I burned down a forest to confirm
Don’t ask it to name an NFL team that doesn’t end with ‘s’
DeepSeek eventually gets it, but it’s DeepThink takes a good ten minutes of racing ‘thoughts’ and loops to figure it out.
I burned down a forest to confirm
Don’t ask it to name an NFL team that doesn’t end with ‘s’
DeepSeek eventually gets it, but it’s DeepThink takes a good ten minutes of racing ‘thoughts’ and loops to figure it out.
no shit. death to ad men. but LLMs aren’t for most of these stunts. that’s part of the problem but it’s like saying my bike is bad at climbing trees. at least the bike isn’t being advertised for arbory
But what is it for? Other than be a bottomless pit for resources.
It does seem cultish to present the thinking machine but when it is presented with easily verifiable tasks it regularly completely shits the bed but we are supposed to blindly trust it with more complicated matters that aren’t easily verified.
it’s not a thinking machine, that’s the advertisers lying again. if you want a thinking machine, that simply doesn’t exist. Maybe wolfram.alpha or IBM’s Watson are better for the tasks you have in mind. an llm would probably give you a correct pythons script to check the last character of each string in an array and even populate that array with NFL team names and that code wold tell you ero of them end with non-“s” chars. it might also end the code with an open-ended block quote you need to delete.
LLMs are statistical models and they’re sometimes useful for outputting text that’s similar to existing related text, this is why they’re sometimes better than google search because search is so degraded by SEO and advertising. They’re very bad at solving new programming tasks so if you wanted to implement something in godot where you’re the first person to be doing it and there’s no tutorial in the training data it’s just going to fuck up constantly.