Apparently it's not obvious to everyone, but if you can't write code, you can't review it. I do know people, and companies, that says: "So what, we ask Claude to write the code, Codex will then do the review". The thing that then strikes me as odd is that they still ask for the code in Python, Java, or some other high level language.... Why? Just ask Claude to dump out assembly, or a compiled binary, but no, they don't trust the LLM that much. They still want to be able to read the code. So they need developers that can read, debug and reason about the code, yet they don't want to give them the training that's required to do this?

“Do you know what the industry term for a project specification that is comprehensive and precise enough to generate a program?

Code. It’s called code.”

- CommitStrip (https://www.reddit.com/r/ProgrammerHumor/comments/1p70bk8/sp...)

I think if you’re doing it right, the core of your code should be the simplest expression of the underlying business logic. Of course there’s always going to be supporting layers, and maybe those don’t need to be reviewed. But if you haven’t read the code, there’s an extent to which you don’t know the business logic.

> For example, have you ever seen an agent follow the boy scout rule? Where they leave code better than they found it? And would you WANT them to try to do this?

Yes, it's in the rules; run profiles, check code coverage, do a critical review, post the report and follow up tasks. 90% of people I've worked with did not follow these boy scout rules nearly as well as today's frontier LLMs.

Is the author implying this is bad?

"It’s about attention and understanding. To keep my attention, I must go beyond ‘read code’ like a passive observer of agents from afar. To really connect with the architecture of the system, it helps to truly experience the code"

I guess the funny answer that is behind this sentence is: You have to train your own mental model. We always argue about code in a very abstract and logical manner. But when coding the subconsciousness makes most of the decision ("this just feels right"). But for this to work you have to train it. And this does only work in a very limited way with code reviews or reading documentation. It requires repetition and deep focus.

When there is an issue in production with this mental model you will be able to point to the cause of an error message instantly. With generated code you'll search for a long time with your slow, conscious part of the brain.

For LLMs to be really helpful, they have to take over complete maintenance of the code. So you can treat them like an external library: Just assume it works. Otherwise this will always be problematic.

I write code all the time I can, outside the KPI metrics that everyone is being pushed to, I only care about AI for smarter code completion.

i write code because i love it. it's something that makes me genuinely happy, so why would i give that up?

At this point I haven't looked at code for many months. Before that, I spent 4 years hand coding a Google Docs competitor in JS without any libraries.

My brain feels equally as exercised (in fact more so as I am not as good at agentic coding as I was at real coding)

But now I'm making highly polished Mac OS apps and I really like that move from JS.

I feel... conflicted.

Note as some may be confused by the "1 hour ago" with comments older than that: this submission was rescued by dang when a previous discussion existed: https://news.ycombinator.com/item?id=48883341

fwiw I think the rationale behind it is counterproductive because the only difference between a OP submitting their article link and someone else submitting their article link is internet points.

>It’s our job to build the software factory - not just the software. Software engineers maintain the assembly line allowing anyone to prompt for a change and ship immediately.

The job of the software engineer increasingly becomes to make himself unnecessary: to empower the nontechnical business users to do as much as reasonably possible, without his/her intervention.

This has, of course, been the dream of computing, since its inception! And the true aim of every "high level" or "beginner friendly" (looking at you javascript!) language.

But finally, now that the computer actually speaks English (and is beginning to stop making completely insane errors), it gradually becomes feasible.

Freeing the masses from the tyranny of the nerds!

I need to write code because otherwise LLMs will write too much code, it’s only when you fully understand the problem you can generalise it enough to not end up with 10k lines and 5 abstraction layers for “hello world”. LLMs are token predictors, so all solutions are you tokens, the more problems to solve == the more tokens (code) to output.

I always hated writing code but loved debugging. LLM super charges systems thinkers & auditors, it’s just a different process and no different than copy and paste from stack overflow. It all comes down to the architecture design and LLM just exposes how bad people are at designing dynamic architectures.

If we look at the progress made from ChatGPT 3.5 (Nov 2022) and up to today...shoot, I'm really starting to wonder if we'll even be reviewing code in 4 years.

And I'm not saying this as some sort of AI maximalist. If progress keeps up, I seriously doubt software engineering and development will, as we know it today, will be a thing in the next 5-10 years. Maybe humans will be left with designing the UI, but everything else will be abstracted away and AI will be doing all the actual work behind the scenes.

I was thinking about an experience I had recently, and how it relates to my feelings about AI... And it bummed me out a lot.

So I took over an open source project called Omnivore. It's a reading app in the vein of Pocket. The hosted version used pdf-lib to inject some functionality into the pdf viewer. Namely, highlighting, note taking, and storing location. pdf-lib is a licensed application, so when taking it to fully self-hosted this needed to change.

I migrated it over to pdf.js. And I went through the entire process. I added all the functionality bit by bit. It didn't take exceptionally long, maybe 1-3 days. But that process was really satisfying. I found a bug, fixed it, and then found a stackoverflow issue where someone was also experiencing the same issue and suggested the fix. https://stackoverflow.com/questions/59151218/pdfjs-error-on-...

