Comment by b33j0r
12 days ago
I usually do most of the engineering and it works great for writing the code. I’ll say:
> There should be a TaskManager that stores Task objects in a sorted set, with the deadline as the sort key. There should be methods to add a task and pop the current top task. The TaskManager owns the memory when the Task is in the sorted set, and the caller to pop should own it after it is popped. To enforce this, the caller to pop must pass in an allocator and will receive a copy of the Task. The Task will be freed from the sorted set after the pop.
> The payload of the Task should be an object carrying a pointer to a context and a pointer to a function that takes this context as an argument.
> Update the tests and make sure they pass before completing. The test scenarios should relate to the use-case domain of this project, which is home automation (see the readme and nearby tests).
I feel that with such an elaborated description you aren't too far away from writing that yourself.
If that's the input needed, then I'd rather write code and rely on smarter autocomplete, so meanwhile I write the code and think about it, I can judge whether the LLM is doing what I mean to do, or straying away from something reasonable to write and maintain.
Yeah, I feel like I get really good results from AI, and this is very much how I prompt as well. It just takes care of writing the code, making sure to update everything that is touched by that code guided by linters and type-checkers, but it's always executing my architecture and algorithm, and I spend time carefully trying to understand the problem before I even begin.
But this is what I don't get. Writing code is not that hard. If the act of physically typing my code out is a bottleneck to my process, I am doing something wrong. Either I've under-abstracted, or over-abstracted, or flat out have the wrong abstractions. It's time to sit back and figure out why there's a mismatch with the problem domain and come back at it from another direction.
To me this reads like people have learned to put up with poor abstractions for so long that having the LLM take care of it feels like an improvement? It's the classic C++ vs Lisp discussion all over again, but people forgot the old lessons.
> Writing code is not that hard.
It's not that hard, but it's not that easy. If it was easy, everyone would be doing it. I'm a journalist who learned to code because it helped me do some stories that I wouldn't have done otherwise.
But I don't like to type out the code. It's just no fun to me to deal with what seem to me arbitrary syntax choices made by someone decades ago, or to learn new jargon for each language/tool (even though other languages/tools already have jargon for the exact same thing), or to wade through someone's undocumented code to understand how to use an imported function. If I had a choice, I'd rather learn a new human language than a programming one.
I think people like me, who (used to) code out of necessity but don't get much gratification out of it, are one of the primary targets of vibe coding.
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I think of it more like moving from sole developer to a small team lead. Which I have experienced in my career a few times.
I still write my code in all the places I care about, but I don’t get stuck on “looking up how to enable websockets when creating the listener before I even pass anything to hyper.”
I do not care to spend hours or days to know that API detail from personal pain, because it is hyper-specific, in both senses of hyper-specific.
(For posterity, it’s `with_upgrades`… thanks chatgpt circa 12 months ago!)
It's not hard, but it's BORING.
I get my dopamine from solving problems, not trying to figure out why that damn API is returning the wrong type of field for three hours. Claude will find it out in minutes - while I do something else. Or from writing 40 slightly different unit tests to cover all the edge cases for said feature.
> it's time to sit back and figure out why there's a mismatch with the problem domain and come back at it from another direction
But this is exactly what LLMs help me with! If I decide I want to shift the abstractions I'm using in a codebase in a big way, I'd usually be discouraged by all the error, lint, and warning chasing I'd need to do to update everything else; with agents I can write the new code (or describe it and have it write it) and then have it set off and update everything else to align: a task that is just varied and context specific enough that refactoring tools wouldn't work, but is repetitive and time consuming enough that it makes sense to pass off to a machine.
The thing is that it's not necessarily a bottleneck in terms of absolute speed (I know my editor well and I'm a fast typist, and LLMs are in their dialup era) but it is a bottleneck in terms of motivation, when some refactor or change in algorithm I want to make requires a lot of changes all over a codebase, that are boring to make but not quite rote enough to handle with sed or IDE refactoring. It really isn't, for me, even mostly about the inconvenience of typing out the initial code. It's about the inconvenience of trying to munge text from one state to another, or handle big refactors that require a lot of little mostly rote changes in a lot of places; but it's also about dealing with APIs or libraries where I don't want to have to constantly remind myself what functions to use, what to pass as arguments, what config data I need to construct to pass in, etc, or spend hours trawling through docs to figure out how to do something with a library when I can just feed its source code directly to an LLM and have it figure it out. There's a lot of friction and snags to writing code beyond typing that has nothing to do with having come up with a wrong abstraction, that very often lead to me missing the forest for the trees when I'm in the weeds.
Also, there is ALWAYS boilerplate scaffolding to do, even with the most macrotastic Lisp; and let's be real: Lisp macros have their own severe downsides in return for eliminating boilerplate, and Lisp itself is not really the best language (in terms of ecosystem, toolchain, runtime, performance) for many or most tasks someone like me might want to do, and languages adapted to the runtime and performance constraints of their domain may be more verbose.
