If AI is so good at coding - where are the open source contributions?
If AI is so good at coding - where are the open source contributions?
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If AI is so good at coding … where are the open source contributions?
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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?
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Baldur Bjarnason (who hates AI slop) has posited precisely this:
My current theory is that the main difference between open source and closed source when it comes to the adoption of “AI” tools is that open source projects generally have to ship working code, whereas closed source only needs to ship code that runs.
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That's basically my question. If the standards of code are different, AI slop may be acceptable in one scenario but unacceptable in another.
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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.
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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?
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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.
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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.
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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.
This is a very generous sentence you have made, haha. My observation is that vast majority of tech companies seem to operate unprofitably (the programming division is pure cost, no measurable financial befit) and with churning bug riddled code that never really works correctly.
Netflix was briefly hugely newsworthy in the technology circles because they... Regularly did disaster recovery tests.
Edit: Netflix made news headlines because someone decided that Kevin in IT having a bad day shouldn't stop every customer from streaming. This made the news.
Our technology "leadership" are, on average, so incredibly bad at computer stuff.
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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.
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Does business internal software need to be optimized?
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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?
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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.
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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.
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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.
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There are commercial open source stuff too
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115 comments