I'm pretty sure an ai could have done all of this. And therein lies my fear and my upset with AI. Not only would it have robbed me of that experience, but it shows that I have in a way been devalued. Because I do think that took a level of skill. And now that's gone...

If you’re not writing code it won’t be long until you get to a point where your agent won’t be able to dig you out of whatever hole you’ve dug for yourself and then you are fubar because you’ve just completely forgotten how.

Unless you want some unmaintainable shitty sloppy app.

Why is this even a legit question? I need to keep writing code to stay relevant, not to forget my craft, be able to review code... So many reasons. AI doesn't change a thing.

I found myself working mostly at the requirements and architecture level, but do not give up proper code-review, creating skills along the way that maintain conventions.

I think fragility is the key reason i intervene in llm code too. Good article.

Do ppl think that programmers just write code from sratch each time..?!

Even without AI I barely write code. 95% of time are spend setting up integrations, configs, copying & adjusting code from previous projects.

All this debate around use LLM or not is tiring and just black and white thinking.

Can I use agents to code a SWE project? yes, with nuances.

Can I write code for a SWE project? yes, with nuances.

Its more options now, I'll write code about projects I deeply care and will use llm at work where its shared slop and forced usage.

Prediction: in 2027 a coding agent will read this as inspiration for why it should code.

Seems like there’s broadly two ways to use LLMs for coding - either as a way to generate the same code you would have written but faster, or as an opaque program-generator where you have no idea what the code is doing. One of these methods results in roughly the same amount of understanding and the other one radically less.

> If we’re building a software factory, details matter. The details that establish architectural patterns. Down to algorithms and performance. Agents push us to evaluate, measure, and guard. They’ve made it cool to add CI into side projects early, not as an afterthought. That’s massive improvement to the state of software.

Why are you building a software factory though, and why weren't you immediately adding CI to every project?

> It’s our job to build the software factory - not just the software. Software engineers maintain the assembly line allowing anyone to prompt for a change and ship immediately.

Again, why? Where are you working where this is considered a good idea? This would mean that the software engineers are not just being completely kicked out of all business decisions, but asked to build a moat that ensures they stay on the other side of it.

Any business that intentionally devalues the insights gained through implementation will eventually starve itself to death by making too many passive thoughtless moves. No insight will ever be gained just spot checking AI. Is their intention really just to make tiny amounts of profit while riding the thing into the ground? Crabs in a bucket, man.

I still exclusively write my code. The quality is higher. I know exactly how it works. It’s more extensible. You don’t have to generate it.

The software factory is exactly what I'm building. The world is changing, we can either be the ones changing it or be forced to change afterwards.

exactly how many times do you plan on posting this here

This is too generic. There's some code I need to write like core abstractions that are going to set the pace for everything. Or tricky steps that can look good without actually working well.

Then there's the mass. I don't need that anymore. The mountains of boilerplate, etc.

I write little islands which need high judgement that are then connected by the obvious goo.

lol what a slopper

"Why cook food in 2026 [while McDonald's exists]?"

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TL;DR: Write it so you’re actively involved and not a passive reviewer. Then a sign up link for his course.

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Recently, even a tourist lost to OAI's model in competitive coding. To be honest, I haven't been able to beat AI at coding since around 5.2. People often say 'AI can't write good code,' but in reality, the quality of AI's output is layered depending on the level of the prompt input. The deeper the prompt, the better the code actually gets.

Usually, when people say AI code is terrible, it's because they either don't understand the theory well but have grown through hands-on experience and can't explain things properly to the AI, or they don't know what they don't know. Or there are the very few who are just far better coders than AI. Some people will say they're among the rare few who can write better code than AI, and for some that may be true. But in my experience, the vast majority are not. Even from my perspective as a beginner, I could see flaws when I looked at their git code. It's a metacognition problem.

Realistically speaking, at the script level, it's quite common to see AI surpass human programmers as you increase the input level. You might disagree, but that's probably because you're a specialist in that field, deeply immersed in a very narrow area—it only holds true in that limited scope. In the general domain, most people would agree that AI writes code well.

Human programmers don't know much outside their own domain. But AI, while it loses in very narrow specialist areas, writes better code than humans across the broader range. It loses in the 1% zone (the expert's domain), but wins in the other 99%. Usually, when that's the case, you have two choices: become the 1%, or learn how to use AI.

Since I'm a non-native English speaker, I'm already at a disadvantage compared to native speakers in programming skills, so I chose the latter. But I still code. Not for any other reason—if I don't maintain at least some typing muscle, I won't be able to review AI code properly.

That's why I think coding is essential. Even if I can't understand the entirety of AI's output, I still need to understand the core business logic. At the very least, the core logic requires human understanding, so coding is necessary.

AI is a tool. Learn how to use it!

Interesting article btw

It seems to me that AI won the code Battle and that humans are just trying to justify the defeat. I will relax and wait for the Next AI generation to see how it fixed its problems. May be, everything will be ok.