I teach machines to see.

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Maria Busygina / Machine Learning + Full-Stack / Dubai, UAE

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software is easier to make than ever. libraries are everywhere, models are increasingly accessible and AI can help turn an idea into working code before you've finished your coffee.

what interests me is what comes before and after that moment: choosing the right problem, understanding why a system behaves the way it does and making something that feels considered.

that's part of what drew me towards machine learning. it isn't only about writing the code that makes a model run. A lot of the work happens in the questions around it: choosing what to measure, understanding the data, interpreting failure, figuring out why a prediction happened in the first place and deciding what that prediction should actually mean.

and somewhere along the way, i started caring about another kind of decision too — how an idea is presented once it leaves the codebase.

two people can be given the same tools and make completely different things. i'm interested in that difference. The choices around a project — what to keep, what to remove, how to explain it, how it behaves, what it feels like — are part of the work, not decoration added afterwards.

maybe that's why i'm less concerned about deciding exactly what i am or what label i want to wear at my next job, but in seeing what I can make. thats why i pursue different types of projects, some of which turn out good, others are left forgotten, but it all teaches me something. exploring different parts of the tech world is far from linear.

and if i've caught your interest