BRM-loading · layering pigment…
BRM-loading · layering pigment…
Thesis
The substrate is measured. The benchmark suite ships verified — 31.9% reasoning gain on the head-to-head, 21× cost reduction, 29.2× lower energy on the UK grid, 96.6% per-call CO2 reduction, 19 physics-acceptance tests passed in 12.91 seconds on a single laptop GPU. Track One granted. The corpus is inscribed on Bitcoin mainnet. The audit chain is public. The file is the proof.
60 days · idea to delivered substrate
Project start 2026-03-15 · launch lock 2026-05-18. Sixty calendar days. Single founder. Domestic silicon (Mac · Windows · ATOM) plus three hired-GPU training runs whose carbon is itself on the ledger.
RUN 01 · 2026-04-04
cloud accelerator · single host
25 models swept · best sub-3B-class loss 0.3501
RUN 02 · 2026-04-09
cloud accelerator · 4-host cluster
314B-class fine-tune at QLoRA r=64 · 0.9010 score · paste-recovery novelty
RUN 03 · 2026-04-10
domestic edge silicon
production sub-3B distillation · 108× token reduction · Ollama-deployable
No retraining for thirty-four days after RUN 03. The patent suite expanded across Wave-16 continuations · eighteen on-chain events anchored · the twelve honest-AI principles authored · the substrate moved into production — all without touching model weights. The substrate compounds; the model is the vehicle.
The substrate delta · what the numbers say
| dimension | industry baseline | BRMSTE | ratio |
|---|---|---|---|
| Per-query cost | industry baseline | $0.001642 / query | 21× cheaper |
| Per-query energy | 1.1652 Wh | 0.0399 Wh | 29.2× less |
| Per-query CO2 | industry baseline | 0.0077 gCO2e | 96.6% reduction |
| Reasoning gain (n=9) | — | 31.9% avg · 80% peak | head-to-head |
| Reliability (1,002 calls) | varies | 100% · 0.854 metaloop floor | 379.6 calls/min |
| Production model size | 70B–400B+ frontier | sub-3B distilled · domestic edge | Ollama-deployable |
| Patent estate | 0 directly comparable | 84 codenames | Track One granted |
| On-chain attestation | 0 | 18 events · 5-layer topology | Bitcoin mainnet |
Every row sourced · see /register
What this means in practice. As deployed AI moves into decision- bearing roles, the bottleneck is not raw capability — it is verifiable compute attestation, tamper-evident output provenance, and Bitcoin-anchored settlement of agentic transactions. BRMSTE delivers all three today: cheaper, leaner, audit-grade.
BRMengitec is to deployed AI what TCP/IP was to the consumer internet. The transport layer had to be neutral, permissionless, and provable before the applications above it could become globally trusted. The substrate is that transport, and it is already running.
Bitcoin provides the settlement neutrality. BRMSTE provides the attestation and provenance layer above that. The agents running on top are free to be whichever model the operator selects — the substrate authenticates, timestamps, and audits their outputs regardless. The visiting buyer reads block 946,772 directly.
Everything on this site is bound by the same discipline. Real silicon. Real reference equations. Real bounds. Real audit chain. Real on-chain inscription. The twelve honest-AI principles bind every routed call · returned in X-Honest-AI on every response. Every number traces to a file on disk and a signed manifest. We add nothing we cannot defend in code.
