Define policy forbidding use of AI code generators

Oh hey, the thing I predicted in my blog titled "yes i will judge you for using AI" happened lol

Basically I think open source has traditionally HEAVILY relied on hidden competency markers to judge the quality of incoming contributions. LLMs throw that entire concept on its head by presenting code that has competent markers but none of the backing experience. It is a very very jarring experience for experienced individuals.

I suspect that virtual or in person meetings and other forms of social proof independent of the actual PR will become far more crucial for making inroads in large projects in the future.

I've started seeing this at work with coworkers using LLMs to generate code reviews. They submit comments which are way above their skill level which almost trick you in to thinking they are correct since only a very skilled developer would make these suggestions. And then ultimately you end up wasting tons of time proving how these suggestions are wrong. Spending far more time than the person pasting the suggestions spent to generate them.

Yep 100%, it is something I have also observed. Frankly has been frustrating to the point I spun up a quick one off html site to rant/get my thoughts out. https://jaysthoughts.com/aithoughts1

funny enough I had coworkers who similarly had a hold of the jargon but without any substance. They would always turn out to be time sinks for others doing the useful work. AI imitating that type of drag on the workplace is kinda funny ngl.

By far the largest review-effort PRs of my career have been in the past year, due to mid-sized LLM-built features. Multiple rounds of other signoffs saying "lgtm" with only minor style comments only for me to finally read it and see that no, it is not even remotely acceptable and we have several uses built by the same team that would fail immediately if it was merged, to say nothing of the thousands of other users that might also be affected. Stuff the reviewers have experience with and didn't think about because they got stuck in the "looks plausible" rut, rather than "is correct".

So it goes back for changes. It returns the next day with complete rewrites of large chunks. More "lgtm" from others. More incredibly obvious flaws, race conditions, the works.

And then round three repeats mistakes that came up in round one, because LLMs don't learn.

This is not a future style of work that I look forward to participating in.

send your blog link please

I'm not really in the field any longer, but one of my favorite things to do with LLMs is ask for code reviews. I usually end up learning something new. And a good 30-50% of the suggestions are useful. Which actually isn't skillful enough to give it a title of "code reviewer", so I certainly wouldn't foist the suggestions on someone else.

People keep telling LLM will improve efficiency, but your comment has proved it's the otherwise.

It look like LLM is not good for cooperation, because the nature of LLM is randomness.

https://jaysthoughts.com/aithoughts1 Bit of a rambly rant, but the prediction stuff I was tongue in cheek referring to above is at the bottom.

I think a future with LLM coding requires much more tests, both testing happy and bad flows.

> Basically I think open source has traditionally HEAVILY relied on hidden competency markers to judge the quality of incoming contributions.

Yep, and it's not just code. Student essays, funding applications, internal reports, fiction, art...everything that AI touches has this problem that AI outputs look superficially similar to the work of experts.

Probabilistic patterns stringed together are something different from an end-to-end intention driven solidly linked chain of thought that is with pylons grounded in relevant context at critical points.

Looks like your blog post got submitted here and then I assume triggered the flame war flag. A lot of people just reading the title and knee jerking in the comments:

https://news.ycombinator.com/item?id=44384610

Funny, as the entire thing starts off with "Now, full disclosure, the title is a bit tongue-in-cheek.".

Just some feedback: your site is hard to read on mobile devices because of the sidebar.

I have learned over time that the actually smart people worth listening to, avoid jargon beyond what is strictly necessary, talk in simple terms with specific goals/improvements/changes in mind.

If I'm having to reread something over and over to understand what they're even trying to accomplish, odds are it's either AI generated or an attempt at sounding smart instead of being constructive.

Trajectory so far has been that AI outputs are converging increasingly not just in superficial similarity but also quality of expert output. We are obviously not there yet, and some might say we never will. But if we do, there is a whole new conversation to be had.

It also needs proper guideline enforcement. If an engineer produces poorly tested and unreviewed code, then the buck stops with them. This is a human problem more than it is a tool problem.

Thank you, I'll get that fixed.

Edit: Mobile should be fixed now

I suppose I did bring that on myself with the title didn't I. I believe I have fixed the site for mobile so hopefully some of those thread complaints have been rectified.

I think the issue is with people taking mental shortcuts and thus no longer properly thinking about design decisions and the bigger picture in terms of concepts of the software.

I suspect that there are at least 1 or 2 more significant discoveries in terms of architecture and general way of models working, before these things become actual experts. Maybe they will never get there and we will discover how to better incorporate facts and reasoning, rather than just ingesting billions of training data points.