Introduction Knowledge Graphs store facts as triples — (head, relation, tail) — but real-world KGs are notoriously incomplete. The productive fact that “M-KOPA raised a Series D round” might be mi...

Introduction Knowledge Graphs represent facts as symbolic triples — (head, relation, tail). But how do we feed these symbolic structures into neural networks? The answer is Knowledge Graph Embeddi...

Introduction The scariest thing about autonomous AI agents isn’t that they fail — it’s that you don’t know they’re failing until it’s too late. An AI agent is a system where an LLM makes deci...

Introduction Retrieval-Augmented Generation (RAG) has become the most widely adopted architecture for grounding LLM outputs in real data. By fetching relevant documents from a knowledge base at in...

Introduction In September 2024, researchers demonstrated a startling result: fine-tuning GPT-3.5 Turbo on just 10 harmful examples was enough to break its safety alignment. The model would comply ...

Introduction Imagine spending $100M training a state-of-the-art model, only to have a competitor reconstruct it for $1,000 by simply querying your API. That’s not a thought experiment — it’s a pub...

Introduction On March 26, 2026, a routine pip install litellm gave thousands of ML teams something they didn’t expect — a credential stealer that harvested SSH keys, cloud provider secrets, and Ku...

Introduction In our previous posts, we built a Knowledge Graph with Neo4j and analyzed it using Graph Algorithms like PageRank. These methods are powerful, but they are often limited to “shallow” ...

Introduction In our previous tutorial, we built a Knowledge Graph of the African Tech Ecosystem. But a graph is more than just a storage mechanism—it’s a powerful structure for analysis. In this ...

Introduction In our previous post, we explored the theory behind Knowledge Graphs (KGs). Now, it’s time to get our hands dirty! In this tutorial, we will build a Knowledge Graph of the African Te...