You've probably been there. You ask Claude to write a complex Python script or a React component, and it spits out something that looks beautiful. It’s clean. It’s commented. It looks like it was written by a senior engineer on their best day. But then you try to actually use it, and you’re stuck staring at a terminal full of red text and dependency errors. Knowing how to run Claude code isn't just about hitting copy-paste; it’s about understanding the sandbox vs. local reality.
Anthropic’s Claude 3.5 Sonnet and Opus models are arguably the best coding assistants on the market right now. They think logically. They debug well. However, the gap between "code in a chat window" and "software running on your machine" is wider than most people admit.
The Built-in Way: Using Claude’s Analysis Tool
Honestly, the easiest way to handle this is to let Claude do the heavy lifting itself. Anthropic introduced a feature called the analysis tool, which is essentially a built-in JavaScript sandbox. When you ask Claude to process a CSV, create a chart, or do some heavy math, you’ll often see a little "Analyzing" window pop up.
It runs the code internally. You don't have to install a single thing.
This is perfect for data science. If you have a massive spreadsheet of sales data and you need a trend line, just upload the file. Claude writes the code, executes it in its own private environment, and shows you the result. It’s seamless. But—and this is a big "but"—it’s limited. It can’t access the internet. It can’t install weird third-party libraries that aren't already in its pre-approved environment. It’s a walled garden.
If you're trying to build a web scraper or a Discord bot, the analysis tool is useless. You have to go local.
Setting Up Your Local Environment for Claude Code
When you move to your own machine, the stakes get higher. You're the DevOps engineer now. To effectively run Claude code locally, you need a workflow that handles the "hallucinated dependency" problem. Sometimes Claude assumes you have a library installed that doesn't even exist, or it uses a version that's three years out of date.
Start with a virtual environment. Seriously. Don't skip this. If you’re on Mac or Linux, use python -m venv venv. On Windows, it's the same. This keeps the junk Claude generates from polluting your global system settings.
I’ve found that the most reliable way to get Claude's output running is to use an "Iterative Shell" approach. Don't just copy the whole file. Copy the requirements.txt content first—if it didn't give you one, ask for it. "Hey Claude, give me the requirements.txt for this." Install those, then run the script.
Dealing with Node and Frontend Snippets
React is a different beast. Claude loves giving you modern, functional components using Tailwind CSS. If you try to run these in a vacuum, they’ll break because your CSS isn't configured right.
The smartest move here is to use a tool like Vite.
- Initialize a Vite project:
npm create vite@latest. - Select React.
- Paste Claude’s code into
App.jsx.
If it asks for components you don't have, like Lucide icons or Radix UI primitives, you’ll need to npm install those specifically. Claude is great, but it can’t reach through the screen and run your terminal commands for you—at least, not yet.
Why Claude Artifacts Changed Everything
Artifacts are the biggest UX win in AI coding lately. When Claude generates a substantial block of code, it opens a dedicated side window. This isn't just a text box. For HTML, CSS, and basic JavaScript, it’s a live preview.
If you want to run Claude code that is purely frontend-based, you might not even need to leave the browser. You can click the "Preview" tab and see the UI in real-time. You can tell it, "Make the button bigger," and watch the preview update.
But here is the catch: Artifacts are ephemeral. If you refresh your browser or start a new chat, that "running" instance is effectively gone. For anything permanent, you’re still looking at a manual export. I usually download the file directly from the Artifacts window rather than copying and pasting to avoid encoding issues with special characters.
Bridging the Gap with Claude Engineer and CLI Tools
For the power users, the "copy-paste" method is dead. There are now several open-source projects—like Claude Engineer or aider—that allow the model to interact directly with your file system.
Aider is particularly impressive. It’s a command-line tool that lets you chat with Claude inside your terminal. You give it access to your local folder, and it doesn't just write the code; it edits your existing files. It runs the git commits. It can even run your test suite and see the errors itself.
This is how you run Claude code like a professional. Instead of being the middleman who moves text from one window to another, you’re just the supervisor. You say "Refactor this function to be more efficient," and the tool handles the file I/O.
Common Pitfalls: Why the Code Fails
Let's be real: sometimes the code just won't run.
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Usually, it’s one of three things. First: API keys. Claude will often write code that requires an OpenAI key, a Google Maps API, or a database connection string. It will leave a placeholder like your_api_key_here. If you don't fill that in, the script crashes. Obvious? Yes. But it’s the #1 reason for "Claude's code is broken" complaints.
Second: Path issues. Claude likes to assume your files are in the same directory as the script. If you're running a script from projects/ai/ but your data is in projects/data/, it’s going to throw a FileNotFoundError.
Third: Versioning. Claude was trained on data up to a certain point. If a library like LangChain or Pydantic has had a major version release (like the jump to Pydantic v2), the syntax Claude provides might be deprecated. You'll have to tell it: "I'm using version X.X, please update the code."
Security Risks You Shouldn't Ignore
Never, ever run Claude code—or any AI code—that you don't at least skim first. Especially if it involves subprocess, os.system, or networking. While Anthropic has guardrails, an AI doesn't always understand the security implications of a "quick and dirty" script.
It might write a script that opens a port on your firewall or deletes files in a way you didn't intend. Always run experimental code in a container like Docker or a restricted VM if you're doing something risky with the file system.
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Actionable Steps to Execute Claude Code Successfully
- Use the Analysis Tool for Data: If it's just a one-off math problem or a chart, stay in the browser. Don't overcomplicate your life.
- Verify Dependencies: Before running a Python script, check the imports. If you see
import polarsand you don't have Polars,pip installit. - Use Artifacts for UI: Use the preview window to iterate on the look and feel before you bother moving it to a local VS Code project.
- Automate with Aider: If you’re a developer, stop copy-pasting. Use a CLI tool that lets Claude write directly to your workspace.
- Debug via Screenshot: If the code fails and you don't know why, take a screenshot of the terminal error and paste it back to Claude. It’s significantly better at fixing errors when it sees the full context of the crash.
Running code from an AI isn't a "set it and forget it" process yet. It requires a bit of human intuition and a solid local setup. Start small, use virtual environments, and always read the code before you execute the command.
Next Steps for Success
Begin by enabling Artifacts in your Claude settings (usually found under the "Feature Preview" section) to get the best visual feedback. For local Python development, always initialize a new directory and a venv before asking Claude for code to ensure a clean slate for dependency installation. If you encounter an error, paste the exact traceback back into the chat—Claude is often more effective at debugging its own mistakes than writing perfect code on the first try.