by the programmer but piece them in new ways suitable for the system. This could lead to new enmergent behaviors that are not understood by the programmer.
I still think that you can build this model in your head even with LLM's but I'm not sure neither one way or the other.
To repeat their last two sentences:
> Even before LLMs we were aware if it: we knew people moving to managerial/PM roles eventually get their coding skills rusted. Well, now we are all in those managerial roles...
There's always some component of a skill that is not merely knowing something but practising something seemingly procedurally. Even if I understand integration well and have done it thousands of times, if I don't continually practise it from scratch, I begin to forget tiny bits and pieces and that accumulates to an eventually lack of understanding. So it is true for all mental tasks. There really isn't such a thing as understanding something completely without working it out by hand, from scratch. AI exacerbates a lack of understanding in this regard, although admittedly layers of abstraction in the coding realm already do that to some extent...
Lots of reactions here, but if it works for you then that's great.
For me I feel that LLMs have exploded (in a good way) my cognitive capabilities. I'm now the general of an army, rather than playing the role of a soldier. Of course that means that I lose the experience of being the lonely soldier, but it is a no-brainer tradeoff for me.
Anyway, I have to go now so I can push my car to the grocery store (so I don't forget how to walk), me and my giant calves will be back in a few hours.
All meant in good spirit. Keep doing what you're doing, thanks for sharing, and hope people are kind and only give good natured ribbings.
I feel like you think the car analogy was a clever parallel to draw here but looking at a car centric north american population and the dimensions of a non trivial slice of them that are unable to walk a 5k, id say you made the opposite point.
You have a point based on the way I wrote it. What I was trying to get at:
If you're going to walk to the store, don't push you car there! Just walk. So if you want to write code, don't retype what the LLM said, create your code from your own brain.
Unfortunately coding is a perishable skill, unlike walking which is at least partially ingrained into our DNA.
y'all have to come up with a better argument than "LLMs are cars"
Managers famously get dumber but think they get smarter and then start to write army metaphors.
Don’t good generals know how everyone else in their army does their jobs? At the very least to the extent they can see through the bullshit.
For example, I don’t handwrite the code that the compiler produces, but I know how to troubleshoot the IR/assembly and fix optimization issues that arise occasionally. That makes me better at my job of directing the compiler.
This does not sound fun. It's better to work on your side projects with manual coding. You will learn more.
Retyping things is inefficient for learning. It's like trying to retype calculus solutions — you don't learn from it. Even if there is an explanation of why the code is written in such a way, you did not come up with it, and you don't know alternative solutions. It is a practice for memorizing, not for building your intuition.
A better option is to write it yourself first and ask LLMs for better options. They are pretty good at it, especially when you need to optimize hot loops.
When I was learning to code at college, by myself (I did a Business degree), I bought a book on iOS development[0]. This book mandated that you type all the examples out line-by-line. I thought the idea was a pretty silly one, but I stuck with it because I didn't know what I was doing and wanted to learn.
14 years later, as a software engineer, I still think about that book and the way I can trace back a lot of my initial improvements to its requirement that code got written line-by-line. I still maintain the habit of rote text copying as a way to pick up new tools and commands (i.e. copying documentation examples exactly into my editor), and also when people make an assertion like:
> It's like trying to retype calculus solutions — you don't learn from it.
Maybe not everyone learns that way, but I sure did!
[0] - https://www.amazon.co.uk/iPhone-iPad-Apps-Absolute-Beginners...
Very much similar experience to yourself, when I was learning from YouTube tutorials I refused to copy/paste from their repos and instead typed everything out. Maybe changing variable names or structure.
I can definitely say it helped me learn a lot more than just blindly copy/pasting everything over.
I like coding katas (or even trying leetcode problems) when learning new languages.
> you don't learn from it
Except that you do. Otherwise you could just sit in school or university and just listen and do nothing, but that way you just learn A LOT less, instead of taking down the lecture/lesson.
Of course you learn more if you retype material than if you do nothing, but it's still a waste of time, comparatively speaking, because there are much better ways of engaging with the material like solving practice problems.
Having to spend 95% of the time taking notes of things that could be distributed in a PDF has only ever hindered me academically. It's mechanical and boring, my mind tunes out and I get nothing out of it.
Taking down the lesson/lecture does relatively little, you can do it on autopilot. If anything, it distracts from listening to the content. When I took computer science at university (mid naughties) we were given digital lecture notes anyway. (Mathematics lectures did have to be written down but there were no typed notes to start with so it was just out of necessity.) It's the exercises afterwards that are useful for learning. Or summarising notes in your words.
When I did a CS degree in the 1980s we had one lecturer who handed out copies of hand-written notes but then would talk about stuff that wasn't really in the notes - sort of verbal annotations.
Guess what he would ask about in the exams?
Even better would be to rewrite it by hand with a pen.
Yes some people teach C this way, for a little while, I don’t forget semicolons as much as people around me later seemed too, may or may not be related
I started programming this way as a kid with books and notes that I'd then periodically would be able to enter into a computer to see if it runs.
I'm not sure I agree about retyping calculus solutions. I often find that writing out a proof or derivation forces me to engage with some minor detail that I hadn't fully appreciated beforehand. That usually raises productive questions.
I think the disconnect here is that you can't only retype the solutions. You have to already sort-of know what's going on and you have to also care to understand the gaps.
So transcription can definitely be helpful when part of a broader, intentional process, while also being insufficient to do much on its own.
And the next logical question to ask is whether there's a better form of intentional behavior that might be more effective.
Retyping calculus solutions is a great way to remember your LaTeX.
In which case it would be the engagement with minor details that does the productive work. Retyping is merely a gateway to (sometimes) trigger the engagement.
The solution in TFA feels like an on-ramp to cargo culting somehow; observing that manual typing and good results often go together, but then thinking that it's the typing that directly causes the good results rather than the thought process that accompanied the typing.
There's a much better article hiding inside the current one that's titled "Prevent cognitive debt by understanding deeply the code that your LLM spits out", but that sounds like hard work and would probably not be very popular. It's much better for audience engagement to provide a simple solution that anyone can do and does not require a lot of deep thought like "manually retype everything the LLM generated", even if it doesn't actually work.
It is far, far less effective than deriving the solution yourself. Don't take my word for it:
> If you absolutely cannot do it then go home and think but for heavens sakes don't look it up in a book till you give up. Looking it up in a book is giving up. > > Paul Halmos (https://www.robots.ox.ac.uk/~adutta/blog/quotations-powerful...)
> you don't learn from it.
I strongly disagree. I used this strategy for learning how to reverse engineer and hook functions in a game with C++ and learned a ton.
I also used this strategy to learn Imgui and it worked great. Before LLMs I did this when learning from books too.
Writing reinforces. You won't learn from blind uncomprehending rewriting, sure, but when you already know the field, writing gives you the space to comprehend and digest. Certainly more than copying and pasting or blind acceptance of generated code. It doesn't have to be fun to be better.
