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Are You Managing Your Manager? (The Agentic Update)
In 2015, I stood in front of a room full of engineers at Code Driven NYC and laid out a formula for how individual contributors could take charge of their own careers. The framework was simple: Dreams, Alignment, Goals, Feedback. Know what you want and make sure you and your boss want the same things by…
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Can AI Solve the Four Burner Problem?
In my last post, Awe in an AI World, I wrote about the reclaimed time that AI is creating and the question that keeps me up at night in a good way: what do we do with all of it? I want to pull on that thread harder, because there is a framework that captures the…
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The Next Platform Giant Is Hiding in Plain Sight
Every major era of software development has produced a defining platform company. The one that quietly became the infrastructure everyone depends on. GitHub didn’t win because git was exciting (but git did solve a lot of issues that SVN/CVS had). It won because it became the system of record for how teams collaborate on code.…
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Awe in an AI World
I’ve spent most of my life implementing systems at scale, watching engineering teams transform how they work (pre- and post-AI), and having thousands of conversations about what it all means. The sense of awe I feel with what is happening today, and what continues to happen strikes me the deepest. We’re living through something rare.…
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Why Neuro-Symbolic AI Matters Today
For the past decade, we’ve watched deep learning transform everything from how we search the web to how we write code. Neural networks have become remarkably good at pattern recognition but they have limitations. There’s a fundamental tension in AI right now that’s worth paying attention to: the gap between what neural networks can learn…
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Putting AI to Work in Your Engineering Workflow Today
A few years ago, I wrote about the creeping danger of debt. Debt being the slow accumulation of manual processes, fragile deployments, and tribal knowledge that eventually drowns engineering teams in busywork. The message was simple: automate or die (slowly). Fast forward to 2026, and we’re not just talking about bash scripts and CI/CD pipelines…
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The Pre-AI to Post-AI Company Transition
Leaders must rethink beyond adding ML models. Transition to AI-native architectures and embrace probabilistic systems for robust outcomes in this new era of software.
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The Augmented Tech Leader: How AI Can Empower Your Engineering Team
AI tools can fundamentally transform the way engineering managers lead teams by not only speed and convenience but deeper insights and impact. By embracing AI-driven solutions in code review, project management, and daily operations, we can unlock new levels of effectiveness in the AI era. Making Code Reviews Smarter Traditional code reviews are time-consuming and…
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AI and Machine Learning: Transforming Observability
As the shift from Observability 1.0 to 2.0 unfolds, several key factors are driving this transformation, such as the increasing complexity of software systems and the need for a more comprehensive approach to monitoring. One of the most significant advancements in Observability 2.0 is the integration of artificial intelligence (AI) and machine learning (ML) into…
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Dynamic Alerting in Observability 2.0: Responding to Ever-Changing Systems
As we continue our exploration of the transition from Observability 1.0 to 2.0, we’ve discussed the importance of metrics, logs, and traces, as well as the impact of AI and ML on monitoring tools. Another crucial aspect of Observability 2.0 is dynamic alerting, which enables organizations to adapt to rapidly changing systems and respond more…
Duncan Grazier
Building AI-powered teams that scale