I think this has put into words a reason why I bounced off using AI this way, when I need something done I often have a rough idea of how I want it done, and how AI does it often doesn't match what I want, but because it's gone off and written a 2,000 lines of code it's suddenly more work for me to go through and say "Ok, so first off, strip all these comments out, you're doubling the file with trivial explanations of simple code. I don't want X to be abstracted this way, I want that...." etc. And then when I give it feedback 2,000 lines of code suddenly switch to 700 lines of completely different code and I can't keep up. And I don't want my codebase full of disjoint scripts that I don't really understand and all have weirdly different approaches to the problem. I want an AI that I have similar opinions to, which is obviously tough. It's like working with someone on their first day.
I don't know if it's giving the tools less self-confidence per se, but I think it's exposing more the design process. Like ideally you want your designer to go "Ok, I'm thinking of this approach, i'll probably have these sorts of functions or classes, this state will be owned here" and we can approve that first, rather than going straight from prompt -> implementation.
Whenever I land on the front page, I check the comments and brace for HN coming and telling me how stupid I am and lighting me aflame in front of my peers.
But sometimes if I manage to nail the right headline, nobody reads my post and just has their own discussion, and I am spared.
When I first tried an LLM agent, I was hoping for an interactive, 2-way, pair collaboration. Instead, what I got was a pairing partner who wanted to do everything themselves. I couldn't even tweak the code they had written, because it would mess up their context.
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.
My approach has generally been to accept, refactor and reprompt if I need to tweak things.
Of course this does artificially inflate the "accept rate" which the AI companies use to claim that it's writing good code, rather than being a "sigh, I'll fix this myself" moment.
I do this too and it drives me nuts. It's very obvious to me (and perhaps anyone without an incentive to maximize the accept rate) that the diff view really struggles. If you leave a large diff, copilot and cursor will both get confused and start duplicating chunks, or they'll fail to see the new (or the old) but if you accept it, it always works.
Have you tried recently? This hasn't been my experience. I modify the code it's written, then ask it to reread the file. It generally responds "I see you changed file and [something.]" Or when it makes a change, I tell it I need to run some tests. I provide feedback, explain the problem, and it iterates. This is with Zed and Claude Sonnet.
I do notice though that if I edit what it wrote before accepting it, and then it sees it (either because I didn’t wait for it to finish or because I send it another message), it will overwrite my changes with what it had before my changes every single time, without fail.
(Zed with Claude 4)
Interesting. I always wait for it to finish with my workflow.
Gemini has insisted on remembering an earlier version of a file even after its own edits.
“We removed that, remember?”
“Yes! I see now …”
Sometimes it circles back to that same detail that no longer exists.
I usually add “discuss first. Don’t modify code yet”. Then we do some back and forth. And finally, “apply”.
Claude Code has "plan mode" for this now. It enforces this behavior. But its still poorly documented.
Claude Code denies that it has a plan mode...
They should add a “cmd-enter” for ask, and “enter” to go.
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.
> 3. Wait a minute - if the AI can change the interface on its own - do we even need to have a single interface for everyone? Perhaps future software would only expose an API and a collection of customizable UI widgets (perhaps coupled with official example interfaces), which each user's "user agent AI" would then continuously adapt to that user's needs?
Nice, in theory. In practice it will be "Use our Premium Agent at 24.99$/month to get all the best features, or use the Basic Agent at 9.99$ that will be less effective, less customizable and inject ads".
I try to be super careful, type the prompt I want to execute in a textfile. Ask the agent to validate and improve on it, and ask it to add an implementation plan. I even let another agent review the final plan. But even then, occasionally it still starts implementing halfway a refining.
Same. I use /ask in Aider so I can read what it's planning, ask follow-up questions, get it to change things, then after a few iterations I can type "Make it so" while sitting back to sip on my Earl Grey.
I had done something slightly different. I would ask LLM to prepare a design doc, not code, and iterate on that doc before I ask them to start coding. That seems to have worked a little better as it’s less likely to go rogue.
You can totally do that. Just tell it to.
If you want an LLM to do something, you have to explain it. Keep a few prompt docs around to load every conversation.
