I scaled from 1,000 to 100,000,000 tasks. Memory stayed at ~3GB the entire time.
Here's the cryptographic proof.
The benchmark: - 1K tasks: ~3GB memory - 100K tasks: ~3GB memory - 1M tasks: ~3GB memory - 10M tasks: ~3GB memory - 100M tasks: ~3GB memory
Same memory. 100,000x scale. Merkle-verified.
Hardware: Intel i7-4930K (2013), 32GB RAM
The proof: Every task is SHA-256 hashed into a Merkle tree. Root hash commits to all 100 million tasks. You can verify individual samples against the root. The math either works or it doesn't.
Root hash: e6caca3307365518d8ce5fb42dc6ec6118716c391df16bb14dc2c0fb3fc7968b
What it is: An O(1) memory architecture for AI systems. Structured compression that preserves signal, discards noise. Semantic retrieval for recall. Built-in audit trail.
What it's not: A reasoning engine. This is a memory layer. You still need an LLM on top. But the LLM can now remember 100M interactions without 100M× the cost.
Background: Solo developer. Norway. 2013 hardware.
Looking for: Feedback, skepticism, verification. Also open to acquisition conversations.
Repo: https://github.com/Lexi-Co/Lexi-Proofs