In the world of software development, few essays have had as much lasting impact as The Cathedral and the Bazaar by Eric S. Raymond. Published in 1999, the essay is more than just a reflection on open-source software—it’s a philosophy that has influenced how software is built, maintained, and distributed in the modern era. By contrasting two distinct models of software development, the cathedral and the bazaar, Raymond provides a compelling argument for the power of open collaboration.

In his 2019 essay The Bitter Lesson, Richard Sutton outlined a key observation in AI research: general-purpose methods that leverage massive computation consistently outperform human-crafted, specialized approaches. While this claim has strong empirical support, its theoretical justification is often overlooked. This post explores the fundamental learning theories, computational complexity principles, and optimization frameworks that underpin The Bitter Lesson.

Distributed systems operate across multiple independent nodes, making it difficult to establish a single global clock. Logical clocks enable efficient event ordering without synchronized physical clocks, ensuring consistency in distributed systems. However, physical clocks may still be preferred in scenarios requiring real-world timestamping, such as financial transactions or legal record-keeping, where absolute time consistency is crucial. This blog post explores the concepts of logical clocks, their types, and their use cases in distributed systems.

Synchronization is a fundamental challenge in distributed systems, where multiple independent nodes must coordinate their actions despite network delays, failures, and asynchrony.

A common example is cloud-based databases, where multiple servers must stay synchronized despite operating independently. Similarly, blockchain networks, such as Ethereum, must ensure all nodes agree on the latest state despite network delays and decentralization.

Unlike traditional single-machine systems, distributed environments lack shared memory and global clocks, making synchronization complex. Various solutions exist to address this. One example is Google Spanner's TrueTime, which uses globally synchronized clocks to mitigate uncertainty. This technique helps ensure timestamps reflect a bounded range rather than a single point, enabling safer transaction ordering and enforcing strict consistency.

Ordering, in the context of distributed systems, refers to the ability to maintain a well-defined sequence of events or operations across multiple independent nodes. It is a fundamental challenge impacting everything from database consistency to consensus protocols and event-driven architectures. Ensuring a well-defined sequence of operations in an unreliable network is inherently difficult. This blog post explores why ordering is hard in distributed systems, common strategies to address it, and trade-offs involved.

In 2019, Richard Sutton, one of the pioneers of reinforcement learning, wrote a short but impactful essay titled "The Bitter Lesson". The core argument is simple: history has repeatedly shown that leveraging more computation trumps human-designed domain knowledge in AI. Despite decades of attempts to encode expert rules and handcrafted heuristics into AI systems, raw computational power combined with scalable methods has consistently outperformed these approaches over time.

In today’s hyper-connected world, the demand for efficient, reliable, and scalable data transmission across networks is ever-increasing. Traditional approaches to data communication rely heavily on forwarding techniques, where intermediate nodes simply relay data packets. While effective in many scenarios, these methods struggle with packet loss, bandwidth constraints, and dynamic network topologies. Enter Random Linear Network Coding (RLNC)—a revolutionary paradigm that leverages coding theory to enhance network performance.

In a world that often feels fragmented and chaotic, the book Thinking in Systems by Donella Meadows offers a lens to see the intricate, interconnected systems that govern much of our lives. Published posthumously in 2008, this book distills Meadows’ years of research and teaching into an accessible guide to understanding systems—from ecosystems and economies to organizations and communities. It’s a must-read for anyone grappling with complexity, whether in business, science, or everyday decision-making.

If you’ve spent any time grappling with the complexities of modern software systems, you’ve likely heard of Martin Kleppmann’s Designing Data-Intensive Applications (DDIA). This seminal work has become an essential guide for architects, engineers, and data professionals navigating the rapidly evolving world of distributed systems and data management.

Traditional finance theories assume that individuals are rational actors who always make decisions to maximize their wealth. However, real-world financial behavior often deviates from this assumption. Enter behavioral finance—a fascinating field that blends psychology, sociology, and finance to understand how human emotions, biases, and heuristics influence financial decisions.