At the beginning of my professional journey I was coding along youtube tutorials to learn, and I can tell you for a fact that all writing reinforces is syntax, which is reinforced regardless when you build your own projects while you develop your decision making skills at the same time.
Writing certainly reinforces more than that when you're not just starting out. It's a poor learning tool. When you have no grasp at all of the meaning, yes, it'll only enforce the syntax you can grasp at that point. I'll maintain that it's a good reinforcement tool, especially to the ends mentioned in the article.
You have implicit assumption that the person rewriting does nothing else. Understanding intention and solution is in there too for most people.
A lot of calculus is rewriting blocks of solutions and applying them to problems. There is a huge chasm between how calculus and real analysis are taught. By your logic calculus should be of very low value. Yet somehow it opens a lot of doors for people to learn other things based on it.
Everyone learns differently. Retyping was extremely helpful for me. The key, for me, was to look up what I didn't understand.
I had fun and also learned a lot when I retyped programs from magazines back in the day. I am not sure if it's suitable now but there is certainly some merit to the idea.
After typing in the program, I found that single stepping the program in a debugger greatly helped with understanding.
I think it depends on what type of cognition you want to stimulate. It's probably useful to familiarize yourself with a suite of API calls, or get a big picture view of how an algorithm runs.
Wouldn't it maybe make more sense to try to use Cursor-style auto complete if you are trying to learn a language in this day and age?
Having Codex/Claude write all of it won't really benefit you imho.
I'll note this one down. I was under the impression that yes, retyping helps learning something about a language and architecture, but I found myself forgetting it after a while...
Trying it first sounds slower, but definitely better for cognitive training :)
For fun I typed out code from old old magazines and it taught me quite a few things.
Also essays too and other texts non code from llm or books, it helps.
One thing it taught me was that sometimes the corrections would be in next month's edition, so I had that long to figure out for myself why the program didn't work as stated.
> Retyping things is inefficient for learning. It's like trying to retype calculus solutions
Says who? You're saying this unequivocally like you have research that supports this.
I used to re-write the notes I took for studying and it was like night and day for how well I did on tests. IT also gave me a chance to tighten the information I was receiving. And it's exactly what's happening here.
OC out here denying the actual learning and reinforcement research because of vibes
It’s very common here on HN from the threads I’ve engaged in - everybody has an idea how learning works but it’s usually based on perceived personal experience and not actual research. I.e. you’re completely right with calling it vibe learning.
I had to re-read it a few times to make sure I was actually interpreting it correctly because I couldn't believe someone would make a claim like that for everyone.
Good advice yesterday, good advice today, and good advice tomorrow.
I don't remember if I read this advice or just intuited it myself (perhaps after some hard lessons), but it's a programming habit I've kept for as long as I can remember (I started coding in the 90s). If I feel rushed, e.g. someone looking over my shoulder, and I copy+paste something, it always leaves me with a sense of unease. It creates a memory & comprehension hole that sticks out like a sore thumb, even for seemingly simple snippets. You can't really be sure it's simple without stepping through it carefully, and simple can be deceptive because it's usually the interactions and assumptions wrt surrounding code that lead to surprises. Typing out code manually gives you time and space to consider the broader picture.
I got made redundant shortly before the release of LLM code generators, so I've never used one, but I did occasionally do this for short snippets from tutorials and suchlike.
> As I manually type every single line of LLM generated code into my editor, I build up a mental model of how it works and fits into my existing codebase. If I don't understand an API or algorithm, I can stop to look it up, or just ask the LLM to explain it.
This is part of how I learned how to code! Well, sans LLMs. Instead of copypasta-ing code blocks from books, Expert Sexchange and Stack Overflow, I manually retyped everything, looked up what I didn't understand and changed what I could.
In my love(?)-hate relationship with "vibecoding", even I tried this approach. For a while it worked, though that "while" didn't last as long as the months OOP has been riding this wave. Though, the vibes have usually been off, so I wish I could keep both thinking of and writing that code which adds negligibly to shareholder value. I say "wish" because a part of me has definitely been hijacked, in much the same way as the addictive type of social media. Sometimes I feel like I need a serious intervention.
Do mathematicians and physicists put away the calculator and computer (this always reminds me of the last scene from Star Wars) and do the computation by hand? A physicist isn't going to manually invert a 10,000x10,000 matrix.
Exactly as we don't write machine code letting the compiler do that, now and in the near future we won't be writing high level programming language code. We are moving towards working on a higher level of abstraction.
When I ask a frontier model to write a loop 10 different ways in Python and TypeScript and test the performance of each using a 1,000,000 iterations, it isn't creating cognitive debt. For the time being, I'm still racking my brain asking the question, how does garbage collection affect the performance.
I feel like we're doomed to respond to these lazy analogies ad infinitum.
Knowledge abstractions have historically been built by people with a detailed understanding of a problem domain and with a period of vetting the abstraction by many people. That is simply not true with LLM-generated code. We do not really understand what techniques LLMs are using to understand directions, value aesthetic/legibility characteristics, assess tradeoffs, retrieve contextual information, generate output, etc. Embedded in everything LLMs do are small decisions.
If you reach for formulas that you don't understand the applicability of, you will similarly get bad outcomes! But a calculator has almost no resemblance to what happens when you give general instructions to an LLM and get a generated code back, because in between that prompt and the result are many layers of decisions you the developer did not make and likely will not understand without carefully assessing the output!
If you have no discomfort with being culpable for something and doing none of the work to understand it, by all means, don't do anything to understand the output! Don't read the code, don't re-type the result, etc. I have concerns that you'll have a job in a year, but that's your decision to make!
A computer used to be a term for an occupation. [0]
> I have concerns that you'll have a job in a year
Me too because I might be the best TypeScript coder on Earth and there is no demand for those skills.
[0] https://en.wikipedia.org/wiki/Computer_(occupation)
What I did is kinda similar, I downgraded to $20 plan and just ask questions and almost never let it write the code, and if I can I use the web ui like the good old days and not spend my CLI tokens.
This is really the distinction between the mentality of a technical manager and an individual contributor.
Managers never got to write and internalize every line of code anyway. Arguably their essential skill is producing useful stuff without needing to do so. So is that 'cognitive debt'? When I use an agent to code, I do the same things: I validate the direction, organization, and core decisions, but delegate the details. It's an intentional calculation of risk, managing the scope of future problems if there's a failure of implementation somewhere.
It's a question of where you put the value of cognitive focus. I don't understand 'line by line' how my car's ICE works, even though it will likely impact me at some point, because while I might be able to fix it myself if it breaks, the likely applied solution will be to delegate that work anyway.
As an aside, back in the days of Stack Exchange I would always type out manually whatever answer I found to make sure I understood WTF I was adding to the system.
I did this too. Almost always I'd rename variables, change formatting, add or remove comments, etc..
Unfortunately this feels less easy to do with modern tools. For example, Claude Code expects to edit your actual source files, and the Claude chat is much worse at writing code.
