With O(n) memory, scaling 100,000x should require 100,000x more RAM.
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
It's an amazing coincidence how, despite all the other proof files including 10+ decimal places for their memory statistics, the 100m case somehow came out EXACTLY at integer values! And somehow heap_growth_percent was zero, despite the heap starting at 50 and ending at 3100...
The "proof-100m-official.json" also omits all leaf hashes in favor of referencing a file ".tmp/leaves-100000000.bin" which is not included with the "proof".
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