- Formulaic code. It basically obviates the need for macros / code gen. The downside is that they are slower and you can't just update the macro and re-generate. The upside is it works for code that is slightly formulaic but has some slight differences across implementations that make macros impossible to use.
- Using apis I am familiar with but don't have memorized. It saves me the effort of doing the google search and scouring the docs. I use typed languages so if it hallucinates the type checker will catch it and I'll need to manually test and set up automated tests anyway so there are plenty of steps where I can catch it if it's doing something really wrong.
- Planning: I think this is actually a very under rated part of llms. If I need to make changes across 10+ files, it really helps to have the llm go through all the files and plan out the changes I'll need to make in a markdown doc. Sometimes the plan is good enough that with a few small tweaks I can tell the llm to just do it but even when it gets some things wrong it's useful for me to follow it partially while tweaking what it got wrong.
Edit: Also, one thing I really like about llm generated code is that it maintains the style / naming conventions of the code in the project. When I'm tired I often stop caring about that kind of thing.
Maybe a good case, that i've used a lot, is using "spreadsheet inputs" and teaching the LLM to produce test cases/code based on the spreadsheet data (that I received from elsewhere). The data doesn't change and the tests won't change either so the LLM definitely helps, but this isn't code i'll ever touch again.
Building a generator capable of handling all variations you might need is extremely hard[1], and it still won't be good enough. An LLM will both get it almost perfect almost every time, and likely reuses your existing utility funcs. It can save you from typing out hundreds of lines, and it's pretty easy to verify and fix the things it got wrong. It's the exact sort of slightly-custom-pattern-detecting-and-following that they're good at.
1: Probably impossible, for practical purposes. It almost certainly makes an API larger than the Moon, which you won't be able to fully know or quickly figure out what you need to use due to the sheer size.
But then why are so many people expect them to do well in actual reasoning tasks?
This seems weird to me instead of just including the spreadsheet as a test fixture.
I think you have to be careful here even with a typed language. For example, I generated some Go code recently which execed a shell command and got the output. The generated code used CombinedOutput which is easier to used but doesn't do proper error handling. Everything ran fine until I tested a few error cases and then realized the problem. In other times I asked the agent to write tests cases too and while it scaffolded code to handle error cases, it didn't actually write any tests cases to exercise that - so if you were only doing a cursory review, you would think it was properly tested when in reality it wasn't.
1. You can iteratively improve the rules and prompts you give to the AI when coding. I do this a lot. My process is constantly improving, and the AI makes fewer mistakes as a result.
2. AI models get smarter. Just in the past few months, the LLMs I use to code are making significantly fewer mistakes than they were.
One concrete example is confusing value and pointer types in C. I've seen people try to cast a `uuid` variable into a `char` buffer to, for example, memset it, by doing `(const char *)&uuid)`. It turned out, however, that `uuid` was not a value type but rather a pointer, and so this ended up just blasting the stack because instead of taking the address of the uuid storage, it's taking the address of the pointer to the storage. If you're hundreds of lines deep and are looking for more complex functional issues, it's very easy to overlook.
2. It's very questionable that they will get any smarter, we have hit the plateau of diminishing returns. They will get more optimized, we can run them more times with more context (e.g. chain of thought), but they fundamentally won't get better at reasoning.
On the foolishness of "natural language programming"
https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...
The improved prompt or project documentation guides every future line of code written, whether by a human or an AI. It pays dividends for any long term project.
> Like there is a reason we are not using fuzzy human language in math/coding
Math proofs are mostly in English.
Discovering private api using an agent is super useful.
One of my most productive uses of LLMs was when designing a pipeline from server-side data to the user-facing UI that displays it.
I was able to define the JSON structure and content, the parsing, the internal representation, and the UI that the user sees, simultaneously. It was very powerful to tweak something at either end and see that change propagate forwards and backwards. I was able to hone in on a good solution much faster that it would have been the case otherwise.
Think of it like being a cook in a restaurant. The order comes in. The cook plans the steps to complete the task of preparing all the elements for a dish. The cook sears the steak and puts it in the broiler. The cook doesn't stop and wait for the steak to finish before continuing. Rather the cook works on other problems and tasks before returning to observe the steak. If the steak isn't finished the cook will return it to the broiler for more cooking. Otherwise the cook will finish the process of plating the steak with sides and garnishes.
The LLM is like the oven, a tool. Maybe grating cheese with a food processor is a better analogy. You could grate the cheese by hand or put the cheese into the food processor port in order to clean up, grab other items from the refrigerator, plan the steps for the next food item to prepare. This is the better analogy because grating cheese could be done by hand and maybe does have a better quality but if it is going into a sauce the grain quality doesn't matter so several minutes are saved by using a food processor which frees up the cook's time while working.