As someone who, at a point, would copy homework from someone else, copy book reports from online, and use the answer sheets to complete assignments, I can tell you this strategy is long known to accumulate and not prevent cognitive debt
> would copy homework from someone else
I was pretty good at it - mostly remembered to change the name at the top of the paper too.
Struggled at moderated exams; think it must have been the time pressure or something.
> I can tell you this strategy is long known to accumulate and not prevent cognitive debt
When you say "long known" it sounds like this is established science. Is there a link you can share?
How so?
u sure?
Retyping LLM code sounds absolutely miserable and soul crushing. Like hand copying the HTML produced by page generators... why would you do that to yourself.
> manually retyping LLM-generated code
This is just a miserable career of "paint-by-number" because people can't be bothered to have a creative thought about their professional work or programming hobbies.
Software developers think that they are being clever with these kinds of strategies to "keep their skills sharp", but unfortunately the entire industry knows about this, and especially the upper management who are already eliminating these assembly line, JIRA-ticket-taker software jobs en masse.
> upper management who are already eliminating these assembly line, JIRA-ticket-taker software jobs en masse
Is there any proof of this?
Some anecdotal trends listed in this report on US demand for Indian tech workers rapidly slowing.
https://thefederal.com/category/news/h1b-visa-indian-tech-wo...
> According to the discussion, foreign hiring at Google has fallen by more than half, while approvals at Amazon have dropped by nearly a third.
> According to Xfino's Active Tech Jobs Outlook, active technology job openings fell to 93,000 in June, down 14 per cent from 108,000 a month earlier.
Right, it's just pretending to be able to delay the inevitable. It's like the assembly programmers of the 70s and 80s keeping their assembly-fu sharp. Yes it might come handy, and it's good to have a grasp of the concepts, but most careers have shifted to not needing to use assembly. Yes, I know that better knowledge of the low level would improve performance and efficiency. But people don't work with this any more, and the goal back then also wasn't to keep retyping a GCC output to keep the skill fresh. It was to get to a higher level of control and think about the organization of structured code, code maintenance issues, thinking at the level of how to make the C++ implementation.
With AI, our role also shifts. It's mainly to know what to spend effort on, to set priorities and, to be able to verbalize requirements, missing social context and unwritten rules, to anticipate what additional documents the agent needs, to prioritize deadlines, feature necessity, and other judgment calls.
We are right at the stage where our coding ability and review ability is still needed though, but this stage won't last long. Soon there will be as little point to a human diving into the code as to trying to beat a chess engine, or humans constructing buildings by hand. Of course the discussion and prioritization may involve looking at the code itself, to get a better idea of why the agent says that a certain feature would be tedious to implement in the current architecture, but then most people will just learn to take its word for it, just as you may want to understand a chess engine's step, but you typically wouldn't want to override it.
> With AI, our role also shifts. It's mainly to know what to spend effort on, to set priorities and, to be able to verbalize requirements, missing social context and unwritten rules, to anticipate what additional documents the agent needs, to prioritize deadlines, feature necessity, and other judgment calls.
Did you not do that before AI? It’s so strange to me when people are calling out these kind of tasks like they were not already a requirement for the job. What were you doing before?
> Soon there will be as little point to a human diving into the code as to trying to beat a chess engine, or humans constructing buildings by hand
Chess is way less complex than coding. The rules are like a few pages. While the specs for an 8 bit chip like the AVR is in the hundreds of pages. Books like “The Linux Programming Interface” are thousand pages long.
Also humans are using tools for building. Tools that do exactly what you control them to do. When you use a drill for a hole, you don’t have to worry that pressing the trigger have a good chance of sending the bit in your guts.
> Did you not do that before AI? It’s so strange to me when people are calling out these kind of tasks like they were not already a requirement for the job. What were you doing before?
No, you didn't have to explicitly say it in words. My mind doesn't run on internal monologue. Many people can just do their work without ever having reflected on it in words. Tacit knowledge, routines, shared assumptions and culture in a team, common knowledge etc. People have a hard time using AI because they are bad at modeling the knowledge state / information context from the AI POV. You need good theory of mind for this, and being a good programmer is distinct from that soft skill. Yes yes blabla soft skills are more important than hard skills blabla, I don't buy it. It used to be valuable to be great at the hard skills even with mid-tier soft skills. You can have a ton of smooth talkers who are attuned to feel each others emotion and desires super well, but the thing has to actually work too.
> What were you doing before?
Wrote code. Yes, you have to explain the outcome to your boss or your team at some point, but people generally have better developed theories of mind for people than for AI.
> Also humans are using tools for building. Tools that do exactly what you control them to do. When you use a drill for a hole, you don’t have to worry that pressing the trigger have a good chance of sending the bit in your guts.
Right. I'm not sure how to reconcile the two though. A tool whose job is to do some of the thinking part seems to be a contradiction to me. If I so much know what there is to do that it's pure execution and can reliably be executed in a way that basically ensures no potential surprises to me, then I wouldn't need more thinking. But I agree, it would be better to somehow find a hybrid that is both doing thinking and feels more like a tool also while using it.
Llms are a relational responsive reasoning interface, not a mind.
The point is that you have to have an idea of what it has access to in its context. Many expect it to work like an omniscient genie and then give up once it turns out that it can't read your mind and you have to be able to halfway-coherently state what you want.
> No, you didn't have to explicitly say it in words. My mind doesn't run on internal monologue. Many people can just do their work without ever having reflected on it in words.
Were you a solo developer? I think the amount of reports and explanations (either written or verbal) dwarfs by large the amount of code I’ve written. From training juniors to drafting a design specs for a feature. That is why people say code is the trivial part of the job because it’s easy. I’m pretty sure the mailing list of the Linux kernel is bigger than the code itself.
I'm in academic ML research. Coding is mostly scoped to myself. And it's often nontrivial. My communication is more in papers, reports on experimental results, deciding what to try next, but how the code is organized is rarely the focus of discussion. Of course this may be entirely different in a software shop building routine features where the difficulty is indeed the social part and the software itself is more clearly understood. But I'd say that's when AI can be even more effective since it can do mundane coding even better proportionally than research code. Of course Amdahl's law kicks in and even if you reduce that drudgery to 0, the proportion is small when taking total work effort into context.
> Of course this may be entirely different in a software shop building routine features where the difficulty is indeed the social part and the software itself is more clearly understood. But I'd say that's when AI can be even more effective since it can do mundane coding even better proportionally than research code
And here’s another fallacy that is always thrown around. Always underestimating the other’s tasks when you are not an expert in it and don’t know intimately what’s involved.
