Aidni's design follows from published work on data center engineering, AI capital intensity, transparent system design and agent interfaces. Each entry lists the modules it informs.
Argues that data centers should be judged at industry scale with transparent math: grid delivery, not server efficiency, is the true bottleneck, and water footprint is a cooling design choice between closed-loop and evaporative systems.
Using 71,702 declared offerings from 130,057 SEC Form D filings, finds no detectable post-2022 AI offering-size premium; the 2026 divergence is a right-tail phenomenon of a few capital-intensive financings. Models how 24–36 month accelerator obsolescence breaks the usual venture J-curve.
Examines how legacy, distributed pricing systems produce rebate accuracy failures, and how unified data models, published formulas, real-time visibility and automated audit address complexity, transparency and accuracy together.
A technical explanation of the Model Context Protocol and how enterprise integration platforms expose capabilities to AI agents.
A practical guide to deploying and governing a managed API gateway.
Whether the system is pharmacy rebates, venture financing, enterprise integration or a data center, the failure is the same: data split across incompatible systems, numbers no one can recompute, and interfaces too broad to audit.
The fix is also the same: one data model, published formulas, and a small interface. That is the Facility Graph, Ledger and Conduit.
A six-week Site Assessment maps the power envelope, the cooling design and the compute plan for one facility, delivered by the engineers who build the platform.