Before I wrote a single line of code professionally, I spent years inside an analytical chemistry lab. I did my Master's in Chemical Biology at UNIFESP (Federal University of São Paulo), working with NMR (nuclear magnetic resonance) for hydrogen, carbon, and two-dimensional experiments, plus HPLC (high-performance liquid chromatography). The LabTrack API, my project built with Python, FastAPI, and PostgreSQL, came out of a very specific problem I lived through during that time. It wasn't a portfolio idea thought up from the outside. It was a real pain point.
The problem: every lab speaks a different language
My lab handled the analytical side: we isolated and characterized compounds. But we didn't do everything alone. To know if a compound had antimicrobial activity, we needed a partner lab specialized in microbiology. To evaluate effects on cells, we needed a lab working with cell culture. Sometimes these partners were in another country.
The result was that each lab had its own protocol for the same type of task. That alone created friction: every new student joining had to be trained not just in the technique, but in how that specific lab recorded things.
The second problem: nobody knew what anyone else was doing
Communication between partner labs happened mostly through email. No shared calendar, no visibility into what each team was running at that moment. To know if an experiment had already been done, when, by whom, and with which reagents, you had to ask the person directly. If that person didn't reply quickly, the experiment simply waited.
This matters more than it sounds, because eventually that data becomes a scientific paper. To publish, you need to know exactly who did what, when, and in which lab, since that goes into acknowledgments and authorship. If the record is informal and inconsistent, reconstructing that history later is pure rework.
The third problem: no standardization even within the same lab
Even inside my own lab, two people using the same HPLC could log its use differently. Without a recording standard, every entry became an individual's interpretation of how to document an experiment.
Where the LabTrack API comes in
The LabTrack API is my attempt to address this with what I'm currently learning: Python, FastAPI, and PostgreSQL. The core idea is simple: a system where every experiment is logged in a standardized way, with authorship, date, reagents used, and status, accessible to partner labs without depending on email to know what's happening.
It's not an ambitious attempt to reinvent a full LIMS (Laboratory Information Management System). It's a learning project, built commit by commit, but solving a problem I know from the inside, not one invented just to have something on GitHub.
Why this matters for my career transition
I moved from the lab bench to the code editor, but the analytical thinking I developed reading NMR spectra and chromatograms is the same thinking I use now to model a database or design an API route: understand the problem before jumping into a solution.
The LabTrack API isn't just a technical project for me. It's proof that this career change doesn't erase my previous experience, it becomes the foundation for thinking through the problems I'm solving now.
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