The solution required reverse engineering and internals knowledge that most human engineers don't even have.
The question is no longer "Can an LLM write code?". It can. The problem is that certain humans refuse to put in the effort required to properly utilize these tools.
LLMs as things that know what's in the manual are AAA+. Extremely helpful. Very good at making a rough draft of something filled with a lot of stupid mistakes and no new abstractions. That's what your transparent window thing is. Something that you could never ship, is probably too big and doing senseless things for no intelligible reason, and definitely has bizarre bugs.
This application, named Seymore, is being evaluated for purchase by several tech companies. (It helps having good industry contacts)
There was a ton of interesting engineering required to make this work at 60 fps+ without resorting to hacks or using private APIs. Most macOS engineers wouldn't even know where to start.
I've shipped code that is used by billions of people and all you just did was spew a bunch of bullshit. As someone who has built their own LLM from scratch, I have an extremely good idea of what they can and can't do.
If you don't know how to use these tools, you'll end up with crap. If you DO know how to use them, they are incredibly useful.
What they are saying is increasingly difficult to defend, but they will do it anyways. I see the same arguments at my job, and I just gave up arguing against
Some engineering problems (such as designing a performant thread-quantum algorithm) require depth accumulated over years of working on real systems at scale. That is not elitism; it is simply how specialized expertise works. Experience is unevenly distributed, and that matters when evaluating technical claims.
When I say I trust LLM generated code, I mean that in a qualified, engineering sense. I do not treat an LLM as a slot machine: enter a prompt, paste the output, and hope. I use a deliberate workflow for decomposition, prompting, review, testing, validation, and integration. Developing that workflow took substantial time and experimentation.
The useful question is not “Can an LLM write code without oversight?” It obviously cannot, at least not reliably for nontrivial work. The question is whether an experienced engineer can build a process that makes LLM output trustworthy enough for particular classes of work. In my experience, the answer is yes. But the process, judgment, and willingness to do the work are the hard parts.