291–300 of 326 posts
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I would say these are two distinct use cases - one is the assistant that remembers my preferences. The other use case is the clean intelligent blackbox that knows nothing about previous sessions and I can manage the context in fine detail. Both are useful, but for very different problems.
Good point. I almost wish for an anonymous mode with chat history.
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He is telling you how it mechanically works. Your comment about it “understanding what that means” because it is an NLP seems bizarre, but maybe you mean it in some other way. Are you proposing that the attention input context is gone, or that the attention mechanism’s context cost is computationally negated in some way, simply because the system processes natural language? Having the attention mechanism selectively…
I'm telling him... and you... that what I meant by the phrase is exactly how the LLM interpreted it. For some reason that imbecile thinks that their failure to understand means they know something that's not relevant How is it relevant what his interpretation of a sentence is if 1. His interpretation is not what I meant 2. The LLM "understood" my intent and behaved in a manner that exactly matched my desire 3. The un…
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Strong agree. For every time that I'd get a better answer if the LLM had a bit more context on me (that I didn't think to provide, but it 'knew') there seems to be a multiple of that where the 'memory' was either actually confounding or possibly confounding the best response. I'm sure OpenAI and Antropic look at the data, and I'm sure it says that for new / unsophisticated users who don't know how to prompt, that thi…
I'm pretty deep in this stuff and I find memory super useful. For instance, I can ask "what windshield wipers should I buy" and Claude (and ChatGPT and others) will remember where I live, what winter's like, the make, model, and year of my car, and give me a part number. Sure, there's more control in re-typing those details every single time. But there is also value in not having to.
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Strong agree. For every time that I'd get a better answer if the LLM had a bit more context on me (that I didn't think to provide, but it 'knew') there seems to be a multiple of that where the 'memory' was either actually confounding or possibly confounding the best response. I'm sure OpenAI and Antropic look at the data, and I'm sure it says that for new / unsophisticated users who don't know how to prompt, that thi…
If I find that previous prompts are polluting the responses I tell Claude to "Forget everything so far" BUT I do like that Claude builds on previous discussions, more than once the built up context has allowed Claude to improve its responses (eg. [Actual response] "Because you have previously expressed a preference for SOLID and Hexagonal programming I would suggest that you do X" which was exactly what I wanted)
I am even more skeptical on a conceptual level. The LLM memories aren't constructing a self-consistent and up to date model of facts. They seem to remember snippets from your chats, but even a perfect AI may not be able to get enough context from your chats to make useful memories. Things you talk about may be unrelated or they get stale but you might not know which memories your answers are coming from but if you did have to manage that manually it would kind of defeat the purpose of memories in the first place.
I don't use any of these type of LLM tools which basically amount to just a prompt you leave in place. They make it harder to refine my prompts and keep track of what is causing what in the outputs. I write very precise prompts every time. Also, I try not work out a problem over the course of several prompts back and forth. The first response is always the best and I try to one shot it every time. If I don't get what…
Strong agree. For every time that I'd get a better answer if the LLM had a bit more context on me (that I didn't think to provide, but it 'knew') there seems to be a multiple of that where the 'memory' was either actually confounding or possibly confounding the best response. I'm sure OpenAI and Antropic look at the data, and I'm sure it says that for new / unsophisticated users who don't know how to prompt, that thi…
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Are you sure? As far as I am aware CC does not have a memory system built-in, other than .md files.
What do you think a memory system even is? Would you call writing things down on a piece of paper a memory system? Because it is. Claude Code stores some of its memory in someway and digests it, and that is enough to be called a memory system. It could be intermediary strings of context that it keeps around, we may not know the internals.
I don't use any of these type of LLM tools which basically amount to just a prompt you leave in place. They make it harder to refine my prompts and keep track of what is causing what in the outputs. I write very precise prompts every time. Also, I try not work out a problem over the course of several prompts back and forth. The first response is always the best and I try to one shot it every time. If I don't get what…
Strong agree. For every time that I'd get a better answer if the LLM had a bit more context on me (that I didn't think to provide, but it 'knew') there seems to be a multiple of that where the 'memory' was either actually confounding or possibly confounding the best response. I'm sure OpenAI and Antropic look at the data, and I'm sure it says that for new / unsophisticated users who don't know how to prompt, that thi…
LLMs are very simply text in and text out. Unless the providers begin to expand into other areas, there's only so much they can do other than simply focus on training better models.
In fact, if they begin to slow down or stop training new models and put focus elsewhere, it could be a sign that they are plateauing with their models. They will reach that point some day after all.
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If I find that previous prompts are polluting the responses I tell Claude to "Forget everything so far" BUT I do like that Claude builds on previous discussions, more than once the built up context has allowed Claude to improve its responses (eg. [Actual response] "Because you have previously expressed a preference for SOLID and Hexagonal programming I would suggest that you do X" which was exactly what I wanted)
Note to everyone - sharing what works leads to complete morons telling you their interpretation... which has no relevance. Apparently they know better even though 1. They didn't issue the prompt, so they... knew what I was meaning by the phrase (obviously they don't) 2. The LLM/AI took my prompt and interpreted it exactly how I meant it, and behaved exactly how I desired. 3. They then claim that it's about "knowing e…
Always starting over and trying to get it all into one single prompt can be much more work, with no better results than iteratively building up a context (which could probably be proven to sometimes result in a "better" result that could not have been achieved otherwise).
Just telling it to "forget everything, let's start over" will have significantly different results than actually starting over. Whether that is sufficient, or even better than alternatives, is entirely dependent on the problem and the context it is supposed to "forget". If your response had been "try just telling it to start over, it might work and be a lot easier than actually starting over" you might have gotten a better reception. Calling everyone morons because your response indicates a degree of misunderstanding how an LLM operates is not helpful.