Are you aware of any mid-large projects that went that path? That sound like an irreversible one way decision, codebase will be not suitable for humans pretty soon after which means from now one you at the mercy of LLMs.

I think it is more like top companies, not top developers, and the problem with developers in top companies was and is - absolute majority of them are not actually directly working on things that increase revenue, so companies can spend a ton of money and see barely if any changes in the product and the bottom line, so companies, at least legacy ones will be reluctant to sponsor that long term.

LLMs spared my neck of the woods for now but is this really how PR review looks like these days for an average SWE or its just an example of a Junior in a team?

A little off the actual topic discussed in the article but I think this is why current AI cycle is over-hyped in terms of its broad impact on the economy. One of the most valuable thing, if not the most valuable, for any business is its ability to come up with new products/services/markets/niches, improvement in that could lead to a huge economic boost but LLMs are atrocious at that or any other truly novel things, that bottleneck still stands.

I have same familiarity with JFrog Artifactory(which they used in this case) and I'm confused with how you can gain access to the internet through it, even in theory. Isn't JFrog just exposed with a basic REST API for package upload/extraction? I'm not a security expert but how is that possible to get internet access through simple GET/PUT request API?

What does it mean, Is it like when somebody used some coding agent to develop a feature and later input prompts and a resulting PR can be used for training by a presumption that final PR was a correct implementation of a prompt?

Does it increases manufacturing and operational cost of such racks?

I was under impression that every time LLMs try to be truly novel and they need to assume things in the area where they didn't have enough data points that there were trained on, results are not good, has that changed?

I wonder if extracting those static reasoning chains make sense given a Rich Sutton's "The Bitter Lesson" and Geoffrey Hinton's "People should stop training radiologists now.". I guess until participants make money they won't stop, not sure if they do, so far it is more about expectation of profitability as I understand.

I'm not an expert, but is this even an option? I mean models must be refreshed with latest knowldge base periodically even without algo/design improvements, otherwise the lag become too noticeable and it will hurt users and their use-case.

Does training of new models go into RnD or cost? And subscription plans' subsidies, are those cost or sales and marketing?

This obsession with growth instead of progress or value rubs me the wrong way, given the trend for enshittification of services sooner or later, also remind me of a video I watched yesterday when a founder gives examples of private equity people trying to force growth no matter what: https://www.youtube.com/watch?v=k4vNIsVY-0Y&t=412s