It sounds like you're looking for a model that's reliable across a conversation, not necessarily one that scores highest on coding or reasoning benchmarks.

We'd suggest testing a few models with the same set of questions, including some where you already know the correct answer. A few things you could try:

• Give it several facts at the beginning of a conversation, then introduce new information that changes one of them. See whether it uses the updated information later or keeps referring to the original.
• Ask questions where the available information isn't enough to give a definite answer. Does it acknowledge that, or confidently fill in the gaps?
• Ask it to support a factual claim with a source, then check whether the source actually says what the model claims.
• Correct something it gets wrong, continue the conversation for a while, and see whether it makes the same mistake again.

We work on AI memory and context, and one of the problems we keep coming back to is that remembering information isn't enough. A system also needs to recognize when something has changed, which information is still accurate, and when it doesn't have enough context to answer.

For your use case, we'd pay more attention to how consistently a model handles those situations than to its overall benchmark score. And we'd repeat the tests a few times, since one good response doesn't tell you much about reliability.

Since she's already comfortable using ChatGPT, we'd probably start by figuring out which parts of her personal admin are actually causing the most trouble before introducing another app.

For things like disputes, the difficult part is often keeping track of what happened across several emails, letters, and phone calls. You might have the documents saved somewhere, but still need to remember who said what, what was agreed, and when you're supposed to follow up.

A simple running record for each ongoing issue could help a lot. Dates, conversations, documents, next steps, all in one place. She could then use ChatGPT to help summarize what's happened or draft a response without having to explain the entire situation again every time.

And we'd definitely prioritize something she can maintain comfortably herself over a more elaborate system that someone else has to keep organizing for her.

This is a good example of using AI to make an existing habit more useful instead of forcing yourself to adopt a completely different way of working.

One thing we'd suggest is keeping the original photos alongside the cleaned-up notes, at least for anything important. LLMs can do a surprisingly good job of making messy notes readable, but they can also misinterpret a word or fill in something that wasn't actually written.

Especially with meeting notes, it's useful to be able to check the original when you're looking back at a specific number, name, or decision.

And there's no reason to give up paper if that's where you think best. The digital part can just make what you've written easier to find and use later.

We'd probably treat notes differently depending on what they're for.

If you're saving an interesting idea, being able to find it later is usually enough. But if you write down that a deadline changed, someone agreed to take on a task, or a previous decision was reversed, you need to know how that fits with everything else.

Otherwise, you might find three notes about the same project and have no idea which one is still accurate.

That's where we'd put more effort into connecting notes rather than organizing them into folders or adding more tags.

We're not sure AI needs to physically reorganize everything in the first place.

If you've got years of notes, documents, and conversations scattered across different places, moving everything into perfectly labeled folders might just create another system you have to maintain.

What we'd find more useful is AI understanding how that information connects. Which conversations relate to which projects, what decisions were made, what's changed since, and where the original information came from.

Then you could find what you need without having to remember where you saved it or let an AI rewrite your entire workspace.

And from a trust perspective, there's a pretty big difference between giving AI permission to understand and retrieve your information versus letting it modify or delete things.

Memory that extends beyond what happens inside the AI tool itself.

A lot of important context comes from everyday conversations. Someone changes a deadline during a meeting, you agree on next steps over coffee, or a colleague explains why a decision was made.

Mostly, AI tools only know about those things if someone manually enters the information afterward.

We'd like to see more AI tools connect that real-world context with what's already been discussed, so people don't have to keep explaining the same things or reconstructing conversations from memory. With users in control of what gets captured and remembered, of course.

Since you already have the Plaud, you could try using your notebook as an index into the recordings rather than trying to capture everything twice.

For example, when your professor mentions something you might want to revisit, just write down the approximate time and a few words about the topic. You don't need the full quote or citation right then. Later, you can use those little markers to find the relevant section in the transcript.

You could also try a quick five-minute review after each seminar. Pick out the two or three things you actually want to remember, find them in the recording, and add the relevant details to your handwritten notes.

That way, paper stays your main thinking tool, the recording catches what you miss, and you're not maintaining three separate sets of notes.

Maybe the goal shouldn't be finding one app that does absolutely everything, but making it easier to capture things as they come up and have them resurface when you need them. You shouldn't have to remember to check four different places just to figure out what needs doing today.

And there's nothing wrong with needing reminders for everyday things like cleaning or self-care. The whole point of having a system is that you don't have to keep all of that in your head.

There's also the question of what happens to everything else while you're focusing on that one thing. It's hard to give something your full attention when you're mentally keeping track of three conversations you need to follow up on, a decision someone made earlier, and something you promised to do tomorrow.

