This is why I strongly dislike all of the terminal based tools and PR based stuff. If you're left to read through a completed chunk of code it is just overwhelming and your cycle time is too slow. The key to productivity is using an IDE based tool that shows you every line of code as it is being written, so you're reading it and understanding where it's going in real time. Augmentation, not automation, is the path forward. Think of it like the difference between walking and having a manual transmission car to drive, not the difference between having a car and having a self driving car.
First I have to review the 20 lines the LLM has produced
Second, if I reject those lines, it has probably shoved the function I had in mind out of my head
It's enormously disruptive to my progress
The suggestion "think in interfaces" is fine; if you spell out enough context in comments, the LLM may be able to guess more accurately, but in spelling out that much context for it, you've likely already done the mental exercise of the implementation.
Also baffled by "wrong or suboptimal," I don't think I've ever seen an LLM come up with a better solution.
Maybe, but the dogshit that Cursor generates is definitely wrong so frankly if it's gonna be my name on the PR then I want it to me my wrong code not hide behind some automated tool
> Think in interfaces, not implementations
In my experience you likely won't know if you've designed the right interface until you successfully implement the solution. Trying to design the perfect interface upfront is almost guaranteed to take longer than just building the thing
What urge? The urge to understand what the software you're about to build upon is doing? If so, uh... no. No thanks.
I've seen some proponents of these code-generation machines say things like "You don't check the output of your optimizing compiler, so why check the output of Claude/Devon/whatever?". The problem with this analogy is that the output from mainstream optimizing compilers is very nearly always correct. It may be notably worse than hand-generated output, but it's nearly never wrong. Not even the most rabid proponent will claim the same of today's output from these code-generation machines.
So, when these machines emit code, I will inevitably have to switch from "designing and implementing my software system" mode into "reading and understanding someone else's code" mode. Some folks may be actually be able to do this context-shuffling quickly and easily. I am not one of those people. The results from those studies from a while back that found that folks take something like a quarter-hour to really get back into the groove when interrupted while doing a technical task suggest that not that many folks are able to do this.
> Think in interfaces...
Like has been said already, you don't tend to get the right interface until you've attempted to use it with a bunch of client code. "Take a good, educated stab at it and refine it as the client implementations reveal problems in your design." is the way you're going to go for all but the most well-known problems. (And if your problem is that well-known, why are you writing more than a handful of lines solving that problem again? Why haven't you bundled up the solution to that problem in a library already?)
> Successive rendering, not one-shot.
Yes, like nearly all problem-solving, most programming is and always has been an iterative process. One rarely gets things right on the first try.
I will admit that it encourages "laziness," on my part, but I'm OK with that (remember when they said that calculators would do that? They were right).
For example, I am working on a SwiftUI project (an Apple Watch app), and forgot how to do a fairly basic thing. I could have looked it up, in a few minutes, but it was easier to just spin up ChatGPT, and ask it how to do it. I had the answer in a few seconds. Looking up SwiftUI stuff is a misery. The documentation is ... a work in progress ...
This was me until about three weeks ago. Then, during a week of holiday, I decided I didn't want to get left behind and tried a few side-projects using agents -- specifically I've been using Roo. Now I use agents when appropriate, which I'd guess is about 50% of the work I'm doing.
I really like the orchestrator and architect personas as is out of the box. I prefer it over Cursor / Windsurf for a few reasons - no indexing (double edged sword) - orchestrator I find much more useful than windsurf cascades - tool usage is fantastic
The no indexing is a double edged sword, it does need to read files constantly, contributing to token burn. However, you don't have to worry about indexed data being on a 3rd party server (cursor), and also since it has to crawl to understand the codebase for it to implement, to me it seems like it is more capable of trickier code implementations, as long as you utilize context properly.
For more complex tasks, I usually either spend 20-30 minutes writing a prompt to give it what I'm looking to implement, or write up a document detailing the approach I'd like to take and iterate with the architect agent.
Afterwards, hand it off to the orchestrator and it manages and creates subtasks, which is to provide targeted implementation steps / tasks with a fresh context window.
If you have a GH Copilot license already, give it a shot. I personally think it's a good balance between control as an architect and not having to tie my time down for implementations, since really a lot of the work in coding is figuring out the implementation plan anyways, and the coding can be busy work, to me personally anyways. I prefer it over the others as I feel Windsurf/Cursor encourages YOLO too much.
