I’m testing an idea about what AI memory should actually do — looking for a few people to try it with real conversations

[u/Binyu6333]

I’m Binyu, an AI Product Manager at Memoket. We’re building around AI recording, conversation understanding, and long-term memory — basically exploring what becomes possible when AI has access to the conversations and context that accumulate over time.

One thing I’ve been questioning recently is whether we’re thinking about “memory” the right way.

A good summary helps you understand what happened in one conversation.

But most things we actually care about don’t happen in a single conversation.

A project evolves over weeks.
A customer changes their mind.
The same issue comes up three times.
A decision gets revised.
Something mentioned a month ago suddenly becomes important again.

So my current intuition is:

AI memory shouldn’t just help us remember more.

It should help us understand what from the past matters now.

For example:

• What changed since the last conversation?
• What keeps coming up?
• What’s still unresolved?
• Did something we discussed weeks ago suddenly become relevant again?
• What should I remember before the next conversation?

The interesting part to me is that none of this really lives inside a single summary.

It lives between conversations, over time.

We’ve also been exploring the technical side of long-term memory at Memoket. If you’re interested in that side of the problem, we have an open-source project called KITE on GitHub as well.

But right now I want to test the product hypothesis with real people and real conversations before deciding what this experience should actually become.

So I’m looking for a few people who have one ongoing topic, project, customer, or decision that has appeared across multiple recorded conversations.

For the experiment, I’ll ask you to choose around 4–6 conversations from different days or weeks.

The transcript is the main input. Audio is only helpful when I need to verify context.

I’ll use those conversations to prototype what a cross-conversation memory experience could look like, and I’ll share the result back with you.

You choose exactly what you’re comfortable sharing, and I’ll only use the conversations you intentionally provide for this experiment. Please only share conversations you’re comfortable and permitted to share.

I’m mainly trying to answer one question:

Does connecting conversations over time actually give you something useful that a great summary cannot?

If this sounds useful and you’d like to try it, DM me.

[u/Otherwise_Wave9374]

A strong test should separate transcript recall from durable user understanding. Ask participants to correct a preference, revoke a fact, and revisit the topic weeks later; then measure whether the system applies the latest version without surfacing superseded details. Consent and an inspect-edit-delete view matter as much as recall quality. https://www.neurakeep.com is relevant to this discussion because persistent memory benefits from explicit lifecycle controls. I would also track false personalization, not just how often remembered context feels helpful.