<ul><li>Building production-grade AI requires a combination of engineering knowledge and practical AI.</li><li>It’s easy to get overwhelmed by the velocity of new developments in the AI ecosystem.</li><li>To avoid becoming less effective, pick one or two core domains to focus on deeply.</li><li>Create focused “no-noise” slots, plan learning in sprints, and implement structured breaks.</li><li>Understand the core principles of classical algorithms and foundational NLP methods.</li><li>Grasp the principles of Convolutional Neural Networks and deep learning frameworks.</li><li>Understand the principles of generative models like GANs, VAEs, and large language models.</li><li>Incremental updates can be exciting but rarely a radical leap.</li><li>Focus on underlying mechanisms, evidence of actual impact, and cut through the noise.</li><li>Critical thinking saves you from chasing every buzzword-laden release.</li></ul>

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