I do this all the time with Claude Code. I’ll accept its changes, make adjustments, then tell it what I did and point to the files or tell it to look at the diff.
Pair programming requires communicating both ways. A human would also lose context if you silently changed their stuff.
Hmm you can tweak fine these days without messing up context. But, I run in “ask mode” only, with opus in claude code and o3 max in cursor. I specifically avoid agent mode because, like in the post, I feel like I gain less over time.
I infrequently tab complete. I type out 80-90% of what is suggested, with some modifications. It does help I can maintain 170 wpm indefinitely on the low-medium end.
Keeping up with the output isn’t much an issue at the moment given the limited typing speed of opus and o3 max. Having gained more familiarity with the workflow, the reading feels easier. Felt too fast at first for sure.
My hot take is that if GitHub copilot is your window into llms, you’re getting the motel experience.
> My hot take is that if GitHub copilot is your window into llms, you’re getting the motel experience.
I’ve long suspected this; I lean heavily on tab completion from copilot to speed up my coding. Unsurprisingly, it fails to read my mind a large portion of the time.
Thing is, mind reading tab completion is what I actually want in my tooling. It is easier for me to communicate via code rather than prose, and I find the experience of pausing and using natural language to be jarring and distracting.
Writing the code feels like a much more direct form of communicating my intent (in this case to the compiler/interpreter). Maybe I’m just weird; and to be honest I’m afraid to give up my “code first” communication style for programming.
Edit: I think the reason why I find the conversational approach so difficult is that I tend to think as I code. I have fairly strong ADHD and coding gives me appropriate amount of stimulation to do design work.
Take a look at aider's watch mode. It seems like a bridge for code completion with more powerful models than Copilot.
In all honesty - have you tried doing what you would do with a paired programmer - that is, talk to them about it? Communicate? I’ve never had trouble getting cursor or copilot to chat with me about solutions first before making changes, and usually they’ll notice if I make my own changes and say “oh, I see you already added XYZ, I’ll go ahead and move on to the next part.”
I’ve never had trouble getting cursor or copilot to chat with me about solutions first before making changes
Never had any trouble.... and then they lived together happy forever.
In my experience the problem is not they are too fast, they are too slow.
Honestly, their speed is just the right amount to make them bad. If they were faster, I could focus on following the code they are writing. But they take so much time for every edit that I tune out. On the other hand if they were slower, I could do other work while they are working, but they are done every 50 seconds to a few minutes which means I can't focus on other tasks.
If they did smaller faster changes it would probably be better.
Ideally though I would prefer them to be more autonomous, and the collaboration mode to be more like going over merge requests than pair programming. I ideally would like to have them take a task and go away for a few hours or even like 30 minutes.
The current loop, provide a task, wait 1 to 3 minutes, see a bunch of changes, provide guidance, repeat is the worst case scenario in my view.
Yeah, I could completely see this. Reminds me of the "Slow Internet vs No Internet" oatmeal comic
Pair programming is also not suitable for all cases
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
Second this. My solution is to have a 'non-AI' IDE and then a Cursor/VS Code to switch between. Deep work cannot be achieved by chatting with the coding bots, sorry.
You should try aider! This is my workflow, essentially
I do this as well and it works quite well for me like that!
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.
I do this too by pasting only the relevant context files into O3 or Claude 4. We have an internal tool that just lets us select folders/files and spit out one giant markdown.
Thirded. It was just completely distracting and I had to turn it off. I use AI but not after every keystroke, jeez.
But but but... "we are an AI-first company".
Yeah, nah. Fourthed!
> > AI-first company
> 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.
> Deep work cannot be achieved by chatting with the coding bots, sorry.
...by you. Meanwhile, plenty of us have found a way to enhance our productivity during deep work. No need for the patronization.
I don't believe you experience deep work the same way I do then
In my mind you cannot do deep work while being interrupted constantly, and LLM agents are constant interruptions
Everyone knows real programmers only need to use a butterfly.
This sounds like an issue with the specific UI setup you are using. I have mine configured so it only starts doing stuff if I ask it to. It never interrupts me.
