You do not have to be a programmer to benefit from GitHub. Think of it as a public workshop where people publish software projects, explain how they work, report faults and propose repairs.

A common barrier for newcomers is not knowing where to start — it is knowing where to start. You do not need a server rack.

When buying a computer for AI, four acronyms appear repeatedly: CPU (Central Processing Unit), GPU (Graphics Processing Unit), NPU (Neural Processing Unit) and RAM (Random Access Memory).

Start with the work, not the chip announcement. Do you want to search a private archive, classify public websites, draft reports, generate images or produce video?

Before buying a GPU (Graphics Processing Unit), allow for the learning curve. Setting up models, drivers, storage and backups takes time.

Connecting to your secure AI machine remotely may sound complex. It is not. With some training — and troubleshooting help from cloud AI assistants — allow time to learn and test the setup.

You do not need a security degree to use AI safely. You need two habits. Rule one: don’t share.

Prompt injection is an attempt to make an AI follow instructions hidden inside material it is supposed to analyse.

GitHub hosts enormous amounts of useful open-source software, including AI tools, model runners and agent skills. It also hosts projects that are unfinished.

Why put some work on a rented virtual private server, or VPS, when there is already a computer in the office?

Welcome to shadow AI — the uncontrolled cousin of shadow IT. It’s happening in most firms right now, and the person asking is right to be uneasy.

Vibe coding: you describe what you want to an AI in plain language, it writes the code, and you keep adjusting by feel — “make the button bigger