It doesn't really make sense economically for me to write software for work anymore. I'm a teacher, architect, and infrastructure maintainer now. I hand over most development to my experienced team of Claude sessions. I review everything, but so does Claude (because Claude writes thorough tests also.) It has no problem handling a large project these days.
I don't mean for this post to be an ad for Claude. (Who knows what Anthropic will do to Claude tomorrow?) I intend for this post to be a question: what am I doing that makes Claude profoundly effective?
Also, I'm never running out of tokens anymore. I really only use the Opus model and I find it very efficient with tokens. Just last week I landed over 150 non-trivial commits, all with Claude's help, and used only 1/3 of the tokens allotted for the week. The most commits I could do before Claude was 25-30 per week.
(Gosh, it's hard to write that without coming across as an ad for Anthropic. Sorry.)
I looked at some stats yesterday and was surprised to learn Cursor AI now writes 97% of my code at work. Mostly through cloud agents (watching it work is too distracting for me)
My approach is very simple: Just Talk To It
People way overthink this stuff. It works pretty good. Sharing .md files and hyperfocusing on various orchestrations and prompt hacks of the week feels as interesting as going deep on vim shortcuts and IDE skins.
Just ask for what you want, be clear, give good feedback. That’s it
It's bitten me several times at work, and I rather not waste any more of my limited time doing the re-prompt -> modify code manually cycle. I'm capable of doing this myself.
It's great for the simple tasks tho, most feature work are simple tasks IMO. They were only "costly" in the sense that it took a while to previously read the code, find appropriate changes, create tests for appropriate changes, etc. LLMs reduce that cycle of work, but that type of work in general isn't the majority of my time at my job.
I've worked at feature factories before, it's hell. I can't imagine how much more hell it has become since the introduction of these tools.
Feature factories treat devs as literal assembly line machines, output is the only thing that matters not quality. Having it mass induced because of these tools is just so shitty to workers.
I fully expect a backlash in the upcoming years.
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My only Q to the OP of this thread is what kind of teacher they are, because if you teach people anything about software while admitting that you no longer write code because it's not profitable (big LOL at caring about money over people) is just beyond pathetic.
Are you working more on operational stuff or on "long-running product" stuff?
My personal headcanon: this tooling works well when built on simple patterns, and can handle complex work. This tooling has also been not great at coming up with new patterns, and if left unsupervised will totally make up new patterns that are going to go south very quickly. With that lens, I find myself just rewriting what Claude gives me in a good number of cases.
I sometimes race the robot and beat the robot at doing a change. I am "cheating" I guess cuz I know what I want already in many cases and it has to find things first but... I think the futzing fraction[0] is underestimated for some people.
And like in the "perils of laziness lost"[1] essay... I think that sometimes the machine trying too hard just offends my sensibilities. Why are you doing 3 things instead of just doing the one thing!
One might say "but it fixes it after it's corrected"... but I already go through this annoying "no don't do A,B, C just do A, yes just that it's fine" flow when working with coworkers, and it's annoying there too!
"Claude writes thorough tests" is also its own micro-mess here, because while guided test creation works very well for me, giving it any leeway in creativity leads to so many "test that foo + bar == bar + foo" tests. Applying skepticism to utility of tests is important, because it's part of the feedback loop. And I'm finding lots of the test to be mainly useful as a way to get all the imports I need in.
If we have all these machines doing this work for us, in theory average code quality should be able to go up. After all we're more capable! I think a lot of people have been using it in a "well most of the time it hits near the average" way, but depending on how you work there you might drag down your average.
[0]: https://blog.glyph.im/2025/08/futzing-fraction.html [1]: https://bcantrill.dtrace.org/2026/04/12/the-peril-of-lazines...
I guess it comes down to how ossified you want your existing code to be.
If it's a big production application that's been running for decades then you probably want the minimum possible change.
If you're just experimenting with stuff and the project didn't exist at all 3 days ago then you want the agent to make it better rather than leave it alone.
Probably they just need to learn to calibrate themselves better to the project context.
Even within the same project, for a given PR, there are some parts of the codebase I want to modify freely and some that I want fixed to reduce the diff and testing scope.
I try to explain up-front to the agent how aggressively they can modify the existing code and which parts, but I've had mixed success; usually they bias towards a minimal diff even if that means duplication or abusing some abstractions. If anyone has had better success, I'd love to hear your approach.
I suspect AI's learned to do this in order to game the system. Bailing out with an exception is an obvious failure and will be penalized, but hiding a potential issue can sometimes be regarded as a success.