Professional cooks multitask using tools in parallel. Maybe coding will move away from being a linear task writing one line of code at a time.
One caveat I wonder about is how this kind of constant context switching combines with the need to think deeply (and defensively with non humans). My gut says I'd struggle at also being the brain at the end of the day instead of just the director/conductor.
I've actively paired with multiple people at once before because of a time crunch (and with a really solid team). It was, to this day, the most fun AND productive "I" have ever been and what you're pitching aligns somewhat with that. HOWEVER, the two people who were driving the keyboards were substantially better engineers than me (and faster thinkers) so the burden of "is this right" was not on me in the way it is when using LLMs.
I don't have any answers here - I see the vision you're pitching and it's a very very powerful one I hope is or becomes possible for me without it just becoming a way to burn out faster by being responsible for the deep understanding without the time to grok it.
That was my favorite part of being a professional cook, working closely on a team.
Humans are social animals who haven't -- including how our brains are wired -- changed much physiologically in the past 25,000 years. Smart people today are not much smarter than smart people in Greece 3,000 years ago, except for the sample size of 8B people being larger. We are wired to work in groups like hunters taking down a wooly mammoth.[0]
[0] https://sc.edu/uofsc/images/feature_story_images/2023/featur...
Societal smartness might be something like an average student knowing that we are made from cells, some germ theory over bodily fluid inbalances causing diseases, etc, very crude understanding of more elements of physics (electronics). Though unfortunately intellectualism is on a fall, and people come out dumber and dumber from schools all over the world.
The invention and innovation of language, agriculture, writing, and mathematics has driven the change in neuroplasticity remodeling, but the overall structure of the brain hasn't changed.
Often in modern societal structures there has been pruning of intellectuals, i.e. the intelligent members of a society are removed from the gene pool, sent to Siberia. However, that doesn't stop the progeneration of humans capable of immense intelligence with training and development, it only removes the culture being passed down.
And, I say, with strong emphasis, not only has the brain of humans been similar for 25,000 years, the potential for sharpening our abilities in abstract reasoning, memory, symbolic thought, and executive control is *equal* across all sexes and races in humans today. Defending that statement is a hill I'm willing to die on.
"Mindset" by Carol Dweck is a good read.
Leonardo Da Vinci would be a PhD student working on some obscure sub-sub-sub field of something and only 6 other people on the world understanding how marvelously genius he is. The reason they don't get to such a status is that human knowledge is like a circle. A single person can work on the circumference of this circle, but they are limited by what they can learn of this circle. As society improved, we have expanded the radius of the circle greatly, and now an expert can only be an expert in a tiny tiny blob on the circumference, while Leonardo could "see" a good chunk of the whole circle.
---
"Thought leader and inventor" are VC terms of no substance and are 100% not who I would consider smart people on average. Luck is a much more common attribute among them.
I do this "let it go do the crap while I think about what to do next" somewhat frequently. But it's mostly for easy crap around the edges (making tools to futz with logs or metrics, writing queries, moving things around). The failure rate for my actual day job code just is too high, even for non-rocket-science stuff. It's usually more frustrating to spend 5 minutes chatting with the agent and then fixing it's stuff than to just spend 5 minutes writing the code.
Cause the bot has all the worst bits of human interactions - like ambiguous incomplete understanding - without the reward of building a long-term social relationship. That latter thing is what I'm wired for.
At the same time refactoring probably takes 10 minutes with AI.
See, also, testing. There's a lot of similar boilerplate for testing. Giving LLMs a list of "Test these specific items, with this specific setup, and these edge cases." I've been pretty happy writing a bulleted outline of tests and getting ... 85% complete code back? You can see a pretty stark line in a codebase I work on where I started doing this vs comprehensiveness of testing.
This is a case where a monorepo should be a big advantage, as you can update everything with a single change.
It seems silly but it’s opened up a lot of extra time for some of this stuff. Heck, I even play my guitar more, something I’ve neglected for years. Noodle around while I wait for Claude to finish something and then I review it.
All in all, I dig this new world. But I also code JS web apps for a living, so just about the easiest code for an LLM to tackle.
EDIT: Though I think you are asking about work specifically. i.e., does management recognize your contributions and reward you?
For me, no. But like I said, I get more done at work and more done at home. It’s weird. And awesome.
Experience shows otherwise. Urging me to embrace a new way of building software that is predicated on benevolent vendors and management seems hostile to me.