So which is it? Is the coding part easy or hard? If it's easy and the task is mainly all the song and dance with the client, the stakeholders, getting approval and whatnot, then AI can take care of that easy coding and at least save that time. But then that is proportionally not much. I do have friends in industry, and they do sit quite a lot of hours in front of an IDE typing and fighting compilation issues, etc. It's not like they are always in meetings. I know people who work in the computer vision industry, 3D perception, and they have relatively few meetings and report to their boss occasionally, are not micromanaged, there is little red tape. They are getting real benefits from AI. It has eliminated several efficiency problems in their code that nobody took the time to fix, like consolidating repeated computations to just being done once, or similar things. They have solved a lot of CI pain with it, where they used to have an entire employee just taking care of keeping the CI in shape and updating it and remembering all the quirks, it's no longer a full time job, though also not fully automated. The CI person of course doesn't like the fact that his arcane knowledge has become un-moated. (I've worked on maintaining servers myself and I was the only one who really knew how it worked or how to fix it when it was broken, how to use it in specific special contexts, e.g. in context of a Slurm cluster. Today a lot of that hard-earned detailed knowledge is obviated by Claude, because it can answer similarly well to how I could, or better. So I have experienced such things myself too.)
I don't think we should be making sweeping claims. But everyone generalizes. The person on top claimed that productivity doesn't increase for competent people and that coding is anyway very easy. That may be true in some contexts but not in others. Some software jobs require a lot of face-to-face, others less. Some managers micromanage, others just care about results on a longer timeframe. Some people build technically difficult, computationally heavy code, deployed on special hardware with efficiency requirements, others deploy website-like iPhone apps and CRUD webapps all day. There's no single shape for a software-related job.
Sounds like a devious plan to turn me from a self diagnosed 10x developer (& Founder, CEO, Serial Entrepreneur) into a plain old 1x regular Joe.
I empathise with this. I run a little open source project called SmallDocs [1][2] which Claude/Coding Agent invokes to generate easily human readable Markdown documents (and render code files).
You can ask Claude to "sdoc me an annotated code walkthrough of this MR" and it will produce something like this:
https://smalldocs.org/s/Ju9GOmWZ0JXTtzqCVfgt1q#k=6HBrpcCjIu7...
I use this a lot to stay in touch with the code the LLM is producing.
[1] https://smalldocs.org [2] https://github.com/espressoplease/smalldocs
This is really cool, ty for posting it. Gonna try it out
This is a new form of prayer for those who can't break their religious addiction to LLM code generators.
It could easily be the other way around - religious addiction for people can't let go of the code.
I don’t think so, because it’s being used as a way to try to remedy one of the new problems brought in by agentic coding - loss of context of what code does.
I'm taking a slightly different approach. I've started a project where I intentionally don't use agentic coding. I use LLMs for researcher and to learn, but write all the code by hand.
The goal is to maintain the taste, for lack of a better word, that I've developed over decades of programming.
Claude put me on to the concept "Étude", so I've taken to calling it my Étude project.
Generate code using LLM on a small project, then copy manually toward your big project. This reduces the context (and the cost) for the LLM and gives you many small projects where you can experiment ideas with more agility.
This is not slightly comical, it is very comical. Side projects are not mandatory, if you are using them to learn something, asking LLMs to generate and for you to type it makes no sense - just do it on your own. If you want to offload tedious boilerplate part to LLM, then by definition no need to learn it as it is tedious, so typing it out is useless.
It seems to me this is the same problem we previously had: how do we understand codebases we didn't write?
Creating while writing allowed us to build a mental context but in a unproductive way, it never scalled.
I believe we need to move onto a new way of reading codebases that go beyond reading line by line.
I know people have explored representing code in 3D spaces. I don't know the solution. But I believe that is the problem.
Ask your AI to chart the data flow through the program. Not that that's a magic solution but it's a pretty good start.
By default, if you ask an AI to "generate documentation for this code" it generates the same broken documentation all the humans do too; an enumeration of all the modules in the code and what their API is. I'm not surprised, the training data is biased probably at least 25:1 in favor of this rather than the useful data flow documentation. Fred Brooks was complaining about this over 50 years ago and the discipline as a whole still gets this wrong.
I'm not saying this is a future solution to all problems, but it is a now solution to some problems.
3D doesn't help. We live in a 3D world but our vision is 2D with a bit of augmentation from a second view point just a bit away. We derive some depth information from that, but we don't really "see in 3D". To do that we'd need to be 4D beings. There's a lot less juice in the 3D squeeze than meets the eye.
I am also worried about "cognitive debt". I hardly remember what I had Claude do, even hours later. Back in May, I advised of a similar mitigation, citing the "generation effect" as the reason typing the code would make you remember it better:
> For your next ticket or feature, engage with your LLM as you normally would to produce a design and implementation plan, but with that plan in hand, make the mechanical edits yourself...You can expect this to improve your knowledge retention compared to merely reading a diff. You want to go slower now so that later you can go at all.
https://www.slater.dev/2026/05/type-your-code/
Jack Kerouac famously sat down and typed "Anna Karenina" on a typewriter because he wanted to feel what it was like to "write a great novel".
I think you're thinking of Hunter S Thompson, Kerouac really doesn't seem like the person that would have the time or care about doing that.
You're right! Thanks for the correction.
This is what I'm doing right now to learn Electron, I essentially had Opus write me a tutorial to write the application I want to have, and I'm modifying bits and pieces as I go. It's been enlightening thus far, and the bot isn't always right so I still need to look up documentation on occasion.
You are cooked if you can’t actually write better code than llm. Try reading some books or documentation
Frontier LLMs write better code at CRUD tasks than 95% of developers today. They’ll get to 95% of most niche coding domains by December and likely all coding tasks sometime next year; 99% better at all tasks by December 2028.
You may be correct now and it doesn’t matter one bit.
I'd say as long as we can clearly see the 5% are working better than an LLM, why not strive for it? Shows what humans are capable of, and it's probably achievable to most.
I'm not hanging my hat yet and can still see where LLMs struggle. I think it's the best bet you can make: keep working for the future where you'll still be needed, because you can't prepare for the future where you're not.
I agree about your general assessment, but I don't see my coding skills of, depending on how you count them two or three decades, being needed in 36 months - so I'm trying to upskill LLM piloting, but that also seems a bit of a dead end since LLMs will be perfectly capable of piloting themselves in approximately the same timeframe - we'll see what needs upskilling in by winter...
How this presents, however, I would think, is that being that top 5% and utilizing LLMs will get you further than either alone.
You are cooked if you think the end goal was the quality of the code and not the quality of the product.
You won't get a quality product built on sloppy code.
Absolutely delusional if you think the two aren't related
What is "better code"?
I think there are far more people who can't write better code than an LLM. Of course, there are a few exceptions, but it's a fact that LLMs are already handling PhD-level mathematics and papers.
I also think I write better code than an LLM in certain areas, but in most programming domains, the LLM knows more than I do across many dimensions. As prompts get deeper, LLMs are already producing PhD-level code—and that's been shown in research. The vast majority of people don't have that level of education. Of course, having a PhD doesn't guarantee good coding, but at least it's clear that LLMs can handle that level of capability.
People might disagree, but my view is firm on this.
> I also think I write better code than an LLM in certain areas, but in most programming domains, the LLM knows more than I do across many dimensions.
