[jamesmcq]

This all looks fine for someone who can't code, but for anyone with even a moderate amount of experience as a developer all this planning and checking and prompting and orchestrating is far more work than just writing the code yourself.

There's no winner for "least amount of code written regardless of productivity outcomes.", except for maybe Anthropic's bank account.

[shepherdjerred]

I really don't understand why there are so many comments like this.

Yesterday I had Claude write an audit logging feature to track all changes made to entities in my app. Yeah you get this for free with many frameworks, but my company's custom setup doesn't have it.

It took maybe 5-10 minutes of wall-time to come up with a good plan, and then ~20-30 min for Claude implement, test, etc.

That would've taken me at least a day, maybe two. I had 4-5 other tasks going on in other tabs while I waited the 20-30 min for Claude to generate the feature.

After Claude generated, I needed to manually test that it worked, and it did. I then needed to review the code before making a PR. In all, maybe 30-45 minutes of my actual time to add a small feature.

All I can really say is... are you sure you're using it right? Have you _really_ invested time into learning how to use AI tools?

[tyleo]

Same here. I did bounce off these tools a year ago. They just didn't work for me 60% of the time. I learned a bit in that initial experience though and walked away with some tasks ChatGPT could replace in my workflow. Mainly replacing scripts and reviewing single files or functions.

Fast forward to today and I tried the tools again--specifically Claude Code--about a week ago. I'm blown away. I've reproduced some tools that took me weeks at full-time roles in a single day. This is while reviewing every line of code. The output is more or less what I'd be writing as a principal engineer.

[delusional]

> The output is more or less what I'd be writing as a principal engineer.

I certainly hope this is not true, because then you're not competent for that role. Claude Code writes an absolutely incredible amount of unecessary and superfluous comments, it's makes asinine mistakes like forgetting to update logic in multiple places. It'll gladly drop the entire database when changing column formats, just as an example.

[tyleo]

I’m not sure what you're doing or if you’ve tried the tools recently but this isn’t even close to my experience.

[skydhash]

> Yesterday I had Claude write an audit logging feature to track all changes made to entities in my app. Yeah you get this for free with many frameworks, but my company's custom setup doesn't have it.

But did you truly think about such feature? Like guarantees that it should follow (like how do it should cope with entities migration like adding a new field) or what the cost of maintaining it further down the line. This looks suspiciously like drive-by PR made on open-source projects.

> That would've taken me at least a day, maybe two.

I think those two days would have been filled with research, comparing alternatives, questions like "can we extract this feature from framework X?", discussing ownership and sharing knowledge,.. Jumping on coding was done before LLMs, but it usually hurts the long term viability of the project.

Adding code to a project can be done quite fast (hackatons,...), ensuring quality is what slows things down in any any well functioning team.

[jamesmcq]

Trust me I'm very impressed at the progress AI has made, and maybe we'll get to the point where everything is 100% correct all the time and better than any human could write. I'm skeptical we can get there with the LLM approach though.

The problem is LLMs are great at simple implementation, even large amounts of simple implementation, but I've never seen it develop something more than trivial correctly. The larger problem is it's very often subtly but hugely wrong. It makes bad architecture decisions, it breaks things in pursuit of fixing or implementing other things. You can tell it has no concept of the "right" way to implement something. It very obviously lacks the "senior developer insight".

Maybe you can resolve some of these with large amounts of planning or specs, but that's the point of my original comment - at what point is it easier/faster/better to just write the code yourself? You don't get a prize for writing the least amount of code when you're just writing specs instead.

[fourthark]

This is exactly what the article is about. The tradeoff is that you have to throughly review the plans and iterate on them, which is tiring. But the LLM will write good code faster than you, if you tell it what good code is.

[reg_dunlop]

Exactly; the original commenter seems determined to write-off AI as "just not as good as me".

The original article is, to me, seemingly not that novel. Not because it's a trite example, but because I've begun to experience massive gains from following the same basic premise as the article. And I can't believe there's others who aren't using like this.

I iterate the plan until it's seemingly deterministic, then I strip the plan of implementation, and re-write it following a TDD approach. Then I read all specs, and generate all the code to red->green the tests.

If this commenter is too good for that, then it's that attitude that'll keep him stuck. I already feel like my projects backlog is achievable, this year.

[fourthark]

Strongly agree about the deterministic part. Even more important than a good design, the plan must not show any doubt, whether it's in the form of open questions or weasel words. 95% of the time those vague words mean I didn't think something through, and it will do something hideous in order to make the plan work

[Degorath]

My experience has so far been similar to the root commenter - at the stage where you need to have a long cycle with planning it's just slower than doing the writing + theory building on my own.

