The Moon Tigress blog.

I've been releasing and distributing my music for just over a year, but my relationship with music production began long before I started sharing my work publicly.

For years, I've continued learning about music production and sound design, studying different digital audio workstations and developing my technical understanding of how music is created. My studies have included professional certifications and formal training in music production, including a programme through Berklee that I completed last year.

I've worked with tools such as Adobe Audition and FL Studio Producer Edition, which remains my primary DAW. Despite that technical investment, there was a gap between what I could produce and what I wanted to hear. I could create freestyle beats and experiment with ideas, but I couldn't always achieve the specific sound I had in mind.

My interests extended beyond music production. I was also developing my technical knowledge in generative AI, full-stack development, large language models, deep research and prompt engineering. Eventually, I began approaching music creation differently: starting with an idea, shaping its atmosphere, then developing the song structure and lyrics.

My workflow gradually evolved to incorporate Suno's browser-based creation environment and Studio. I began combining generations and mixes, editing the material and working toward a final arrangement. The process became more than a way to generate musical material; it became a way to experiment, make decisions and shape the result.

What matters most to me, however, isn't the software or the novelty of the technology. It's the ability to translate what I feel into sound.

I've never approached music with the primary ambition of becoming a performer, achieving fame or making a fortune. My motivation has been more personal. I want to create music that expresses something I may not otherwise be able to communicate in the same way.

For me, one of the most significant things this process has made possible is a sense of belonging—not necessarily to a particular genre, scene or community, but through the act of creating itself. Taking something internal and giving it a form I can hear, shape and return to has become meaningful.

I am still learning. My workflow continues to evolve, and there are still things I want to understand and achieve. But I have found a way of working that brings me closer to the music I imagine—and somewhere in that process, I found something I hadn't been looking for through technical achievement alone: a sense of belonging.

Over the past year, I've been expanding my technical skills across artificial intelligence, machine learning, digital design, and audio engineering. What started as an interest in individual tools has gradually become an exploration of how these disciplines connect.

I've completed courses covering Python and machine learning, advanced large language models (LLMs), retrieval-augmented generation (RAG), generative AI, and professional audio production. I've also explored AI in creative software and tools such as Microsoft Copilot.

Different disciplines can share surprisingly similar principles

In audio production, I'm working with frequency, equalisation, filtering, and the relationship between a useful signal and unwanted noise. In machine learning, I'm exploring data, features, algorithms, and how models identify meaningful patterns. These aren't identical processes, but both encourage understanding the material, identifying what's important, and making informed adjustments.

Understanding the foundations makes advanced AI less intimidating

My machine learning studies introduced Python fundamentals, decision trees, and data modelling before LLM evaluations, grounding techniques, and RAG architecture. That progression has helped me appreciate why foundations matter. Generative AI isn't only about producing an output; it's also about understanding where information comes from, evaluating reliability, and knowing when a system needs better grounding.

I'm particularly interested in practical LLM development: how retrieval, evaluation, and thoughtful system design can make AI applications more useful and reliable. I'm still developing these skills, but each topic gives me another piece of the bigger picture.

Creative AI is about more than generating something

My background in graphic design and digital media shapes how I approach creative technology. I'm interested not only in what AI can generate, but how it can become part of an established workflow. Exploring generative AI in Adobe Photoshop and Creative Cloud has given me another perspective on creative decisions and technical tools.

The same curiosity extends to music production. A generated image, an audio recording, and an AI-assisted workflow all require decisions about what to keep, what to change, and what the result should communicate. The technology can assist, but creative direction, editing, judgement, and responsible use still matter.

Interdisciplinary learning helps me see the bigger picture

AI and machine learning offer ways to explore data, automate tasks, and build intelligent systems. Digital design offers a space to experiment visually and communicate ideas. Audio engineering brings together technical analysis, listening, and creative problem-solving. I'm interested in how these disciplines can complement one another.

This doesn't mean becoming an expert in everything. It means developing a solid foundation, understanding the limits of your knowledge, and recognising when another discipline can help you approach a problem differently.

Looking ahead

My learning journey across AI, creative technology, and audio production is ongoing. There is plenty to explore, from practical Python projects to LLM architectures and refining my audio engineering skills. I'm building a combination of skills that reflects my interests in technology, design, and music.

The idea of the Renaissance Technologist resonates with me because it leaves room for curiosity across disciplines while valuing technical depth. For me, the value of learning across domains is not simply collecting skills, but developing new ways to think, create, and solve problems.