A 60x speedup is way more than I've seen even in its best case for things like that.
Like I mentioned, it's literally just global find and replace.
Slightly embarrassing thing to have even asked Cursor to do for me, in retrospect. But, you know, you get used to the tool and to being lazy.
GitHub's value proposition was that mediocre coders can appear productive in the maze of PRs, reviews, green squares, todo lists etc.
LLMs again give mediocre coders the appearance of being productive by juggling non-essential tools and agents (which their managers also love).
A better name for vibecoding would be larpcoding, because you are doing a live action role-play of managing a staff of engineers.
Now not only even a junior engineer can become a manager, they will start off their careers managing instead of doing. Terrifying.
From this perspective, English-as-spec is a natural progression in the direction we’ve been going all along.
Friction.
A lot of people are bad at getting started (like writer's block, just with code), whereas if you're given a solution for a problem, then you can tweak it, refactor it and alter it in other ways for your needs, without getting too caught up in your head about how to write the thing in the first place. Same with how many of my colleagues have expressed that getting started on a new project from 0 is difficult, because you also need to setup the toolchain and bootstrap a whole app/service/project, very similar to also introducing a new abstraction/mechanism in an existing codebase.
Plus, with LLMs being able to process a lot of data quickly, assuming you have enough context size and money/resources to use that, it can run through your codebase in more detail and notice things that you might now, like: "Oh hey, there are already two audit mechanisms in the codebase in classes Foo and Bar, we might extract the common logic and..." that you'd miss on your own.
> I still don't understand the benefit of relying on someone/something else to write your code and then reading it, understand it, fixing it, etc.
What they're saying is that they never have coworkers.
Spending the whole day chatting with AI agents sounds like a worst-of-both-worlds scenarios. I have to bring all of my complex, subtle soft skills into play which are difficult and tiring to use, and in the end none of that went towards actually fostering real relationships with real people.
At the end of the day, are you gonna have a beer with your agents and tell them, "Wow, we really knocked it out of the park today?"
Spending all day talking to virtual coworkers is literally the loneliest experience I can imagine, infinitely worse than actually coding in solitude the entire day.
EDIT: You can quibble on the exact rate of people's worth of work versus the cost of these tools, but look at what a single seat on Copilot or Cursor or Windsurf gets you, and you can see that if they are only barely more productive than you working without them, the economics are it's cheaper to "hire" virtual juniors than real juniors. And the virtual juniors are getting better by the month, go look at the Aider leaderboards and compare recent models to older ones.
If my employer said, "Hey, you're going to keep making software, but also once a day, we have to slap you in the face." I might choose to keep the job, but they'd probably have to pay me more. They're making the work experience worse and that lowers my total compensation package.
Shepherding an army of artificial minions might be cheaper for the corporation, but it sounds like an absolutely miserable work experience so if they were offering me that job, they'd have to pay me more to take.
But when I do code reviews, I don't enjoy reviewing the code itself at all. The enjoyment I get out of the process comes from feeling like I'm mentoring an engineer who will remember what I say in the code review.
If I had to spend a month doing code reviews where every single day I have to tell them the exact same corrections, knowing they will never ever learn, I would quit my job.
Being a lead over an army of enthusiastic interns with amnesia is like the worst software engineering job I can imagine.
* the wall of how much you can review in one day without your quality slipping now that there's far less variation in your day
* the long-term planning difficulties around future changes when you are now the only human responsible for 5-20x more code surface area
* the operational burden of keeping all that running
The tools might get good enough that you only need 5 engineers to do what used to be 10-20. But the product folks aren't gonna stop wanting you to keep churning out the changes, and the last 2 years of evolution of these models doesn't seem like it's on a trajectory to cut that down to 1 (or 0) without unforeseen breakthroughs.
public T MyMethod<T>(/*args*/) /*type constraints*/
{
//TODO: Implement this method using the following requirements:
//1 ...
//2 ...
//...
}
It could do this in code. I didn't have to type anywhere near as much and 1.5 sets of eyes were on it. It did a pretty accurate job and the followup pass was better.
This is just an example I had time to type before my morning shower
Another way to look at this is you’re outsourcing your understanding to something that ultimately doesn’t think.
This means 2 things: your solution could be severely suboptimal in multiple areas such as security and two because you didn’t bother understanding it yourself you’ll never be able to identify that.
You might think “that’s fine, the LLM can fix it”. The issue with that is when you don’t know enough to know something needs to be fixed.
So maybe instead of carts and oxen this is more akin to grandpa taking his computer to Best Buy to have them fix it for him?