Remember that the quality of the LLMs code in the areas you don’t know is as good as the quality as the area you do know. You’re only able to gauge the quality of what you do know.
Your main target seems to be the view that software quality is a matter of 'aesthetic intuition,' but I see it differently. I think because it's 'engineering,' there must be measurable indicators.
Executable specifications
Unit, integration, and property tests
Official API documentation
All of these provide ways to judge quality. There are so many metrics that the problem is actually choosing which ones to use.
1.Does it produce output A for input B? 2.Can it process 100,000 records within 5ms? 3.Is memory usage within the defined limit? 4.Does the protocol handle error conditions properly?
You don't need to be an expert to test these. People forget that programming is one of the few fields where judgment criteria can be easily translated into machine executable tests.
The biggest problem with epistemological objections is that they often assume a binary: 'experts can judge, non-experts cannot.' But in reality, it's a continuous process.
You run the code, notice something odd, look up the terminology, compare it with official documentation and reference implementations, add tests, and gradually build up judgment in that area. Rewriting LLM generated code compresses that entire learning curve.
In the past, entering an unfamiliar domain meant starting from a blank file and reading documentation. Now, you start with a working hypothesis and modify it. That's far cheaper than starting from scratch.
If you treat LLM generated code as executable teaching material and a falsifiable hypothesis, it's a very powerful resource.
I feel uncomfortable when people reduce programming to aesthetic quality alone. We were all trained to measure things.
My "aesthetic" sense is more about how modular the code is, how DRY (which is also an aesthetic balancing act), how clean the abstractions are and how well they fit the domain and the workflow.
Also, error handling.
Tests, specs, and docs are are all downstream of that.
So far I've found that AI does an adequate-to-very-good job up to a certain amount of code, then things tend to fall apart. The solution is modularity and clean interfaces - as it always was.
I'm not really sure. I'm mostly thrown into existing codebases and just modify things to fit the existing style, so I'm almost always evaluated purely by metrics. I do think your (TheOtherHobbes) standards are really good, though.
>You’re only able to gauge the quality of what you do know.
That's not true. You can follow the logic step by step, search for similar domains, and form a comparison group.
Realistically, if we take this logic to its conclusion, it's like asking: 'You're just a spectator, so can you really judge whether a football player is good at football?
Realistically, LLMs write code well. I'm a C# programmer, and as you know, the current trend in modern programming is to refine error handling—using try/catch at higher layers, working with monads, preserving computable contexts, and branching accordingly. That kind of code typically takes 1 to 2 hours of modeling before you even start writing.
But an LLM can do it in just 5 minutes.
So I think the opposite way: if LLMs can do this well in a domain I'm familiar with, they're probably just as capable in other domains too. And that makes them feel trustworthy.
And more importantly, no matter how great a human programmer is, once you step outside your domain, you're in unknown territory. In those areas, LLMs are surprisingly helpful.
Epistemologically, it might be true that you can't evaluate code in a domain you don't know. But people keep forgetting the fact that you can learn through that code. This very post is itself an example of learning through LLM-typed code.
And programming actually provides clearer results than many other domains. It's not based on subjective UX like UI design. You have:
1.Executable specifications
2.Performance measurements
3.Comparisons with official documentation
These are clear engineering outcomes that allow for objective comparison. For example, you can verify whether a program produces results within 5ms when executed.
Many epistemological arguments tend to overlook this.
A lot of code is open source. You can find mature implementations anywhere. Finding a repo from someone claiming 10 or 20 years of experience and running a differential comparison is not difficult at all."
> As prompts get deeper, LLMs are already producing PhD-level code—and that's been shown in research.
This gave me a chuckle, "PhD-level code" is gross actually. Have you ever looked at the code of research papers?
I've had opportunities to look at code from Korean and Chinese companies, not just academia. From what I've seen, AI generated code is often better than the average code in those codebases.
When I talk about PhD level code, I'm not referring to research code quality. I'm talking about research level algorithms.
Production code is built around reusability, policies, monadic chaining, and various code hygiene practices. Research code isn't. So what I'm saying is: 'Yes, I've collaborated with professors from top Korean universities.'
That said, I'm not writing PhD level code myself. The reason is that I take existing algorithms and apply code hygiene to them. I'm not actually developing new algorithms at a PhD level of understanding
So from what you're saying, it sounds like:
'Are you talking about those horrible research paper codes?'
But what exists in those research papers is: 'The domain modeling itself is difficult.'
I learned about code hygiene in production code—things like using functional approaches, and so on. Research code often lacks those things, so it might look bad. But that means the code quality is bad—not that the modeling itself is bad. And in fact, the core value is in being able to produce modeling at that level of difficulty.
In fact, most programmers, if trained like me, can do these things well enough. But domain modeling is different.
This touches on the distinction Frederick Brooks made in The Mythical Man-Month between 'Essential Complexity' and 'Accidental Complexity.' Code hygiene, functional chaining, monadic structures—these are techniques for reducing accidental complexity. With enough training, you can learn them. But domain modeling deals with essential complexity. It's about how difficult the problem itself is, and it's not easily solved through training alone. That's why the depth of modeling that researchers produce should be evaluated on a different axis from code quality.
This is what we call 'Essential Complexity.' From that perspective, saying code is 'good' usually means that the essential complexity is handled well on average. What I'm calling 'PhD-level' usually refers to modeling problems that are commonly difficult to solve.
I see it 'very often.' Most research paper code has no reusability—they just implement the algorithm.
So I can see why it might be considered low-quality. They don't use things like Result or policy types like we do in production code. But they're modifying or creating new algorithms, right?
The thing is, they don't need to do those things, so it looks lower-level—but the algorithm implementations themselves are actually pretty good, aren't they?
PhD thesis code often looks low-quality simply because there's no reason to reuse it like production code. The tensor computations inside are things that typical programmers can't easily do.
The point I'm making is that the core algorithmic work is something most programmers can't handle. And that's natural—highly skilled programmers have already built deep libraries for that.
When I talk about PhD-level code, I'm not referring to overall code quality. I'm talking about the level of the algorithms themselves.
> there's no reason to reuse it like production code.
This strikes me as a self-fulfilling prophecy. There are probably many people who would like to use the code published with PhD level research, but they don't because the code is not easy to adapt (or sometimes even to get running).
Then a decade later someone implements that same algorithm in a library with a well-designed interface and it suddenly becomes a useful tool for others. So ultimately there was a reason to reuse it, it was the quality of some aspects of the code that held it back.
At that point I feel like you might as well do the implementation yourself and just plan with an LLM
Related, a writing advice I stole from Neal Stephenson is to write the first draft by hand. The thing is, there are a lot of small corrections where you kinda should change the text but nah, and if you already committed to copy the entire thing than you are already working at that sentence anyhow.
When I was younger and took over a codebase I open it up in one window and then type it back into another window. Not only did I catch/fix a crazy number of bugs, but I became a near expert overnight. Doing the typing would cause me to question everything, ask why we import something, why x is used and not y, etc.
