BERYO 🎨

AI term of the day: MCP (Model Context Protocol) MCP is an open standard that lets AI apps connect to outside tools and data in the same way every time. The problem it solves: Before MCP, every AI app needed custom code for every tool. A separate connection for Gmail, another for Google Drive, another for your database. How it works: • A tool or data source runs an MCP server • An AI app (the client) connects to that server • The server tells the AI what it can do: read files, search, run queries • The AI uses those abilities when your request needs them Why it matters: Build the connection once, and any AI app that supports MCP can use it. People call it the USB-C of AI: one plug that works everywhere. Anthropic introduced it in November 2024. OpenAI, Google and Microsoft now support it too.

Building an agent is the easy part. The hard part is knowing it won't embarrass you when real users start interacting with it. That's why testing AI agents matters. 🧵

Good morning o. Whatever you dey face this week, no give up. Your own turnaround dey come.

Training vs Inference Training • The model learns patterns from huge amounts of data • It runs for weeks on thousands of GPUs • It costs millions and happens once (or each time a new version is trained) • The result is a fixed model Inference • The trained model uses what it learned to answer you • It runs in seconds, one token at a time • It happens every time anyone sends a prompt • This is the part you pay for, per token Why it matters: Training decides how smart the model is. Inference decides how fast and how cheap it is for you to use. Training is school. Inference is the exam.