Own your knowledge.
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keccak-256
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Your identity unified in one reactive graph. |
⟐🤖 ⇄ ⇆ 🤖⟐ Robots That Understand Context — Same object, different meaning.
∴ 🏙️ ◉ 📡 ⌬ Smart City — A city reacting as one connected graph.
𓀠𓀠 ⟐👤 ⇄ 👥 ⌬ ∴ 𓀠 Social Graph — Identity, trust, and relationships.
🏪 ⇄ 📦 ⇄ 📈 CoffeeShops — Inventory and operations as a graph.
💳 ⇄ 👥 ⌬ ⚖️ ∴ Splitting your Bill — Shared expenses with automatic settlement.
🌐 ⇄ ⌬ 𓇳 ⌬ ⇄ 🌐 Hemisphere Scale — 1 million sensors. One flips. Only 6 recompute. The other 999,994 untouched. That's O(k).
⚡⚡⚡ ⟶ ⌬⌬⌬⌬ Extreme Fan-Out — One write updates 100k dependents.
.me uses an infinite proxy — any path you write becomes a node in the graph.
No schema. No migrations. No declarations upfront.
me.city.population = 700_000
me.city.name = "Veracruz"
// derived — recomputes automatically
me.city.density = () => me.city.population / me.city.area
// context-aware
me.robot.canProceed = () => me.robot.canLift && !me.robot.needsHumanReview
// stealth — structurally invisible to outside observers
me.wallet["_"].balance = 1000
// explain any value
me.explain("city.density")
// → { value: 3500, expression: "population / area", dependsOn: [...] }
// query across the graph
me.robots[r => r.canProceed === true].nameWrite anything. Chain anything. The kernel figures out the dependencies. If it changes, everything that depends on it updates — automatically.
- Structural Privacy — Private data is structurally invisible (not just hidden by rules).
- Full Explainability — Every derived value can explain exactly how it was computed.
- Subjective Reality — Same graph, different views per agent.
Local compute makes memory an OS primitive.
Cloud makes it a service.
Even with 100,000 nodes needing a simultaneous recompute, you're looking at about 62 microseconds per node (6252ms / 100k) for the full propagation. That’s incredibly consistent.
The same object can mean completely different things depending on context — and everything updates automatically when something changes.
.me uses true O(K) reactivity — when a value changes, only its actual dependents update. Not the whole graph.
- 1 million nodes in memory
- 1 sensor changed → exactly 6 dependent nodes recomputed
- Time to propagate: 0.256ms
- K=6 out of 1,000,000 — the rest of the graph is untouched
Scale the graph to 10 million nodes — if your change has 6 dependents, it still takes the same time. Data that thinks. Logic that explains itself.
𓅓 Own your intelligence.
suiGn MIT License © 2025 · neurons.me