@plexus In the end, software engineering is about creating solutions to problems other people have. The solutions are not a byproduct, but the primary purpose. To the majority of users, the inner workings and the creation process of software is opaque. The qualities that software exposes on the outside are largely independent of its inner workings.

This means that for most people in the software industry, adapting to the new tooling that makes the creation process more efficient is 1/

@hanshuebner You are replying to Rich Felker, primary developer of the musl C library for Linux, a shining example of software at a low layer of the stack developed with meticulous attention to quality. True, quality that business people probably don't appreciate, but if software at all layers were developed with this attention to quality, I think users would feel the difference.

@dalias @plexus

@matt @dalias @plexus Is the reality not that not all software is developed with meticulous attention to quality? In my experience, most software is primarily written with the intent to solve a problem. The engineering challenge is to make it maintainable as requirements evolve. Success is when the software fulfills its purpose.

I love writing beautiful code, but don't expect anyone to pay me for it - not only because beauty is in the eye of the beholder, but also because users don't care.

@hanshuebner @matt @plexus Software written without any concern that it's doing the wrong thing does not "solve any problem" except "how to line venture capitalists' pockets".

Unless it's just being written for fun and not actual deployment to real-world applications, software is responsible for people's safety.

It controls deadly machines like cars and airplanes.It's used to design buildings and bridges. It guards people's communications against abusive partners, stalkers, governments. It controls people's money. It controls who gets need-based benefits. It decides whether people will be wrongly accused of embezzlement and driven to suicide.

@hanshuebner @dalias (Dropping the original author as they already warned you that they're in no mood for your arguments.)

IMO, code is not something to be cranked out en masse. Every detail matters; as such, we should write every line deliberately, with care, as the clearest, most direct expression of our understanding of how to solve the problem, certainly clearer and more precise than a natural-language prompt.

@matt @dalias "Our understanding" is often incomplete, leading to code that is just a reflection of the process of understanding the task at hand. Code often suffers from that in that the person working on it learned faster than they could or would refactor. The resulting reality is that code, by and large, is messy.

Not everyone is working the same way, but it is certainly true that not everyone is a genius. Thus, bad, human code prevails.

@dalias @matt I live in capitalism as a software developer. I don't get to choose what tools I use, I'm getting paid to do the work. I can change my profession, or I can pick up what I need to know in order to sustain myself. This is me personally.

Then: LLMs create code that is comparable to human written code in that frame of reference. There is better code, but there is also much worse.

Finally: LLMs create shitty prose, shitty images and shitty music. I hate all of that.

@matt @dalias You are absolutely right, but here's the thing: Code review also does not prevent subtle bugs from creeping into the code base when humans wrote the code. Review is just one of the tools that ensure software quality.

This is to say that code written by LLMs and humans suffer from similar issues, require similar care and review and can fail in similar ways. There is more LLM code, though, and there are new challenges because scaling with LLMs works differently than with humans.

@matt @dalias Statistically, you will have more bugs because you have more software. But also, you can easily create tests, refactor and make executable requirements.

Making good software with LLM support is hard work and takes time. If you look at the stuff that people make with three prompts and then post to LinkedIn, you know what I mean.

A good program requires attention to detail, no matter what the tool does for you.

@matt @hanshuebner @dalias Just jumping into this thread to point out the elephant in the room: empirically, using LLMs to write code makes the process slower, not faster. Once you wade through the anecdotes and the puff-pieces and the LLM-developer-sponsored "research" using metrics like "lines of code" you'll find solid academic articles observing slower time-to-task-completion despite (incorrect) subjective perception of a speedup:

https://arxiv.org/abs/2507.09089

The entire premise of "well we've got to adopt it to keep up" is just plain not supported by a preponderance of credible evidence. "This is the future" is a pure marketing line and seems likely to be an outright lie. Accepting it as the framing of the argument cedes *way* too much ground to the AI pushers.

@tiotasram @matt @hanshuebner @dalias Honestly, I don't think even the AI labs are ignoring these issues. At the very least Anthropic has been fairly up front about these concerns and reporting them in their research and surveywork.

Their most recent one is pretty comprehensive: https://www.anthropic.com/features/81k-interviews

@neal @matt @hanshuebner @dalias this is... A "study" conducted by an AI company who used their own tool to summarize and classify the results. The quotes pulled at the top and even the design of the study are designed to take as an unspoken assumption "AI is the future" and their recruitment method selects for people who likely believe that too. The quotes they pull are split between AI boosterism & doomerism, both of which help feed the hype around AI capabilities that helps sell their product.

I'll bet that some of the Anthropic employees involved think this is honest self-reflection, but I'll bet twice as much that a marketing team signed off on the copy. It puts serious harms life psychological dependence next to questionable benefits, and makes this seem like a good thing.

It mentions productivity as a category and shows that more respondents *feel* they are getting productivity gains than feel their productivity decreased (37% vs 17%). Impossible with this design to quantify how much productivity actually increased or decreased, but of course that wasn't a study goal. If the results in the study I linked about overestimation of productivity with LLM "help" hold up, they're consistent with this data.

Not mentioned anywhere in this report (by design): opinions of people who don't want to use AI at all and who want harmful AI development processes like those at Anthropic to stop.

Finally, one main thrust of the report is that individual users will directly benefit from the supposed productivity increases LLMs are delivering. I've already written about this misconception, which amounts to a trap designed to prevent the masses of workers who stand to lose by the machinations of big AI companies from actually opposing them:

https://cs.wellesley.edu/~pmwh/advice/aiProductivity.html

TL;DR: Anthropic briefly gives out free cookies to everyone and says "see, don't you love having more cookies?" In a few years, either your boss fires you because they can get cookies from Anthropic directly or they demand you hand over the extra cookies to them.

@tiotasram @matt @hanshuebner @dalias So you're saying you'd prefer slanted research as long as it favors your point of view that "AI is bad"? That wall of text you wrote basically oversimplifies everything to a negative bias.

Even Anthropic acknowledged that this is from Claude users, but you discount the weight of the opinions of people simply because of that?

And the sample size is more than sufficiently large to be considered rigorous.

@neal @tiotasram @matt @hanshuebner Slanted research? The "slanted research" is that which was funded by a party with a financial interest in a particular outcome.

All of the real researchers have been defunded/fired for publishing things that reflected poorly on the industry, going all the way back to Timnit Gebru.

Do better.

@dalias @tiotasram @matt @hanshuebner You do better, too. So far I've seen nothing from this thread that attempts to connect with people in a way that would make them want to consider your position. I have been wary of this tech. But as an "ordinary" freelance software engineer, the pressure to be as good as a genius is real.

I've already stated this once before in another thread, and I'll say it again here: your way of communicating this is making people consider the opposite as reasonable.

@neal @tiotasram @matt @hanshuebner When you cite clearly biased industry-sponsored "research" as a credible source, it makes it hard to believe you have the same goals and values on this as I do.

And a belief that we do have shared goals and values is a necessary prerequisite for taking serious any advice you might give on how to achieve those goals.

Without that it comes across as concern trolling.