Claude Code: AI That Writes, Runs and Fixes Code
Claude Code is Anthropic’s coding assistant that lives inside your terminal. The headline is simple. You describe what you want, it writes the code, runs it, reads the error messages, and keeps going until the thing works.
The point is not that it types faster than you. The point is that it closes the loop between writing code and making it actually run. That closed loop is what separates Claude Code from anything that came before it.
Most AI coding tools up to this point have been autocomplete. You write a line, the model suggests the next one, you accept or reject. That helps, but it leaves all the hard bits with you. The wiring up, the failing tests, the typo in a config file, the import that is not where the model thinks it is.
Claude Code takes that whole loop and owns it. It writes, runs, reads the output, spots the problem, and fixes it, then runs it again. That is what makes it feel different from any autocomplete tool you have used before.
How Claude Code actually works
In practice, the tool changes how you work. You open a terminal, start it in your project folder, and describe a task in plain English. Add a login page. Write a script that pulls sales data from a CSV and charts it.
The model reads the relevant files, plans the change, edits the code, and runs the relevant command. It reports back when done. If the test still fails, it keeps going. If it is unsure about something risky, it asks you first.
The technology underneath is the same Claude model you may have used in a chat window. The difference is the harness around it. The model gets access to three things: your file system, your shell, and the tools your project already ships with.
Run npm, run pytest, run a linter, run the actual app. When the model can read the results of those commands, it can correct itself. That feedback loop is what separates autonomous AI coding from the autocomplete era. It is also what allows the model to handle multi-step tasks without you stepping in at each point.
Understanding how much of your project the model can process at once matters. It can handle a small-to-medium project without much guidance. For larger projects, you direct it toward the files that matter. Our explanation of what a context window is covers that technical limit in detail.
This also makes the tool part of a broader shift in how AI is designed. Earlier tools were built to help you think. These newer ones are built to act.
The distinction matters because the value comes from execution, not suggestion. A suggestion you still have to implement yourself is a hint. An action the model completes and verifies is actual work.
Who gets the most out of Claude Code
There is a common view that this kind of tool is only for senior engineers. The reality is the opposite. The people who get the biggest lift are those who can describe what they want but get blocked on syntax, setup, and dependency errors.
A skilled developer might save fifteen percent of their time. A less experienced developer trying to build something small often saves eighty percent. The parts that used to block them are exactly the parts the tool handles best.
In the UK, Claude Code is being used by a varied group of people. Solo founders use it to ship faster. Finance analysts who were taught Excel but never Python use it to automate the reports they used to produce by hand. NHS data teams are building internal dashboards without waiting months for a developer.
It also shows up inside larger engineering teams for the chores nobody wants. Test coverage, migrations, dependency upgrades. These are exactly the kind of repetitive tasks the tool handles well. They pile up through the week and eat Friday afternoon.
What Claude Code cannot do well
Claude Code is not good at holding an entire large codebase in its head at once. For a small project it can see almost everything. For a large enterprise monorepo, it cannot.
You have to guide it. Tell it which files matter, give it the context, and keep the task narrow. The skill you need as the human is writing a clear brief. The code it produces is only as good as the description you start with.
Writing that brief well is a learnable skill. The more specific you are about what you want, where the relevant files live, and what a correct outcome looks like, the better the output. Vague briefs produce vague code. Precise briefs produce code that runs first time.
The safety model is worth understanding. Claude Code asks before it runs anything destructive. Editing a file is one approval step. Running a shell command is another.
Deleting something, installing a package, or touching a file outside the current project usually triggers a prompt. You can run it in a less-cautious mode, but the default is conservative on purpose. A model with access to a shell is powerful. Everyone involved wants you to stay in control.
Getting started: pricing and first steps
The pricing is worth knowing because it catches people out. Claude Code runs on the Anthropic API, which bills by the token. A typical coding task uses a few thousand tokens of input and a few thousand of output. That works out at pennies for small tasks and a few pounds for larger ones.
Heavy users on a subscription plan pay a fixed monthly fee for a set allowance. If you are trying it for the first time, the cost is almost always lower than the time you save. But unattended tasks can add up faster than you expect, so keep an eye on usage.
A common comparison is to GitHub Copilot or Cursor. Those tools are strong at in-editor autocomplete and suggestion. This tool is stronger at multi-step autonomous tasks: building a feature from scratch, fixing a failing test suite, or refactoring a module end-to-end. They serve different stages of the development workflow.
One overlooked strength is that it is also useful for reading code, not just writing it. If you have inherited a codebase you do not understand, point the model at the folder and ask it to walk you through the architecture. It will identify the key files and the likely places a bug might live.
For non-programmers who need to understand a system they cannot rebuild, this is quietly the most valuable thing the tool does. To understand how Claude compares with ChatGPT and Gemini, the AI models compared post walks through the main differences.
The practical takeaway is this. If you write any code at all, try Claude Code on a single small task this week. Fix a bug. Add a feature you have been putting off.
Write a script that automates the thing you do every Monday morning. You will either save an hour, in which case the tool has paid for itself, or you will see its limits firsthand. Either way is useful. Anthropic publishes official setup documentation, covering installation, permissions, and first tasks.