The following post challenges the epistemological assumption that quantitative data provides the most reliable and objective form of knowledge. It argues that our obsession with counting and measuring has led to a distorted understanding of reality, where numbers replace the richness and complexity of human experience. This critique aligns with postmodern and phenomenological perspectives, which emphasize the importance of subjective experience and the limitations of objective measurement. We’ve become prisoners of our invention.
Author: William David Louth
The Lost Dimension of Meaning
For decades, we’ve built computational systems under a grand illusion: that by mastering data collection, storage, and movement, intelligence and insight will naturally emerge. The reality we face today across observability, digital transformations, data engineering, and artificial intelligence proves otherwise. Our systems are efficient but not effective. They process but don’t understand. They inform but don’t enlighten. This isn’t just a technical failure—it’s philosophical. We’ve engineered complexity but not comprehension. We’ve built means for data but not for meaning.
From Configuration Management to Situational Awareness
We trace the evolution of system management from manual configuration to dynamic situational awareness, highlighting the shift from maintaining static states to enabling systems to self-regulate and adapt in real-time. As we consider the future of system observability, it has become evident that data collection alone is insufficient. We require systems capable of comprehending the significance of the data they generate. This is where our Semiotic Twin concept comes into play. Just as human experts interpret numbers beyond mere numerical values, our monitoring systems must transcend mere metric recording to attain genuine situational awareness.
The Uncomputable Moment
In our quest to model human experience through computation, we often forget that a situation is more than the sum of its measurable parts. While we can map events, log data, and predict patterns, we cannot fully capture the ineffable quality of human awareness—the way a mother’s intuition sparks before conscious thought, or how a shared glance between strangers can rewrite social rules in an instant.
The Situation Room of the Known Unknown
In the drive to “observe all the things,” we’ve built comprehensive pipelines that churn out terabytes of data. Yet data alone isn’t understanding. Without context, without synthesis, even the most powerful observability suite is just a loudspeaker blaring noise.
Semiosphere: A Foundation for Perception and Control
In our exploration of system observability and situational intelligence, we established a universal pattern for understanding system behavior through sources, subjects, signs, and signals (including states). This recursive pattern is foundational to what we call Semiosphere, a living semiotics system, which builds upon Substrates and Signetics—a computational execution model enabling the processing, routing, and transformation of signs and signals. Together, these elements create an architecture that supports situational awareness, digital twins, and business process intelligence.
Observability X – Containers
In this post, we introduce the concept of Container in the Substrates API. A Container is a collection of Conduits of the same emittance data type. With the concept of Container, we introduce a new element in the Subject hierarchy. So instead of a Conduit being parented by a Circuit, it can now also be parented by a Container which is then parented by a Circuit.
COBOL, Zombies, and Lipstick: The Observability Crisis
In an industry that prides itself on innovation and progress, the observability sector stands as an anomaly - a field seemingly trapped in amber, where genuine evolution has been replaced by an endless cycle of marketing-driven rebranding. Despite decades of supposed advancement, the fundamental approaches to understanding system behavior remain stubbornly unchanged, masked only by increasingly elaborate terminology and ever-growing data collection.
From Mechanical Sympathy to System Sympathy
Software and systems performance engineering needs to undergo a fundamental shift. While the traditional focus on hardware optimization—known as mechanical sympathy—remains valuable, the increasing complexity of modern distributed systems demands a more comprehensive approach. This document outlines system sympathy, a new mindset for understanding and optimizing system-wide performance in enterprise environments.
Preserving Situational Awareness in AI-Assisted Software Development
The future of software development doesn’t lie in relinquishing our understanding to AI, but rather in establishing a genuine partnership that augments our cognitive faculties while preserving the profound comprehension that enables the creation of exceptional software. As we progress with the integration of artificial intelligence, maintaining this equilibrium between automation and awareness will be paramount for the ongoing evolution of our discipline.