I wonder how this extrapolates to general Q&A. Do models find ways to sound convincing enough to make the user feels satisfied and the go away? I've noticed models often use "it's not X, it's Y", which is a binary choice designed to keep the user away from thinking about other possibilities. Also they often come up with a plan of action at the end of their answer, a sales technique known as the "assumptive close", which tries to get the user to think about the result after agreeing with the AI, rather than the answer itself.
They are trained on human feedback, so there is no other way this goes. Every bit of every response is pointed toward subversion of the assumed evaluator.
I can't help but read complaints about the capabilities of AI – and I'm certainly not accusing you of complaining about AI, just a general thought – and think "Yet" to myself every time.
I've spent far more time pitting one AI context against another (reviewing each other's work) than I have using AI to build stuff these days.
The benefit is that since it mostly happens asynchronously, I'm free to do other stuff.
1. I have no real understanding of what is actually happening under the hood. The ease of just accepting a prompt to run some script the agent has assembled is too enticing. But, I've already wiped a DB or two just because the agent thought it was the right thing to do. I've also caught it sending my AWS credentials to deployment targets when it should never do that.
2. I've learned nothing. So the cognitive load of doing it myself, even assembling a simple docker command, is just too high. Thus, I repeatedly fallback to the "crutch" of using AI.
It has about doubled my development pace. An absolutely incredible gain in a vacuum, though tiny compared to what people seem to manage without these self-constraints. But in exchange, my understanding of the code is as comprehensive as if I had paired on it, or merged a direct report's branch into a project I was responsible for. A reasonable enough tradeoff, for me.
anonu has explicitly said that they've wiped a database twice as a result of agents doing stuff. What sort of diff would help against an agent running commands, without your approval?
The diff: +8000 -4000
Day 1: Carefully handles the creds, gives me a lecture (without asking) about why .env should be in .gitignore and why I should edit .env and not hand over the creds to it.
Day 2: I ask for a repeat, has lost track of that skill or setting, frantically searches my entire disk, reads .env including many other files, understands that it is holding a token, manually creates curl commands to test the token and then comes back with some result.
It is like it is a security expert on Day 1 and absolute mediocre intern on Day 2
( This was low-stakes test creds anyway which I was testing with thankfully. )
I never pass creds via env or anything else it can access now.
My approach now is to get it to write me linqpad scripts, which has a utility function to get creds out of a user-encrypted share, or prompts if it's not in the store.
This works well, but requires me to run the scripts and guide it.
Ultimately, fully autotonous isn't compatible with secrets. Otherwise, if it really wanted to inspect it, then it could just redirect the request to an echo service.
The only real way is to deal with it the same way we deal with insider threat.
A proxy layer / secondary auth, which injects the real credentials. Then give claude it's own user within that auth system, so it owns those creds. Now responsibilty can be delegated to it without exposing the original credentials.
That's a lot of work when you're just exploring an API or DB or similar.
1. Everything is specified, written and tested by me, then cleaned up by AI. This is for the core of the application.
2. AI writes the functions, then sets up stub tests for me to write. Here I’ll often rewrite the functions as they often don’t do what I want, or do too much. I just find it gets rid of a lot of boilerplate to do things this way.
3. AI does everything. This is for experiments or parts of an application that I am perfectly willing to delete. About 70% of the time I do end up deleting these parts. I don’t allow it to touch 1 or 2.
Of course this requires that the architecture is setup in a way where this is possible. But I find it pretty nice.
[1] except perhaps read-only credentials to help diagnose problems, but even then I would only issue it an extremely short-lived token in case it leaks it somehow
Only helps if we listen to it :) which is fun b/c it means staying sharp which is inherently rewarding
> python <<'EOF'
> ${code the agent wrote on the spot}
> EOF
I mean, yeah, in theory it's just as dangerous as running arbitrary shell commands, which the agent is already doing anyway, but still...
By default these shell commands don't have network access or write access outside the project directory which is good, but nowhere near customizable enough. Once you approve a command because it needs network access, its other restrictions are lifted too. It's all or nothing.
I'm not trying to be offense, so with all due respect... this sounds like a "you" problem. (And I've been there, too)
You can ask the LLMs: how do I run this, how do I know this is working, etc etc.
Sure... if you really know nothing or you put close to zero effort into critically thinking about what they give you, you can be fooled by their answers and mistake complete irrelevance or bullshit for evidence that something works is suitably tested to prove that it works, etc.
You can ask 2 or 3 other LLMs: check their work, is this conclusive, can you find any bugs, etc etc.
But you don't sound like you know nothing. You sound like you're rushing to get things done, cutting corners, and you're getting rushed results.
