AI
AI models, agents and the tools people use at work.
AI Temperature Explained: Why Answers Vary
How temperature changes token sampling, how top-p differs, and why consistent wording does not prove a fact.
Embeddings Explained: Meaning as Coordinates
How text becomes a vector, how semantic search ranks neighbors, and why similarity still needs evaluation.
Content Credentials Explained: What a C2PA Label Proves
What a signed C2PA record on an image or video contains, what valid and trusted mean, and what it cannot prove.
Prompt Injection Explained: When an AI Tool Obeys a Stranger
How text in an email, page or file can redirect an AI tool, and which controls limit the damage.
Retrieval-Augmented Generation Explained: How AI Answers From Your Documents
How RAG tools search your documents before answering, and how to check what they say.
Context Window Explained: What an AI Tool Can Keep in View
What fills an AI tool's context window, how tokens are counted and why long chats lose track.
Why AI Hallucinates: The Mechanics Behind Confident Wrong Answers
How AI tools learn and how they are scored push them to guess. Where the errors come from and how to catch them.
Before You Give an AI Agent Access to Your Inbox
Reading mail and sending it are different permissions. Start with the job you actually want done.
Why an AI Benchmark Win May Not Help With Your Work
A leaderboard measures a test. Your workflow adds documents, tools, delays and costly mistakes.
Running AI on Your Laptop: What to Check First
Memory, model files and cloud settings matter more than a screenshot of a local chat window.
Start with the permissions an inbox agent needs, then examine what a benchmark can and cannot tell you. The local-model guide covers memory, context and the difference between a desktop interface and computation on your own machine. Each piece gives you a practical way to check a claim before relying on the tool.
Visit the AI desk for current reports alongside these explainers.