We did it in an engineering setting and had very mixed results. Big 800 page machine manuals are hard to contextualise.

I worked in an industry for five years and I could feasibly build a competitor product that I think would solve a lot of the problems we had before, and which it would be difficult to pivot the existing ones into. But ultimately, I could have done that before, it just brings the time to build down, and it does nothing for the difficult part which is convincing customers to take a chance on you, sales and marketing, etc. - it takes a certain type of person to go and start a business.

Nobody’s talking about starting businesses. The article is specifically about pypi packages, which don’t require any sales and marketing. And there’s still no noticeable uptick in package creation or updates.

My understanding reading it was that PyPi packages is just being used as a proxy variable

Yes, you are correct. The parent is not following the conversation. They probably didn't even read the article.

They had some great video series too which seem to have stopped. Their War Stories gaming interviews were brilliant.

In my PhD more than a decade ago, I ended up using png image file sizes to classify different output states from simulations of a system under different conditions. Because of the compressions, homogenous states led to much smaller file size than the heterogenous states. It was super super reliable.

I'm not sure why this is against 'frameworks' per se; if we were sure that the code LLMs could generate was the best possible, we might as well use Assembly, no, since that'd lead to best performance? But we don't generally, we still need to validate, verify and read it. And in, that, there is still some value in using a framework since the code generated is likely, on the whole, to be shorter and simpler than that not using a framework. On top of that, because it's simpler, I've at least found that there's less scope for LLMs to go off and do something strange.

> Costs for all features are shared by all users.

To a degree but most enterprise focused software usually has differential pricing. Often that pricing isn't public so different companies get different quotes.

The other thing is bringing in the knowledge about what other customers in the same field want. For business-focused software this can be a boon, customers often can't really envision the solution to their problem, it's like the Henry Ford attributed "If I had asked people what they wanted, they would have said faster horses"

Until a given company decides they need access control for their contractors that's different from their employees, etc. etc. etc. - seen it all before with internal often data scientist written applications that they then try to scale out and run into the security nightmare and lack of support internally for developing and taking forward. Usually these things fizzle out when someone leaves and it stops working.

Bingo; the exact same arguments against regular-coding it in-house apply to vibe-coding it in-house.

I just don't buy it.

Most people who've been in a business SaaS environment know that writing the software is relatively the easy part aside from in very difficult technical domains. The sales cycle + renewals and solution engineering for businesses is the majority of the work, and that's going nowhere.

With this, they've not provided an upper bound the error on the kernel expanded with N terms which I think is a big missing piece.