Letting a robot write code for me, however tedious it would be to write manually, made me feel like I was working in someone else's codebase. It reminds me of launching a videogame and letting someone else play through the boring parts. I might as well not be playing. Why bother at all?

I understand this behaviour if you're working for a company on some miserable product, but not for personal projects.

LLM-agents have made making products, especially small ones, a lot easier, but sacrifice much of the crafting of details and, if the project is small enough, the architecture. I've certainly enjoyed using them a lot over the last year and a half, but I've come to really miss fully wrapping my head around a problem, having intimate knowledge of the details of the system, and taking pride in every little detail.

edit - an interesting facet of AI progress is that the split between these two types of work gets more and more granular. It has led me to actively be aware of what I'm doing as I work, and to critically examine whether certain mechanics are inherently toilistic or creative. I realized that a LOT of what I do feels creative but isn't - the manner in which I type, the way I shape and format code. It's more in the manner of catharsis than creation.

> That includes code outside of the happy path, like error handling and input validation. But also other typing exercises like processing an entity with 10 different types, where each type must be handled separately. Or propagating one property through the system on 5 different types in multiple layers.

With AI, I feel I'm less caught up in the minutia of programming and have more cognitive space for the fun parts: engineering systems, designing interfaces and improving parts of a codebase.

I don't mind this new world. I was never too attached to my ability to pump out boilerplate at a rapid pace. What I like is engineering and this new AI world allows me to explore new approaches and connect ideas faster than I've ever been able to before.

Just don't expect to run a successful restaurant based on it.

And there was something nice about still having to put in the manual work in those cases. It let me process what the code is actually doing and gave me the opportunity to internalize it in a way that just doesn't happen with AI. It also sort of gave me a thinking break where I was engaged at just the right level to let the thoughts about the more interesting parts float around in my head. With AI writing all the code, I feel like I'm either fully engaged with those thoughts or not engaged at all. And that's a bit of a problem because aha moments often happen when the idea is in that middle area of thought.

I'm excited to work on more things that I've been curious about for a long time but didn't have the time/energy to focus on.

I’m working on library code in zig, and it’s very nice to have AI write the FFI interface with python. That’s not technically difficult or high risk, but it is tedious and boring.

Realistically having a helper to get me over slumps like that has been amazing for my personal productivity.

AI takes the craft out of being an IC. IMO less enjoyable.

AI takes the human management out of being an EM. IMO way more enjoyable.

Now I can direct large-scope endeavors and 100% of my time is spent on product vision and making executive decisions. No sob stories. No performance reviews. Just pure creative execution.

More concrete examples to illustrate the core points would have been helpful. As-is the article doesn't offer much - sorry.

For one, I am not sure what kind of code he writes? How does he write tests? Are these unit tests, property-based tests? How does he quantify success? Leaves a lot to be desired.

What's worse, the more I rely on the bot, the less my internal model of the code base is reinforced. Every problem the bot solves, no matter how small, doesn't feel like a problem I solved and understanding I'd gained, it feels like I used a cheat code to skip the level. And passively reviewing the bot's output is no substitute for actively engaging with the code yourself. I can feel the brainrot set in bit by bit. It's like I'm Bastian making wishes on AURYN and losing a memory with every wish. I might get a raw-numbers productivity boost now, but at what cost later?

I get the feeling that the people who go on about how much fun AI coding is either don't actually enjoy programming or are engaging in pick-me behavior for companies with AI-use KPIs.

This is too low level. You’d be better off describing the things that need testing and asking for it to do red/green test-driven development (TDD). Then you’ll know all the tests are needed, and it’ll decide what tests to write without your intervention, and make them pass while you sip coffee :)

> I don’t trust it yet is when code must be copy pasted.

Ask it to perform the copy-paste using code - have it write and execute a quick script. You can review the script before it runs and that will make sure it can’t alter details on the way through.

I legitimately enjoy scale practice when I'm playing piano. In a similar way I've always found some pleasure in writing boilerplate and refactoring.

There is joy/peace and instructive value in the "boring" parts of almost every discipline. It's perhaps more meditative and subtle, but still very much there in abundance. And primes you much better for a real flow state.

Ever since AI exploded at my day job, I haven't legitimately been in anything resembling a programming flow state at work.

I feel more like a software producer or director than an engineer though.

imo, this isn't paranoid at all, and it very likely filters through the LLM, unless you provide a tool/skill and explicit instructions. Even then you're rolling the dice, and the diff will have to be checked.

My work often entails tweaking, fixing, extending of some fairly complex products and libraries, and AI will explain various internal mechanisms and logic of those products to me while producing the necessary artifacts.

Sure my resulting understanding is shallow, but shallow precedes deep, and without an AI "tutor", the exploration would be a lot more frustrating and hit-and-miss.

These kind of pain points usually indicated too much of or a wrong architecture. Being able to fee these kind of things when the clanker does the work is a thing we must think about.

Just like how, in writing a story, a writer must also toil over each sentence, and should this be an emdash or a comma? and should I break the paragraph here or there? All this minutia is just as important to the final product as grand ideas and architecture are.

If you don't care about those little details, then fine. But you sacrifice some authorship of the program when you outsource those things to an agent. (And I would say, you sacrifice some quality as well).

This is the hidden super power of LLM - prototyping without attachment to the outcome.

Ten years ago, if you wanted to explore a major architectural decision, you would be bogged down for weeks in meetings convincing others, then a few more weeks making it happen. Then if it didn't work out, it feels like failure and everyone gets frustrated.

Now it's assumed you can make it work fast - so do it four different ways and test it empirically. LLMs bring us closer to doing actual science, so we can do away with all the voodoo agile rituals and high emotional attachment that used to dominate the decision process.

Pick your favorite GoF design pattern. Is that they best way to do it for the computer or the best way to do it for the developer?

I'm just making this up now, maybe it's not the greatest example; but, let's consider the "visitor" pattern.

There's some framework that does a big loop and calls the visit() function on an object. If you want to add a new type, you inherit from that interface, put visit() on your function and all is well. From a "good" engineering practice, this makes sense to a developer, you don't have to touch much code and your stuff lives in it's own little area. That all feels right to us as developers because we don't have a big context window.

But what if your code was all generated code, and if you want to add a new type to do something that would have been done in visit(). You tell the LLM "add this new functionality to the loop for this type of object". Maybe it does a case statement and puts the stuff right in the loop. That "feels" bad if there's a human in the loop, but does it matter to the computer?

Yes, we're early LLMs aren't deterministic, and verification may be hard now. But that may change.

In the context of a higher-level language, y=x/3 and y=x/4 look the same, but I bet the generated assembly does a shift on the latter and a multiply-by-a-constant on the former. While the "developer interface", the source code, looks similar (like writing to a visitor pattern), the generated assembly will look different. Do we care?