Some of the most enjoyable learning experiences are the ones that make you feel…

Some of the most enjoyable learning experiences are the ones that make you feel like a beginner again."
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"What has fascinated me about diving into the Speech & Audio (ASR) domain is realizing that many of the design choices in modern speech models are not arbitrary. They are deeply rooted in the physical constraints of how humans hear and speak. Discovering these connections across acoustics, signal processing, neuroscience, and AI has been incredibly rewarding."
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"At Agent Boutique AI, these sessions are about more than understanding a single model. We are consciously embedding the roots of ASR domain knowledge into our engineering teams, starting from first principles and building a shared mental model that helps us reason about architectures, trade-offs, and future innovations instead of treating models as black boxes."
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"My learning process has been equally enjoyable:"
"• Reading papers, code, documentation, and manuals from first principles."
"• Chasing every "why?" and unpacking unknowns with NotebookLM until the concepts truly click."
"• Building small demo applications, vibe coded with Claude, to see the concepts in action instead of only understanding them theoretically."
"• Creating every presentation slide by hand. Not because tools like Gemini Canvas or GenSpark are not capable, but because the process gives me time to reflect, organize my thoughts, and turn detailed concepts into stories that are easier for engineers to absorb."
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"I always belive that teaching is one of the best ways to learn. Every session reveals gaps in my understanding, encourages better questions, and helps me move from remembering details to understanding the core ideas."
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"While our internal sessions began with the foundations of sound, signal processing, and Whisper, I am now enjoying the next phase of the journey by exploring how speech architectures have evolved beyond Whisper through models like Moonshine AI, Parakeet, Qwen, and others. It is fascinating to see how each generation revisits the same constraints while making different engineering trade-offs for latency, quality, efficiency, and deployment."
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"A heartfelt thanks to the team at Agent Boutique AI and Kannan Ramamoorthy for the opportunity and also creating an environment where curiosity, first-principles thinking, and continuous learning are encouraged. "
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"Looking forward to sharing more of these learnings with the broader engineering community in the coming days.