If humans are so good at coding, how come there are 8100000000 people and only 1500 are able to contribute to the Linux kernel?
I hypothesize that AI has average human coding skills.
this post was submitted on 22 May 2025
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The average coder is a junior, due to the explosive growth of the field (similar as in some fast-growing nations the average age is very young). Thus what is average is far below what good code is.
On top of that, good code cannot be automatically identified by algorithms. Some very good codebases might look like bad at a superficial level. For example the code base of LMDB is very diffetent from what common style guidelines suggest, but it is actually a masterpiece which is widely used. And vice versa, it is not difficult to make crappy code look pretty.
"Good code" is not well defined and your example shows this perfectly. LMDBs codebase is absolutely horrendous when your quality criterias for good code are Readability and Maintainability. But it's a perfect masterpiece if your quality criteria are Performance and Efficiency.
Most modern Software should be written with the first two in mind, but for a DBMS, the latter are way more important.
My theory is not a lot of people like this AI crap. They just lean into it for the fear of being left behind. Now you all think it's just gonna fail and it's gonna go bankrupt. But a lot of ideas in America are subsidized. And they don't work well, but they still go forward. It'll be you, the taxpayer, that will be funding these stupid ideas that don't work, that are hostile to our very well-being.
Ask Daniel Stenberg.
who makes a contribution made by aibot514. noone. people use ai for open source contributions, but more in a 'fix this bug' way not in a fully automated contribution under the name ai123 way
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Mostly closed source, because open source rarely accepts them as they are often just slop. Just assuming stuff here, I have no data.
And when they contribute to existing projects, their code quality is so bad, they get banned from creating more PRs.
To be fair if a competent dev used an ai "auto complete" tool to write their code, I'm not sure it'd be possible to detect those parts as an ai code.
I generally dislike those corporate AI tools but gave a try for copilot when writing some terraform script and it actually had good suggestions as much as bad ones. However if I didn't know that well the language and the resources I was deploying, it'd probably have led me to deep hole trying to fix the mess after blindly accepting every suggestion
They do more than just autocomplete, even in autocomplete mode. These Ai tools suggest entire code blocks and logic and fill in multiple lines, compared to a standard autocomplete. And to use it as a standard autocomplete tool, no Ai is needed. Using it like that wouldn't be bad anyway, so I have nothing against it.
The problems arise when the Ai takes away the thinking and brain functionality of the actual programmer. Plus you as a user get used to it and basically "addicted". Independent thinking and programming without Ai will become harder and harder, if you use it for everything.
People seem to think that the development speed of any larger and more complex software depends on the speed the wizards can type in code.
Spoiler: This is not the case. Even if a project is a mere 50000 lines long, one is the solo developer, and one has a pretty good or even expert domain knowledge, one spends the mayor part of the time thinking, perhaps looking up documentation, or talking with people, and the key on the keyboard which is most used doesn't need a Dvorak layout, bevause it is the "delete" key. In fact, you don't need yo know touch-typing to be a good programmer, what you need is to think clearly and logically and be able to weight many different options by a variety of complex goals.
Which LLMs can't.
I don't think it makes writing code faster, just may reduce the number of key presses required
Creator of curl just made a rant about users submitting AI slop vulnerability reports. It has gotten so bad they will reject any report they deem AI slop.
So there’s some data.
AI is just the lack of privacy, Authoritarian Dragnet, remote control over others computers, web scraping, The complete destruction of America's art scene, The stupidfication of America and copyright infringement with a sprinkling of baby death.
As a dumb question from someone who doesn't code, what if closed source organizations have different needs than open source projects?
Open source projects seem to hinge a lot more on incremental improvements and change only for the benefit of users. In contrast, closed source organizations seem to use code more to quickly develop a new product or change that justifies money. Maybe closed source organizations are more willing to accept slop code that is bad but can barely work versus open source which won't?
Maybe closed source organizations are more willing to accept slop code that is bad but can barely work versus open source which won’t?
Because most software is internal to the organisation (therefore closed by definition) and never gets compared or used outside that organisation: Yes, I think that when that software barely works, it is taken as good enough and there's no incentive to put more effort to improve it.
