Comment by zOneLetter

Here are the cases where it helps me (I promise this isn't ai generated even though im using a list...)

- 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.

I am beginning to love working like this. Plan a design for code. Explain to the LLM the steps to arrive to a solution. Work on reading, understanding, fixing, planing, ect. while the LLM is working on the next section of code. We are working in parallel.

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.

On one codebase I work with, there are often tasks that involve changing multiple files in a relatively predictable way. Like there is little creativity/challenge, but a lot of typing in multiple parts/files. Tasks like these used to take 3-4 hours complete before just because I had to physically open all these files, find right places to modify, type the code etc. With AI agent I just describe the task, and it does the job 99% correct, reducing the time from 3-4 hours to 3-4 minutes.

Some people cannot do anything without a tool. These people are early adopters and power users, who then evangelize their latest discovery.

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).

I felt the same way until recently (like last Friday recently). While tools like Windsurf / Cursor have some utility, most of the time I am just waiting around for them while I get to read and correct the output. Essentially, I'm helping out with the training while paying to use the tool. However, now that Codex is available in ChatGPT plus, I appreciate that asynchronous flow very much. Especially for making small improvements , fixing minor bugs, etc. This has obvious value imo. What I like to do is queue up 5 - 10 tasks and the. focus on hard problems while it is working away. Then when I need a break I review / merge those PRs.

I kinda consider it a P!=nP type thing. If I need to write a simple function, it will almost always take me more time to implement it than it will to verify if an implementation of it suits my needs. There are exceptions, but overall when coding with LLMs this seems to hold true. Asking the LLM to write the function then checking it's work is a time saver.

> 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.

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.

As a senior developer you already spend a significant amount of time planning new feature implementations and reviewing other people's code (PRs). I find that this skill transitions quite nicely to working with coding agents.

My most productive use of LLMs has been to stub out individual methods and have them fill in the implementations. I use a prompt like:

  public T MyMethod<T>(/*args*/) /*type constraints*/
  {
    //TODO: Implement this method using the following requirements:
    //1 ...
    //2 ...
    //...
  }

Anything beyond this and I can't keep track of which rabbit is doing what anymore.

I'm categorizing my expenses. I asked the code AI to do 20 at a time, and suggest categories for all of them in an 800 line file. I then walked the diff by hand correcting things. I then asked it to double check my work. It did this in a 2 column cav mapping.

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

You’re clinging to an old model of work. Today an LLM converted my docker compose infrastructure to Kubernetes, using operators and helm charts as needed. It did in 10 minutes what would take me several days to learn and cobble together a bad solution. I review every small update and correct it when needed. It is so much more productive. I’m driving a tractor while you are pulling an ox cart.

if you work on a team most code you see isn’t yours.. ai code review is really no different than reviewing a pr… except you can edit the output easier and maybe get the author to fix it immediately

Just to draw a parallel (not to insult this line of thinking in any way): “ Maybe it's because I only code for my own tools, but I still don't understand the benefit of relying on someone/something else to _compile_ your code and then reading it, understand it, fixing it, etc”

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.

I think there are 2 types of software engineering jobs: the ones where you work on a single large product for a long time, maintaining it and adding features, and the ones that spit out small projects that they never care for again.

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.

> I just don't like reading other people's code lol.

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?

The LLM has a much larger "working vocabulary" (so to speak) than I. It's more fluent.

It's easier to read a language you're not super comfortable with, than it is to write it.

Fast prototyping for code I'll throw away anyway. Sometimes I just want to get something to work as a proof of concept then I'll figure out how to productionize it later.

It is just faster and less effort. I can't write code as quickly as the LLM can. It is all in my head, but I can't spit it out as quickly. I just see LLMs as getting what is in my head quickly out there. I have learned to prompt it in such a way that I know what to expect, I know its weakspots and strengths. I could predict what it is going output, so it is not that difficult to understand.

> I still don't understand the benefit of relying on someone/something else to write your code and then reading 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.

It's an intentional (hopefully) tradeoff between development speed and deep understanding. By hiring someone or using an agent, you are getting increased speed for decreased understanding. Part of choosing whether or not to use an agent should include an analysis of how much benefit you get from a deep understanding of the subsystem you're currently working on. If it's something that can afford defects, you bet I'll get an agent to do a quick-n-dirty job.

> I just don't like reading other people's code lol.

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 use it almost like an RSI mitigation device, for tasks I can do (and do well) but don't want to do anymore. I don't want to write another little 20-line script to format some data, so I'll have the machine do it for me.

I'll also use it to create basic DAOs from schemas, things like that.

If you give a precise enough spec, it's effectively your code, with the remaining difference being inconsequential. And in my experience, it is often better, drawing from a wider pool of idioms.

When you write code, you have to spend time on ALL of the code, no matter how simple or obvious it is.

When you read code, you can allocate your time to the parts that are more complex or important.

No different than most practices now. PM write a ticket, dev codes it, PRs it, then someone else reviews it. Not a bad practice. Sometimes a fresh set of eyes really helps.

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