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@Okonu

Ian Okonu

@Okonu·Nairobi, Kenya·Joined December 2021

ML &AI Architect

About

LLM Orchestration · RAG Pipelines · Multi-Agent Systems | Production AI for Fintech, Legal & Gov on Azure · GCP · AWS

Available for

I am available for ML & AI Engineering roles, Open Source Contributions and learning opportunities

Ian Okonu's blogs

Okonuokonu.hashnode.dev22 posts

About

LLM Orchestration · RAG Pipelines · Multi-Agent Systems | Production AI for Fintech, Legal & Gov on Azure · GCP · AWS

Available for

I am available for ML & AI Engineering roles, Open Source Contributions and learning opportunities

Ian Okonu's blogs

Okonuokonu.hashnode.dev22 posts

Articles Comments1

Recently published

IOIan Okonuinokonu.hashnode.dev·5d ago · 19 min read

How a Retry Storm and ORDER BY RAND() Took Down Our Platform in Under an Hour

A postmortem on two unrelated bugs that compounded into a full platform outage — and the fixes deployed under fire. Preface This is a postmortem. Not a theoretical discussion of queue design or datab

IOIan Okonuinokonu.hashnode.dev·5d ago · 26 min read

Anatomy of a Self-Healing WordPress Malware Attack: The Medusa "Vitrin Gate" Kit

A technical incident response report and research analysis of a sophisticated redirect malware — its architecture, its evasion techniques, the cleanup mistakes that let it survive, and the historical

IOIan Okonuinokonu.hashnode.dev·May 2 · 11 min read

When Confidence Becomes Overconfidence

Calibration Collapse After RLHF; and How to Fix It Without Retraining Reinforcement Learning from Human Feedback makes language models more helpful and less harmful. It also makes them systematically

IOIan Okonuinokonu.hashnode.dev·Apr 12 · 18 min read

AI Agents Are Not Products: Embedding Intelligence Into Business Operations

Abstract AI agent products are proliferating faster than the operational frameworks needed to deploy them well. Most enterprise and SME deployments fail not because the underlying models are incapable

IOIan Okonuinokonu.hashnode.dev·Apr 5 · 20 min read

When Does Similarity Stop Working?

The Concentration Problem at the Heart of Vector Search Every major embedding model produces hundreds or thousands of dimensions. You use cosine similarity on them. There is a theorem that says this