How Cursor, Claude Code, and llama.cpp Actually Work

Six latest blog posts, covering search by meaning, two AI coding tools, the memory trick that keeps long sessions alive, and how big models get small enough to run on our own machine. Here’s a quick look at each, in our own words. Let’s get started.

Semantic Search finds documents by their meaning, not by their exact words.

Keyword search only matches words that are spelled the same. It does not understand meaning at all. The word “car” and the word “automobile” mean the same thing, but keyword search treats them as two completely unrelated words. Humans search by meaning, but keyword search matches by spelling.

So, here comes Semantic Search to the rescue. In simple words, Semantic Search understands what the user is asking, and it returns documents that mean the same thing, even if they use completely different words.

Read here: https://outcomeschool.com/blog/how-does-semantic-search-work

Cursor is an AI code editor. It is a place where we write our code, but with an AI helper sitting right next to us.

So, Cursor takes a normal code editor and adds a powerful AI brain on top of it. That is the whole idea.

Here, the most important word is context. The AI model has never seen our project before. It does not know our files. So, Cursor must send the right pieces of our code to the AI model, so that the AI can give a useful answer. This is the real magic of Cursor. It is very good at picking the right pieces of our code and sending them to the AI.

Read here: https://outcomeschool.com/blog/how-does-cursor-work

Context compaction is the technique of shrinking the old conversation into a short summary, so the important facts stay while the box gets free space again.

In simple words, instead of deleting old messages, we squeeze them. We replace a long pile of old text with a small note that keeps the key facts. The box gets emptier, but the memory survives.

We do not throw away our clothes when the suitcase is full. We fold them tightly to make room. Context compaction does the same thing with words.

Read here: https://outcomeschool.com/blog/how-does-context-compaction-work

Claude Code is a coding agent from Anthropic that runs in the terminal. We give it a task in plain English, and it completes the task by reading our code, editing files, running commands, and checking its own work.

The heart of Claude Code is the agent loop. When we give Claude Code a task, it works through three phases: gather context, take action, and verify results. And, it repeats these phases again and again until the task is complete.

Claude Code gives the AI model the two things it never had: eyes and hands. The eyes and hands are called tools. The model is the brain that decides what to do, and the tools are what actually do it.

Read here: https://outcomeschool.com/blog/how-does-claude-code-work

llama.cpp is a program, written mainly in C and C++, that runs large language models on our own computer.

In simple words, llama.cpp is a small, fast engine that lets us run a large language model on a normal laptop, without needing a giant server in a data center.

It shrinks a giant model with quantization, packs it into a single GGUF file, loads it instantly with memory mapping, runs the math quickly using the CPU’s SIMD power, shares the work with the GPU layer by layer, and produces the answer one token at a time, all on the device we already own.

Read here: https://outcomeschool.com/blog/how-does-llama-cpp-run-llms-on-everyday-hardware

Model Quantization is the process of storing and computing a model’s numbers at lower precision, so the model takes less memory and runs faster.

In simple words, quantization means we use smaller, simpler numbers in place of big, detailed numbers.

Through all of it, we balance one simple trade-off: fewer bits gives a smaller, faster model, at the cost of a little accuracy. When we pick the right level, we lose almost nothing and gain a lot. This is the real power of quantization. It takes models that once needed expensive data-center hardware and brings them to our laptop, our phone, and our single GPU.

Read here: https://outcomeschool.com/blog/how-does-model-quantization-work

That’s it for now.