It’s quite amusing to see you complain about patronisation, and then see you turn about and do it yourself one comment later.
Would be informative if both sides share what the problem domain is when providing their their experiences.
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'
For what it’s worth, the deepest-thinking and most profound programmers I have met—hell, thinkers in general—have a peculiar tendency to favour pen and paper. Perhaps because once their work is recognised, they are generally working with a team that can amplify them without needing to interrupt their thought flow.
> You could have asked for tips since I said I've found a way to work deeply with them
How do you work deeply with them? Looking for some tips.
Analysis in the last 5-10 years has shown the Dunning-Kruger effect may not really exist. So it’s a poor basis on which to be judgmental and condescending.
I recently got a new laptop and had to setup my IDE again.
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.
It's strange the pure llm workflow and boring. I still write most of my own code and will llms when I'm too lazy to write the next piece.
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.
> who even wants their next cursor position predicted
I'm fascinated by how different workflows are. This single feature has saved me a staggering amount of time.
> Also, Cursor was exceptional at interrupting any kind of "flow" - who even wants their next cursor position predicted?
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.
Same, I like agents or nothing in between.
AI "auto-complete" or "code suggestions" is the worst, especially if you are in a strongly-type language because it's 80% correct and competing with an IDE that can be 100% correct.
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.
I’ve always seen it as primarily an _education_ tool; the purpose of pair programming isn’t that two people pair programming are more productive than two people working individually, they’re generally not. So pair programming with a magic robot seems rather futile; it’s not going to learn anything.
LLMs in their current incarnation will not, but there's nothing inherently preventing them from learning. Contexts are getting large enough that having a sidecar database living with each project or individual as a sort of corpus of "shit I learned pairing with Justin" is already completely achievable, if only a product company wanted to do that.
I love the autocomplete, honestly use it more than any other AI feature.
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.
I’m a Vim user and couldn’t agree more.
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.
Zed has a "subtle" mode, hopefully that feature can become table stakes in all AI editor integrations
Code regularly, and use ai to get unblocked if you do so or review code for mistakes.
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.
As a developer who doesn't use AI for coding, except for the occasional non-project specific question to a chat bot, I am wondering if you use it for client projects or only for your own projects. If you do use it for client projects, do you have some kind of agreement that you're going to share their code with a third-party? I'm asking because most clients will make you sign a contract saying that you shouldn't disclose any information about the project to a third-party. I even once had a client who explicitly stated that AI should not be used. Do you find clients willing to make an exception for AI coding agents?
I basically only use it in the workplace, and largely because of one of those AI mandates.
I don't think it actually saves me enough time (or for many tasks, any time) so I wouldn't pay for it for my own projects, and also for my own projects, the enjoyability is a big factor, and I enjoy doing more than prompting.
Thank you for the reply. What do you mean by "AI mandates"? Does it mean your company has an explicit policy allowing sharing code with AI services?
Sadly, I mean my current employer is doing the whole "tracking to see AI usage rates" and basically checking in performance reviews if people are using as much AI as the AI sales people told the CEO people need to use.
We're a SaaS company so we own all our code.
Klarna, Shopify and either Google or Meta made a lot of press promoting policies like that and also the AI companies themselves are selling this kind of approach in the "how to make the best use of our tools" advice they give to execs.
I have a client that actively asks me to use AI more and more. They expect to get better quality code faster, ie. to reduce costs. (That's not my experience but that's beside the point).
I don't share anything with openai/anthropic that I wouldn't feel comfortable pasting into a web search prompt.
So no AI autocomplete I suppose?
I assume AI autocomplete may send any part of your code base or even all of it to a third-party.
No, I don't. This goes for internal projects as well, we're not going to share code unless payed to do so.
We commonly work with personal information so it would also introduce rather harsh legal risks if usian corporations could reach it.
I have mixed feelings about this situation. I have committed myself to learning how to use it as effectively as possible and to utilising it extensively for at least one month. Through my company, I have access to multiple products, so I am trying them all.
I can say that I am more productive in terms of the number of lines written. However, I cannot claim to be more productive overall.