What do you expect?
Their work is cheap. They can pump out $50k+ worth of features in a $200/mo subscription with minimal baby-sitting. Be EAGER to reject their work. Send it back to them over and over again to do it right, for architectural reviews, to check for correctness, performance, etc.
They are not expensive people with feelings you need to consider in review, that might quit and be hard to replace. Don't let them cut corners. For whatever reason, they are EAGER to cut corners no matter how much you tell them not to.
https://vivekhaldar.com/articles/when-compilers-were-the--ai...
We are completely comfortable now letting the compilers do their thing, and never seem to worry that we "don't know what is actually happening under the hood".
I am not saying these situations are exactly analogous, but I am saying that I don't think we can know yet if this will be one of those things that we stop worrying about or it will be a serious concern for a while.
LLMs are nothing like that
> Many assembly programmers were accustomed to having intimate control over memory and CPU instructions. Surrendering this control to a compiler felt risky. There was a sentiment of, if I don’t code it down to the metal, how can I trust what’s happening? In some cases, this was about efficiency. In other cases, it was about debuggability and understanding programming behavior. However, as compilers matured, they began providing diagnostic output and listings that actually improved understanding.
I would 100% use LLMs more and more aggressively if they were more transparent. All my reservations come from times when I prompt “change this one thing” and it rewrites my db schema for some reason, or adds a comment that is actively wrong in several ways. I also think I have a decent working understanding of the assembly my code compiles to, and do occasionally use https://godbolt.org/. Of course, I didn’t start out that way, but I also don’t really have any objections to teenagers vibe-coding games, I just think at some point you have to look under the hood if you’re serious.
Isn't that what git is for, though? Just have your LLM work in a branch, and then you will have a clear record of all the changes it made when you review the pull request.
A non deterministic compiler is probably defective and in any case much less useful
Although, while the compiler devs might know what was going on in the compiler, they wouldn't know what the compiler was doing with that particular bit of code that the FORTRAN developer was writing. They couldn't possibly foresee every possible code path that a developer might traverse with the code they wrote. In some ways, you could say LLMs are like that, too; the LLM developers know how the LLM code works, but they don't know the end result with all the training data and what it will do based on that.
In addition, to the end developer writing FORTRAN it was a black box either way. Sure, someone else knows how the compiler works, but not the developer.
I think we should move to semi-autonomous steerable agents, with manual and powerful context management. Our tools should graduate from simple chat threads to something more akin to the way we approach our work naturally. And a big benefit of this is that we won't need expensive locked down SOTA models to do this, the open models are more than powerful enough for pennies on the dollar.
How do you emulate that with llm’s? I suppose the objective is to get variance down to the point it’s barely noticeable. But not sure it’ll get to that place based on accumulating more data and re-training models.
The idea being that if you're working in an area, you should refactor and tidy it up and clean up "tech debt" while there.
In practice, it was seldom done, and here we have LLMs actually doing it, and we're realising the drawbacks.
At times even when a function is right there doing exactly what's needed.
Worse, when it modifies a function that exists, supposedly maintaining its behavior, but breaks for other use cases. Good try I guess.
Worst. Changing state across classes not realising the side effect. Deadlock, or plain bugs.
I spent some time dealing with this today. The real issue for me, though, was that the refactors the agent did were bad. I only wanted it to stop making those changes so I could give it more explicit changes on what to fix and how.
This is horrible practice, and very typical junior behavior that needs to be corrected against. Unless you wrote it, Chesterton's Fence applies; you need to think deeply for a long time about why that code exists as it does, and that's not part of your current task. Nothing worse than dealing with a 1000 line PR opened for a small UI fix because the code needed to be "cleaned up".
Tech debt needs to be dealt with when it makes sense. Many times it will be right there and then as you're approaching the code to do something else. Other times it should be tackled later with more thought. The latter case is frequently a symptom of the absence of the former.
In Extreme Programming, that's called the Boy Scouting Rule.
https://furqanramzan.github.io/clean-code-guidelines/princip...
"Refactor-as-you-go" means to refactor right after you add features / fix bugs, not like what the agent does in this article.
If LLMs are doing sensible and necessary refactors as they go then great
I have basically zero confidence that is actually the case though
I think they're in here, last edited 8 months ago: https://github.com/nreHieW/fyp/blob/5a4023e4d1f287ac73a616b5...
Over-editing is definitely not some long gone problem. This was on xhigh thinking, because I forgot to set it to lower.
Cross entropy loss steers towards garden path sentences. Using a paragraph to say something any person could say with a sentence, or even a few precise words. Long sentences are the low perplexity (low statistical “surprise”) path.
