Years ago, one of my mentor gave me a piece of advice that sounded oddly simplistic at the time: “Give ’em the bones, and they’ll play with it.” I laughed when I first heard it. Back then, I interpreted it as a shortcut mindset. A way of saying “just throw something out there” Over the years, working across enterprise programs, modernization initiatives, and high pressure delivery environments, I realized it meant something far more practical. It was never about lowering standards. It was about understanding how organizations react to uncertainty. As architects, we operate in an uncomfortable middle layer: The…
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I recently worked on an AI project that was quite unusual. It was about analyzing the past, present, and future of a person’s life. Yes, you guessed it right. Astrology. More specifically, this project focused on Vedic Astrology using the Kerala system. As someone who builds AI systems for a living, and being someone who loves solving challenging problems, I was excited. Modern AI tools make it incredibly easy to spin up apps quickly. But here is the reality check. The whole concept of “vibe coding” is still evolving, especially when it comes to complex data analysis, probabilistic workflows, and…
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If you treat governance as a static rulebook, you stall innovation. But if you treat it as an active engineering layer embedded in your strategy, frameworks, and runtime controls, you actually move faster. I broke down the 3 essential layers of modern AI governance (Strategy, Frameworks, and Technical Controls) in the article below. I have also included an implementation reference sample. Read: https://www.linkedin.com/pulse/ai-governance-isnt-just-rules-its-art-praveen-xfeqc
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I see that many people are still confused by the term Copilot in the Microsoft context. Are Microsoft Copilot, M365 Copilot, and Microsoft Copilot Studio different from one another? If so, how are they different?
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If you are building an AI-powered application today, the way you expose your models through APIs can make a big difference in scalability and developer experience. FastAPI has become one of the most popular choices for creating robust, production-ready APIs, especially for AI and LLM-based workloads. It is fast, type-safe, asynchronous, and easy to work with, which makes it ideal for developers who want both speed and clarity. While Flask, Django, BentoML, and Ray Serve are all valid alternatives, FastAPI provides a good balance between simplicity and performance. For enterprise-level applications, however, more powerful frameworks like Ray Serve or BentoML…
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Technically, I am not a fan of this but tired of laughing without facial expressions throughout my career, especially when talking to sales teams. Thanks to ChatGPT, here is the most abused jargons.
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