For every task it completes, it often performs some inexplicable actions that undo or disrupt other elements, sometimes unrelated ones. The tests it generates initially appear impressive, but upon examining other metrics, such as coverage, it becomes evident that its performance is lacking. The amount of time required to guide it to the correct outcome makes it feel as though I am taking many steps backwards before making any significant progress forward—and not in a beneficial way. On one occasion, it added 50,000 unnecessary import lines into a module that it should not have been altering.
On another occasion, one of the agents completely dismantled the object-oriented programming hierarchy, opting instead to use if/else statements throughout, despite the rules I had set.
The issue is that you can never be certain of its performance. Sometimes, for the same task, it operates flawlessly, while at other times, it either breaks everything or behaves unpredictably.
I have tried various techniques to specify what needs to be done and how to accomplish it, yet often, for similar tasks, its behaviour varies so significantly between runs that I find myself needing to review every change it makes each time. Frustratingly, even if the code is nearly correct and you request an update to just one part, it may still behave erratically.
My experience thus far suggests that it is quite effective for small support tools, but when dealing with a medium-sized codebase, one cannot expect it to function reliably every time.
Writing out hundreds of lines of code is not what I meant by proactive tools…
Where are the proactive coding tools? https://austinhenley.com/blog/proactiveai.html
LLM agents don't know how to shut up and always think they're right about everything. They also lack the ability to be brief. Sometimes things can be solved with a single character or line, but no they write a full page. And, they write paragraphs of comments for even the most minuscule of changes.
They talk at you, are overbearing and arrogant.
I was trying out sonnet 4 yesterday and it spent 15 minutes changing testing changing etc just to get one config item changed. It ended up changing 40 files for no reason. Also kept trying to open a debugger that didn’t exist and load a webpage that requires auth.
They’re far from perfect that’s for sure.
I expect a lot of the things people don't like ("output too long, too many comments in code") are side effects of making the LLM good in other areas.
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.
Just add to the prompt not to include comments and to talk less.
I have a prompt document that includes a complete summary of the Clean Code book, which includes the rules about comments.
You do have to remind it occasionally.
You can, but I would expect code correctness to be reduced, you're removing one mechanism the model uses to dump local reasoning immediately prior to where it's needed.
Finally someone said it, they're overconfident in their approach, don't consult us with the details of the implementation, they're trained to create mock APIs that don't follow structure, leading to lot of rework. The LLM actions should be measured, collaborative, ask for details when it's not present. It is impossible to give every single detail in the initial prompt, and a follow up prompt derails the train thought and design of the application.
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.
Most stacks now support some form of "plan" workflows. You'd want to first do this, and see if it improves your experience.
One workflow that works well for me, even with small local models, is to start a plan session with something like: "based on @file, and @docs and @examples, I'd like to _ in @path with the following requirements @module_requirements.md. Let's talk through this and make sure we have all the info before starting to code it."
Then go back and forth, make sure everything is mentioned, and when satisfied either put it into a .md file (so you can retry the coding flow later) or just say "ok do it", and go grab a cup of coffee or something.
You can also make this into a workflow with .rules files or .md files, have a snippets thing from your IDE drop this whenever you start a new task, and so on. The idea with all the advancements in LLMs is that they need lots of context if you want them to be anything other than what they were trained on. And you need to try different flows and see what works on your specific codebase. Something that works for projectA might not work for projectB ootb.
Also giving them more details seems to confuse them. There is probably a way around this, though. They are pretty good in finding a tiny silver of information out of the ocean. What I hate is that the industry is all geared toward the same model (chat bot). Imagine if we never invented the keyboard, mouse, GUI, touch screen, etc...
Yes, this is exactly why the "planning" approach never seems to work for me. Like every ounce of planning I do with the LLM it becomes a pound stupider at implementation time
[dead]
A week or so ago I needed to convince chatgpt that following code will indeed initialize x values in struct
struct MyStruct
{
int x = 5;
};
...
MyStruct myStructs[100];
I even showed msvc assembly output and pointed to the place where it is looping & assigning all x values and then it started hallucinating about msvc not conforming the standards. Then I did it for gcc and it said the same. It was surreal how strongly it believed it was correct.