I see this take brought up quite a bit and it’s honestly just plain wrong.
For starters Junior engineers can be held accountable. What we see currently is people leaving gaping holes in software and then pointing at the LLM which is an unthinking tool. Not the same.
Juniors can and should be taught as that is what causes them to progress not only in SD but also gets them familiar with your code base. Unless your company is a CRUD printer you need that.
More closely to the issue at hand this is assuming the “senior” dev isn’t just using an LLM as well and doesn’t know enough to critique the output. I can tell you that juniors aren’t the ones making glaring mistakes in terms of security when I get a call.
So, no, not the same. The argument is that you need enough knowledge of the subject call bs to effectively use these tools.
This is no different than, say, the typical anecdote of a junior engineer dropping the database. Should the junior be held accountable? Of course not - it's the senior's fault for allowing that to happen at the first place. If the junior is held accountable, that would more be an indication of poor software engineering practices.
> More closely to the issue at hand this is assuming the “senior” dev isn’t just using an LLM as well and doesn’t know enough to critique the output.
This seems to miss the point of the analogy. A senior delegating to a junior is akin to me delegating to an LLM. Seniors have delegated to juniors long before LLMs were a twinkle in Karpathy's eye.
Of course the junior should be held accountable, along with the senior. Without accountability, what incentive do they have to not continue to fuck up?
Dropping the database is an extreme example because it's pretty easy to put in checks that should make that impossible. But plenty of times I've seen juniors introduce avoidable bugs simply because they did not bother to test their code -- that is where teaching accountability is a vital part of growth as an engineer.
You read this quote wrong. Senior devs outsource _work_ to junior engineers, not _understanding_. The way they became senior in the first place is by not outsourcing work so they could develop their understanding.
> I review every small update and correct it when needed
How can you review something that you don't know? How do you know this is the right/correct result beyond "it looks like it works"?
Or you’re assembling prefab plywood homes while they’re building marble mansions. It’s easy to pick metaphors that fit your preferred narrative :)
Which one are there more of nowadays, hm?
If you haven't learned how all this stuff works, how are you able to be confident in your corrections?
> I’m driving a tractor while you are pulling an ox cart.
Are you sure you haven't just duct taped a jet engine to your ox cart?
You just hope you are on a tractor.
LLM is a teacher that can help you learn by doing the work you want to be doing and not some fake exercise.
The more you learn though, the more you review the code produced by the LLM and the more you'll notice that you are still able to reason better than an LLM and after your familiarity with an area exceeds the capabilities of the LLM the interaction with the LLM will bring diminishing returns and possibly the cost of babysitting that eager junior developer assistant may become larger than the benefits.
But that's not a problem, for all areas you master there will be hundreds of other areas you haven't mastered yet or ever will and for those things the LLM we have already today are of immediate help.
All this without even having to enter the topic of how coding assistants will improve in the future.
TL;DR
Use a tool when it helps. Don't use it when it doesn't. It pays to learn to use a tool so you know when it helps and when it doesn't. Just like every other tool
And you can't ask "why" about a decision you don't understand (or at least, not with the expectation that the answer holds any particular causal relationship with the actual reason)... so it's like reviewing a PR with no trust possible, no opportunity to learn or to teach, and no possibility for insight that will lead to a better code base in the future. So, the exact opposite of reviewing a PR.
Instead of the downcast, chastened look of a junior developer, it responds with a bulleted list of the reasons why it did it that way.
Maybe this is the source of the confusion between us? If I see someone writing overly convoluted code to avoid an allocation, and I ask why, I will take different actions based on those two answers! If I get the answer "because it avoids an allocation," then my role as a reviewer is to educate the code author about the trade-off space, make sure that the trade-offs they're choosing are aligned with the team's value assessments, and help them make more-aligned choices in the future. If I get the answer "because Bob told me I should," then I need to both address the command chain issues here, and educate /Bob/. An answer is "useful" in that it allows me to take the correct action to get the PR to the point that it can be submitted, and prevents me from having to make the same repeated effort on future PRs... and truth actually /matters/ for that.
Similarly, if an LLM gives an answer about "why" it made a decision that I don't want in my code base that has no causal link to the actual process of generating the code, it doesn't give me anything to work with to prevent it happening next time. I can spend as much effort as I want explaining (and adding to future prompts) the amount of code complexity we're willing to trade off to avoid an allocation in different cases (on the main event loop, etc)... but if that's not part of what fed in to actually making that trade-off, it's a waste of my time, no?
> it's like reviewing a PR with no trust possible, no opportunity to learn or to teach, and no possibility for insight that will lead to a better code base in the future
The first part is 100% true. There is no trust. I treat any LLM code as toxic waste and its explanations as lies until proven otherwise.