I also tried where I would print out the code (with color) and then go read it with a red pen. Faster and similar results of forcing me to be able to read it enough to be "an editor" to the code.
And I have done the same for LLM-generated code and text, back in '23 this meant more taking their so-so output and then turning it into my own, but between now and then come up with a number of techniques to improve the AI output to more acceptable out of the gate so more learning than being inspired.
These are good techniques as it causes your own brain to rapidly learn the material, but no matter how good it is I have never met anyone else that does it so the real ponderable is assuming that everyone else doesn't do this and what does that mean?
I don't manually retype the code from pull requests of other engineers.
It's important to retain the what why and how as a team to the degree that development can be efficient, extensible, and maintainable. (And ideally a good devx)
Needing to manually touch every line of code is not scalable.
This was true 10 years ago. It's still true today.
I never had so little free time as I have these days. Most of my time is spent at work or with my lovely family.Love my family, and work is great.
The thing is that, probably like many of you, I love going deep in a side project, even if it lands nowhere. With so little time, working on them has become a very frustrating activity.
This is where I found my trap... do more with very little time by delegating to an LLM. You get dopamine shots, the feeling of achieving something but the cognitive dept is just crazy. So much that the activity becomes almost meaning less. After couple of months doing this, I'm not even sure it's a good use of this time. I get very little satisfaction on the long run.
I don't have a solution to this problem, not even sure there one. I think I have to accept that this is an activity that takes time, and only time gives the real gratification.
This resonates with me. The concept of cig it I've debt was something I've been experiencing but didn't have a name for it.
I think it is worth noting that not all code is equal... One could argue that adding a library is in some ways similar to copy/pasting code in as much as, one doesn't know what the code is doing, and yet that doesn't leave me with a sense of unease!
So it might be that as I'm working with an LLM there are parts (boring, as the author calls them) that are not worth "knowing" how they work, something uninteresting or that a correct output is all that us needed, I'm totally fine having the agent write that code, but the sections I need to know how they work, I think it might make sense to write those by hand!
> One could argue that adding a library is in some ways similar to copy/pasting code in as much as, one doesn't know what the code is doing, and yet that doesn't leave me with a sense of unease!
It does, to me. Been burned enough times that I now, at minimum, audit the source code of third-party libraries before I use them.
Hard disagree, it's proven that its the actual discovery process that's makes us improve at tasks. Blindly typing will make you just good at typing.
I do think there is absolutely no way a learner should be using ai for code generation. I think code analysis is the only acceptable usecase.
I've used LLMs in a similar way and I'm reminded of learning to draw / paint.
At the very beginning stages you might be tempted to trace over an image but this builds a very shallow understanding. Instead you should quickly move onto replicating work you admire by sight. Consider the form, volume and values, conceptualise them in your mind and try to apply them in whatever medium you are using. This helps form your own mental model of the process. Eventually you can start using those techniques on original works.
I'm currently rethinking language learning too in a similar manner. Comprehensible input, shadowing. That sort of thing.
Alternatively, what I tend to do after receiving generated code is a lot of asking "why?".
I've learned things I wouldn't otherwise have learned because I hadn't considered using the tools the LLM recommends. It's also a way to eliminate some hallucinating, given that critical questions are posed as unbiased as possible. For that, I also like to open a new chat with a different model and asking open-ended questions about a recommended tool I don't know much about, to double-check that the original LLM was likely correct in its recommendation in the first place.
I don't know if retyping is the solution, but for me is clear that we need something additional to a terminal and a code editor. I envision a software that an agent can use and showcase to you how it did implement the changes and why. The same way a colleague would do, focusing on the important bits, then ending with the trivial stuff. Something in between pair programing and code reviews.
Right. I want an AI that sees my screen, sees my mouse cursor, has my audio transcript synced to the mouse movements, and it can similarly do TTS and and pointer movements or put things like circles and rectangles and background highlights on things while it talks, and slows down if I say so etc, like a human discussion partner.
Diffity has a “tour” feature that can be directed to explain a local code change. Walks you through the important bits of the change with explanations.
https://github.com/nilbuild/diffity
As others mentioned retyping is not fun. My approach is to let it write the code, but only in small portion. Not "implement this feature". But "open this file and make these changes". Each small change is easily reviewable and often times I end up asking it about better options and iterate a bit. Still feels like I'm in charge. Still feels like I'm learning stuff.
We're living in such a stupid time.
It’s just bonkers
Manually typing in code is an underappreciated trick in a lot of circumstances. It's one of the fastest ways to get to grips with a certain piece of code, a new library, or some methodology.
This was true when I first learned to program, and is still true today. And I do find myself manually typing in really critical code. In those cases even if I do have an LLM alongside these days, LLM suggestions also then get manually typed.
This is the workflow that Vs Code Copilot does. All the AI generated code changes are in a git worktree and you can step through them all. This is what I missed after Claude forced third parties to start charging API pricing and now I have to use Claude directly and I have to do this same review process in a clunkier way via my git client.
"Prevent sub-optimal code by manually retyping compiler-generated assembly"
I don't think this is a practice which will be sustainable for very long
LLMs are plenty far from compilers.
I already wrote my opinion on this, which I don't think anyone read, but my idea is to let AI code the working system, and then prompt it to teach you, give you challenges, and grade your work.
If you write, you should write in your own words, to demonstrate your own understanding - the so-called Feynman technique. Never verbatim. That's as true for coding as it is for study notes.
My counterpoint to that is, you never know when the factuality of its analysis is mistaken because you're making it the point of authority over knowledge you should be working to acquiring.
In math classes back at school, it didn't matter how much the math professor explained how the formula works. What mattered is me putting in the effort to understand it. The implication to your example is, I should already be familiar enough to understand the generated code to the point where all the explanation that it's doing is effectively a "Quality of Life feature".
The most I enjoy working with AI is my special workflow.
I ask it to plan the feature in a separate worktree.
In parallel I start coding without being biased by AI and vice versa.
At some point I read its plan and iterate on it all the while I am in implementation mode. This helps me improve my own vision.
Finally I ask the AI to review my implementation. It flags off bugs and gaps which are usually straightforward for it to fix.
I ask the LLM to generate the code but mask the last token. Then I do softmax and give it an answer. Pretty soon my perceptrons are more connected than ever.
Filing this away
Whenever I don't know something, I ask it for a tutorial, programming-magazine style. Then I just follow the tutorial.
I wonder how effective it finally will be. At first glance it reminds me painting by numbers a d I'm not sure if that will help the real painter to keep his skills and surely won't teach aspiring painter much about the craft.
This is not about an aspiring painter though. This method is intended for an already accomplished painter. Not saying how effective it is but your comparison is not relevant.
Pair programming with the LLM is a better approach. You can take either role and even take turns. It’s slower but gives ample time to read everything and push back on decisions or receive feedback and review on your own decisions.
