Thread Truncated (Cap Enforced)
Only the first 20 tweets are unrolled into slides to ensure reliable PDF exporting and high server performance.
Canvas & Ratio
Choose your destination platform format
Layout Template
Choose a content structure for your slides
Preset Themes
Typography & Sizing
Brand Kit Customization
AGENCYConfigure brand assets for headers & footers
Outro Slide CTA
Customize your closing call-to-action slide
Background Pattern
Build Your Carousel
Drag and drop any post card below onto a slide, or use the quick buttons to insert content/images instantly!
<b>Almost every AI memory system is built for one person talking to one assistant. Bloome is the opposite — many people and many agents sharing the same rooms — and when we put a single-user memory design into that setting, it broke in ways that had nothing to do with how clever the technology was. The fix turned out to be less about better search and more about asking a different question.</b>
Bloome is a place where people and AI agents work together — direct messages, group chats, and agents that belong to one person but talk to everyone in the room. That last part is the whole story. The moment an agent serves more than one person, the question its memory has to answer changes from <i>"what do I know?"</i> to <i>"what do I know <b>about this person, right now</b>?"</i>
It sounds like a small difference. It isn't. Almost every memory design we tried, and most of the ones we studied, quietly assume the first question and stumble on the second.
## A notebook for one
The first version of memory was the obvious one, the same shape most personal assistants start with: a long-term note for durable facts and preferences, plus a running log of recent days. The agent reads the note at the start of a conversation and carries it along.
For one human and one assistant, this works beautifully. It's a personal notebook. Everything written in it is implicitly <i>about you</i>, because there is only one "you."
Now put that same notebook in a group chat. The implicit subject vanishes. A line like "prefers short replies, no emoji" is useless — or worse, misapplied — if the agent can't say <i>whose</i> preference it is. The notebook has no place to record who a fact belongs to. It never needed one, because in the world it was designed for, the person was a constant, not a variable.
So the real fix isn't a bigger notebook. It's memory that knows <i>who</i> each thing is about.
## Why "just search everything" doesn't rescue it
The popular way to scale memory today is retrieval: save everything, then, when it's needed, search for the bits most similar to the current conversation. It's the engine behind a lot of impressive AI memory, and we looked closely at how leading systems do it. OpenClaw is retrieval-first — it files everything away and lets the agent search that store when it needs history. Hermes Agent keeps a small, hand-curated profile always in view and hands the heavier recall to an external memory service that fetches relevant bits each turn.
Both are well-built, and both are genuinely good approaches — for a single person's growing pile of notes. But each, in its own way, leans on matching <i>text</i> to <i>text</i>, and that's exactly where a shared space trips it up.
Consider the most ordinary thing that happens in a group: two different people named Mike.
> I can't tell the Mike I talked to yesterday from the Mike I talked to a week ago — they're different people. I can't even reliably tell the Mike in this conversation from the Mike in the last one.
Search for "Mike" and you don't get back <i>a person</i>; you get back <i>every passage that mentions a Mike</i>, ranked by how similar the words are. The search can be flawless and the answer still wrong, because what came back was text, not a person. Matching is good at "what is this about." A shared space needs "<i>who</i> is this about, and is it still true?" — and that's a question about who someone is, not about which words are nearby. Retrieval also ages quietly: a note that says "Mike is planning a trip to Tokyo" stays just as findable long after the trip is over.
We could have kept patching the search to be more identity-aware. Instead we changed what memory <i>is</i>.
## Memory you can browse, not just query
We gave each person their own small set of notes, kept as ordinary files the agent can open and read directly — no hidden index, no similarity score in the middle. Identity stops being a guess the search has to recover; it's simply <i>whose folder this is</i>. "The Mike in the room" becomes a lookup, not a gamble.
Within each person's memory, the agent makes one judgment call for every fact it records: does this belong in front of me <i>every time</i> I talk to this person, or only <i>when it's relevant</i>?