The second part I disagree somewhat. I've learned plenty of things from AI output and analysis. You can't teach it to analyze allocations or code complexity, but you can feed it guidelines or samples of code in a certain style and that can be quite effective at nudging it towards similar output. Sometimes that doesn't work, and that's fine, it can still be a big time saver to have the LLM output as a starting point and tweak it (manually, or by giving the agent additional instructions).
To be fair, humans are also very capable of post-hoc rationalization (particularly when they're in a hurry to churn out working code).
But yes, these juniors take minutes versus days or weeks to turn stuff around.
At a certain point you won’t have to read and understand every line of code it writes, you can trust that a “module” you ask it to build works exactly like you’d think it would, with a clearly defined interface to the rest of your handwritten code.
"A certain point" is bearing a lot of load in this sentence... you're speculating about super-human capabilities (given that even human code can't be trusted, and we have code review processes, and other processes, to partially mitigate that risk). My impression was that the post you were replying to was discussing the current state of the art, not some dimly-sensed future.
The latter category is totally enamored with LLMs, and I can see the appeal: they don't care at all about the quality or maintainability of the project after it's signed off on. As long as it satisfies most of the requirements, the llm slop / spaghetti is the client's problem now.
The former category (like me, and maybe you) see less value from the LLMs. Although I've started seeing PRs from more junior members that are very obviously written by AI (usually huge chunks of changes that appear well structured but as soon as you take a closer look you realize the "cheerleader effect"... it's all AI slop, duplicated code, flat-out wrong with tests modified to pass and so on) I still fail to get any value from them in my own work. But we're slowly getting there, and I presume in the future we'll have much more componentized code precisely for AIs to better digest the individual pieces.
Do you work for yourself, or for a (larger than 1 developer) company? You mention you only code for your own tools, so I am guessing yourself?
I don't necessarily like reading other people's code either, but across a distributed team, it's necessary - and sometimes I'm also inspired when I learn something new from someone else. I'm just curious if you've run into any roadblocks with this mindset, or if it's just preference?
It's easier to read a language you're not super comfortable with, than it is to write it.
Maybe the key is this: our brains are great at spotting patterns, but not so great at remembering every little detail. And a lot of coding involves boilerplate—stuff that’s hard to describe precisely but can be generated anyway. Even if we like to think our work is all unique and creative, the truth is, a lot of it is repetitive and statistically has a limited number of sound variations. It’s like code that could be part of a library, but hasn’t been abstracted yet. That’s where AI comes in: it’s really good at generating that kind of code.
It’s kind of like NP problems: finding a solution may take exponentially longer, but checking one takes only polynomial time. Similarly, AI gives us a fast draft that may take a human much longer to write, and we review it quickly. The result? We get more done, faster.
The bottle neck is in the architecture and the details. Which is exactly what AI gets wrong, and which is why any engineer who respects his craft sees this snake oil for what it is.
I agree entirely and generally avoided LLMs because they couldn't be trusted. However a few days ago i said screw it and purchased Claude Max just to try and learn how i can use LLMs to my advantage.
So far i avoid it for things where they're vague, complex, etc. The effort i have to go through to explain it exceeds my own in writing it.
However for a bunch of things that are small, stupid, wastes of time - i find it has been very helpful. Old projects that need to migrate API versions, helper tools i've wanted but have been too lazy to write, etc. Low risk things that i'm too tired to do at the end of the day.
I have also found it a nice way to get movement on projects where i'm too tired to progress on after work. Eg mostly decision fatigue, but blank spaces seem to be the most difficult for me when i'm already tired. Planning through the work with the LLM has been a pretty interesting way to work around my mental blocks, even if i don't let it do the work.
This planning model is something i had already done with other LLMs, but Claude Code specifically has helped a lot in making it easier to just talk about my code, rather than having to supply details to the LLM/etc.
It's been far from perfect of course, but i'm using this mostly to learn the bounds and try to find ways to have it be useful. Tricks and tools especially, eg for Claude adding the right "memory" adjustments to my preferred style, behaviors (testing, formatting, etc) has helped a lot.
I'm a skeptic here, but so far i've been quite happy. Though i'm mostly going through low level fruit atm, i'm curious if 20 days from now i'll still want to renew the $100/m subscription.
I'll also use it to create basic DAOs from schemas, things like that.
When you read code, you can allocate your time to the parts that are more complex or important.
You could insert sanity checks by humans at various points but are any of these tasks outside the capabilities of an LLM?