If you can afford it, why not. For certain phases of projects like a proof-of-concept, you need to move fast and validate several ideas. Once it's locked down, rewrite from scratch, and here, if you can afford it, type or write the code manually.
better: before submitting the code, ask LLM to quiz you for understanding the code.
It's better than nothing perhaps, but reminds me of UK highschool in the 80's (is it any different now?) where we had to manually copy everything down that the teacher was writing on the blackboard rather than the teacher giving handouts so you could pay attention to the teaching. The act of copying everything down was a negative rather than a positive.
Of course agentic coding tools are not trying to peer code or teach/inform you what they are doing, so being present in the moment doesn't help, but I suspect that copying it all down later doesn't help much either.
When you are/were developing software without AI, even for pretty large projects you do end up internalizing (memorizing, but not deliberately so) a lot of detail, but from my own experience I'd say it's more the design than the code. The design is what you put effort into, thought about, etc, so is both what you naturally end up memorizing, and is what you need to know to have a mental map of the project and therefore understand how best to modify it. The code itself was naturally always the last thing you did, and followed automatically from the design and module/component interfaces - not something you typically think much about other than while in the flow of just "coding it up".
By retyping LLM-generated code, it seems you are mostly going to be gaining familiarity with the wrong thing - the code and not the design. Memorizing the code is not going to help much in grokking the design.
Nice workflow! I'll give it a try. I'm struggling with building mental model of AI-generated code. And code review fatigue is real. This may be the way.
I like the "cognitive debt" term. With the latest models, what I've observed is that they are really good, but I don't use them to write main code because I need to know what I'm doing.
The article is not wrong though that it pays off to have some imagination on how to use the models. For example, I want to use SIMD instructions in an ESP32-P4 CPU. Those instructions are undocumented for the most part, with just a couple of handwavey blog posts and some infuriatingly vague marketing material. So I just asked an LLM to create a `SIMD_P4.md` document with all the details. Lo and behold, it practically reverse-engineered the ISA. Now I can program in assembler by hand all I want and build that skill in my own brain, and whenever I find a slightly unclear op in the document, I ask the LLM to refine the documentation in that op.
I don't understand the concern about "cognitive debt". I frequently have to maintain code I wrote, or someone else wrote, weeks/months/years ago and I have NFI what's going on. Now I say to the LLM "tell me what's going on" and it tells me. I can ask it some follow up questions, and build up my understanding. It's SO much faster than grepping through the source myself and I can do it for as multiple issues in parallel. The notion of reading every line of code is absurd to me, the notion of RETYPING it beggars belief. Surely this is satire.
My rule is: I only let AI code for me, I don’t let it think for me.
Since writing is thinking, coding is thinking since coding is writing. That means any time I am not certain how I’m gonna implement some feature or bug fix, I have to code it myself because that’s the only way I can force myself to think through it. Only when I get to a point where I’m line “ok I know exactly what to do now- all that’s left to do is type it out” that’s when AI can be employed - essentially as a autocomplete.
This is only for projects where I will be held responsible for outcomes and must understand how it works. For hackathon / personal projects, I vibe away.
I also use AI to brainstorm at the outset of the task when I don’t know where to start at all.
Is this inefficient? My take: no. It’s maximally efficient. Over the long term it gives me an edge over any teammates who just vibe code everything because I actually, you know, understand how stuff works. I become the guy who can save the day at 3am when the team’s business critical app goes down. I become the guy that gets pulled into meetings so the suits can ask “is this possible?”. I see opportunities and problems before my teammates because I have a relationship with our code and system that they never took the time to develop or think about.
If you copy/paste code from a teaching book, you will probably not learn as well as if you type it.
Typing itself is irrelevant, it is the timing spent, even if only seconds, pondering at what each word or syntactic element is and why use it.
Being slower does not automatically make you learn better, focus on the learning is what makes the difference.
If you don't have the opportunity to learn, the time to actually think, then a faster tool is not helping.
TL;DR: what matters is why you are doing something, is it solely to get the task done or is it primary to learn, or both?
Would one retype assembly language for C generate code?
Having LLMs write out their design and reviewing it seems more efficient. Have LLMs, maybe with a different model, check that the implementation meets the design.
Why use LLMs at all if you're doing this?
Because typing is a small percentage of the time spent writing software.
For learning? You can use llms to help you with stuff and still learn new things in the process. Its really surprising that so many people don't understand this.
Are people really out there just mass copy pasting llm code without even trying to understand it! lol
But this way you move way slowly even on personal projects, like you will not even get the basic UI for the app done in a few days? Is that OK for you?
It is meant to offset not knowing why everything degrades and you can't make progress after the first month. Is that OK for you?
No you misunderstand me. I support such a view but cannot hold it because my pace at work is so much fast. And hand coding like this will make personal projects s slow and choreful with no visible progress. Like where is the joy in that?
The bottleneck is very rarely the typing.
Has this been your first hand experience?
And if so, in what work, and have you tried debugging issues with SOTA models?
From my experience it is certainly not the case that you cannot make progress after the first month.
I work on native mobile applications.
I work on mobile native applications.
Without an active harness (eg. Appium) that can end-to-end deterministically verify the changes you make continue to work correctly it is almost impossible to continue to keep the same pace on the app.
Unsupervised LLMs (even fabel) are categorically incapable of running parallel unsupervised mobile app feature development.
That is my personal, first hand experience working in a team in this space.
What you are (I guess?) experiencing is user-in-the-loop light touch LLM development where you can 80% most tasks quite quickly (much faster than without assistance!) with a small number of human developers working on largely unrelated features and manually verifying they are correct and manually fixing the platform specific issues you encounter.
Maintaining a strong appium end-to-end test suite is still extremely challenging with notifications and maps.
Honestly, it blows my mind you could even being to claim that of all things, native apps using obscure languages like swift are suitable for this, compared to the much much easier path of web + react.
You might say “yeah yeah, but one month? Come on!”
…but have you actually seen how much code fabel can write in a month?
Its a lot.
So sure, you say, work at a slower pace. Don't just endlessly run a frontier model in unsupervised feature development mode.
Yes… you see, thats the point. Thats what the op is saying.
Move more slowly, and you can avoid building a spaghetti castle (ok sure! If you dont wanna, maybe don't retype every character by hand, but the point of that practice is not upping your wpm typing speed. :p It is to take the time to think, design and collaborate, not rush rush rush)
I have no idea what "an active harness" is referring to, Appium looks like a cross platform testing framework.
I doubt it brings any advantage over XCUITest here.
What is supposed to be the problem with parallel development? I use worktrees, and it works just fine with five agents in parallel.
Automated end-to-end testing on mobile is notoriously flaky, but that's nothing new, and I think it is now much easier to deal with.
Depends what they're doing... I can crack out a basic UI in a few hours at my job, and I don't use LLMs at all, and I wouldn't class myself as an expert developer or anything
AKA preventing cognitive debt
Do you really find your typing speed to be the bottleneck in getting things done? I suppose that's pretty easily fixed, at least.
