AI on Chinese Man

Reasoning Models: What "Thinking" and "Effort" Actually Mean

Wed, 15 Jul 2026 00:00:00 +0000

You may have noticed newer AI models pause and “think” before they answer, sometimes showing a summary of their working-out. These are often called reasoning models, and the pause is a real, deliberate feature, not a loading spinner. Understanding what it does explains why these models are better at hard problems, and why they can be slower and more expensive.

The old way: answer immediately #

A standard language model predicts its answer one chunk at a time and starts producing the final response right away. For simple questions this is perfect. But for a problem that needs several steps of reasoning (a math word problem, a tricky bit of logic, a multi-part coding task) answering immediately is like being forced to blurt out the answer to a hard question with no time to think. Sometimes you get it right, often you do not.

What Is MCP (Model Context Protocol)?

Wed, 15 Jul 2026 00:00:00 +0000

Once you understand that AI agents work by using tools, an obvious problem appears. Every useful integration (connecting an AI to your files, your GitHub, your calendar, your database) has to be built. And historically, every AI app built every one of those connections itself, in its own way. MCP, the Model Context Protocol, is the standard that fixes that.

The problem: everyone reinvents the same plug #

Say you want AI tools to be able to read your company’s wiki. Without a standard, the situation is a mess:

What Is RAG (Retrieval-Augmented Generation)?

Wed, 15 Jul 2026 00:00:00 +0000

A language model only knows what it absorbed during training, frozen at some point in the past. It has never seen your company’s documents, your notes, or anything that happened after its training cutoff. So how do AI tools answer questions about exactly those things? The most common answer is RAG, short for retrieval-augmented generation, and the idea is simpler than the name.

The problem RAG solves #

Ask a plain model “what is our refund policy?” and it has two options, both bad. It can admit it does not know, or it can make something up that sounds like a refund policy. It was never trained on your policy, so it genuinely cannot know.

Prompt Engineering: How to Actually Get Good Answers

Tue, 14 Jul 2026 00:00:00 +0000

“Prompt engineering” sounds like a specialized skill, but it is mostly common sense once you know how a language model works. Since a model just continues your text in the most plausible way, the way you ask shapes what you get. A vague prompt gives a vague answer. A clear, well-set-up prompt gives a sharp one. Here are the techniques that actually move the needle.

Be specific about what you want #

The most common mistake is being too vague and expecting the model to read your mind. It cannot. It fills ambiguity with the most generic plausible answer.

Running AI Models Locally: Open Weights and Quantization

Tue, 14 Jul 2026 00:00:00 +0000

Most people assume AI only lives in the cloud: you send your text to a company’s servers, they run a giant model, and they send an answer back (and bill you per token). But you can also run capable models directly on your own computer or phone. No cloud, no per-message cost, nothing leaving your device. Here is how that works and when it is worth doing.

Open-weight models #

The models you can run yourself are called open-weight models. The “weights” are the giant set of numbers that make up a trained model, the thing it learned during training. When a lab releases those weights openly, anyone can download them and run the model on their own hardware, instead of it living locked on a company’s servers.

Why LLMs Hallucinate, and How to Reduce It

Tue, 14 Jul 2026 00:00:00 +0000

The most important thing to know about AI is that it will sometimes state something false with total confidence. A made-up statistic, a citation to a paper that does not exist, an API function that was never real. This is called hallucination, and it is not a bug that will be patched away next month. It comes straight from how these models work, and understanding why is the key to working around it.

How Large Language Models Actually Work

Mon, 13 Jul 2026 00:00:00 +0000

Every AI chatbot, coding assistant, and agent you have used is built on a large language model, or LLM. They can feel like they understand you, reason with you, and know things. Underneath, they are doing something far simpler than that impression suggests, and understanding what it is explains most of their strange behavior.

The one-sentence version #

An LLM predicts the next chunk of text, given everything before it, over and over.

What Is a Token, and Why It Shows Up on Your Bill

Mon, 13 Jul 2026 00:00:00 +0000

If you spend any time around AI, you will keep bumping into the word “token.” The context window is measured in tokens. Pricing is per token. Bills go up when you use more of them. It is the basic unit everything in AI is counted in, and it is worth five minutes to understand.

What a token actually is #

A token is a chunk of text. Not quite a word, not quite a letter, but something in between. Before a language model can work with your text, it chops it into these chunks, because the model does not operate on raw letters or whole words, it operates on tokens.

Context Windows, and Why AI Agents Forget

Sun, 12 Jul 2026 00:00:00 +0000

If you have used an AI tool for a long session, you have probably felt it start to slip: it forgets something you said earlier, loses track of a decision, or repeats itself. That is not the model being dim. It is running into the context window, the single most important constraint to understand about how these tools behave.

What a context window is #

A language model can only consider so much text at one time. That maximum is its context window, and everything the model is working with has to fit inside it: your instructions, the entire conversation so far, any files or data it has been given, and the results of every tool it has used.

Tool Use and Function Calling, Explained

Sat, 11 Jul 2026 00:00:00 +0000

Here is a puzzle. A language model does exactly one thing: it produces text. It cannot search the web, read a file, or query a database. And yet AI agents do all of those things constantly. How?

The answer is tool use (also called function calling), and it is the mechanism that underpins every agent. Once it clicks, the whole idea of agents stops seeming magical. The model still only produces text. The trick is what that text is allowed to say.

How Coding Agents Work Under the Hood

Fri, 10 Jul 2026 00:00:00 +0000

Coding agents (the tools that read your codebase, write changes, and run your tests) are the most mature kind of AI agent, and the most useful window into how agents work in general. Underneath the polish, a coding agent is just the agent loop pointed at a specific set of tools. Here is what those tools are and how the whole thing hangs together.

The tools a coding agent has #

An agent can only affect the world through its tools, so the tool set defines what a coding agent can do. The common ones are exactly what a developer uses:

The Agent Loop: How AI Agents Actually Work

Thu, 09 Jul 2026 00:00:00 +0000

If you understand one thing about how AI agents work, make it this: the agent loop. It is the single mechanism that turns a language model from a thing that answers questions into a thing that gets work done. Once you see the loop, agents stop being mysterious.

The loop in one paragraph #

An agent runs in a cycle. The model looks at the current situation and the tools available to it. It either produces a final answer, in which case the loop ends, or it decides to use a tool. If it calls a tool, your program runs that tool for real, captures the result, and hands the result back to the model. The model looks at the new situation, including what the tool just returned, and decides again. This repeats until the model has nothing left to do and gives its final answer.

What Is an AI Agent?

Wed, 08 Jul 2026 00:00:00 +0000

“AI agent” is one of those phrases that gets used constantly and explained rarely. Strip away the hype and it is a simple idea: an agent is a language model that has been given tools and a goal, and that works toward the goal in steps instead of answering in a single reply.

That one difference, acting in steps versus answering once, is the whole distinction. Everything else is detail.