It's an okay mental energy saver for simpler things, but for me the self review in an actual production code context is much more draining than writing is.

I guess we're seeing the split of people for whom reviewing is easy and writing is difficult and vice versa.

[hathawsh]

Several months ago, just for fun, I asked Claude (the web site, not Claude Code) to build a web page with a little animated cannon that shoots at the mouse cursor with a ballistic trajectory. It built the page in seconds, but the aim was incorrect; it always shot too low. I told it the aim was off. It still got it wrong. I prompted it several times to try to correct it, but it never got it right. In fact, the web page started to break and Claude was introducing nasty bugs.

More recently, I tried the same experiment, again with Claude. I used the exact same prompt. This time, the aim was exactly correct. Instead of spending my time trying to correct it, I was able to ask it to add features. I've spent more time writing this comment on HN than I spent optimizing this toy. https://claude.ai/public/artifacts/d7f1c13c-2423-4f03-9fc4-8...

My point is that AI-assisted coding has improved dramatically in the past few months. I don't know whether it can reason deeply about things, but it can certainly imitate a human who reasons deeply. I've never seen any technology improve at this rate.

[Kiro]

> but I've never seen it develop something more than trivial correctly.

What are you working on? I personally haven't seen LLMs struggle with any kind of problem in months. Legacy codebase with great complexity and performance-critical code. No issue whatsoever regardless of the size of the task.

[nojito]

>I've never seen it develop something more than trivial correctly.

This is 100% incorrect, but the real issue is that the people who are using these llms for non-trivial work tend to be extremely secretive about it.

For example, I view my use of LLMs to be a competitive advantage and I will hold on to this for as long as possible.

[jamesmcq]

The key part of my comment is "correctly".

Does it write maintainable code? Does it write extensible code? Does it write secure code? Does it write performant code?

My experience has been it failing most of these. The code might "work", but it's not good for anything more than trivial, well defined functions (that probably appeared in it's training data written by humans). LLMs have a fundamental lack of understanding of what they're doing, and it's obvious when you look at the finer points of the outcomes.

That said, I'm sure you could write detailed enough specs and provide enough examples to resolve these issues, but that's the point of my original comment - if you're just writing specs instead of code you're not gaining anything.

[cowlby]

I find “maintainable code” the hardest bias to let go of. 15+ years of coding and design patterns are hard to let go.

But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns.

Specs are worth it IMO. Not because if I can spec, I could’ve coded anyway. But because I gain all the insight and capabilities of AI, while minimizing the gotchas and edge failures.

[girvo]

> But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms. So maintainable has to evolve from good human design patterns to good AI patterns.

How do you square that with the idea that all the code still has to be reviewed by humans? Yourself, and your coworkers

[Jweb_Guru]

> But the aha moment for me was what’s maintainable by AI vs by me by hand are on different realms

I don't find that LLMs are any more likely than humans to remember to update all of the places it wrote redundant functions. Generally far less likely, actually. So forgive me for treating this claim with a massive grain of salt.

[kaydub]

Yes to all of these.

Here's the rub, I can spin up multiple agents in separate shells. One is prompted to build out <feature>, following the pattern the author/OP described. Another is prompted to review the plan/changes and keep an eye out for specific things (code smells, non-scalable architecture, duplicated code, etc. etc.). And then another agent is going to get fed that review and do their own analysis. Pass that back to the original agent once it finishes.

Less time, cleaner code, and the REALLY awesome thing is that I can do this across multiple features at the same time, even across different codebases or applications.

[reg_dunlop]

To answer all of your questions:

yes, if I steer it properly.

It's very good at spotting design patterns, and implementing them. It doesn't always know where or how to implement them, but that's my job.

The specs and syntactic sugar are just nice quality of life benefits.

[jmathai]

You’d be building blocks which compound over time. That’s been my experience anyway.

The compounding is much greater than my brain can do on its own.

[hghbbjh]

> In all, maybe 30-45 minutes of my actual time to add a small feature

Why would this take you multiple days to do if it only took you 30m to review the code? Depends on the problem, but if I’m able to review something the time it’d take me to write it is usually at most 2x more worst case scenario - often it’s about equal.

I say this because after having used these tools, most of the speed ups you’re describing come at the cost of me not actually understanding or thoroughly reviewing the code. And this is corroborated by any high output LLM users - you have to trust the agent if you want to go fast.

Which is fine in some cases! But for those of us who have jobs where we are personally responsible for the code, we can’t take these shortcuts.

[kaydub]

There's comments like this because devs/"engineers" in tech are elitists that think they're special. They can't accept that a machine can do a part of their job that they thought made them special.

[streetfighter64]

I mean, all I can really say is... if writing some logging takes you one or two days, are you sure you _really_ know how to code?

