Everyday Data Science: applied AI, agentic systems, machine learning, and AI in Africa, written by practitioners for people who build.
Your Pipeline Passed. Your Dashboard Is Still Wrong.
A green pipeline only proves the code ran. This practical guide shows how contracts, freshness checks, semantic tests, lineage, and safe promotion catch schema drift before it corrupts a dashboard.
Free Weekly Newsletter
Stay ahead in AI, ML & data science
Every week: curated AI research, agentic system breakdowns, Africa tech spotlights, and career intelligence, written by practitioners, for practitioners.
Agentic AI
All articles →LangGraph vs AutoGen vs CrewAI: The 2026 Comparison Is Already Out of Date
The popular three-way comparison is stale: AutoGen is now maintenance-only, folded into the Microsoft Agent Framework (1.0, April 2026). The real live choice is LangGraph vs CrewAI, and the honest first question is whether you need a framework at all.
Ibrahim Denis Fofanah·7 min
Research BriefAgent Accuracy Went Up. Agent Reliability Barely Moved.
Twelve metrics, four dimensions, 14 agentic models. The finding: reliability gains lag well behind capability gains. Why a single success rate hides the flaws that actually break deployments.
Ibrahim Denis Fofanah·6 min
Deep DiveThe Agent Revolution Is Here, and Most Organizations Are Not Ready
Gartner says 40%+ of agentic AI projects will be cancelled by 2027, and only ~130 of thousands of "agentic" vendors are real. Here's what the research actually shows, and what separates the teams shipping from the ones stuck in pilot purgatory.
Ibrahim Denis Fofanah·7 min·1
ML & Data Science
All articles →Your Pipeline Passed. Your Dashboard Is Still Wrong.
A green pipeline only proves the code ran. This practical guide shows how contracts, freshness checks, semantic tests, lineage, and safe promotion catch schema drift before it corrupts a dashboard.
Ibrahim Denis Fofanah·7 min·1
TutorialYour Train-Test Split Is Leaking the Future
A random split can make a forecasting model look excellent by training on records that occur after its test rows. Here is how to build a time-aware evaluation that rehearses production instead of leaking the future.
Ibrahim Denis Fofanah·10 min
ExplainerReasoning Models Explained: What Test-Time Compute Actually Costs You
Letting a model think longer is a real capability (DeepSeek-R1 lifted AIME accuracy from 15.6% to 71.0%) and a real bill you pay in tokens and latency on every query. When it is worth paying, and when it is not.
Ibrahim Denis Fofanah·7 min
Research Digest
All papers →Claude Formalized Fermat’s Last Theorem. It Did Not Discover a New Proof.
Claude generated a 13-million-line Lean formalization of Fermat’s Last Theorem in 11 days. The real advance is not a new proof—it is verification throughput, agent scaffolding, and a public artifact that exposes exactly what the kernel checked.
Ibrahim Denis Fofanah·9 min
Benchmark WatchSame Model, Same Benchmark: 54.8% or 99.9% Depending on the Harness
GPT-6 Astra scored 54.82% or 99.95% on ARC-AGI-3 at the same reasoning level. The model did not change; the evaluation harness did. That gap is a lesson for anyone measuring agents.
Ibrahim Denis Fofanah·9 min
AnalysisBrain Waves to Words: What Brain2Qwerty Actually Does, and What It Doesn't
Meta's Brain2Qwerty decodes typed sentences from brain activity with no surgery, at 61% word accuracy. The catch: the scanner is a room, and the participants could type.
Ibrahim Denis Fofanah·6 min
Tools & Resources
📚 Agentic AI: Concepts, Architectures & Applications
Ibrahim Denis Fofanah's book, the definitive practitioner guide to building agent systems.
Get the book →Africa AI Spotlight
Building Intelligent Systems for the World's Fastest-Growing Markets
Africa is not just adopting AI, it's inventing new architectures for low-resource languages, unreliable infrastructure, and mobile-first contexts. Everyday Data Science brings you the stories nobody else is covering.
Africa · AI Ecosystem
AI in Africa in 2026: The Real State of Play
Ibrahim Denis Fofanah · 6 min read
AI in Africa · Language Models
A 125M-Parameter Model for 11 Languages Is Not a Small Story
Ibrahim Denis Fofanah · 6 min read
Metric Reliability
The Broken Ruler: Why "LLMs Now Translate African Languages" Doesn't Mean What You Think
Ibrahim Denis Fofanah · 7 min read
AI in Africa · Rwanda
Rwanda Is Betting It Can Lead African AI Without Owning the Compute
Ibrahim Denis Fofanah · 7 min read
Ibrahim Denis Fofanah
Data Scientist & AI Researcher
From the Editor
Why I Built This, and Who It's For
I'm a data scientist and AI researcher at Pace University's Seidenberg School, and founder of the Rise Africa Foundation for STEM and Innovation in Sierra Leone. My research keeps circling one question: what happens to the people a system doesn't see? That question produced Artificial Frictional Unemployment, the finding that automated hiring systems reject qualified people not for lack of skill, but because of how algorithms read them (arXiv:2601.14534). And it produced the first machine learning model for crop yield prediction in Sierra Leone, where a country plans its food security largely blind, not because the technology is hard, but because nobody had built it (arXiv:2606.13959). I'm also the author of Understanding Agentic AI. I started Everyday Data Science because I got tired of reading AI writing by people who don't build things. Everything here is meant to be usable, and honest about what doesn't work, including my own results.