Articles

The concept of covariance of entropies can be understood as a way of quantifying how uncertainties in different signals vary together. Rather than monitoring each metric independently, the focus shifts to the relationships between sources of uncertainty. When signals that typically exhibit aligned behavior diverge, this can provide an early indicator of system anomalies. While this terminology is uncommon, the underlying idea overlaps strongly with established constructs in information theory, particularly mutual information. Mutual information measures the reduction in uncertainty about one random variable given knowledge of another, and has been widely applied to anomaly detection and monitoring tasks. ...

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: ...

Many systems already know a lot about themselves — you just have to listen. MongoDB change streams (CDC) emit a continuous feed of inserts, updates, and deletes. With a little routing into a fast analytical database like ClickHouse, you can let the system “discover itself”: jobs, runs, schedules, dependencies, and even the fingerprints of human intervention. 1. Capture the Raw Feed First, set up a connector: MongoDB → Kafka → ClickHouse In ClickHouse, land the JSON envelopes losslessly: ...

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.

Karma now ingests, normalizes, and routes events from any CDC-like source into a shared ledger, optional graph, and an action loop — setting the stage for learned expectations and autonomous intervention.

In Karma, every action is just another event. This post explains the pattern for turning anomalies and rules into commands, tracking their execution, and feeding the results back into the same event pipeline.

Karma uses a single normalized event stream to feed both a ClickHouse ledger and an optional graph database — but through separate pipelines for flexibility, scalability, and clarity.

When you already have systems tracking their own state in MongoDB, you can turn that into a real-time stream of structured events without rewriting application logic. This approach captures every meaningful change from Mongo, tags it with relevant metadata, and makes it instantly queryable in ClickHouse — all through a generic, reusable pattern. The idea: One fixed event envelope for all sources Dynamic tags/attributes defined in config files No code changes when onboarding new collections 1. The Fixed Event Envelope Every CDC message has the same top-level structure, no matter what source system or collection it came from: ...