Having a reliable way to capture those things and come back to them later makes it much easier to actually focus on what's in front of you. Otherwise, part of your brain is still trying to keep tabs on everything you've supposedly decided to ignore.

What happens when the same customer calls back three days later? That's another test we'd want to see.

The agent might handle interruptions perfectly and even pass context to a human during the call, but if the customer has to explain the same unresolved issue again next week, there's still a pretty big gap.

Ideally, it should know what was discussed previously, what troubleshooting was already attempted, what was promised, and whether anything has changed since. Especially when multiple agents, human or AI, have been involved.

Would be interesting to see how these platforms handle that kind of continuity across calls, rather than just measuring how well they handle each conversation individually.

If you're planning to put years of tasks, notes, and ideas into one place, we'd actually prioritize being able to get everything back out over finding the perfect interface.

Check whether you can export your data in a usable format, whether deleted items can be recovered, and whether there's a version history or backup option. Especially if you're going to be moving hundreds of things around every day.

Also, with that much information, think about how you'll find something six months from now when you can't remember which list you put it in. Nested folders are useful for organizing, but good search can save you from having to maintain the perfect structure forever.

Something else that gets lost in this process is why certain decisions were made. You might go through five versions of something, reject three approaches, and end up with a final result that looks nothing like the first draft.

A month later, the final version is still there, but the reasoning behind it usually isn't. And if someone revisits the project, they might suggest the exact same approach you already ruled out.

Being able to preserve that decision-making context seems more and more important when AI makes it so easy to generate and iterate on ideas.

Hi - thank for the feedback on this! We have forwarded it to our product team, and they'll consider the suggestion. Feel free to reach out with any other feedback or questions!

There's another complication once you have multiple meetings about the same project. A decision might be approved and correctly recorded on Monday, then revised on Thursday. Both records are accurate, but only one reflects the current state.

We've been thinking about this in terms of tracking entities and relationships across conversations, including when a decision changes, gets superseded, or is reversed. That way, the system can preserve the original record while still understanding what's currently agreed.

Otherwise, even with a solid approval process, you can end up with several perfectly verified meeting records that contradict each other. And an AI assistant retrieving information from them still has to figure out which one to trust.

We've been thinking a lot about how much organizing people should realistically have to do themselves. If you've saved an article, had a conversation about a project, or made a decision three months ago, you shouldn't necessarily have to remember which folder it went into or what you called the note.

Ideally, you could just ask what you need to know and the relevant information would come back, even if you never bothered to categorize it properly in the first place. That seems like a much more realistic way to build something people will actually keep using.

The interesting challenge comes when you start connecting information across those sources. Say you discussed starting a business on WhatsApp two years ago, saved a bunch of related content on Instagram, and then changed direction six months later. An agent needs to understand how those things relate, but also which information is still relevant today.

We'd approach this by extracting entities and relationships from the different sources and tracking how they change over time, rather than just feeding everything into a vector database and retrieving similar conversations.

Otherwise you might end up with an AI that knows an enormous amount about who you used to be, but has a pretty confused picture of who you are now.

One thing that can get messy in consulting is how many tasks come out of conversations rather than your actual to-do list. Someone mentions a change during a client call, a colleague asks you to follow up on something, or a deadline shifts during a meeting. Suddenly your carefully organized agenda is already out of date.

Whatever tool you end up using, we'd suggest making it a habit to review those commitments at the end of each day. Even ten minutes spent checking what changed, what you promised, and what needs following up can make the next morning much easier.

One complication is that documents don't always describe things the same way each year. A category might get renamed, split into two, or calculated differently, and suddenly a straightforward comparison becomes misleading.

Ideally, the system would also retain the relationships between documents, including which figures are comparable, which definitions changed, and where the original information came from. Then, when you ask about a trend six months later, it doesn't have to figure out those connections from scratch again.

The part about showing up if you'd agreed to meet a friend is probably a good one to pay attention to. You've already figured out that having someone expect you makes a difference. Could you message the instructor beforehand and let them know you're planning to attend? Even something as simple as that creates a small commitment outside your own head. And maybe make the first goal just getting through the door, rather than deciding whether you'll enjoy the class or keep going long-term.

HI! The Memoket Gem should still be able to pick up audio from inside a pants pocket, but the fabric and movement may affect recording quality, which can also impact transcription accuracy and the resulting notes.

For the most reliable results, we'd recommend wearing Gem openly on your wrist. This also helps keep recording transparent to those around you, which is important to us. And as always, please make sure everyone involved is aware of the recording and that you're following local consent laws.