Anyway the author did address that
> Using LLMs this way allows me to work faster than not using LLMs at all, but I'm still slower than those who are willing to allow the machine to think for them. Instead of being 10x faster, I'm probably only 2x faster. But what I lose out on in terms of speed, I gain in terms of a deeper understanding of my code.
Cognitive debt is an idiotic phrase.
nah thanks;
my workflow:
- ask not only for a solution to a problem but also for specific code (= tell the agent about your mental model of the codebase)
- ask for small stacked 'PRs/branches' and review/refactor heavily also using the agent (= refine your mental model of the codebase)
When I got my first corporate job, I was placed in a group of 20 trainees in a rigorous COBOL course. We were given assignments and a schedule to complete them.
Most of us read the specs, then raced into the coding phase, hands to IBM mechanical keyboards. One guy took a different approach. He took a legal pad and pencil, and wrote his whole program on paper before he ever approached a terminal. He’d do his own bug checking and syntax checking, instead of having the compiler do it ( compiles took longer in those days, and required JCL ). He avoided the entire compile/wait/read-with-dismay/quickly-try-again loop.
He was one of the top students, of course. And a lot less stressed, as I recall.
I feel like this will do almost nothing?
Mindlessly typing something is not much better than copy and pasting?
I could maybe see it if you asked it to spit out pseudocode you had to rewrite. At least there’s some translation there…
But this is bizarre. Write it yourself at that point. Is it any faster (or faster at all frankly) to prompt what you want, manually write it out, and maybe even make adjustments as you go? I’d argue not.
The way I wrote code in the past was to just first comment out what I wanted to do, and then underneath write the syntax for it. You could maybe do this too? Take the LLM code, and go through commenting what each section does to be able to effectively break it up? It still seems dumb.
Funny that there's another trending post titled, "Don't be a meat proxy," just above this proposal that we literally meat-proxy all the code.
Whenever I encounter an especially preposterous proposal like this one, I like to imagine a USMC Drill Instructor wandering into the open plan office and having an interaction something like this:
USMC Drill Instructor: "What the actual fuck are you doing?!"
Smelly Recruit: "Sir, I'm hand typing the LLM output. Sir!"
USMC Drill Instructor: "Are you fucking with me recruit?! I said I wanted a SASS App, not a typing tutor! Drop and give me 20!"
Smelly Recruit: "SIR, YES SIR!"
I don’t disagree with this if you code for a hobby.
Buy if you code for a job, good luck justifying this to management. “Yeah Claude already gave me the solution, I’ll take the rest of the week to type it out”
I use LLM code for hobby/fun projects only (I also don't code for a living) and I still wouldn't want to type up the code it gives me. Instead what I dream of doing is one day to rewrite all the little things from scratch (well, with a blueprint of a working result). Your management wouldn't even talk to me, rightly so :P
I don't think it's bad to manually retype code as a way of learning.
Isn't a working program itself the best textbook? It's just a difference in learning methods. Depending on Stack Overflow is also a dependency, and searching for code on GitHub is also a dependency. How much dependency you allow is purely a personal difference, and it varies depending on your own study habits and learning style. Whether your learning method is superior or not likely depends on how your brain works.
People tend to think that the more painful something is, the better it is.
I don't deny that there are talented people who can read the manual and build everything from scratch. But I think that analyzing and rebuilding a working template step by step is also valuable.
I agree with the view that LLMs may cause cognitive decline. But if you go down that path, Socrates already criticized writing for weakening human memory. And how did that turn out? Books became a universal medium for knowledge. Then the internet came along. When Stack Overflow appeared, there was opposition, but it also had explosive adoption. LLMs are just the next step in that sequence.
If there is cognitive decline, I think there's also compensation in other areas. Using LLMs clearly causes some cognitive decline. And I think there are areas that need to be reinforced to compensate.
But having a baseline to work from—modifying already-working code—is genuinely helpful. I don't see what's wrong with using that as a way to learn.
I just give them smaller tasks
In my current workflow, I've settled into a three tier system when coding:
1. HIGH-VALUE CODE:
I write it all myself. I will occasionally use AI for mostly mechanical changes, like cleaning up variable names or mass-changes when a function signature has changed. Either way, every line is read carefully. Sometimes this means isolating my high-value code as a library in a separate repo. Usually it's just a note in AGENTS.md, or even a well-written comment at the top of certain files. I'm not obsessive about it, though, as it can't hide from git. And learning what it's trying to change is sometimes a useful insight.
That doesn't stop me from using AI as a consultant. This is the one time I'll use a beast like Fable. Ask it to write a technical/security analysis on a section of code and damn it can pull out some impressive insights. It can't write new code particularly well, but it can inspect code like a boss. But that all stays in the chat window. (And despite being so infrequent, they ends up costing significantly more than all my other AI costs combined!)
2. BOILERPLATE/PROCEDURAL CODE:
I'll write the first draft, but once I've set the tone, I'll allow AI to build and maintain it. I keep on top of things like a senior manager, just to make sure it's not doing stupid things. Every few days I tell it to mow its own grass: AI is good at recognising its own stupidity, you just need to give it an opportunity to look.
3. TEST/HARNESS CODE:
Bring on the slop. If I get nothing else from the AI revolution, it's not having to write another stupid test unit. Nothing makes me happier than setting the AI to work writing every permutation of test I can think of. I will slop this code all day, and I won't read a single line of it. Why should I? If I ever doubt whether a particular test is correct, I'll test the test by breaking the code, not by reading the test. But I almost never catch it out. In my experience, AI is especially good at writing tests. Perhaps more than anything else.
Tests don't just take the form of a few mocks and props in a test harness. In one recent case, my project involved writing a library for the API of an obscure commercial microcontroller-powered device. I took the API documentation and made AI build me a complete simulator. I then made it write a full suite of tests using my client library within the test code. I then got it to run that test suite against real hardware and identify any inconsistencies. From there it could recursively modify the simulator until it became unreasonably good at mimicking the real hardware. I haven't read a single line of its code. But it's now core to the library's CI.
> 3. TEST/HARNESS CODE:
> Bring on the slop.
Bring on Volkswagen tests, right. Because reliably confirming that your code work is not a critical port of the project at all. /s
> Because reliably confirming that your code work
I can't reliably confirm that you read my post all the way to the end. I pointed out multiple ways where tests are proven. One is to verify the test by breaking the code under test. Another way is to build a fully independent, highly complex test rig that would never be (commercially) feasible without AI.
This method doesn't seem bad.
Realistically, LLMs write code much better than most people. In my domain, there are areas where I still write better code than an LLM, especially when it comes to physical constraints it might not understand, but there are far more domains where the LLM writes much better code than I do. In that sense, writing code with an LLM and keeping track of it feels more helpful than I expected.
Practicing solo coding for an hour a day often ends up being mechanical and not very useful. This might actually be more helpful.
...what, that's terrible advice. If you gonna waste time on that just waste time on writing the code from scratch
there is a simpler way, make a complete mental model of the changes and ask questions to confirm your understanding. so much faster.