This is a very academic approach to the subject - read what other people have written about it without ever doing it yourself. Study what someone said about LLM coding 50 years ago, before they were even invented, to see what you think about it.

I would strongly suggest to the author that you just give it a go, and see what you think, without the preconception of other people's opinions.

My experience has been remarkable, and, like others, I'm finding real joy in being able to move past the code to actually design and play with whole systems and architectures.

It gets to the essence of code; which is not about the code, but about the system that code implements. Being able to write code in 3 minutes not 30 minutes does not bog us down in review (the LLM is perfectly capable of reviewing code too). It frees us to explore systems and architectures without worrying about the sunk cost of the existing code, or the effort of changing it.

> (although I’m personally skeptical of the “10x programmer” concept, the software industry overall does seem to accept it as true)

To be fair, this statement from Brooks doesn't entirely match with the "10x programmer" we talk about. My take on it is when someone says "10x programmer" today, they mean 10x more productive than the average, not 10x more productive than the worst. Brooks' statement is about the latter. If he'd looked at the difference between average and best, I would assume you'd get something more like a 2x or 4x programmer.

(Sorry for bikeshedding, but you can't discuss an article if you can't read it.)

You could fetch some unfinished github repos or download free templates. It’s actually faster than LLMs, still no body would do it.

I don’t start my project with the ecommerce nextjs starter repo. I build it from scratch, because it’s faster...

Use the damn thing or don't.

It's that simple.

The author didn't seem to read the Brooks essay for comprehension. There is an entire section about expert systems that foreshadows agents. While there is no singular silver bullet, Brooks explores the most promising techniques to reduce essential complexity that were anticipated in 1986.

> The most powerful contribution of expert systems will surely be to put at the service of the inexperienced programmer the experience and accumulated wisdom of the best programmers. This is no small contribution.

Furthermore, his objection to automatic programming was simply an argument from incredulity, which is an understandable opinion at the time, yet quite vacuous in hindsight.

I think the biggest benefit language models have provided me is in the auxiliary aspects to programming: search, debugging, rubber ducking, planning, refactoring. The actual code generation has been mixed.

I had an LLM try and implement a fairly involved feature the other day, providing it with API spec details, examples from other open source libraries, and plenty of specifications. It's also something readily available in training data as well, but still fairly involved.

On first glance it looked great, and had I not spent the time to investigate deeper I would have missed some glaring deficiencies and omissions that render its implementation worthless. I am now going back and writing it by hand, but with language models providing assistance along the way, and it's going much better.

I think people are being unrealistic by thinking that the usage of language models in their side projects represent something broader. It's almost the perfect situation for language models: small, greenfield code bases, no review, no responsibility, and no users. It goes up on GitHub with a pretty readme, and then off to social media where they post about how developers are "cooked". It's just not a very realistic test.

In the end we will probably see large productivity increases by integrating language models, but they won't be replacing developers but rather augmenting them.

Design patterns in an older (programming) language become core language features in a newer one. As we internalize and abstract away the best patterns for something, it becomes accidental but it's only obvious in retrospect.

The article quotes Brooks (quoting Parnas) about just that (later, in context of LLMs):

> automatic programming always has been a euphemism for programming with a higher-level language than was presently available to the programmer. [...] Once those accidents have been removed, the remaining ones are smaller, and the payoff from their removal will surely be less.

Considering this was written when C was the hot new stuff, let's compare the ability to code a CRUD web app in Python/Django vs C. What Brooks and Parnas are saying that Python/Django cannot bring big improvements in building a CRUD web app when compared to C because they can only make it easier to program, reducing accidental complexity. But we've since redefined "accidental" and I would argue that you can write a CRUD web app in Python/Django at least 100x faster than in C (and probably at least 100x more secure), although it may take 1000x as more CPU and RAM while running.

So "we removed most of the accidental difficulties and the most that remains is essential" is a kind of "end of history" argument.

> I’d be surprised if there’s even a doubling of productivity still available from a complete elimination of remaining accidental difficulty.

It's good that this statement has a conditional subjective guard, because that's just punditry.

> LLM coding does not represent a silver bullet

Here I agree with the author completely, but probably not for the same reasons. The definition of "silver bullet" the article uses (quoting Brooks):

> There is no single development, in either technology or management technique, which by itself promises even a single order-of-magnitude improvement within a decade in productivity, in reliability, in simplicity.

AI-assisted development is not a single technique, the same way "devops" or "testing" or "agile" is not a single technique. But more importantly, I agree it will take time to find best practices, for the technology change to slow down, and for the best approaches to diffuse across the industry.

The article's conclusion:

> You should be adopting and perfecting solid foundational software development practices like version control, comprehensive test suites, continuous integration, meaningful documentation, fast feedback cycles, iterative development, focus on users, small batches of work… things that have been known and proven for decades, but are still far too rare in actual real-world software shops.

These are great and I'm gonna let him/her finish, but it's curious actual coding isn't mentioned anywhere. The author doesn't suggest "polish your understanding of C pointer semantics" or "Rust ownership model" or "Django ORM" or to really, deeply, understand B-trees. Looks like pedestrian detailes like those are left as an excercise for the reader ... or the reader's LLM.

I'm reminded of this scene from the Matrix: https://www.youtube.com/watch?v=cD4nhYR-VRA where the older wise man discusses societies reliance on AI

"Nobody cares how it works, as long as it works"

We're done. I for one welcome our new AI Overlords, or more accurately still welcome the tech bro billionares who are pulling the strings

Until recently. dramatic pause

And then AI happened.

I honestly couldn't force myself to finish yet another blog post about how "we're not yet sure what impact LLMs will have on society" or whatever beleaguered point the author was attempting to make.

"Some random person's take on LLMs" was maybe interesting in 2024. Today it is not even remotely interesting.

There are a gazillion more interesting things happening today that ought to be of interest to the median HN reader. Can we talk about those instead?

The article goes on to assume there’s no 10x gain to be had but misses one big truth.

Needing to type the code is an enormous source of accidental difficulty (typing speed, typos, whether you can be arsed to put your hands on the keyboard today…) and it is gone thanks to coding agents.