Codex also has a tendency to apply unwanted styles everywhere.
I see similar tendencies in backend and data work, but I somehow find it easier to control there.
I'm pretty much all in on AI coding, but I still don't know how to give these things large units of work, and I still feel like I have to read everything but throwaway code.
But yeah, I saw a suggestion about adding a long-lived agent that would keep track of salient points (so kinda memory) but also monitor current progress by main agent in relation to the "memory" and give the main agent commands when it detects that the current code clashes with previous instructions or commands. Would be interesting to see if it would help.
Purely anecdotal.
That kind of thing has made me much more cautious about judging these tools purely by whether the immediate error went away.
I feel like this was one of the most valuable skills an engineer could learn, as it protects the integrity of the system by making minimum viable changes. If you need to refactor something, it should be clear that that is the task.
But we're all tokenmaxing now.
In this case I would ask for smaller changes and justify every change. Have it look back upon these changes and have it ask itself are they truly justified or can it be simplified.
The cynic in me thinks it's done on purpose to burn more tokens. The pragmatist however just wants full control over the harness and system prompts. I'm sure this could be done away with if we had access to all the knobs and levers.
We do, just tell it what you want in your AGENTS.md file.
Agents also often respond well to user frustration signs, like threatening to not continue your subscription.
I've had success with greenfield code followed by frustration when asking for changes to that code due to over editing
And prompting for "minimal changes" does keep the edits down. In addition to this instruction, adding specifics about how to make the change and what not to do tends to get results I'm looking for.
"add one function that does X, add one property to the data structure, otherwise leave it as is, don't add any new validation"
Not surprised to see this, since once again, because some of us didn’t like history as a subject, lines of code is a performance measure, like a pissing contest.
It's impossible to properly review this in a reasonable time and they always introduce tons of subtle bugs.
I am surprised Gemini 3.1 Pro is so high up there. I have never managed to make it work reliably so maybe there's some metric not being covered here.
"Do not modify any code; only describe potential changes."
I often add it to the end when prompting to e.g. review code for potential optimizations or refactor changes.
With LLMs, you glimpse a distant mountain. In the next instant, you're standing on its summit. Blink, and you are halfway down a ridge you never climbed. A moment later, you're flung onto another peak with no trail behind you, no sense of direction, no memory of the ascent. The landscape keeps shifting beneath your feet, but you never quite see the panorama. Before you know it, you're back near the base, disoriented, as if the journey never happened. But confident, you say you were on the top of the mountain.
Manual coding feels entirely different. You spot the mountain, you study its slopes, trace a route, pack your gear. You begin the climb. Each step is earned steadily and deliberately. You feel the strain, adjust your path, learn the terrain. And when you finally reach the summit, the view unfolds with meaning. You know exactly where you are, because you've crossed every meter to get there. The satisfaction isn't just in arriving, nor in saying you were there: it is in having truly climbed.
With LLM-assisted coding, you skip the trek and you instantly know that’s not it.
The version it puts down into documents is not the thing it was actually doing. It's a little anxiety-inducing. I go back to review the code with big microscopes.
"Reproducibility" is still pretty important for those trapped in the basements of aerospace and defense companies. No one wants the Lying Machine to jump into the cockpit quite yet. Soon, though.
We have managed to convince the Overlords that some teensy non-agentic local models - sourced in good old America and running local - aren't going to All Your Base their Internets. So, baby steps.
The solution to this is to use quality gates that loop back and check the work.
I'm currently building a tool with gates and a diff regression check. I haven't seen these problems for a while now.
I re-did an entire UI recently, and when one of the elements failed to render I noticed the old UI peeking out from underneath. It had tried just covering up old elements instead of adjusting or replacing them. Like telling your son to clean their room, so they push all the clothes under the bed and hope you don't notice LOL
It saves 2 hours of manual syntax wrangling but introduces 1 .5 hours of clean up and sanity checking. Still a net productivity increase, but not sure if its worth how lazy it seems to be making me (this is an easy error to correct, im sure, but meh Claude can fix it in 2 seconds so...)
Too many people are treating the tools as a complete replacement for a developer. When you are typing a text to someone and Google changes a word you misspelled to a completely different word and changes the whole meaning of the text message do you shrug and send it anyway? If so, maybe LLMs aren't for you.
...and that led me to believe that AI might be very capable to develop over-engineered audio equipment. Think of all the bells and whistles that could be added, that could be expressed in ridiculous ways with ridiculous price tags.
Counterpoint: no it isn't
> makes this job dramatically harder
No it doesn't
