Who cleans up after the vibe-coding party? | Hacker News Reader

Who cleans up after the vibe-coding party?

ft.com

23 pointsmsephton21 comments

Their assertion that cURL is used to fetch the web page you're reading seems...misplaced. Browsers almost certainly do not use libcurl to fetch web pages. Though I do agree that libcurl is ubiquitous.

Thanks, learned something.

Had to over-simplify things a bit on cURL to keep this legible to a general audience, particularly in the first few paragraphs. The idea is that at some point down the stack from your request, libcurl is almost certainly involved (as a dependency, or maybe installed on your internet router, for example), not that your browser is necessarily running libcurl code directly in making the request. But yes, it's slightly hand-wavy in service of the larger point ("this is a thing you use all of the time without realizing it")

Thanks for responding! It is hard to strike the right balance; I wouldn't want to be a writer :)

In terms of software projects themselves, consulting companies do, the same that now are pushing for AI solutions, which are the trenches I find myself in.

Now for the whole economic chaos, unfortunately the whole society will, but not the big tech folks.

IT outsourcing was arguably a sort of precursor to vibecoding, and consulting companies are arguably still eating well cleaning up the legendary mess it created.

As someone that regularly is involved in such projects, fashion driven industry is great for consulting, which is one reason it exists in first place.

I work in Salesforce consulting ecosystem, notorious for producing awful code (occasionally passing project until you find someone code for $1 per hour (I kid you not)).

Now within last year it improved to point I can trust it produce decent quality that beats like 95% of human slop.

Vibecoding for anything beyond prototypes is a bad idea. Software engineering principles still matter. Good software that is maintainable and has quality in functional, non-functional, and operational axes still matters.

I wrote about this a few weeks ago in my blog. As an industry, we need to move past vibecoding and into rightcoding.

https://sgopalanbtg.substack.com/p/andrej-karpathy-didnt-mea...

> Now, suddenly, it’s completely normal to have to pay $100 or $200 per month in order to do software development

And those prices are still subsidized. OpenAI, Anthropic, etc are nowhere close to generating profits.

Yes, but:

1) They are investing Loony Toons levels of money and using Loony Toons financing a lot of the time for DC buildout. Those are actual physical buildings that once built out, exist and don’t need to be built again.

2) They are pointedly ignoring FPGA and ASIC. With the current model quality, would it really be so bad to burn Claude irrevocably on a chip and have a non-modifiable, cheap to mass produce, order of magnitude faster Claude-in-a-chip? This is what happened to Bitcoin ultimately, there are huge performance gains we know exist lying on the table just because they don’t exist for training. And even for training, TPUs make the first steps in that direction.

> They are pointedly ignoring FPGA and ASIC. With the current model quality, would it really be so bad to burn Claude irrevocably on a chip and have a non-modifiable, cheap to mass produce, order of magnitude faster Claude-in-a-chip? This is what happened to Bitcoin ultimately, there are huge performance gains we know exist lying on the table just because they don’t exist for training. And even for training, TPUs make the first steps in that direction.

Yeah this is copium if I ever saw it.

Isn't GPUs a purpose made ASIC for parallell matrix math allready?

No? You can program a GPU, you can’t program ASIC. TPUs are closer to ASIC than GPUs.

An ASIC won't help until models are compute bound. They're still memory bound at all levels.

Models are memory bound.

Startups burning through VC money to build massive warehouses full of hardware those models need to run on currently seem to be quite power grid connector bottlenecked. A more efficient chip would help.

That's the approach https://www.cerebras.ai/ is taking.

Inference is probably the cheapest part of it all.

The buildings will last for years, decades even, but the GPUs need to be replaced every 2-3 years in perpetuity.

True, but only because they are choosing to actively burn VC money on training.

But it’s not like the models we have now would stop to exist if training stopped. Other than the occasional retraining to get the latest data in, if they stopped wanton experimentation with models, that admittedly is pushing the models forward, the training costs could plummet and inference would be the thing to optimize and scale.

Without new models how would AI companies compete then?

Other features can easily be copied (eg Claude Code, ChatGPT, etc).

If AI services are a commodity then they can only compete on price.

Well, yeah. Which is where energy efficiency of FPGA and ASIC comes into play.

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