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344

How Vibe Coding Is Killing Open Source (hackaday.com)

submitted 1 month ago by mesamunefire@piefed.social to c/technology@lemmy.world

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[–] WanderingThoughts@europe.pub 185 points 1 month ago* (last edited 1 month ago) (26 children)

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.

[–] percent 6 points 1 month ago (16 children)

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.

[–] XLE@piefed.social 4 points 1 month ago (3 children)

Can you cite some sources on the increased efficiency? Also, can you link to these lower priced, efficient (implied consumer grade) GPUs and TPUs?

[–] percent 2 points 1 month ago (1 children)

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:

Models are getting better, and tooling built around them are getting better, so hopefully we can get to a point where small models (capable of running on consumer-grade hardware) become much more useful.
Some modern data center GPUs and TPUs compute more per watt-hour than previous generations.

[–] XLE@piefed.social 2 points 1 month ago (1 children)

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).

[–] percent 1 points 1 month ago* (last edited 1 month ago)

Can you provide evidence the "more efficient" models are actually more efficient for vibe coding? Results would be the best measure.

Did I claim that? If so, then maybe I worded something poorly, because that's wrong.

My hope is that as models, tooling, and practices evolve, small models will be (future tense) effective enough to use productively so we won't need expensive commercial models.

To clarify some things:

I'm mostly not talking about vibe coding. Vibe coding might be okay for quickly exploring or (in)validating some concept/idea, but they tend to make things brittle and pile up a lot of tech debt if you let them.
I don't think "more efficient" (in terms of energy and pricing) models are more efficient for work. I haven't measured it, but the smaller/"dumber" models tend to require more cycles before they reach their goals, as they have to debug their code more along the way. However, with the right workflow (using subagents, etc.), you can often still reach the goals with smaller models.

There's a difference between efficiency and effectiveness. The hardware is becoming more efficient, while models and tooling are becoming more effective. The tooling/techniques to use LLMs more effectively also tend to burn a LOT of tokens.