So this:
between idiots believing AI is the devil and idiots believing AI is their new lord and savior
wasn't mud-slinging, in your opinion? Calling someone an idiot isn't an attack?
Maybe because when they actually try them out they're discovering that AI coding agents are pretty good?
And you're here standing in between the two sides, casting mud in both directions and thinking you're not getting any mud on yourself in the process?
When did I ever claim "proof" of anything, let alone with scientific rigor? Here was my original comment, verbatim:
If everyone you thought was smart are disagreeing with you, perhaps take another look at your position on the matter? Might be a reason beyond spontaneous mass "psychosis."
This is advising uncertainty, not certainty. The opposite of what you appear to be accusing me of here.
I pointed out that several smart people were disagreeing with TootSweet. You responded:
Big “100 physicists have signed a letter saying you’re wrong, Mr. Einstein” energy.
Who else would "Einstein" be in this analogy?
I'm actually kind of pleased by this because it means they all know that this is an intensely unpopular action and nobody wants to be left with the blame.
Local models are capable of assisting coding in even enormous, complex pieces of software. That's where having the right harness really shines - it provides the model with the tools it needs to focus in on just the bits of code it's currently working on. Sort of like how a regular human programmer can do a much better job working on a large project with Visual Assist or equivalent in their IDE. I've also installed some tools like Context7 that provides the LLM with API documentation (no more hallucinated APIs) or Firecrawl (it can just go search whatever it needs like a regular person). It's not so great at large-scale architecture questions, but that's not what 90% of coding usually involves.
If you want a step up from what can run on just commodity desktops, some of the bigger models are also open-weight and a company or studio with a programming department could run one of those on their own hardware in the server room. GLM 5.3 is right up there next to Claude in the benchmarks, for example, and doesn't take a data center to run.
Or you could go halfsies on the "big tech" thing and have an API subscription for a big model that your coding harness uses for doing the large-scale architecture planning and then hands off to small local models for implementing the plans the big model wrote. That's a very common pattern too.
The key point is that there's a lot of options here, it's not "pay Anthropic or nothing."
This is still assuming that TootSweet is equivalent to Einstein here.
And is Linux equivalent to McDonald's in this scenario? The analogy is becoming rather unclear.
This also assumes that TootSweet is Einstein in this scenario, rather than just some guy on the Fediverse. If he's got cred on par with Steve Yegge or Linus Torvalds the comparison might hold some weight, but I'm dubious about that.
They check to make sure that the code continues producing the expected results. For example if I had some code that added up numbers, I might have a unit test that calls that function with the parameters "2" and "2" and checks to make sure the result is "4". If something changes that makes it start returning something else then a flag is immediately raised.
If everyone you thought was smart are disagreeing with you, perhaps take another look at your position on the matter? Might be a reason beyond spontaneous mass "psychosis."
Indeed, my code has never been more rich in unit tests now that coding assistants make them so easy to add. If the coding assistant subsequently does something weird that changes program behaviour it knows immediately.
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