When Nick Bostrom published Superintelligence in 2014, many of its ideas seemed closer to science fiction than an imminent technological challenge. Had someone recommended the book to me even a few years ago, I probably would have dismissed it as an interesting but highly speculative thought experiment. The rapid emergence of generative AI, followed by increasingly capable AI agents, completely changed my perspective. Curious to revisit the subject with fresh eyes, I finally picked up Bostrom's now-classic work. Despite being over a decade old, it remains remarkably relevant. Drawing on his background in philosophy, physics and computational neuroscience, Bostrom goes far beyond the technical aspects of AI to explore one of the deepest questions of our time: if humanity succeeds in creating a superintelligence, how can we ensure that it remains aligned with our interests? These are the ideas that stayed with me after six weeks of reading.
Agent-Based Modeling (ABM) offers a fascinating way to study complex systems through the interactions of many simple agents. Long before the recent hype around AI agents, researchers were already using ABMs to explore phenomena ranging from traffic congestion and flocking birds to social and biological systems. In this post, I discuss the core ideas behind ABM and share my experience starting the Santa Fe Institute’s Introduction to Agent-Based Modeling course using NetLogo.
Pedalytics is a small cycling tracking and analytics application that I recently built using an AI-assisted development workflow centered around GPT 5.5 and Codex. In this post, I walk through the evolution of the project from an old Flash-based cycling dashboard developed around 2010 to a modern TypeScript/Svelte application generated from a carefully designed seed prompt. I also discuss the architectural constraints, technology choices, and lessons learned while using AI to bootstrap an MVP from scratch.
Can AI build a real web application from a single prompt? I recently experimented with this idea by developing a personal librarian app using tools like ChatGPT, Copilot, and Codex. In less than 30 hours, the project went from an initial prompt to a working application—and along the way it revealed where AI dramatically accelerates development and where engineers still need to take the wheel.
This post reviews Beyond Coding by Addy Osmani, cutting through the hype around vibe coding to draw a clear line between prompt-only experimentation and professional AI-assisted software engineering. It highlights why the book’s perspective—backed by its O'Reilly Media pedigree—matters for real-world teams, with practical frameworks, classifications, and hard-earned warnings about quality, security, and skills atrophy. The takeaway is refreshingly balanced: AI is already reshaping how we build software, but engineers remain firmly responsible for turning AI-generated code into maintainable, production-ready systems.
Google’s generous free-tier limits once made Gemini an ideal platform for experimenting with LLM-powered applications. When those limits quietly changed, a YouTube recommendation system that had worked reliably for months suddenly stopped functioning. This post recounts that experience and explores what it reveals about the hidden risks of building on third-party AI platforms.
After four years on DigitalOcean, I explored cheaper VPS alternatives and evaluated OVH as a potential replacement. Through load testing and hardware analysis, OVH proved faster, more scalable, and better equipped to handle traffic thanks to more CPU cores, additional RAM, and efficient PHP process management. This comparison highlights the importance of assessing both hardware and software configuration when choosing a hosting provider.
I am not a web designer by training, yet web design has been a recurring part of my work for nearly two decades. Building single page applications means dealing with HTML, CSS, and layout decisions whether one enjoys it or not. For a long time, I approached web design pragmatically, learning just enough to make things work, while never feeling fully in control of the result. Recently, that changed.