What does it mean when an AI is open source vs. closed?
When you start looking into the world of artificial intelligence, you’ll quickly run into two camps: the “open” crowd and the “closed” crowd. Companies like OpenAI, Anthropic, and Google generally keep their most powerful models behind a digital curtain, while others like Meta and DeepSeek release models that anyone can download and run.
At its simplest, this debate is about who gets to see how the engine works and who is allowed to drive the car. But the lines have blurred, and a new term—“open weight”—has become just as important for understanding how your favorite AI tools actually function.
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What is a large language model, actually?
If you’ve used ChatGPT, Claude, or Gemini lately, you’ve probably noticed they feel different than they did a year or two ago. They don’t just spit out answers instantly anymore; they often pause to “think” for a few seconds before responding. We’ve moved past the era of simple chatbots and into the age of the “reasoner.”
But what is a Large Language Model (LLM) at its core, and why has the way they work changed so much in late 2025? To understand what’s happening inside those digital brains, we have to look at how they were built and how they are evolving into something much more powerful than a fancy autocomplete.
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What is a prompt and why does how you ask matter?
If you’ve spent any time with an AI chatbot lately, you’ve already written a prompt. Simply put, a prompt is the instruction or question you give to an AI model to get it to do something. It’s the digital equivalent of a person-to-person request, but instead of talking to a colleague or a friend, you’re talking to a vast network of statistical patterns that “understands” language.
As we head into 2026, the way we interact with technology has fundamentally shifted. We no longer just click buttons or select menu items; we use natural language to describe what we want. This shift has turned “prompting” from a niche technical curiosity into a core skill for anyone who wants to get things done efficiently.
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Why AI chatbots make things up: hallucination explained
If you’ve spent any time using modern AI like ChatGPT or Claude, you’ve probably had a “wait, what?” moment. You ask a question, and the chatbot gives you a perfectly formatted, highly confident answer that is completely, 100% wrong. In the industry, we call this “hallucination,” and it remains one of the most fascinating and frustrating quirks of artificial intelligence.
Even with the massive leap forward we’ve seen with “reasoner” models—which use test-time compute to “think” before they speak—AI still sometimes pulls facts out of thin air. It isn’t trying to lie to you; it’s simply doing exactly what it was designed to do: predict the next most likely word in a sequence. Understanding why this happens can help you use these tools more effectively and, more importantly, know when to take their answers with a grain of salt.
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