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Comment by corry | Hacker News Reader

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 this is a handy crutch (even if it's bad here and there) to make sure they get SOMETHING useable.

But for the HN crowd in particular, I think most of us have a feeling like making the blackbox even more black -- i.e. even more inscrutable in terms of how it operates and what inputs it's using -- isn't something to celebrate or want.

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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.

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.

I'd imagine 99% of ChatGPT users see the app as the former. And then the rest know how to turn the memory off manually.

Either way, I think memory can be especially sneakily bad when trying to get creative outputs. If I have had multiple separate chats about a theme I'm exploring, I definitely don't want the model to have any sort of summary from those in context if I want a new angle on the whole thing. The opposite: I'd rather have 'random' topics only tangentially related, in order to add some sort of entropy in the outout.

Good point. I almost wish for an anonymous mode with chat history.

Would that just be the ability to chat without making new memories while using existing memories?

In chatgpt at least if you start a temporary chat it does not have access to memories.

Well you're in luck! They have that feature and talk about it in the article

I've found this memory across chats quite useful on a practical level too, but it also has added to the feeling of developing an ongoing personal relationship with the LLM.

Not only does the model (chat gpt) know about my job, tech interests etc and tie chats together using that info.

But also I have noticed the "tone" of the conversation seems to mimick my own style some what - in a slightly OTT way. For example Chat GPT wil now often call me "mate" or reply often with terms like "Yes mate!".

This is not far off how my own close friends might talk to me, it definitely feels like it's adapted to my own conversational style.

I mostly find it useful as well, until it starts hallucinating memories, or using memories in an incorrect context. It may have been my fault for not managing its memories correctly but I don't expect the average non power user will be doing that.

until you ask it why you have trouble seeing when driving at night and it focuses on you need to buy replacement wiper blades.

Claude, at least in my use in the last couple weeks, is loads better than any other LLMs at being able to take feedback and not focus on a method. They must have some anti-ADHD meds for it ;)

You can leave memory enabled and tell it to not use memory in the prompt of it's interfering.

Like valid, but also just ?temporarychat=true that mfer

Both of you are missing a lot of use cases. Outside of HN, not everyone uses an LLM for programming. A lot of these people use it as a diary/journal that talks back or as a Walmart therapist.

Walmart therapist?

People use LLMs as their therapist because they’re either unwilling to see or unable to afford a human one. Based on anecdotal Reddit comments, some people have even mentioned that an LLM was more “compassionate” than a human therapist.

Due to economics, being able to see a human therapist in person for more than 15 minutes at a time has now become a luxury.

Imo this is dangerous, given the memory features that both Claude and ChatGPT have. Of course, most medical data is already online but at least there are medical privacy laws for some countries.

This is exactly why the two use cases need to be delineated.

As in cheap.

Anecdotally, LLMs also get less intelligent when the context is filled up with a lot of irrelevant information.

This is well established at this point, it’s called “context rot”: https://research.trychroma.com/context-rot

Yeah, though this paper doesn't test any standard LLM benchmarks like GPQA diamond, SimpleQA, AIME 25, LiveCodeBench v5, etc. So it remains hard to tell how much intelligence is lost when the context is filled with irrelevant information.

Nah, they don't look at the data. They just try random things and see what works. That's why there's now the whole skills thing. They are all just variations of ideas to manage context basically.

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.

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.

It can't forget everything, but it can and probably does have an effect on how much attention it gives to those particular tokens.

I mean I am telling you what has actually worked for me so far - and being a NLP the system (should) understand what that means... as should you...

LLMs literally can’t forget. If it’s in the context window, it is known regardless of what you put in the context next.

That said, if the ‘pretend forget’ you’re getting works for you, great. Just remember it’s fake.

it may be possible to add - or rather, that they've already added - an mcp function that clears the context?

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 you clearly don't.

I didn't expect it to delete the internet, the world, the universe, or anything, it didn't read my request as an instruction to do so... yet you and that other imbecile seem to think that that's what was meant... even after me saying it was doing as I wanted.

/me shrugs - now fight me how your interpretation is the only right one... go on... (like you and that other person already are)

One thing I am not going to miss is the toxic "We know better" responses from JUNIORS

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 scheduled programming: if it works, great. This is prompt engineering, not magic, not humans, just tools. It pays to know how they work, though.

It's beyond possible that the LLM Chat Agent has tools to self manage context. I've written tools that let an agent compress chunks of context, search those chunks, and uncompress them at will. It'd be trivial to add a tool that allowed the agent to ignore that tool call and anything before it.

>the things you told it before are still taken into the matrix multiplication mincers and influence outputs just the same.

