I worked for a Microsoft studio for a while that took forever to allow Copilot to be used internally, and they were all Microsoft. It was very frustrating, especially given that permission was finally granted for Copilot use just a matter of weeks before the whole studio got abruptly shut down anyway.
Corporate bureaucracy. Weird and dumb.
Well, open weight is not open source. There's a lot of black box still left in those models.
It's still far more open to investigation and analysis than the likes of ChatGPT, though. Lots of work has been done to allow some degree of investigation into how the "brains" of an LLM work and in the case of Deepseek you can actually apply those techniques to the model you know you're using.
There are different levels of trust one can apply. In the case of Deepseek you can run the model on your very own hardware with no external connection to China at all. You can abliterate and fine-tune the model's weights yourself. You can observe all of its reasoning text as it works.
So I think I'd trust Deepseek over the likes of OpenAI, who insist you send all your data to them and be happy with whatever they choose to send back to you.
Remember that it's congress that sets the budget.
And also remember who's up for reelection in 16 days.
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.