LLM's don't have beliefs, so "convincing" them of this or that is a a waste of your time. The way to handle such cases is to start anew with a clean context and just add your insight to the prompt so that it lands on the right track from the beginning. Remember these models are ultimately just next-token predictors and anthropomorphizing them will invariably lead to suboptimal interactions.
That is not even valid C code, so you would have to seriously convince me, too.
What makes it invalid is "= 5", and lack of "struct" before "MyStruct" (could have used typedef).
It is a c++ code.
I use an LLM as a reference (on-demand), and don't use agents (yet). I was never into pair programming, anyway, so it isn't a familiar workflow for me.
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 ...
> I use an LLM as a reference (on-demand), and don't use agents (yet)
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.
Roo looks interesting. How does it compare with Cursor and Windsurf?
It burns tokens if you BYOK but you can hook into GH Copilot LLMs directly
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've been considering a... protocol? for improving this. Consider this repo:
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?
I think coding will eventually go away in favor of models with metadata built around them.
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.
I don't understand all of what you wrote but a lot of it is very old news and usually done with deterministic tooling you don't have to wait for, some of which you should have built or configured yourself to get it tailored to the type of work you do.
And some of it we've had under the RAD umbrella, basically using configuration files and tools to generate those that are used to generate large portions of systems.
This guy needs a custom prompt. I keep a prompt doc around that is constantly updated based on my preferences and corrections.
Not a few sentences but many many lines of examples and documentation
Gist an example of what you mean? My experience with very large prompts and exacting custom instructions has been drastically eroded "intelligence"
> Allow users to pause the agent to ask a clarifying question or push back on its direction without derailing the entire activity or train of thought
I think I’ve seen Zed/Claude do kind of this. A couple times, I’ve hit return, and then see that I missed a clarifying statement based on the direction it starts going and I put it in fast, and it corrects.
I basically jump away from Cursor to ChatGPT when I need to think thoroughly on something like an architecture decision or an edge case etc. Then when I've used ChatGPT to come up with an implementation plan, I jump back to Cursor and have Claude do the actual coding. O3 and ChatGPT's search functionality are just better (at least for myself) currently for "type 2" thinking tasks.
LLM agents are very hard to talk about because they're not any one thing. Your action-space in what you say and what approach you take varies enormously and we have very little body of common knowledge about what other people are doing and how they're doing it. Then the agent changes underneath you or you tweak your prompt and it's different again.
In my last few sessions I saw the efficacy of Claude Code plummet on the problem I was working on. I have no idea whether it was just the particular task, a modelling change, or changes I made to the prompt. But suddenly it was glazing every message ("you're absolutely right"), confidently telling me up is down (saying things like "tests now pass" when they completely didn't), it even cheerfully suggested "rm db.sqlite", which would have set me back a fair bit if I said yes.
The fact that the LLM agent can churn out a lot of stuff quickly greatly increases 'skill expression' though. The sharper your insight about the task, the more you can direct it to do something specific.
For instance, most debugging is basically a binary search across the set of processes being conducted. However, the tricky thing is that the optimal search procedure is going to be weighted by the probability of the problem occurring at the different steps, and the expense of conducting different probes.
A common trap when debugging is to take an overly greedy approach. Due to the availability heuristic, our hypotheses about the problem are often too specific. And the more specific the hypothesis, the easier it is to think of a probe that would eliminate it. If you keep doing this you're basically playing Guess Who by asking "Is it Paul? Is it Anne?" etc, instead of "Is the person a man? Does the person have facial hair? etc"
I find LLM agents extremely helpful at forming efficient probes of parts of the stack I'm less fluent in. If I need to know whether the service is able to contact the database, asking the LLM agent to write out the necessary cloud commands is much faster than getting that from the docs. It's also much faster at writing specific tests than I would be. This means I can much more neutrally think about how to bisect the space, which makes debugging time more uniform, which in itself is a significant net win.
I also find LLM agents to be good at the 'eat your vegetables' stuff -- the things I know I should do but would economise on to save time. Populate the tests with more cases, write more tests in general, write more docs as I go, add more output to the scripts, etc.
