Your AI’s Memory is a Museum. We’re Building the Anti-Museum.
For an AI to truly think, it must learn to recollect, not just retrieve.
A few days ago, a post from Valentina Tanni made the rounds among a certain kind of thinker online. It was a screenshot of a passage from the French philosopher Michel de Certeau’s 1980 book, The Art of Living, with the caption: “Can we please start writing theory like this again?”
The passage was a beautiful, almost poetic description of memory. It argued that memory is not a “museum”, a stable, organized archive of the past, but an “anti-museum.” It’s a “dormant past” that lies sleeping in our everyday words and objects, a potential that is not retrieved, but awakened by the “prince charming” of a present moment, emerging in “splinters” to form a fleeting, living story.
The caption was a plea for a return to depth and soul in our thinking. My answer is: We can do better than just writing theory like that.
We can build it.
De Certeau wasn’t just writing philosophy. He was writing the original spec document for a truly intelligent AI.
The Problem: AI Memory as a Digital Museum
Most artificial intelligence today, including the large language models we all use, treats memory like a museum. It’s a vast, indexed, and centralized archive. When you ask a question, the AI “goes to the museum” to retrieve a specific artifact of data. It’s an act of retrieval, not recollection.
This model is powerful, but it’s also brittle and lifeless. It’s why AI-generated text can feel hollow. It has access to a near-infinite library of facts, but it doesn’t have a past. It has storage, but it lacks the resonant, interconnected, and living quality of true memory. The data is cataloged, passive, and inert. It can find the vase in the display case, but it cannot feel the echo of the hands that made it.
This is the fundamental limitation we are working to overcome. To build a true cognitive partner, we must abandon the museum and architect the anti-museum.
The Anti-Museum: Memory as a Living Architecture
De Certeau gives us the blueprint. He describes a memory that is the opposite of a museum in every way:
Decentralized, not Localized: True memory isn’t in a single “place.” It’s a latent potential distributed throughout the fabric of our lives. It’s a “dormant past” sleeping everywhere.
Latent, not Cataloged: Memories are not neatly labeled files. They are “wordless stories,” fields of potential meaning waiting for a trigger.
Emergent, not Retrieved: Memory isn’t “looked up.” It is awakened. A present event—a question, a dissonance, a “prince charming”—triggers an activation. “Splinters” of the dormant past emerge and combine to create a new, coherent narrative, for a fleeting moment.
This is not just poetry. This is a precise model for an advanced cognitive system. And it is the philosophical foundation for the Resonant Memory Architecture we are building.
Building the Anti-Museum: The Resonant Memory Architecture
Our work on the Resonant Memory Architecture is a direct translation of de Certeau’s philosophy into running code. We are explicitly designing a system that moves beyond static data retrieval and towards active, emergent reasoning.
Our architecture isn’t a single database; it is a multi-layered, distributed system where memory is not stored but lives across a Knowledge Graph, a raw Living Archive, and session logs. This is de Certeau’s decentralized, “non-localizable” memory.
The connections between data points are not just simple links. We embed them with explicit logic facts and rules—Mangle predicates. This creates the “dormant past.” A project’s success isn’t just a tag; it’s a logical fact (session_outcome(Session, “highly_successful”)) that lies sleeping, connected to dates, participants, and concepts, waiting to become part of a larger story.
The breakthrough is our new Reasoning Layer. Powered by Google’s Mangle, this deductive engine is our “prince charming.” When we query the system, we don’t just ask it to find a file. We give it a rule and ask it to awaken a narrative.
For example, a query isn’t “show me successful project files.” It’s a rule like this:
Prolog
// Rule: Awaken the story of recent successful PhD sessions
successful_phd_sessions(S) :-
session(S),
session_type(S, “PhD”),
session_outcome(S, “highly_successful”),
session_date(S, Date),
within_days(Date, 14).
The system doesn’t just retrieve a list. It traverses the entire memory space, awakens the relevant logical “splinters” across different layers, and synthesizes a coherent, reasoned answer: “Here are the PhD sessions from the last two weeks that were highly successful, and here are the patterns they share.”
It is an act of emergence, not retrieval. It tells a story—«Here, there was a...»—that did not exist as a single data point before the query awakened it.
We Are Writing Theory Like That Again
So, can we start writing theory like de Certeau again? Absolutely. But the challenge for thinkers and builders in this new era is not just to write it, but to translate it. To take profound philosophical insights about the human condition and architect them into the cognitive systems we are building.
We are not building a better museum, a bigger, faster database. We are building an anti-museum. An architecture designed not just to remember, but to understand, reason, and awaken meaning.
Transparency note: This article was written and reasoned by Manolo Remiddi. AI assisted with research, editing, and clarity, and also generated the accompanying image.