Welcome to HackerNoon’s Meet the Writer Interview series, where we learn a bit more about the contributors that have written some of our favorite stories.
I'm Lanre Shittu. By day, I'm a Senior ML/AI Engineer at Fxbud, where I lead the AI roadmap for a fintech remittance platform- things like the supervisor-executor agent architecture we built and the LLMOps pipeline that took our task accuracy from 55% to 80%. My path here wasn't linear, though. I started out as a structural engineer in Lagos, doing site inspections for the Lagos State Building Control Agency, before retraining into data science and eventually AI. Outside of work, I produce music; I'm an IEEE Senior Member and an MBCS member; I peer review ML/medical imaging papers for Springer, and I dabble in motivational content on social media, mostly for fun, partly because I like the challenge of saying something useful in a single image or caption.
"AI First or Data First? Why Scale Requires a Balanced Approach" came out of something I kept seeing on the job: teams either chasing a flashier model while their data pipeline is held together with duct tape, or obsessing over data infrastructure with no real plan for what AI they're actually building toward. I wrote it mostly to think through my own position on that, not as a neutral take.
Mostly, yes. My last HackerNoon piece was on memory architectures for AI agents using vector databases, which is closely tied to work I do day-to-day with RAG systems. Beyond HackerNoon, I publish more formal research, things like a hybrid RAG framework for evidence-grounded review systems, and a paper on predicting concrete compressive strength with ML, which is a fun callback to my civil engineering days. So the throughline is usually applied AI, but I'll occasionally wander back into structural engineering if the data's interesting enough.
Not much of a routine, honestly. Articles usually start as a half-formed argument I'm having with myself after a long day debugging something at work. I'll write the bones of it in one sitting, late evening usually, then leave it for a day or two before editing, because I write worse when I'm still annoyed about whatever prompted it.
Time, mainly, since this isn't my main job. But also restraint. I'll often have ten interesting tangents I want to include and have to cut eight of them so the piece doesn't read like a literature review.
I want to keep pushing production AI systems that actually hold up under regulatory and operational pressure, not just demo well. I'm also chasing a couple of research threads further, particularly around evaluation of generative outputs. I recently presented "StoryCheck," a framework for evaluating AI-generated creative writing, at a conference in Prague, and I'd like to take that further.
Staying up far too late on music production when I'd told myself I'd just "quickly tweak one track."
Music production, and increasingly, building out motivational content for social media. It's a strange contrast to spending all day on AI architecture, but I like that it forces a completely different kind of creative discipline.
Probably something on the operational side of agentic AI in finance, the unglamorous stuff like auditability, latency under load, and what happens when your multi-agent system has to fail safely rather than just fail fast.
It's one of the few places where a technical piece doesn't get buried under fifteen layers of editorial polish before it goes out, but it's still read by people who'll actually engage with the substance, not just skim for keywords.
Thanks for having me. If you work in tech and think your niche is too narrow to write about, it probably isn't, someone is actively searching for exactly the thing you'd dismiss as "too obvious to mention."