I listen to music in a variety of scenarios, depending on my mood and what I’m doing. During deep work or coding sessions, I prefer instrumental or ambient tracks to stay focused. When I’m at the gym or going for a walk, I go for high-energy songs to stay motivated. Music also helps me unwind — I often listen to mellow or nostalgic tracks at night to relax or reflect. Sometimes, I just explore new genres out of curiosity, especially when I want to discover fresh sounds or emotions.

My favorite song makes me feel like I’m suspended in time — everything slows down and the world fades into the background. It evokes a strange mix of nostalgia and calm, like remembering something beautiful that never actually happened. The melodies wrap around me like a warm blanket, while the lyrics strike something deeply personal every time I hear them.

Yes, I’ve explored music both as a hobby and a study subject. I have basic training in [instrument/vocal if any, e.g., guitar or piano], which gave me a foundational understanding of rhythm, scales, and harmony. Beyond performance, I’ve always been curious about how people connect with music emotionally and culturally, which led me to casually study music theory and explore recommendation systems used by platforms like Spotify or Last.fm. As a tech enthusiast, I’m especially fascinated by how machine learning models can interpret listener behavior and personalize recommendations — which is why I'm really interested in what Tamber.ai is doing. My background in [programming/data science/AI if applicable] and my passion for music make this intersection very exciting to me.