Making practical AI knowledge easier to reach
Most people meeting AI for the first time get either hype or a research paper. This is the work of explaining what is actually true about building with it, in public.
The problem
Public conversation about AI splits into hype and inaccessible research, and very little of it explains what building with these systems actually involves, what it costs, and where it fails.
Why it mattered
Practical AI adoption is limited less by access to models than by access to plain explanation and technical mentorship, and that gap is widest for people furthest from the industry.
The context
A public AI leadership and technical education effort, run alongside the day job rather than instead of it, aimed at practical adoption, mentorship and industry-wide literacy.
What I did
I co-host FabAcademic Unfiltered alongside Prof. Mamokgethi Phakeng, delivering strategic AI insight to a global audience. I publish technical leadership writing that distils distributed systems, MLOps, AI governance and enterprise ML architecture into something readable. I also architected and deployed Thabang AI, a retrieval-grounded assistant built on a multi-model architecture with a custom AI gateway, so the material can be interrogated rather than only read.
What changed
A large audience gets an accurate account of what building with AI involves, from someone doing it in production rather than describing it from outside.
Who benefited
Practitioners, students and people considering the field, and the technical audience reading the writing each month.
What remained
A body of public technical writing, a recorded conversation series, and a grounded assistant that cites its sources.
Technical context
RAG architecture, multi-model orchestration (Claude, Gemini, OpenAI), a custom AI gateway, serverless deployment.
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