My past year (and more) of programming business-internal applications have been characterised by upper management imperatives to “use Generative AI, and we expect that to make you nerd faster” without any effort spent to figure out whether there is any net improvement in the result.
Certainly there's no effort spent to determine whether it's a net drain on our time and on the quality of the result. Which everyone on our teams can see is the case. But we are pressured to continue using it anyway.
I'd argue the two aren't as different as you make them out to be. Both types of projects want a functional codebase, both have limited developer resources (communities need volunteers, business have a budget limit), and both can benefit greatly from the development process being sped up. Many development practices that are industry standard today started in the open source world (style guides and version control strategy to name two heavy hitters) and there's been some bleed through from the other direction as well (tool juggernauts like Atlassian having new open source alternatives made directly in response)
No project is immune to bad code, there's even a lot of bad code out there that was believed to be good at the time, it mostly worked, in retrospect we learn how bad it is, but no one wanted to fix it.
The end goals and proposes are for sure different between community passion projects and corporate financial driven projects. But the way you get there is more or less the same, and that's the crux of the articles argument: Historically open source and closed source have done the same thing, so why is this one tool usage so wildly different?
Historically open source and closed source have done the same thing, so why is this one tool usage so wildly different?
Because, as noted by another replier, open source wants working code and closed source just want code that runs.
When did you last time decide to buy a car that barely drives?
And another thing, there are some tech companies that operate very short-term, like typical social media start-ups of which about 95% go bust within two years. But a lot of computing is very long term with code bases that are developed over many years.
The world only needs so many shopping list apps - and there exist enough of them that writing one is not profitable.
most software isn't public-facing at all (neither open source nor closed source), it's business-internal software (which runs a specific business and implements its business logic), so most of the people who are talking about coding with AI are also talking mainly about this kind of business-internal software.
Does business internal software need to be optimized?
Does business internal software need to be optimized?
Need to be optimised for what? (To optimise is always making trade-offs, reducing some property of the software in pursuit of some optimised ideal; what ideal are you referring to?)
And I'm not clear on how that question is related to the use of LLMs to generate code. Is there a connection you're drawing between those?
So I was trying to make a statement that the developers of AI for coding may not have the high bar for quality and optimization that closed source developers would have, then was told that the major market was internal business code.
So, I asked, do companies need code that runs quickly on the systems that they are installed on to perform their function. For instance, can an unqualified programmer use AI code to build an internal corporate system rather than have to pay for a more qualified programmer's time either as an internal hire or producing.
do companies need code that runs quickly on the systems that they are installed on to perform their function.
(Thank you, this indirectly answers one question: the specific optimisation you're asking about, it seems, is optimised speed of execution when deployed in production. By stating that as the ideal to be optimised, necessarily other properties are secondary and can be worse than optimal.)
Some do pursue that ideal, yes. For example: many businesses seek to deploy their internal applications on hosted environments where they pay not for a machine instance, but for seconds of execution time. By doing this they pay only when the application happens to be running (on a third-party's managed environment, who will charge them for the service). If they can optimise the run-time of their application for any particular task, they are paying less in hosting costs under such an agreement.
can an unqualified programmer use AI code to build an internal corporate system rather than have to pay for a more qualified programmer’s time either as an internal hire or producing.
This is a question now about paying for the time spent by people to develop and maintain the application, I think? Which is thoroughly different from the time the application spends running a task. Again, I don't see clearly how "optimise the application for execution speed" is related to this question.
I'm asking if it worth spending more money on human developers to write code that isn't slop.
Everyone here has been mentioning costs, but they haven't been comparing them together to see if the cost of using human developers located in a high cost of living American city is worth the benefits.
There are commercial open source stuff too
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I created this entirely using mistral/codestral
https://github.com/suoko/gotosocial-webui
Not a real software, but it was done by instructing the ai about the basics of the mother app and the fediverse protocol
I think it's established genAI can spit straightforward toy examples of a few hundred lines. Bungalows aren't simply big birdhouses though.
Still they're just birdhouses with some more infrastructure you can read instructions about how to build it.