AI Explained
Concepts, models and trends explained clearly, without the jargon and without dumbing them down.
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“Open Source” AI Usually Isn’t. Here’s the Three-Part Test.
Most models called open source only ship the weights. The three things the Open Source AI Definition asks for, and the Llama clauses that actually bite.
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Fine-Tuning vs RAG vs Prompting: Which One You Actually Need
Want an AI that knows your business? You probably need a better prompt, not training. A symptom-to-fix table, plus OpenAI’s fine-tuning wind-down dates.
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MCP Explained: What You’re Actually Connecting
MCP is the standard behind your AI app’s connectors menu. What it is, the difference between a remote connector and a local one, and what to check first.
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Reasoning Models Explained: What AI “Thinking” Actually Is
The thinking you watch scroll past is a summary, not the model’s working. What reasoning tokens are, what they cost, and when the extra wait is worth it.
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RAG Explained: What “Upload Your Documents” Actually Does
Upload a file to an AI tool and it never reads the whole thing. What retrieval actually does to your document, why answers still miss, and when to skip it.
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Tokens, Pricing and Why Your AI Bill Looks Like That
Why an AI chatbot costs several times what its visible text suggests, worked through one 10-turn chat at Claude, OpenAI and Gemini prices checked 13 Sep 2026.
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Why AI Forgets Mid-Conversation: Context Windows, Explained
A context window is everything an AI model can see at once. How tokens fill it, why answers slip long before it is full, and what to do in long chats.
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AI Agents Explained: What They Actually Do
An AI agent is a model that can take actions and keep going until a task is done. That loop is the whole difference — and it is also where the failures come from.
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Why AI Makes Things Up (And What to Do About It)
AI models invent facts because inventing facts and recalling them are the same operation. Here is what causes it, what it looks like in the wild, and the habits that catch it.