I want a pairing partner where I can write a little, they write a little, I write a little, they write a little. You know, an actual collaboration.
Separately, if I were at cursor (or any other company for that matter), I’d have the AI scouring HN comments for “I wish x did y” suggestions.
They talk at you, are overbearing and arrogant.
Long output correlates with less laziness when writing code, and higher performance on benchmarks due to the monotone relationship between number of output tokens and scores. Comment spam correlates with better performance because it's locally-specific reasoning it can attend on when writing the next line of code, leading to reduced errors.
foo.py
bar.py
bar.py.vibes.md
bar.py.vibes.md would contain whatever human-written guidance describes how bar should look. It could be an empty file, or a few paragraphs, or it it could contain function signatures and partially defined data types.
If an LLM wants to add a new file, it gets a vibes.md with whatever prompt motivated the addition.
Maybe some files keep their assiciated *.vibes.md forever, ready to be totally rewritten as the LLM sees fit. Maybe others stick around only until the next release, after which the associated code is reviewed and the vibes files are removed (or somehow deactivated, I could imagine it being useful for them to still be present).
What do people think, do we need handcuffs of this kind for our pair programming friends the LLMs?
How many times did you have a mutation operation where you had to hand code the insert of 3 or 4 entities and make sure they all come back successful, or you back out properly (and perhaps this is without a transaction, perhaps over multiple databases).
Make sure the required fields are present Grab the created inserted ID Rinse, repeat
Or if you're mutating a list, writing code that inserts a new element, but you don't know which one is new. And you end up, again, hand coding loops and checking what you remember to check.
What about when you need to do an auth check.
And the hand coder may fail to remember one little thing somewhere.
With LLM code, you can just describe that function and it will remember to do all the things.
An LLM with a model + metadata - we won't really need to think of it as editing User.java or User.py anymore. Instead User.yaml - and the LLM will just consume that, and build out ALL of your required biz-logic, and be done with it. It could create a fully authenticating/authorizing REST API + GraphQL API with sane defaults - and consistent notions throughout.
And moving into UIs- we can have the same thing. The UI can be described in an organized way. What fields are required for user registration. What fields are optional according to the backend. It's hard to visualize this future, but I think it's a no-code future. It's models of requirements instead.
Easily solved. Use less compute. Use slower hardware. Or put in the prompt to pause at certain intervals.
I don't know if I'm using it right, I'd love to know more if that's the case. In a way the LLM should improve on being iterative, take feedback, maybe it's a hard problem to add/update the context. I don't know about that either, but love to learn more.
bluefirebrand•4h ago
Maybe not for many cases
I mentioned this elsewhere but I find it absolutely impossible to get into a good programming flow anymore while the LLM constantly interrupts me with suggested autocompletes that I have to stop, read, review, and accept/reject
It's been miserable trying to incorporate this into my workflow
amazingamazing•4h ago
Or have the ai write the entire first draft for some piece and then you give it a once over, correcting it either manually or with prompts.
meesles•4h ago
morkalork•4h ago
latentsea•4h ago
Yeah, nah. Fourthed!
mdp2021•3h ago
Does anybody introduce itself like that?
It's like when your date sends subtle signals, like kicking sleeping tramps in the street and snorting the flour over bread at the restaurant.
(The shocking thing is that the expression would even make sense when taken properly - "we have organized our workflows through AI-intelligent systems" -, while at this time it easily means the opposite.)
neilv•2h ago
> Does anybody introduce itself like that?
Yes, I've started getting job posts sent to me that say that.
Declaring one's company "AI-first" right now is a great time-saver: I know instantly that I can disregard that company.
gen220•4h ago
soulofmischief•3h ago
...by you. Meanwhile, plenty of us have found a way to enhance our productivity during deep work. No need for the patronization.
bluefirebrand•3h ago
In my mind you cannot do deep work while being interrupted constantly, and LLM agents are constant interruptions
soulofmischief•3h ago
Essentially, this is a skill issue and you're at the first peak of the Dunning–Kruger curve, sooner ready to dismiss those with more experience in this area as being less experienced, instead of keeping an open mind and attempting to learn from those who contradict your beliefs.
