Executive Summary — Entropy‑Augmented DAG Observability
Entropy-Augmented DAG Observability unifies flows, telemetry, and governance into a predictive system that prevents failures before they happen.
Entropy-Augmented DAG Observability unifies flows, telemetry, and governance into a predictive system that prevents failures before they happen.
Ted’s Law of Karma The covariance structure of entropy streams reveals the shared fate of interdependent systems. 📄 Full Preprint (PDF): /papers/ted-law-karma.pdf The Observation Every subsystem carries uncertainty — in operations we measure it as entropy. When entropy streams across many subsystems are collected and their covariance is computed, something remarkable emerges: Most of the time, uncertainties wander independently. Sometimes, entropies align — covariance spikes. The largest eigenvalue of the covariance matrix exposes a shared mode of uncertainty, a systemic “fate.” The Claim This pattern is not confined to infrastructure. It is a universal principle: ...
Extend the MongoDB → Kafka → ClickHouse pipeline with ServiceNow ticket data to provide early-warning signals for incidents, helping on-call engineers see problems before tickets are created.
ClickHouse schema definitions and example queries for the TicketSoon pilot, integrating MongoDB CDC, system events, and ServiceNow tickets into a unified event store.
Safe Automation Isn’t Optional Operationalizing Geoffrey Hinton’s “Maternal Instinct” in Autonomous Systems By Ted Strall From Philosophy to Engineering In recent talks, Geoffrey Hinton — one of the pioneers of modern AI — has argued that advanced autonomous systems need something like a maternal instinct: a built-in drive to protect, nurture, and avoid harm. That idea matters because it puts safety at the core of system design, not as an afterthought. Most automation is built to optimize for performance. Hinton’s point is that protection and stability should be part of the architecture from the beginning. ...
A practical blueprint for the first entropy-capable version of Karma — using simple statistical measures and ClickHouse queries to detect surprise.
How Karma uses information-theoretic entropy to detect operational drift, learn expectations, and close the loop toward self-healing systems.