Gray Smoke

The second law of thermodynamics tells us that entropy drives systems toward equilibrium and disorder. In his final shareholder letter as CEO of Amazon, Jeff Bezos referenced Richard Dawkins' "The Blind Watchmaker" to illustrate how natural selection counters entropy by building complex life through energy-driven processes. Bezos urged Amazon to resist the entropy-like pull of complacency by staying in "Day 1" mode—continuously growing, innovating, and dynamically adapting to trends rather than following predetermined designs. While this advice resonates for maintaining corporate vitality, a deeper examination through Dawkins' evolutionary lens reveals a more insidious challenge lurking within organizations themselves. The very mechanisms that help companies survive external entropy may be generating internal evolutionary pressures that undermine efficiency and threaten long-term survival. The Gene's-Eye View of Organizations Richard Dawkins' rev...

      The Three Paradigms of Software Karpathy's Framework: Andrej talks about how Software is fundamentally changing for the first time in 70 years, having changed approximately twice in recent years Software 1.0: Traditional code written by humans for computers Software 2.0: Neural network weights (not written directly but tuned through datasets and optimizers) Software 3.0: Prompts in English that program Large Language Models (LLMs) My Perspective: As an engineering practitioner, I see fundamental challenges in "prompt engineering" that threaten its scalability and reliability: Standardization Crisis : Unlike traditional programming languages with formal syntax, natural language prompts suffer from inherent ambiguity. While researchers have identified 26 design principles for prompt engineering, we lack universal standards comparable to programming language specifications. Cross-model and Version Compatibility : Currently, "there is no such thing as prompt po...

In 1933, Bell Labs engineer Karl Jansky was investigating the static interfering with transatlantic radio communications. Rather than simply filtering out the unwanted noise, Jansky methodically studied its patterns. What he discovered in that static revolutionized our understanding of the universe—the noise was actually radio waves from the center of the Milky Way galaxy. His investigation of "unwanted" interference founded the field of radio astronomy and led to discoveries that would eventually earn Nobel Prizes and fundamentally expand human knowledge of cosmic phenomena. Jansky's story illustrates a principle we often overlook in our need for certainty and clarity: noise is not merely an impediment to understanding—it is an intrinsic component of authentic signals. When we encounter suspiciously "clean" information, we should question not just its content, but the filtering mechanisms that produced such artificial clarity. The Physics of Imperfection The ...

In 2023, at age 49, I received the diagnosis that transformed my understanding of myself: Attention Deficit Hyperactive disorder (ADHD). For years, I had navigated episodes of anxiety, depression, and anger, developing coping mechanisms that somehow kept me afloat. But that year, the depression became overwhelming. I needed help. What pushed me to seek support was my sister. During her medical studies, she had studied ADHD extensively. With the frankness only a sister can provide, she said, "You're a textbook case of ADHD." Her words were simultaneously shocking and deeply familiar. The psychiatrist confirmed both types: inattentive and hyperactive. The moment he spoke those words, a weight lifted from my chest—a burden I hadn't realized I'd been carrying. I had always known I was different, but could never name it. Now I had a map to understand my own mind. The Medication Revelation My doctor prescribed atomoxetine, starting low. For weeks, nothing changed. ...

As a technology leader constantly seeking wisdom in unexpected places, I've discovered that some of my most profound leadership insights come not from business books or conference talks, but from watching the intricate dance of an Indian kitchen. In my last article, " The Indian Kitchen Philosophy: Transforming the Ordinary into Extraordinary ," I wrote about how the Indian kitchen follows principles that differ from those of the western kitchen. The same principles that transform humble ingredients into extraordinary meals offer remarkable parallels for navigating the complexities of modern product development and engineering management. Making Magic with What You Have The Indian kitchen's greatest strength lies in its ability to create abundance from scarcity. A skilled cook can transform whatever vegetables are available at the local market into a feast, just as successful product and engineering teams learn to excel with the resources actually at the...

While European and Japanese cuisines celebrate the pristine flavour of individual ingredients and limited seasons, Indian cooking follows a fundamentally different philosophy: transformation through technique. This approach creates an entirely different relationship with food – one where the magic happens not in sourcing the perfect ingredient, but in the alchemy of combining, cooking, and layering flavors. The Art of Amalgamation Indian cuisine operates on a revolutionary principle – that the whole can be infinitely greater than the sum of its parts. Unlike cuisines that aim to highlight a single star ingredient, Indian cooking embraces complexity through amalgamation. Every dish becomes an orchestra where individual instruments blend into something entirely new. Take the beloved sambar , a South Indian lentil soup that exemplifies this philosophy perfectly. Here, vegetables, lentils, tamarind, and spices merge into such a harmonious blend that it doesn't matter whether you use ...

  GenAI's shift from deterministic to non-deterministic systems breaks traditional development approaches. Having worked on scaling GenAI at big-tech, I've learned that success requires working backwards from desired outcomes—starting with precise problem boundaries and data quality requirements. These first two steps determine whether your GenAI product scales or remains an impressive demo. One of the biggest differences between existing computer systems and new GenAI systems is indeterminism—and it's breaking everything we thought we knew about building scalable products. For decades, computers excelled at doing the same task thousands of times with identical results. They weren't necessarily smarter than humans, but they had almost no variance or bias. GenAI deliberately introduces variance through "temperature" settings to enable creativity. Zero temperature produces identical, factual outputs. Higher temperature brings creativity and individuality. Th...