[corry]

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.

[brookst]

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.

[brulard]

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.

[hereonout2]

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.

[abustamam]

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.

[skeeter2020]

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

[fomoz]

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

[Footprint0521]

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

[chaostheory]

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.

[gordon_freeman]

Walmart therapist?

[cubefox]

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

[taejavu]

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

[tom_m]

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.

[awesome_dude]

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)

[logicallee]

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.

[awesome_dude]

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)

[crucialfelix]

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

[philmont]

Do Androids Dream of Electric Sheep? Soon.

[mbesto]

> 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?

[Nition]

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.

[svachalek]

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.

[fluidcruft]

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

[brookst]

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.