Only until AI investor money dries up and vibe coding gets very expensive quickly. Kinda how Uber isn't way cheaper than a taxi now.
This.
until AI investor money dries up
Is that the latest term for "when hell freezes over"?
Microsoft steeply lowered expectations on the AI Sales team, though they have denied this since they got pummelled in their quarterly and there's been a lot of news about how investors are not happy with all the circular AI investments pumping those stocks. When the bubble pops (and all signs point to that), investors will flee. You'll see consolidation, buy-outs, hell maybe even some bullshit bailouts, but ultimately it has to be a sustainable model and that means it will cost developers or they will be pummeled with ads (probably both).
A Majority of CEOs are saying their AI spend has not paid off. Those are the primary customers, not your average joe. MIT reports 95% generative AI failure rate at companies. Altman still hasn't turned a profit. There are Serious power build-out problems for new AI centers (let alone the chips needed). It's an overheated reactionary market. It's the Dot Com bubble all over again.
There will be some more spending to make sure a good chunk of CEOs "add value" (FOMO) and then a critical juncture where AI spending contracts sharply when they continue to see no returns, accelerated if the US economy goes tits up. Then the domino's fall.
I wouldn't be surprised if that's only a temporary problem - if it becomes one at all. People are quickly discovering ways to use LLMs more effectively, and open source models are starting to become competitive with commercial models. If we can continue finding ways to get more out of smaller, open-source models, then maybe we'll be able to run them on consumer or prosumer-grade hardware.
GPUs and TPUs have also been improving their energy efficiency. There seems to be a big commercial focus on that too, as energy availability is quickly becoming a bottleneck.
So far, there is serious cognitive step needed that LLM just can't do to get productive. They can output code but they don't understand what's going on. They don't grasp architecture. Large projects don't fit on their token window. Debugging something vague doesn't work. Fact checking isn't something they do well.
They don't need the entire project to fit in their token windows. There are ways to make them work effectively in large projects. It takes some learning and effort, but I see it regularly in multiple large, complex monorepos.
I still feel somewhat new-ish to using LLMs for code (I was kinda forced to start learning), but when I first jumped into a big codebase with AI configs/docs from people who have been using LLMs for a while, I was kinda shocked. The LLM worked far better than I had ever experienced.
It actually takes a bit of skill to set up a decent workflow/configuration for these things. If you just jump into a big repo that doesn't have configs/docs/optimizations for LLMs, or you haven't figured out a decent workflow, then they'll be underwhelming and significantly less productive.
(I know I'll get downvoted just for describing my experience and observations here, but I don't care. I miss the pre-LLM days very much, but they're gone, whether we like it or not.)
This sounds a lot like every framework, 20 years ago you could have written that about rails.
Which IMO makes sense because if code isn't solving anything interesting then you can dynamically generate it relatively easily, and it's easy to get demos up and running, but neither can help you solve interesting problems.
Which isn't to say it won't have a major impact on software for decades, especially low-effort apps.
Can you cite some sources on the increased efficiency? Also, can you link to these lower priced, efficient (implied consumer grade) GPUs and TPUs?
Oh, sorry, I didn't mean to imply that consumer-grade hardware has gotten more efficient. I wouldn't really know about that, but I assume most of the focus is on data centers.
Those were two separate thoughts:
Can you provide evidence the "more efficient" models are actually more efficient for vibe coding? Results would be the best measure.
It also seems like costs for these models are increasing, and companies like Cursor had to stoop to offering people services below cost (before pulling the rug out from them).
Vibe coding is a black hole. I've had some colleagues try and pass stuff off.
What I'm learning about what matters is that the code itself is secondary to the understanding you develop by creating the code. You don't create the code? You don't develop the understanding. Without the understanding, there is nothing.
Yes. And using the LLM to generate then developing the requisite understanding and making it maintainable is slower than just writing it in the first place. And that effect compounds with repetition.
TheRegister had an article, a year or 2 ago, about using AI in the opposite way: instead of creating the code, someone was using it to discover security-problems in it, & they said it was really useful for that, & most of its identified things, including some codebase which was sending private information off to some internet-server, which really are problems.
I wonder if using LLM's as editors, instead of writers, would be better-use for the things?
_ /\ _
A second pair of eyes has always been an acceptable way to use this imo, but it shouldnt be primary or only
LLMs definitely kills the trust in open source software, because now everything can be a vibe-coded mess and it's sometimes hard to check.
LLMs definitely kills the trust in ~~open source~~ software, because now everything can be a vibe-coded mess and it's sometimes hard to check.
Might make open source more trustworthy, It can't be any harder to check than closed source.
A week or two back there was a post on Reddit where someone was advertising a project they'd put up on GitHub, and when I went to look at it I didn't find any documentation explaining how it actually worked - just how to install it and run it.
So I gave Gemini the URL of the repository and asked it to generate a "Deep Research" report on how it worked. Got a very extensive and detailed breakdown, including some positives and negatives that weren't mentioned in the existing readme.
I don't know yet how good Gemini about it,but I think https://deepwiki.com/ this tool will overkill anything for now
Yeah, LLMs do a decent job explaining what code does.
yeah it's to the point now where if I see emojis in the readme.md on the repo I just don't even bother.
I used to use emojis in my documentation very lightly because I thought they were a good way to provide visual cues. But now with all the people vibe coding their own readme docs with freaking emojis everywhere I have to stop using them.
Mildly annoying.
✨ especially this one ✨
Is the ✨sparkly emoji✨ the <BLINK> of the 21st century? Discuss.
ttbomk, emojis are legal function-names in both Swift & Julia..
The Swift example was damned incomprehensible, & .. well, it was Apple stuff, so making it look idiotic might have been some kind of cultural-exclusivity intention..
The Julia stuff, though, means that you can use Greek symbols, etc, for functions, & get things looking more like what they should..
Also, I think emojis are actually better than my all-text style, for communicating intonation/emotion ( I'm old: learned last century ), & maybe us old geezers ought to adapt a bit, to such things..
That does NOT mean that cartoon "code" is good-enough, whether it's cartoonish in plaintext or in emojis, though..
I'm just trying to keep the cultural-prejudice & the code-quality being distinct-categories of judgement, you know?
( & cultural-prejudice is an actual thing, though it's usually called "religious wars", isn't it, in geekdom? )
_ /\ _
Check out this one I came across earlier - https://github.com/Jtensetti/fediverse-career-nexus/blob/main/README.md
It's a federated LinkedIn. ofc it's vibe coded.
Man... of all the vibe coding tools, Lovable has gotta be one of the most useless, too.
I work with people (all middle managers) who love Loveable because they can type a two sentence description of an app and it will immediately vomit something into existence. But the code it generates is an absolute disaster and the UIs it designs (which is supposed to be its main draw) is some of the most generic crap I've ever seen.
0/10, do not recommend.
How AI is killing everything.
If the abominable intelligence is killing every corner of things we consider good its time to start killing the "AI"...
Open source is not only about publishing code: it's about quality, verifiable, reproducible code at work. If LLMs can't do that, those "vibe coding" projects will hit a hard wall. Still, it's quite clear they badly impact the FOSS ecosystem.
Eh. I never considered myself some hard-core old professional, but:
The LLM will not interact with the developers of a library or tool, nor submit usable bug reports, or be aware of any potential issues no matter how well-documented
If an LLM introduces a dependency, I will sure as hell go see it myself. Enough people do not do that for this to become a problem?
This isn’t the problem with the AI, it’s the problem with the user. If you don’t know enough to select the library and make the AI use it, maybe you were never gonna finish the project without AI anyway.
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