An idea: your AI remembers you, even when you switch AIs
Most of us aren't using just one AI anymore. You're on Claude until you hit a limit, then you switch to Codex, then maybe back. That's just how it works now. Every time you switch, you start over. You have to re-explain who you are, how you like to work, what…
Most of us aren't using just one AI anymore. You're on Claude until you hit a limit, then you switch to Codex, then maybe back. That's just how it works now. Every time you switch, you start over. You have to re-explain who you are, how you like to work, what the project is, what feature you're building, what's already decided, what's left. The model never remembers, so you're the one carrying it all in your head and retyping it each time. The idea is simple. What if that context wasn't tied to one AI at all? Just one place that holds who you are and where each project stands, and any AI you switch to can pick it up mid task instead of you re-teaching it from scratch. With a little logic in between so the handoff doesn't get messy just because the models think differently. Not a memory feature inside one app. Something that sits above all of them, so switching AIs feels like switching tabs, not onboarding someone new. I'm thinking about actually building a small version of this to test it. Curious what you all think, is this a real problem for you too, or is it just me overcomplicating my own workflow?
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I totally like, but I had a different approach because we have different AID working on the project and memory keeps on getting upgraded. I didn't want to give them an option to directly override on the memory. I wanted to create a pipeline such as GitHub where agent creates a PR where we can review it before it actually made any change to the context and we can see the difference between how the context evolves around over time, not just a plain text as well as sub agent which keeps on track on what we are building and have we actually documented it properly in our context. So you can assume a complete KT, which runs on autopilot between different AI agents.
Yes exactly…. Finally someone understood the USP of the idea thx
Glad it resonated.
I will fix ur problem once in for all my child... lol 😂
this is a massive pain point for devs, i find myself copy pasting context blocks constantly. have u thought about how u would manage the privacy aspect though, becuase u definately dont want ur proprietary codebase leaking across different models just to keep the chat history synced
I was thinking in terms of version control of context across team using the platform where ppls + agents can use and update the context. If person's A claude + person's B codex wants to update the context for feature flag then they can create a PR (Just a metaphor for now) where the someone from there team can review and approve so that other ppls can start using it. It let them create a clean version of the context timeline b/w the everyone
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That's a great way to put it. The challenge isn't just retaining context, it's making that context actionable. Knowing what was tried , why it didn't work, and what changed is what keeps agents from repeating the same mistakes.
the pain is real, i switch between claude and codex constantly and feel exactly this. so no, not just you. fair warning though: this specific idea (portable memory that sits above all the AIs) got a lot less novel in the last few months. MCP made it easy to build, and a few tools are already doing the "one context layer, any assistant reads it" thing. so "is it a real problem" is a clear yes, but "is it open space" is closer to no. which means the interesting part isn't the memory, it's the curation. a context store you have to manually keep updated is just another doc you'll abandon in a week. the version that wins auto-extracts what actually matters from your sessions and keeps it current without you babysitting it. that extraction quality is basically the whole product, and it's the part everyone underinvests in. if you build it, build it there.
exactly. maintaining state is the real bottleneck. we see it in ops workflows constantly, the agent doesn't just need to know the right answer, it needs to know what was already tried yesterday across three different tools. context assembly is the hard part.
I see this becoming more common a teams use multiple AI tools. The hard part isn't sharing memory, it's keeping that memory accurate as the project evolves. The biggest opportunity is preserving the project's state i.e. what decisions have been made, what's already been tried, and what's next. That saves far more time than simply remembering user preferences.
yeah this happens.. i spend more time reexplaining context than doing the actual task sometimes
Built it, give it a try: https://github.com/RakuenSoftware/aimee It's also heavily self-learning, and will learn to adapt to your style and expectations. It also doesn't care about what models you use, and can translate between OpenAi and Anthropic. Hell, it can use claude code subscriptions. The hard part is memory, and it was brutal. It took me over 4 months to get the memory right and I had to teach myself a bunch of new math knowledge/techniques for it, the rest was fairly straightforward.
This feels real to me. The annoying part isn’t just re-prompting, it’s losing all the tiny decisions between tools. I’ve had the best luck with one lightweight project brief + next-action note outside the AI, otherwise every switch turns into a reset.
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