Posts covering methods to prioritize product features, including frameworks and tools.
Feature Prioritization
I’m a proud nerd—a lifelong fan of anime and manga (among other nerdy things!)—and I’ve found these stories often reveal surprising truths about resilience, strategy, and leadership. One of my favorite examples is Dr. Stone, which imagines a post-apocalyptic future where humanity has been petrified for
AI
When humanity arrives at the doorsteps of these massive technological leaps—whether it’s artificial intelligence, quantum computing, or breakthroughs in energy—the question isn’t just how we innovate, but why and at what cost. I recently read a book by Batya Friedman, founder of the Value Sensitive Design
Product Leadership
We’re witnessing a pivotal moment in the world of computing. It’s not every day that a single breakthrough can redefine our perception of what’s possible. Yet that’s precisely what Google’s Willow chip represents—a quantum leap that promises to reshape how we solve problems, build
Agile Development
In an era where inflation is eroding the availability of cheap capital, designing efficiently has never been more critical. As product managers and technologists, we're challenged to optimize resources, innovate under constraints, and deliver maximum value with minimal waste. SpaceX's recent successes offer a vivid illustration
AI
Remember the not-so-distant past of telecommunications. There was a time when making a phone call meant planning your day around finding a phone booth, ensuring you had change in your pocket, and hoping the person you were calling was near a phone. Fast forward to today, and telecommunications are no
Tools & Technologies
The past few years have made one thing abundantly clear: tech companies, like many industries, go through cycles of expansion and contraction. In this context, we often talk about two distinct phases—lean times, where resources are constrained and companies must focus on doing more with less, and buffered times,
AI
AI development, unlike traditional software engineering, isn’t a straight line. Models need to adapt to new data, user behaviors, and shifting business conditions. In fast-paced environments, agility becomes more than just a buzzword—it’s a necessity. The key to success in AI is to build systems that can