[boxedemp]

Ever worked on a distributed system with hundreds of millions of customers and seemingly endless business requirements?

Some things are complex.

[shepherdjerred]

You're right, you're better than me!

You could've been curious and ask why it would take 1-2 days, and I would've happily told you.

[jamesmcq]

I'll bite, because it does seem like something that should be quick in a well-architected codebase. What was the situation? Was there something in this codebase that was especially suited to AI-development? Large amounts of duplication perhaps?

[shepherdjerred]

It's not particularly interesting.

I wanted to add audit logging for all endpoints we call, all places we call the DB, etc. across areas I haven't touched before. It would have taken me a while to track down all of the touchpoints.

Granted, I am not 100% certain that Claude didn't miss anything. I feel fairly confident that it is correct given that I had it research upfront, had multiple agents review, and it made the correct changes in the areas that I knew.

Also I'm realizing I didn't mention it included an API + UI for viewing events w/ pretty deltas

[streetfighter64]

LOL, so why would I have asked you if the answer is self-admittedly not particularly interesting? It'd be like asking somebody why they took two days to put together an IKEA cupboard, of course the answer is uninteresting.

[fendy3002]

Well someone who says logging is easy never knows the difficulty of deciding "what" to log. And audit log is different beast altogether than normal logging

[streetfighter64]

Audit logging is different because it's actually more straightforward than "normal logging". You just make a log entry for each state change, basically. Especially if you're storing the log entries as "objects" instead of plain text.

Besides, do you think that a LLM would be better at deciding what to log than a human that has even just a little experience with the actual system in question?

[therealdrag0]

Audit logging is different than developer logging… companies will have entire teams dedicated to audit systems.

[fragmede]

We're not as good at coding as you, naturally.

[streetfighter64]

You're being sarcastic, but clearly what you're saying is literally true. So...

[psvv]

I'd find it deeply funny if the optimal vibe coding workflow continues to evolve to include more and more human oversight, and less and less agent autonomy, to the point where eventually someone makes a final breakthrough that they can save time by bypassing the LLM entirely and writing the code themselves. (Finally coming full circle.)

[pjio]

You mean there will be an invention to edit files directly instead of giving the specific code and location you want it to be written into the prompt?

[skeledrew]

Researching and planning a project is a generally usefully thing. This is something I've been doing for years, and have always had great results compared to just jumping in and coding. It makes perfect sense that this transfers to LLM use.

[skydhash]

> planning and checking and prompting and orchestrating is far more work than just writing the code yourself.

This! Once I'm familiar with the codebase (which I strive to do very quickly), for most tickets, I usually have a plan by the time I've read the description. I can have a couple of implementation questions, but I knew where the info is located in the codebase. For things, I only have a vague idea, the whiteboard is where I go.

The nice thing with such a mental plan, you can start with a rougher version (like a drawing sketch). Like if I'm starting a new UI screen, I can put a placeholder text like "Hello, world", then work on navigation. Once that done, I can start to pull data, then I add mapping functions to have a view model,...

Each step is a verifiable milestone. Describing them is more mentally taxing than just writing the code (which is a flow state for me). Why? Because English is not fit to describe how computer works (try describe a finite state machine like navigation flow in natural languages). My mental mental model is already aligned to code, writing the solution in natural language is asking me to be ambiguous and unclear on purpose.

[roncesvalles]

Well it's less mental load. It's like Tesla's FSD. Am I a better driver than the FSD? For sure. But is it nice to just sit back and let it drive for a bit even if it's suboptimal and gets me there 10% slower, and maybe slightly pisses off the guy behind me? Yes, nice enough to shell out $99/mo. Code implementation takes a toll on you in the same way that driving does.

I think the method in TFA is overall less stressful for the dev. And you can always fix it up manually in the end; AI coding vs manual coding is not either-or.

[dmix]

Most of these AI coding articles seem to be about greenfield development.

That said, if you're on a serious team writing professional software there is still tons of value in always telling AI to plan first, unless it's a small quick task. This post just takes it a few steps further and formalizes it.

I find Cursor works much more reliably using plan mode, reviewing/revising output in markdown, then pressing build. Which isn't a ton of overhead but often leads to lots of context switching as it definitely adds more time.

[kburman]

Since Opus 4.5, things have changed quite a lot. I find LLMs very useful for discussing new features or ideas, and Sonnet is great for executing your plan while you grab a coffee.

[keyle]

I partly agree with you. But once you have a codebase large enough, the changes become longer to even type in, once figured out.

I find the best way to use agents (and I don't use claude) is to hash it out like I'm about to write these changes and I make my own mental notes, and get the agent to execute on it.

