[coffeebeqn]

I don’t know if most software engineers build toy CRUD apps all day? I have found the state of the art models to be almost completely useless in a real large codebase. Tried Claude and Gemini latest since the company provides them but they couldn’t even write tests that pass after over a day of trying

[flir]

Our current architectures are complex, mostly because of DRY and a natural human tendency to abstract things. But that's a decision, not a fundamental property of code. At core, most web stuff is "take it out of the database, put it on the screen. Accept it from the user, put it in the database."

If everything was written PHP3 style (add_item.php, delete_item.php, etc), with minimal includes, a chatbot might be rather good at managing that single page.

I'm saying code architected to take advantage of human skills, and code architected to take advantage of chatbot skills might be very different.

[rafaelmn]

LOL how does the AI keep track of all the places it needs to update once you make a logic change ? I have no idea what you're doing but almost nothing I do is basic CRUD - there's always logic/constraints around data flow and processes built on top.

People didn't move away from PHP3 style of code because it's a natural human tendency - they moved away because it was impossible to maintain that kind of code at scale. AI does nothing to address that and is in fact incredibly bad at it because it's super inconsistent, it's not even copy paste at that point - it's the "whatever flavor of solution LLM chose in this instance".

I don't understand what's your background to think that this kind of thing scales ?

[flir]

I can see how it would be confusing.

[svilen_dobrev]

long time ago, in one small company, i wrote an accounting system from 1st principles and then it was deployed to some large-ish client. It took several months of rearranging their whole workflows and quarelling with their operators to enable the machine to do what it is good at and to disable all the human-related quirky +optimizations -cover-asses. Like, humans are good at rough guessing but bad at remembering/repeating same thing. Hence usual manual accounting workflows are heavily optimized for error-avoidability.

Seems same thing here.. another kind of bitter lesson, maybe less bitter :/

[dbdoskey]

This is IMHO where the interesting direction will be. How do we architecture code so that it is optimized around chatbot development? In the past areas of separation were determined by api stability, deployment concerns, or even just internal team politics. In the future a rep might be separated from a monolith repo to be an area of responsibility that a chatbot can reason about, and not get lost in the complexity.

[az09mugen]

IMHO we should always architect code to take advantage of human skills.

1°) When there is an issue to debug and fix in a not-so-big codebase, LLMs can give ideas to diagnose, but are pretty bad at fixing. Where your god will be when you have a critical bug in production ?

2°) Code is meant for humans in the first place, not machines. Bytecodes and binary formats are meant for machines, these are not human-readable.

As a SWE, I pass more time reading than writing code, and I want to navigate in a the codebase in the most easy possible way. I don't want my life to be miserable or more complicated because the code is architected to take advantage of chatbot skills.

And still IMHO, if you need to architect your code for not-humans, there is a defect in the design. Why force yourself to write code that is not meant to be maintained by a human when you will in any case maintain that said code ?

[flir]

This human quite likes having everything on one page, to be honest. And not having a leaky ORM layer between me and the SQL.

[the_duke]

Agreed in general, the models are getting pretty good at dumping out new code, but for maintaining or augmenting existing code produces pretty bad results, except for short local autocomplete.

BUT it's noteworthy that how much context the models get makes a huge difference. Feeding in a lot of the existing code in the input improves the results significantly.

[nradov]

This might be an argument in favor of a microservices architecture with the code split across many repos rather than a monolithic application with all the code in a single repo. It's not that microservices are necessarily technically better but they could allow you to get more leverage out of LLMs due to context window limitations.

[gmadsen]

this is a short term issue though. The available context window has been increasing exponentially over the past 2 years

[tough]

if your microservices become more verbose overall, now you have handicapped your ability to cram the whole codebase into a context window.

I think AI is great but humans know the why's of the code needs to exist AI's don't need stuff, only generate it

[the_duke]

The LLMs would only need to have API information for the service, not the whole code, which would be small.

[kypro]

Most senior SWEs, no. But most technical people in software do a lot of what the parent commenter describes in my experience. At my last company there was a team of about 5 people whose job was just to make small design changes (HTML/CSS) to the website. Many technical people I've worked with over the years were focused on managing and configuring things in CMSs and CRMs which often require a bit of technical and coding experince. At the place I currently work we have a team of people writing simple python and node scripts for client integrations.

There's a lot of variety in technical work, with many modern technical jobs involving a bit of code, but not at the same complexity and novelty as the types of problems a senior SWE might be working on. HN is full of very senior SWEs. It's really no surprise people here still find LLMs to be lacking. Outside of HN I find people are far more impressed/worried by the amount of their job an LLM can do.

[fl0id]

Same. Like Claude code for example will write some tests. But what they are testing is often incorrect