this podcast is a good window into the sf tech/ai hive mind and how it's vastly different from someone who's been in ai longer than them
bitter lesson pilled? llms don't capture the essence of it
agi leading to asi? huh? what asi?
ai safety? lmao
this podcast is a good window into the sf tech/ai hive mind and how it's vastly different from someone who's been in ai longer than them
bitter lesson pilled? llms don't capture the essence of it
agi leading to asi? huh? what asi?
ai safety? lmao
the idea that running a thousand simulated dates between ai clones is somehow better than normal recommendation algos is funny
the only reason why i can think some people are even considering it to be an interesting idea is because of that one black mirror ep
its interesting to see how openai still struggles with finetuning the vibe of their models. they either full send with sycophancy or have this terseness which just makes their models feel unhelpful
tweet 1: ha! llms suck! geoff, i've bested you finally! sweet vindication at last!
tweet 2: hmm, actually llms are not that bad, but they still make mistakes, ha!
tweet 3: on second thought, they're probably not that bad even based on this paper's experiments. but llms suck!!…
tweet 1: ha! llms suck! geoff, i've bested you finally! sweet vindication at last!
tweet 2: hmm, actually llms are not that bad, but they still make mistakes, ha!
tweet 3: on second thought, they're probably not that bad even based on this paper's experiments. but llms suck!!…
a good name makes things instantly cool
wanna use embeddings? how about matryoshka embeddings? look into matryoshka embeddings. matryoshka
have i really looked into how they're trained? no but it must be cool bc it sounds so good
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