It is rather soul crushing how fast LLMs spit out decent code.
In my experience, LLMs are idiot savant coders--but currently more idiot than savant. Claude 3.7 (via cursor and roo) can comment code well, create a starter project 10x faster than I could, and they spit out common crud apps pretty well.
However I've come to the conclusion that LLMs are terrible at decision making. I would much rather have an intern architect my code than let AI do it. It's just too unreliable. It seems like 3 out of 4 decisions that it makes are fine. But that 4th decision is usually asinine.
That said, I now consider LLMs a mandatory addition to my toolkit because they have improved my developer efficiency so much. I really am a fan. But without a seasoned dev to write detailed instructions, break down the project into manageable chunks, make all of the key design decisions, and review every line of code that it writes, today's AI will only add a mountain of technical debt to your project.
I guess I'm trying to say: don't worry because the robots cannot replace use yet. We're still in the middle of the hype cycle.
But what do I know? I'm just an average meat coder.
LLMs currently can generate a few thousand lines of coherent code but they cannot write a cohesive large scale code base.
But LLMs are very good at writing SQL and Cypher queries that I would spend hours or days figuring out how to write.
Aider does everything right. Stop using Cursor or any other agentic environments. Try Aider, it works exactly as suggested here.
I've been wanting to use Aider but I want to use Copilot as a provider (to stay compliant with the wishes of my employer). Haven't gone down that road yet because Aider copilot support seems a bit tentative. I see they have some docs about it up now though: https://aider.chat/docs/llms/github.html
"The easiest path is to sign in to Copilot from any JetBrains IDE"
Somebody must've made a standalone login script by now right? I wonder if `gh auth login` can be used to get a token?
>LLM agents make bad pairs because they code faster than humans think.
Easily solved. Use less compute. Use slower hardware. Or put in the prompt to pause at certain intervals.
>LLM agents make bad pairs because they code faster than humans think
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.
If I have a 20 line function in my mind and the LLM injects 20 lines for me to accept or reject, I have two problems
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 hard truth here is in accepting that the 20 lines in your head were probably wrong, or suboptimal, and letting go of that urge. Think in interfaces, not implementations. Successive rendering, not one-shot.
This is just fundamentally not the case most of the time. LLMs guess where you're going, but so often what they produce is a "similar looking" non sequitur relative to the lines above it. It guesses, and sometimes that guess is good, but as often, or more, it's not.
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 it's the domain I work in, or the languages I use, but the 20 lines the LLM comes up with is almost certainly wrong.
I agree with the last two sentences but simultaneously think that starting to defacto believe you cannot have an equal or better solution compared to the AI is the start of atrophy of those skills.
> The hard truth here is in accepting that the 20 lines in your head were probably wrong, or suboptimal, and letting go of that urge.
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
> ...and letting go of that urge.
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.
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At the start of prompt before project requirements I copy paste paragraph about the code I want.
No emojis, no comments, no console log statements, no read me file, no error handling. Act as a senior developer working with other experienced developers.
Otherwise it happily generates bunch of trash that is unreadable. Error handling generated will most of the times just hide errors instead of actually dealing with them.
Personally, I’ve gone from working with the AI to code to working on it to develop specifications. It’s also useful at troubleshooting issues.
I’m no longer a developer by trade and it’s more a hobby or specific problem solving scenario now. But I find using it to identify gaps in my thinking and edit English is ultimately better than getting random code — I love editing English text, but find editing code without consistent style a drag for my purposes.
Yeah, and then you just wind up feeling exhausted AND unsatisfied with where you wind up. You are exactly who I posted this for.
100% of my positive experiences with agent coding are when I don't have reason to care about the intrinsic qualities of the code (one-off scripts or genuine leaf node functions that can't impact anything else.
The trick is to have rules specific to the project and your programming style & preferences.
Think of the AI like an outsourced consultant who will never say no. It'll always do everything it can to solve the problem you've given it. If it doesn't know how, it'll write a thousand lines when a single additional dependency and 2 lines of code would've done it.
You need to tune it.