Claude’s memory architecture is the opposite of ChatGPT’s
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Re: Claude’s memory architecture is the opposite of ChatGPT’s
#2ChatGPT is quickly approaching (perhaps bypassing?) the same concerns that parents, teachers, psychologists had with traditional social media. It's only going to get worse, but trying to stop the technological process will never work. I'm not sure what the answer is. That they're clearly optimizing for people's attention is more worrisome.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#3This post was great, very clear and well illustrated with examples.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#4I love Claude's memory implementation, but I turned memory off in ChatGPT. I use ChatGPT for too many disparate things and it was weird when it was making associations across things that aren't actually associated in my life.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#5> Anthropic's more technical users inherently understand how LLMs work.
good (if superficial) post in general, but on this point specifically, emphatically: no, they do not -- no shade, nobody does, at least not in any meaningful sense
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#6> Anthropic's more technical users inherently understand how LLMs work. good (if superficial) post in general, but on this point specifically, emphatically: no, they do not -- no shade, nobody does, at least not in any meaningful sense
Thanks for this generalization, but of course there is a broad range of understanding how to improve usefulness and model tweaks across the meat populace.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#7ChatGPT is quickly approaching (perhaps bypassing?) the same concerns that parents, teachers, psychologists had with traditional social media. It's only going to get worse, but trying to stop the technological process will never work. I'm not sure what the answer is. That they're clearly optimizing for people's attention is more worrisome.
Seems like either a huge evolutionary advantage for the people who can exploit the (sometimes hallucinating sometimes not) knowledge machine, or else a huge advantage for the people who are predisposed to avoid the attention sucking knowledge machine. The ecosystem shifted, adapt or be outcompeted.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#8> Anthropic's more technical users inherently understand how LLMs work. good (if superficial) post in general, but on this point specifically, emphatically: no, they do not -- no shade, nobody does, at least not in any meaningful sense
Understanding how they work in the sense that permits people to invent and implement them, that provides the exact steps to compute every weight and output, is not "meaningful"?
There is a lot left to learn about the behaviour of LLMs, higher-level conceptual models to be formed to help us predict specific outcomes and design improved systems, but this meme that "nobody knows how LLMs work" is out of control.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#9The link to the breakdown of ChatGPT's memory implementation is broken, the correct link is: https://www.shloked.com/writing/chatgpt-memory-bitter-lesson
This is really cool, I was wondering how memory had been implemented in ChatGPT. Very interesting to see the completely different approaches. It seems to me like Claude's is better suited for solving technical tasks while ChatGPT's is more suited to improving casual conversation (and, as pointed out, future ads integration).
I think it probably won't be too long before these language-based memories look antiquated. Someone is going to figure out how to store and retrieve memories in an encoded form that skips the language representation. It may actually be the final breakthrough we need for AGI.
Re: Claude’s memory architecture is the opposite of ChatGPT’s
#10> Anthropic's more technical users inherently understand how LLMs work. good (if superficial) post in general, but on this point specifically, emphatically: no, they do not -- no shade, nobody does, at least not in any meaningful sense
If we are going to create a binary of "understand LLMs" vs "do not understand LLMs", then one way to do it is as you describe; fully comprehending the latent space of the model so you know "why" it's giving a specific output.
This is likely (certainly?) impossible. So not a useful definition.
Meanwhile, I have observed a very clear binary among people I know who use LLMs; those who treat it like a magic AI oracle, vs those who understand the autoregressive model, the need for context engineering, the fact that outputs are somewhat random (hallucinations exist), setting the temperature correctly...