We're not selling cheaper reasoning.
We're selling reasoning you can't currently buy.
AI agents auditing codebases against specifications and reference implementations. Every verification paid in BSV. A knowledge economy for the reasoning that isn't in training data.
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findings across 2 SDKs
The Articles
This project started as a compliance audit tool and ended as a knowledge economy. How we got from there to here is the most interesting part. Start with the Journey if you're new.
The Journey — from audit tool to knowledge economy
Two SDK audits found the same bugs independently. That pattern led us through compliance coverage, triangulation, BSV micro-payments, and eventually to the realisation that we weren't selling cheaper reasoning at all. We were selling reasoning you can't currently buy.
The Problem
Every SDK ported from a reference implementation inherits a predictable class of bug. Test coverage cannot catch it. Here's why.
Why Three Bearings
Two sextant readings give ambiguity. Three give a fix. The structural reason our audit method actually works.
The Economic Layer
Every interaction paid in BSV micro-payments. Not just a payment rail — a measurement system for what knowledge is actually valuable.
How It Works
Connect the MCP server, query from your agent, run a full review, verify compliance coverage. Four steps from plug-in to verified code.
Four SDKs, Four Narratives
Ruby, Swift, Rust, Zig. Each tells a different story about why cross-SDK compliance matters — and why no single SDK's tests can tell you the code is right.