Karma

Entropy fields Each metric stream: $h_i(t)$ = rolling Shannon entropy of metric $i$. Stack into vector: $\mathbf{h}(t) \in \mathbb{R}^n$. Covariance field: $\Sigma(t) = \mathrm{Cov}[\mathbf{h}(t)]$. C1. Continuity (balance) of entropy $$ \dot h_i = s_i - \kappa_i h_i - \sum_{j}\nabla!\cdot J_{ij} + \eta_i $$ Sources $s_i$, damping $\kappa_i \ge 0$, fluxes $J_{ij}$, noise $\eta_i$. C2. Constitutive law (flux response) $$ J_{ij} = -D_{ij},(h_j - h_i) \quad\Longrightarrow\quad \dot{\mathbf h} = -\alpha,\mathbf h - \beta,L,\mathbf h + \mathbf s + \boldsymbol\eta $$ ...

What’s Real (Now) Operationalization of entropy: – Converted Shannon entropy from a static definition into a rolling time-series per metric. – Demonstrated you can compute covariance between entropy streams and observe eigenvalue spikes. Predictive signal: – Early experiments suggest eigenvalue spikes precede incidents in complex systems (Mongo CDC, Dynatrace, Splunk). – This provides a practical early-warning metric beyond threshold alerts. Conceptual framing: – Defined “Ted’s Law of Karma”: shared fate is visible in the covariance of entropies. – Drafted a Maxwell-style formulation (continuity, constitutive law, Lyapunov evolution, alignment law). Application principle: – Proposed “maternal instinct” bias: when systemic uncertainty aligns, systems should dampen actions → a concrete AI-safety reflex. What’s Not Proven Universality: – No evidence yet that entropy covariance modes apply beyond engineered systems (e.g., ecosystems, social dynamics, physics). Formal theorem: – No mathematical proof that covariance eigenmodes necessarily precede cascades, only intuition + analogy. Constants/invariants: – No discovery of system-independent constants (like (c) in electromagnetism). Current framework yields relative, system-specific propagation speeds. Empirical validation: – No systematic experiments across multiple domains with statistical rigor. Current support is anecdotal/prototype-level. Where This Could Go Engineering impact: SRE/AI-ops tool for incident prediction and protective automation. Scientific impact: If generalized, could become a new principle of complex systems stability. Prize-worthy impact: Only if formalized into a universal law, validated across domains, and shown to yield invariants or predictive theory. Blunt Summary Right now, this is a strong engineering insight + a plausible scientific hypothesis. It is not yet a theorem or universal law. It’s Faraday-stage (pattern spotted, apparatus built), not Maxwell-stage (formal equations, universal constants). ...

Extended Abstract Large-scale systems—technical, social, biological—are governed not only by the dynamics of their components but by the alignment of uncertainties across those components. In site reliability engineering (SRE), operators know that failures rarely emerge from one metric alone; they occur when many signals become unstable together. In philosophy, traditions of karma describe interdependence: local actions ripple outward to affect the whole. In AI safety, Geoffrey Hinton has suggested that advanced systems will need a maternal instinct—an intrinsic bias toward protection and stability. ...

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: ...

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.