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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…
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The Role of End-to-End Visibility in Observability 2.0
Throughout our series on the transition from Observability 1.0 to 2.0, we’ve explored topics such as data correlation, AI and machine learning integration, and dynamic alerting. Another essential aspect of Observability 2.0 is end-to-end visibility, which allows teams to gain a comprehensive view of their entire system and its dependencies. In this post, we’ll discuss…
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Observability-Driven Development: Aligning Monitoring and Software Development
Our series on the transition from Observability 1.0 to 2.0 has discussed various aspects of modern monitoring, such as data correlation, AI and machine learning integration, dynamic alerting, and end-to-end visibility. One crucial element of Observability 2.0 is the integration of observability practices into the software development process itself. In this post, we’ll discuss the…
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Cannabis: A New Strain of E-commerce Technology – SXSW 2023
Online ordering, on-demand delivery, audience targeting, AI-assistants and digital transactions are all e-commerce capabilities that we take for granted, but without this kind of tech, today’s growing legal cannabis industry would be struggling to come to fruition. Though still young, the legal cannabis industry is emerging at a moment when e-commerce technology is just mature…
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Embracing Context: The Power of Correlation in Observability 2.0
As we’ve discussed in previous posts, the transition from Observability 1.0 to 2.0 is driven by the growing complexity of modern software systems and the need for more comprehensive monitoring solutions. A key aspect of Observability 2.0 is its emphasis on context and correlation, which enables teams to link data across multiple sources and gain…