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.

No.

I think that you are misunderstanding EVERYTHING

Answer this:

1. Why would I care what the other interpretation of the wording I GAVE is?

2. What would that interpretation matter when the LLM/AI took my exact meaning and behaved correctly?

Finally - you think you "know how it works"????

Because you tried to correct me with an incorrect interpretation?

F0ff

Well ask it to tell you what it forgot. Over and out.

> I am reminded how thankful I am that AI is around now, because it understood what you clearly don't.

We understand what you're saying just fine but what you're saying is simply wrong as a matter of technical fact. All of that context still exists and still degrades the output even if the model has fooled you into thinking that it doesn't. Therefore recommending it as an alternative to actually clearing the context is bad advice.

It's similar to how a model can be given a secret password and instructed not to reveal it to anyone under any circumstances. It's going to reject naive attempts at first, but it's always going to reveal it eventually.

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 context continue to apply, even if the model is convincingly pretending to have forgotten everything. Therefore your original interjection of "instead of clearing the context you can just ask it to forget" was mistaken and misleading.

These conversations would be way easier if you didn't go around labeling everyone an idiot, believing that we're all incapable of understanding your rather trivial point while ignoring everything we say. In an alternative universe this could've been:

> You can ask it to forget.

> Models don't work like that.

> Oh, I didn't know that, thanks!

Just because it's not mechanically actually forgetting everything doesn't mean the phrase isn't having a non trivial effect (that isn't 'pretend'). Mechanically, based on all current context, Transformers choose how much attention/weight to give to each preceding token. Very likely, the phrase makes the model pay much less attention to those tokens, alleviating the issues of context rot in most (or a non negligible amount of) scenarios.

You should probably stop resorting to personal attacks as it's against hn rules.

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 isolate context on command would be an important technical discovery.

I wonder if the AI companies will eventually just have a tool that lets the llm drop it's context mid convo when the user requests it.

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 universe was not deleted (Ok, that would be stupid... like the other individuals stupidity... but here we are)

Calling other people making comments in good faith “imbecile” or stupid is not awesome dude. It’s against HN rules and the spirit of this site.

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 exactly what's going on" ... even though they didn't and they got it wrong.

This is the advantage of an LLM - if it gets it wrong, you can tell it.. it might persist with an erroneous assumption, but you can tell it to start over (I proved that)

These "humans" however are convinced that only they can be right, despite overwhelming evidence of their stupidity (and that's why they're only JUNIORS in their fields)

There are problems with either approach, because an LLM is not really thinking.

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.

All those moments will be lost in time, like tears in rain.

Do Androids Dream of Electric Sheep? Soon.

> For every time that I'd get a better answer if the LLM had a bit more context on me

If you already know what a good answer is why use a LLM? If the answer is "it'll just write the same thing quicker than I would have", then why not just use it as an autocomplete feature?

That might be exactly how they're using it. A lot of my LLM use is really just having it write something I would have spent a long time typing out and making a few edits to it.

Once I get into stuff I haven't worked out how to do yet, the LLM often doesn't really know either unless I can work it out myself and explain it first.

That rubber duck is a valid workflow. Keep iterating at how you want to explain something until the LLM can echo back (and expand upon) whatever the hell you are trying to get out of your head.

Sometimes I’ll do five or six edits to a single prompt to get the LLM to echo back something that sounds right. That refinement really helps clarify my thinking.

…it’s also dangerous if you aren’t careful because you are basically trying to get the model to agree with you and go along with whatever you are saying. Gotta be careful to not let the model jerk you off too hard!

Yes, I have had times where I realised after a while that my proposed approach would never actually work because of some overlooked high-level issue, but the LLM never spots that kind of thing and just happily keeps trying.

Maybe that's a good thing - if it could think that well, what would I be contributing?

You don't need to know what the answer is ahead of time to recognize the difference between a good answer and a bad answer. Many times the answer comes back as a Python script and I'm like, oh I hate Python, rewrite that. So it's useful to have a permanent prompt that tells it things like that.

But myself as well, that prompt is very short. I don't keep a large stable of reusable prompts because I agree, every unnecessary word is a distraction that does more harm than good.

For example when I'm learning a new library or technique, I often tell Claude that I'm new and learning about it and the responses tend to be very helpful to me. For example I am currently using that to learn Qt with custom OpenGL shaders and it helps a lot that Claude knows I'm not a genius about this

Because it's convenient not having to start every question from first principles.

Why should I have to mention the city I live in when asking for a restaurant recommendation? Yes, I know a good answer is one that's in my city, and a bad answer is on one another continent.

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