Which means that, yes, we're using languages that have more boilerplate and scaffolding to do than strictly ideally necessary, which is part of why we like LLMs, but that's just the thing: LLMs give you the boilerplate eliminating benefits of Lisp without having to give up the massive benefits in other areas of whatever other language you wanted to use, and without having to write and debug macro soup and deal with private languages.
There's also how staying out of the code writing oar wells changes how you think about code as well:
If you think through a problem as you're writing the code for it, you're going to end up the wrong creek because you'll have been furiously head down rowing the entire time, paying attention to whatever local problem you were solving or whatever piece of syntax or library trivia or compiler satisfaction game you were doing instead of the bigger picture.
Obviously, before starting writing, you could sit down and write a software design document that worked out the architecture, the algorithms, the domain model, the concurrency, the data flow, the goals, the steps to achieve it and so on; but the problem with doing that without an agent is then it becomes boring. You've basically laid out a plan ahead of time and now you've just got to execute on the plan, which means (even though you might even fairly often revise the plan as you learn unknown unknowns or iterate on the design) that you've kind of sucked all the fun and discovery out of the code rights process. And it sort of means that you've essentially implemented the whole thing twice.
Meanwhile, with a coding agent, you can spend all the time you like building up that initial software design document, or specification, and then you can have it implement that. Basically, you can spend all the time in your hammock thinking through things and looking ahead, but then have that immediately directly translated into pull requests you can accept or iterate on instead of then having to do an intermediate step that repeats the effort of the hammock tim
What you’re describing makes sense, but that type of prompting is not what people are hyping
The more accurate prompt would be “You are a mind reader. Create me a plan to create a task manager, define the requirements, deploy it, and tell me when it’s done.”
And then you just rm -rf and repeat until something half works.
"Here are login details to my hosting and billing provider. Create me a SaaS app where customers could rent virtual pets. Ensure it's AI and blockchain and looks inviting and employ addictive UX. I've attached company details for T&C and stuff. Ensure I start earning serious money by next week. I'll bump my subscription then if you deliver, and if not I will delete my account. Go!"
I haven't tried it, but someone at work suggested using voice input for this because it's so much easier to add details and constraints. I can certainly believe it, but I hate voice interfaces, especially if I'm in an open space setting.
You don't even have to be as organised as in the example, LLMs are pretty good at making something out of ramblings.
This is a good start. I write prompts as if I was instructing junior developer to do stuff I need. I make it as detailed and clear as I can.
I actually don't like _writing_ code, but enjoy reading it. So sessions with LLM are very entertaining, especially when I want to push boundaries (I am not liking this, the code seems a little bit bloated. I am sure you could simplify X and Y. Also think of any alternative way that you reckon will be more performant that maybe I don't know about). Etc.
This doesn't save me time, but makes work so much more enjoyable.
> I actually don't like _writing_ code, but enjoy reading it.
I think this is one of the divides between people who like AI and people who don't. I don't mind writing code per se, but I really don't like text editing — and I've used Vim (Evil mode) and then Emacs (vanilla keybindings) for years, so it's not like I'm using bad tools; it's just too fiddly. I don't like moving text around; munging control structures from one shape to another; I don't like the busy work of copying and pasting code that isn't worth DRYing, or isn't capable of being DRY'd effectively; I hate going around and fixing all the little compiler and linter errors produced by a refactor manually; and I really hate the process of filling out the skeleton of an type/class/whatever architecture in a new file before getting to the meat.
However, reading code is pretty easy for me, and I'm very good at quickly putting algorithms and architectures I have in my head into words — and, to be honest, I often find this clarifies the high level idea more than writing the code for it, because I don't get lost in the forest — and I also really enjoy taking something that isn't quite good enough, that's maybe 80% of the way there, and doing the careful polishing and refactoring necessary to get it to 100%.
I don't want to be "that guy", but I'll indulge myself.
> I think this is one of the divides between people who like AI and people who don't. I don't mind writing code per se, but I really don't like text editing — and I've used Vim (Evil mode) and then Emacs (vanilla keybindings) for years, so it's not like I'm using bad tools; it's just too fiddly.
I feel the same way (to at least some extent) about every language I've used other than Lisp. Lisp + Paredit in Emacs is the most pleasant code-wrangling experience I've ever had, because rather having to think in terms of characters or words, I'm able to think in terms of expressions. This is possible with other languages thanks to technologies like Tree-sitter, but I've found that it's only possible to do reliably in Lisp. When I do it in any other language I don't have an unshakable confidence that the wrangling commands will do exactly what I intend.
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This is similar to how I prompt, except I start with a text file and design the solution and paste it in to an LLM after I have read it a few times. Otherwise, if I type directly in to the LLM and make a mistake it tends to come back and haunt me later.