My First-Hand Experience with Claude’s Computer Use: When AI Takes Control
So, Anthropic has just released a groundbreaking new AI feature — computer use. Imagine an AI that can actually control your desktop, moving the mouse, clicking buttons, and typing text just like a human would. When I heard about this, I knew I had to try it out, and today I’m sharing my experience with you all.
Before diving into my experience, let’s talk about what we’re dealing with.
According to Anthropic’s official announcement, this is a truly revolutionary capability. As they explain, ‘developers can direct Claude to use computers the way people do — by looking at a screen, moving a cursor, clicking buttons, and typing text.’
Claude 3.5 Sonnet is the first frontier AI model to offer computer use in public beta.
The upgraded Claude 3.5 Sonnet model is capable of interacting with tools that can manipulate a computer desktop environment.
However, it’s important to note that this is still in beta, which means it won’t always work perfectly. Think of it as an early access to something potentially game-changing.
How Does It Actually Work?
The process is fascinating. Claude uses a series of computer use tools to interact with your system:
- First, you give Claude access to computer use tools and tell it what you want it to do (like “Save a picture of a cat to my desktop”).
- Claude then evaluates whether it needs to use any tools to complete your request. If it does, it formats a proper tool use request.
- These tools are executed in a container or Virtual Machine (for safety), and the results are sent back to Claude.
- Claude keeps using tools as needed until your task is complete, creating what’s called an ‘agent loop.’
This back-and-forth between Claude and the computer continues until your requested task is finished. It’s like watching someone remotely control a computer, except that ‘someone’ is an AI.
Setting Up Claude’s Computer Use
Now, let’s get our hands dirty with the actual setup. Before you start, you’ll need two essential things:
- Docker installed on your system — This is crucial because Claude runs in an isolated container for safety
- An Anthropic API key — You can get this from your Anthropic Console
Once you have these prerequisites ready, the setup is fairly straightforward. Here’s the command you’ll need to run in your terminal:
export ANTHROPIC_API_KEY=%your_api_key%
docker run \
-e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
-v $HOME/.anthropic:/home/computeruse/.anthropic \
-p 5900:5900 \
-p 8501:8501 \
-p 6080:6080 \
-p 8080:8080 \
-it ghcr.io/anthropics/anthropic-quickstarts:computer-use-demo-latest
If everything goes well, you’ll see this exciting message:
✨ Computer Use Demo is ready!
➡️ Open http://localhost:8080 in your browser to begin
Once you open the browser, you’ll see a split-screen interface: a chat window on the left where you can interact with Claude, and a virtual desktop on the right where Claude will perform actions.
I decided to try two different scenarios:
- Booking flight tickets
- Data analysis by loading data into a spreadsheet
Task 1: Flight Booking
I started with a prompt : “Book my flights from New Delhi to Mumbai for 1st December 2024”
Browser Navigation-
Claude first opened Firefox and skillfully typed “makemytrip.com” — one of India’s popular flight booking platforms. The precision with which it entered the URL was impressive, though what happened next revealed both the potential and current limitations of this technology.
Dealing with Pop-ups
For any first time user the website makemytrip opens with a sign-in popup. The model initially struggled with this pop up, i think its still learning about the pop-ups. I added a prompt to close the popup, which Claude then managed to do successfully.
Typing Texts
Once past the popup issue and an error, it located and clicked the ‘From’ field, and strated typing “Mumbai” and ‘To’ was pre filled in the website.
After a few more trials and tool use, it could type “New Delhi” in the ‘From’, then filled the ‘To’ field with “Mumbai”.
What was particularly fascinating was watching it navigate the calendar interface to select December 1st, 2024.
While Claude couldn’t complete the actual booking (which is understandable given the beta version), what it achieved was remarkable. It successfully navigated through multiple steps of a real-world task.
Task 2: Loading Data in Spreadsheet
For my second test, I wanted to see how Claude would handle spreadsheet tasks. I asked it to download the titanic dataset and load it into a spreadsheet. The process was surprisingly smooth and efficient.
It found the dataset I requested, and downloaded it with precision. What impressed me was how it then seamlessly opened a spreadsheet application and imported the data. The entire process felt fluid and intuitive — from downloading to organizing the data in neat columns and rows. Unlike the flight booking task, here Claude showed remarkable competence in handling the entire workflow.
I was genuinely amazed by how smoothly it handled the data transfer and organization. The excitement of seeing it work so efficiently got me wondering — what else could it do?
What else could it do?
I thought of trying a follow up prompt on the titanic data which was uploaded in the spreadsheet.
“Can you create a chart to show the distribution of age with sex ?”
It walked me through its thought process, explaining that it needed to install Python libraries and prepare the data. It also able to elevate the permissions to sudo access for installations, watching it troubleshoot and resolve these issues was impressive. It proceeded to install pandas, numpy, matplotlib, and seaborn — all the essential data science libraries.
It was able to do pip install of the required libraries.
Tool Use: bash
Input: {'command': 'pip3 install pandas numpy matplotlib seaborn'}The visualization was clean and professional, with males represented in blue and females in orange, spanning ages from 0 to 120 years. Claude even provided a detailed interpretation of the chart, pointing out that males had a mean age of 45 years while females averaged 48 years, both with similar standard deviations of 15 years.
This kind of capability could revolutionize how we handle data analysis tasks, making data preprocessing and organization much more efficient.
What’s truly remarkable is seeing an AI system actually navigate a computer interface like a human would. It’s no longer just providing suggestions or code; it’s actively performing tasks in real-time.
AI that can not just suggest solutions but actually implement them — handling everything from routine administrative tasks to complex data analysis workflows. We’re moving from an era of LLMs that can only advise to AI Agents that can actually do.
As these systems mature, we are witnessing the early days of truly autonomous AI agents — ones that can understand our intent and independently navigate the digital world to help us achieve our goals.
This experience was a revelation. Not only could Claude handle basic computer tasks, but it could also set up a development environment, write Python code, and create meaningful data visualizations — all while explaining its process clearly. It felt like having a data analyst, programmer, and teacher all rolled into one.
Building Your Own - Custom Computer Use Environment
While Anthropic provides a reference implementation to get started with computer use, it’s worth noting that the feature is designed to be customizable for different use cases. For developers interested in building their own implementations, you’ll need four key components: a virtualized environment for security, tool implementations that align with Anthropic’s definitions, an agent loop to handle communications, and a user interface.
However, as this is still a beta feature, I recommend starting with the reference implementation to understand the capabilities and limitations before attempting any custom development. The technology is evolving, and future updates may bring changes to how custom implementations can be built.
Safety and Risk Considerations
While the capabilities of Claude’s computer use feature are exciting, it’s crucial to address the important safety considerations that come with this technology. As Anthropic emphasizes in their official documentation, computer use poses unique risks that differ from standard AI interactions. When using this feature, it’s essential to implement several security measures: always use a dedicated virtual machine or container with minimal privileges to prevent system vulnerabilities, avoid sharing sensitive data or login credentials with the model, and maintain strict control over internet access through domain allowlisting.
Additionally, human oversight remains crucial — any decisions with significant real-world consequences, financial transactions, or actions requiring explicit consent should always be verified by a human operator. It’s worth noting that Claude may be susceptible to instruction injection through webpage content or images, which could potentially override user commands. Therefore, whether you’re a developer implementing this technology or an end user exploring its capabilities, understanding and acknowledging these risks is paramount. As we venture into this new frontier of AI capabilities, maintaining a balance between innovation and security will be key to responsible implementation.
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