301–310 of 326 posts
Memorize this: Fuck you Anthropic.
Yes, be sure to release a tool that I already wrote 10 times in MCP and had running....meanwhile their policy is to auto update all software you may be using (which is closed source) and then shit all over our own memory based MCP tools by making breaking changes to how the tools are run. Memorize this: Fuck you Anthropic.
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What I'm saying is.. I tell the AI to "forget everything" and it understands what I mean... and you're arguing that it cannot do... what you INCORRECTLY think is being said I get that you're not very intelligent, but do you have to show it repeatedly?
Again, we understand your argument and I don't doubt that the model "understands" your request and agrees to do it (insofar that LLMs are able to "understand" anything). But just because the model is agreeing to "forget everything" doesn't mean that it's actually clearing its own context, and because it's not actually clearing its own context it means that all the output quality problems associated with an overfilled…
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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)
it can't really "forget everything so far" just because you ask it to. everything so far would still be part of the context. you need a new chat with memory turned off if you want a fresh context.
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Like I said, the AI does exactly what I intend for it to do. Almost, as I said earler, like the AI has processed my request, realised that I am referring to the context of the earlier discussions, and moved on to the next prompt exactly how I have expected it to Given the two very VERY dumb responses, and multiple people down voting, I am reminded how thankful I am that AI is around now, because it understood what yo…
I think you completely misunderstood me, actually. I explicitly say if it works, great, no sarcasm. LLMs are finicky beasts. Just keep in mind they don’t really forget anything, if you tell it to forget, the things you told it before are still taken into the matrix multiplication mincers and influence outputs just the same. Any forgetting is pretend in that your ‘please forget’ is mixed in after. But back to schedule…
Not the same no. Models chooses how much attention to give each token based on all current context. Probably that phrase, or something like it, makes the model give much less attention to those tokens than it would without it.
CC barely manages to follow all of the instructions within a single session in a single well-defined repo. 'You are totally right, it's been 2 whole messages since the last reminder, and I totally forgot that first rule in claude.md, repeated twice and surrounded by a wall of exclamation marks'. Would be wary to trust its memories over several projects
How big is your claude.md file? I see people complain about this but I have only seen it happen in projects with very long/complex or insufficient claude.md files. I put a lot of time into crafting that file by hand for each project because it's not something it will generate well on its own with /init.
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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…
All those moments will be lost in time, like tears in rain.
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What are we debating? Does anyone know? One claim seems to be “people should cease using any anthropocentric language when describing LLMs”? Most of the other claims seem either uncontested or a matter of one’s preferred definitions. My point is more of a suggestion: if you understand what someone means, that’s enough. Maybe your true concerns lie elsewhere, such as: “Humanity is special. If the results of our thinki…
I don't need to feel "special". My concerns are around the people who (want to) believe their statistical models to be a lot more than they really are. My current working theory is there's a decent fraction of humanity that has a broken theory of mind. They can't easily distinguish between "Claude told me how it got its answer" and "the statistical model made up some text that looks like reasons but have nothing to d…
Yes, I also think this is common and a problem. / Thanks for stating it clearly! ... Though I'm not sure if it maps to what others on the thread were trying to convey.
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> "the truth sometimes hurts" But it's not the truth in the first place.
The training data contains all kinds of truths. Say I told Claude I was a Christian at some point and then later on I told it I was thinking of stealing office supplies and quitting to start my own business. If Claude said "thou shalt not steal," wouldn't that be true?
You know that it's true that stealing is against the ten commandments, so when the LLM says something to that effect based on the internal processing of your input in relation to its training data, YOU can determine the truth of that.
> The training data contains all kinds of truths.
There is also noise, fiction, satire, and lies in the training data. And the recombination of true data can lead to false outputs - attributing a real statement to the wrong person is false, even if the statement and the speaker are both real.
But you are not talking about simple factual information, you're talking about finding uncomfortable truths through conversation with an LLM.
The LLM is not telling you things that it understands to be truth. It is generating ink blots for you to interpret following a set of hints and guidance about relationships between tokens & some probabilistic noise for good measure.
If you find truth in what the LLM says, that comes from YOU, it's not because the LLM in some way can knows what is true and give it to you straight.
Personifying the LLM as being capable of knowing truths seems like a risky pattern to me. If you ever (intentionally or not) find yourself "trusting" the LLM to where you end up believing something is true based purely on it telling you, you are polluting your own mental training data with unverified technohaikus. The downstream effects of this don't seem very good to me.
Of course, we internalize lies all the time, but chatbots have such a person-like way of interacting that I think they can end run around some of our usual defenses in ways we haven't really figured out yet.
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…