What I write
Published writing on AI engineering, career reflections, African tech, and hands on tutorials, across Medium and beyond.
Notes from living between two roles, building production AI systems and teaching the next generation of practitioners. What overlaps, what doesn't, and what both require.
In a world chasing transformers and trillion parameter models, the fundamentals still matter. A case for why statistical rigour is the most underrated skill in modern ML.
Three years of production ML, the insights that surprised me, the challenges nobody warned about, and the lessons I had to learn the hard way.
On structural barriers, geographic luck, and what it really takes for talent from underrepresented places to reach global stages. A personal essay.
What it felt like to represent Africa on a European stage, the prep, the nerves, the moment on stage, and what happened after. Part one of the Belgrade story.
Part two: the memorable side conversations, unexpected connections, and why international visibility still feels like a privilege that should be a right.
A look at the tools, automation trends, and AI driven shifts reshaping how teams build, collaborate, and ship, and what remains irreducibly human.
Building a food recognition app trained on Nigerian cuisine: why existing models fail at local dishes, how we collected the data, and the architecture we chose.
The technical deep dive: model optimisation for mobile, deployment choices, on device inference constraints, and lessons from shipping to real users.
How a 72 hour sprint became a globally recognised project, from idea to top 10 out of 3,000+ teams worldwide. The full story of NASA Space Apps 2022.
A year of building and learning, seen through the lens of psychology and verse. A developer's introspective look at growth, setbacks, and showing up anyway.
A hands on walkthrough of building intent classification for conversational AI using Rasa's NLU framework, with working examples and common pitfalls to avoid.
Before you write a single line: the mental models, architectural choices, and practical traps worth understanding before starting any chatbot project.
A beginner friendly introduction to the two libraries every data practitioner depends on, clear examples, common patterns, no fluff.