Introduction In 2023, Auto-GPT — an experimental open-source AI agent — was given a budget and a set of tools. Its goal? Research and buy a domain name. What happened next was a glimpse into the f...

Introduction In the modern AI development workflow, very few teams train models from scratch. Instead, they download pre-trained models from Hugging Face, TensorFlow Hub, or PyTorch Hub, then fine...

Introduction If prompt injection is about making an LLM do something unintended, jailbreaking is the specific subset where an attacker bypasses safety guardrails to make the model produce harmful ...

Introduction In August 2024, security researchers at PromptArmor demonstrated a chilling attack: by posting a message in a public Slack channel, an attacker could steal data from private channels ...

Introduction Large Language Models (LLMs) are remarkable — they write poetry, debug code, and answer questions across thousands of domains. But there’s a dirty secret: they hallucinate. Ask GPT-4 ...

Introduction You built a Knowledge Graph. It works beautifully on your laptop — 10,000 nodes, 50,000 edges, crisp Cypher queries, and a Streamlit dashboard that makes you look like a data wizard a...

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...