Agents don't get tired, they don't start fat fingering stuff at 4pm, the quality doesn't suffer. And they can be parallelised.

Finally, this allows me to stay at a higher level and not get bogged down of "right oh did we do this simple thing again?" which wipes some of the context in my mind and gets tiring through the day.

Always, 100% review every line of code written by an agent though. I do not condone committing code you don't 'own'.

I'll never agree with a job that forces developers to use 'AI', I sometimes like to write everything by hand. But having this tool available is also very powerful.

[jamesmcq]

I want to be clear, I'm not against any use of AI. It's hugely useful to save a couple of minutes of "write this specific function to do this specific thing that I could write and know exactly what it would look like". That's a great use, and I use it all the time! It's better autocomplete. Anything beyond that is pushing it - at the moment! We'll see, but spending all day writing specs and double-checking AI output is not more productive than just writing correct code yourself the first time, even if you're AI-autocompleting some of it.

[skeledrew]

For the last few days I've been working on a personal project that's been on ice for at least 6 years. Back when I first thought of the project and started implementing it, it took maybe a couple weeks to eke out some minimally working code.

This new version that I'm doing (from scratch with ChatGPT web) has a far more ambitious scope and is already at the "usable" point. Now I'm primarily solidifying things and increasing test coverage. And I've tested the key parts with IRL scenarios to validate that it's not just passing tests; the thing actually fulfills its intended function so far. Given the increased scope, I'm guessing it'd take me a few months to get to this point on my own, instead of under a week, and the quality wouldn't be where it is. Not saying I haven't had to wrangle with ChatGPT on a few bugs, but after a decent initial planning phase, my prompts now are primarily "Do it"s and "Continue"s. Would've likely already finished it if I wasn't copying things back and forth between browser and editor, and being forced to pause when I hit the message limit.

[keyle]

This is a great come-back story. I have had a similar experience with a photoshop demake of mine.

I recommend to try out Opencode with this approach, you might find it less tiring than ChatGPT web (yes it works with your ChatGPT Plus sub).

[skeledrew]

I actually don't have a subscription; just started ramping up my usage, and still primarily evaluating. TBH also the main reason I'm using ChatGPT for this project is because Claude kept timing out on my initial prompt, maybe because too much for their free plan. But it turned out well as ChatGPT has a higher message limit, and I still use Claude to resolve bugs that stump ChatGPT (and me); I consider it my "big gun" that I resort to in extraordinary circumstances, or for things I'm pretty sure it'll handle in a few rounds. ChatGPT is more for "grunt work".

[Quothling]

I think it comes down to "it depends". I work in a NIS2 regulated field and we're quite callenged by the fact that it means we can't give AI's any sort of real access because of the security risk. To be complaint we'd have to have the AI agent ask permission for every single thing it does, before it does it, and foureye review it. Which is obviously never going to happen. We can discuss how bad the NIS2 foureye requirement works in the real world another time, but considering how easy it is to break AI security, it might not be something we can actually ever use. This makes sense on some of the stuff we work on, since it could bring an entire powerplant down. On the flip-side AI risks would be of little concern on a lot of our internal tools, which are basically non-regulated and unimportant enough that they can be down for a while without costing the business anything beyond annoyances.

This is where our challenges are. We've build our own chatbot where you can "build" your own agent within the librechat framework and add a "skill" to it. I say "skill" because it's older than claude skills but does exactly the same. I don't completely buy the authors:

> “deeply”, “in great details”, “intricacies”, “go through everything”

bit, but you can obviously save a lot of time by writing a piece of english which tells it what sort of environment you work in. It'll know that when I write Python I use UV, Ruff and Pyrefly and so on as an example. I personally also have a "skill" setting that tells the AI not to compliment me because I find that ridicilously annoying, and that certainly works. So who knows? Anyway, employees are going to want more. I've been doing some PoC's running open source models in isolation on a raspberry pi (we had spares because we use them in IoT projects) but it's hard to setup an isolation policy which can't be circumvented.

We'll have to figure it out though. For powerplant critical projects we don't want to use AI. But for the web tool that allows a couple of employees to upload three excel files from an external accountant and then generate some sort of report on them? Who cares who writes it or even what sort of quality it's written with? The lifecycle of that tool will probably be something that never changes until the external account does and then the tool dies. Not that it would have necessarily been written in worse quality without AI... I mean... Have you seen some of the stuff we've written in the past 40 years?

[stealthyllama]

There is a miscommunication happening, this entire time we all had surprisingly different ideas about what quality of work is acceptable which seems to account for differences of opinion on this stuff.

[phantomathkg]

Surely Addy Osmani can code. Even he suggests plan first.

https://news.ycombinator.com/item?id=46489061