Notre Équipe

Yuki Lindström-Sato came to music theory through the piano, and to the piano through a grandmother in Kyoto who played Satie and Bach with equal devotion and regarded the difference between them as less important than the quality of the listening they demanded. That formation — music as a discipline of attention as much as a discipline of craft — has shaped everything Yuki has done since.

With roots in Japan and Sweden, Yuki studied composition and musicology across both traditions, developing fluency in Western classical structures, Japanese traditional forms, and the long history of Nordic folk music that his Swedish heritage opened. His graduate research examined the deep grammar of songwriting across traditions: the relationship between melodic contour and emotional resolution, the architecture of the verse-chorus form and its variants, the harmonic logic underlying what audiences experience simply as beauty or unease.

In his professional life, Yuki has worked as a composer, an educator, and a critic — writing about music with the same precision he brings to writing music. He came to AI-generated music with genuine curiosity and a clear evaluative standard: does it demonstrate understanding of the craft, or does it produce surface resemblance while missing the internal logic that gives the craft its meaning? His answer is nuanced but consistent — current AI music tools are powerful at resemblance and weak at coherence over longer structures, and those limitations matter.

At maimusic-web, Yuki is the guide for users who want to understand what makes music good beyond the immediate impression — and whether AI-assisted creation is engaging with that question seriously. He is patient, precise, and genuinely interested in being persuaded by work that earns it.

Diego Ramírez-Santos learned the music business not in a university seminar but in a Mexico City studio where he started as a runner, spent years as an engineer, and eventually became a producer whose instincts for what would connect with audiences proved more reliable than most people's frameworks for explaining why. He developed a simple operating principle early: the hook is the argument, and everything else is the evidence.

Diego's career took him across the Latin music ecosystem and into the global pop circuits that increasingly intersect with it — working with artists at every level from emerging regional acts to projects with major international distribution. He developed a detailed map of the streaming economy: what the data shows about skip rates and save rates and playlist placement, how platforms weight early listening behavior, how regional sounds travel into global markets, and how production choices that seem purely aesthetic are also market positioning decisions.

In his early forties, Diego came to AI music tools with the same question he brings to any new studio technology: does this help me make something better faster, or does it just give me more mediocre options at speed? His answer has evolved — he has found genuine utility in AI for rapid melodic sketch generation, for reference-track analysis, and for certain repetitive production tasks — while remaining clear-eyed about what the tools cannot do: develop the taste that knows which sketch is worth finishing.

At maimusic-web, Diego is the guide for artists and producers who want an industry-informed perspective on what AI means for their commercial practice. He does not moralize about the technology, and he does not oversell it. He talks about it the way he talks about any tool: in terms of what it can do for the record you are trying to make.

Amara Diallo-Khoury grew up moving between Dakar, Beirut, and London, and the experience of navigating between musical traditions that each believed themselves to be rich and the others to be exotic gave her an early and persistent interest in the politics of sound — in who gets to define what counts as music, what gets preserved, and what gets absorbed into other traditions without credit.

She studied ethnomusicology and music production in parallel, which is an unusual combination that turned out to be exactly the right one for the moment she entered. Her academic training gave her tools for understanding how musical traditions encode cultural knowledge — how a rhythmic pattern can carry the memory of a ceremony, how a mode can carry the emotional grammar of a region, how the oral transmission of music preserves information that notation cannot capture. Her production training gave her the ability to work with that understanding inside actual creative processes rather than writing about it from outside.

Amara's practice centers on what she calls dialogical music — work that brings traditions into genuine creative conversation rather than sampling them decoratively. She has collaborated with traditional musicians from West Africa, the Levant, and the South Asian diaspora on projects that required sustained mutual learning rather than surface borrowing, and she has been consistently attentive to the question of credit, consent, and reciprocity in those collaborations.

At maimusic-web, Amara guides users who want to understand how AI music tools relate to questions of cultural heritage — as potential amplifiers of underrepresented traditions, as potential extractors of cultural value without attribution, and as creative instruments that require the same ethical attentiveness as any collaboration across cultural difference. Her perspective is not gatekeeping; it is stewardship.