You could have asked for tips since I said I've found a way to work deeply with them, but instead chose to assume that you knew better. This kind of attitude will stunt your ability to adopt these programs in the same way that many people were dismissive about personal computers or the internet and got left behind.
girvo•3h ago
soulofmischief•1h ago
jcranmer•1h ago
soulofmischief•14m ago
antihipocrat•2h ago
It's possible that the domain or the complexity of the problems are the deciding factor for success with AI supported programming. Statements like 'you'll be left behind' or 'it's a skill issue' are as helpful as 'It fails miserably'
JumpCrisscross•2h ago
soulofmischief•1h ago
I think there's still room for thought augmentation via LLMs here. Years back when I used Obsidian, I created probably the first or second copilot-for-Obsidian plugin and I found it very helpful, even though GPT-3 was generally pretty awful. I still find myself in deep flow, thinking in abstract, working alongside my agent to solve deep problems in less time than I otherwise would.
JumpCrisscross•10m ago
I agree. I use search-enabled LLMs constantly as a research assistant.
hnthrowaway121•2h ago
soulofmischief•1h ago
pushing back against judgement and condescension is not judgemental and condescending.
> may not really exist
I'm open to reading over any resources you would like to provide, maybe it's "real", maybe it isn't, but I have personally both experienced and witnessed the effect in myself, other individuals and groups. It's a good heuristic for certain scenarios, even if it isn't necesarily generalizable.
8note•3h ago
thallada•1h ago
NicoSchwandner•35m ago
Additionally, when working on microservices and on issues that don’t seem too straightforward, I use o3 and copy the whole code of the repo into the prompt and refine a plan there and then paste it as a prompt into cursor. Handy if you don’t have MAX mode, but a company-sponsored ChatGPT.
flessner•4h ago
After a couple hours of coding something felt "weird" - turns out I forgot to login to GitHub Copilot and I was working without it the entire time. I felt a lot more proactive and confident as I wasn't waiting on the autocomplete.
Also, Cursor was exceptional at interrupting any kind of "flow" - who even wants their next cursor position predicted?
I'll probably keep Copilot disabled for now and stick to the agent-style tools like aider for boilerplate or redundant tasks.
johnfn•4h ago
I'm fascinated by how different workflows are. This single feature has saved me a staggering amount of time.
ipaddr•3h ago
If I give it to an llm most of my time is spent debugging and reprompting. I hate fixing someone elses bug.
Plus I like the feeling of the coding flow..wind at my back. Each keystroke putting us one step closer.
The apps I made with llms I never want to go back to but the apps I made by hand piece by piece getting a chemical reaction when problems were solved are the ones I think positively about and want to go back to.
I always did math on paper or my head and never used a calculator. Its a skill I never have forgotten and I worry how many programmers won't be able to code without llms in the future.
baq•16m ago
Me, I use this all the time. It’s actually predictable and saves lots of time when doing similar edits in a large file. It’s about as powerful as multi-line regex search and replace, except you don’t have to write the regex.
jumploops•3h ago
Didn’t like any of the AI-IDEs, but loved using LLMs for spinning up one off solutions (copy/paste).
Not to be a fan boy, but Claude Code is my new LLM workflow. It’s tough trying to get it to do everything, but works really well with a targeted task on an existing code base.
Perfect harmony of a traditional code editor (Vim) with an LLM-enhanced workflow in my experience.
brianpan•55m ago
AI agents are much better for me because 1) they don't constantly interrupt your train of thought and 2) they can run compile, run tests, etc. to discover they are incorrect and fix it before handing the code back to you.
dinosaurdynasty•54m ago
But I'm forced to write in Go which has a lot of boilerplate (and no, some kind of code library or whatever would not help... it's just easier to type at that point).
It's great because it helps with stuff that's too much of a hassle to talk to the AI for (just quicker to type).
I also read very fast so one line suggestions are just instant anyway (like non AI autocomplete), and longer ones I can see if it's close enough to what I was going to type anyway. And eventually it gets to the point where you just kinda know what it's going to do.
Not an amazing boost, but it does let me be lazy writing log messages and for loops and such. I think you do need to read it much faster than you can write it to be helpful though.