The way developers use AI is quietly changing.
For the past few years, the default workflow looked like this:
- Open an editor
- Connect to a cloud AI (Copilot, Codex, Claude)
- Send code → wait for response
It worked. But it came with trade-offs:
- API costs
- Latency
- Privacy concerns
- Limited control
Now, with Google DeepMind’s Gemma 4 and Ollama , that model is breaking.
I decided to test something bold:
Can a fully local AI coding agent replace Codex?
Short answer: Surprisingly, yes (with some caveats).
This guide walks you through:
- Setting up your own local coding agent
- Building features with it
- Fixing bugs autonomously
- And where it still falls short
Why Local AI Is Becoming a Big Deal
Before jumping into setup, let’s understand why this matters.
1. Zero API Cost
Once downloaded, Gemma 4 runs locally — no per-token pricing.
2. Full Privacy
Your code never leaves your machine.
Huge for:
- startups
- enterprise projects
- sensitive repos
3. Low Latency
No network calls = faster iteration.
4. Full Control
You can:
- Customize prompts
- Add tools
- Build agents on top
This is the shift from AI as API → AI as infrastructure
Set up: Your Local AI Coding Agent
We’re going to combine:
- Ollama → runs models locally
- Gemma 4 → the brain
- Claude Code → agent interface
Step 1: Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
Verify:
ollama --version
Step 2: Downloading and Running Gemma 4 Locally
In this step, we pull and run the Gemma 4 E2B model using Ollama.
ollama run gemma4:e2b
When you run this command for the first time, Ollama automatically:
- Downloads the model (~7.2 GB)
- Verifies its integrity
- Prepares it for local execution
Once completed, you’ll see a >>> prompt — this means:
Your AI model is now running locally and ready to accept inputs.
Step 3: Testing the Model — Understanding “Thinking Mode”
After successfully running the model, we send a simple input:
Hey
What happens next is interesting.
The Gemma 4 doesn’t just respond — it thinks before answering.
You can see:
- Input analysis
- Context understanding
- Response planning
- Final answer generation
Step 4: Installing the Coding Agent (Claude Code)
Now that the model is running locally, the next step is to install an agent interface that can actually perform coding tasks.
We install Claude Code using:
npm install -g @anthropic-ai/claude-code
After that, we complete the setup with the native installer:
claude install
Step 5: Launching the AI Coding Agent with Gemma 4
With everything installed, we now connect the agent to our local model.
Run:
ollama launch claude --model gemma4:e2b
What This Does
This command links:
- Claude Code → the agent
- Gemma 4 E2B → the brain
Result: a fully functional local AI coding agent
What You’ll See
Once launched, you’ll get an interactive terminal interface with:
- A welcome screen
- Model details (gemma4:e2b)
- A prompt ready for input
This confirms that:
- The agent is running
- The model is connected
- Your local AI system is ready
Step 6: Generating and Explaining Code with the AI Agent
Now comes the most important part — actually using the agent.
We give a simple prompt:
Explain how a basic Express server works with an example
What the Agent Does
Using Claude Code powered by Gemma 4 E2B , the system:
- Explains the concept of an Express server
- Generates a complete working example
- Provides setup steps (npm init, install, etc.)
- Includes real code for handling routes
Output Highlights
The agent produces:
- Project setup commands
- Express server code
- Middleware usage (express.json())
- Example API endpoint (POST /submit)
- Instructions to run the server
Output
Explain how a basic Express server works with an example
⏺ A basic Express.js web server is a very popular way to build web applications and APIs
in Node.js. Express is a minimal and flexible Node.js web application framework that
makes it easy to build web and mobile applications and APIs.
Here is a complete, simple example of an Express server that handles a GET request:
1. Setup the Project
First, you need to initialize a Node.js project and install Express.
# Create a new directory for your project
mkdir express-example
cd express-example
# Initialize a Node.js project
npm init -y
# Install Express
npm install express
2. Create the Server File
Create a file named server.js (or index.js) and add the following code:
server.js
// 1. Import the express module
const express = require('express');
// 2. Create an Express application instance
const app = express();
// 3. Define the port the server will listen on
const port = 3000;
// 4. Define a route handler for the root URL ("/")
// When a GET request is made to the root URL, this function runs.
app.get('/', (req, res) => {
// Send a simple text response back to the client
res.send('Hello World from Express!');
});
// 5. Define another route handler for a different URL (e.g., "/about")
app.get('/about', (req, res) => {
// Send a more detailed response
res.send('This is a simple Express server.');
});
// 6. Start the server and listen on the specified port
app.listen(port, () => {
// This message will be printed to the console when the server starts successfully
console.log(`Express server listening at http://localhost:${port}`);
});
3. Run the Server
Execute the file using Node.js from your terminal:
node server.js
You should see the output in your console:
Express server listening at http://localhost:3000
4. Test the Server
Open your web browser and navigate to the following addresses:
1. For the root route: http://localhost:3000
- Result: You will see the text: Hello World from Express!
2. For the about route: http://localhost:3000/about
- Result: You will see the text: This is a simple Express server.
Explanation of the Code
1. const express = require('express');: This line imports the Express framework, making
its functions and methods available to your script.
2. const app = express();: This creates the main Express application object. This
object holds all the routing, middleware, and configuration for your server.
3. const port = 3000;: This defines the network port the server will listen on.
4. app.get('/', (req, res) => { ... });: This is routing. It tells the server: "If a
client sends an HTTP GET request to the root path (/), execute the provided function."
- req (Request): Contains information about the incoming request (headers, query
parameters, etc.).
- res (Response): Used to send data back to the client.
- res.send(...): Sends the response back to the client and ends the request cycle.
5. app.listen(port, () => { ... });: This is the final step. It tells the server to
start listening for incoming network connections on the specified port. When it
successfully starts, it executes the callback function, which is perfect for logging a
success message.
Next Steps (Handling POST Requests and Data)
Real-world applications use POST requests to send data to the server. You can handle
this using middleware like express.json() to parse incoming JSON data:
// Import middleware for parsing JSON bodies
app.use(express.json());
// Example of handling a POST request
app.post('/submit', (req, res) => {
// req.body contains the data sent in the request body (if the client sent JSON)
const data = req.body;
console.log('Received data:', data);
// Send a response back
res.status(200).json({ message: 'Data received successfully', received: data });
});
app.listen(port, () => {
console.log(`Express server listening at http://localhost:${port}`);
});
✻ Sautéed for 32s
Output
Create a simple Express server with one GET route that returns "Hello World" and show
how to run it
⏺ Here is a simple example of a Node.js/Express server.
1. Project Setup
First, make sure you have Node.js installed. Then, create a new directory for your
project and initialize a Node.js project:
mkdir my-express-app
cd my-express-app
npm init -y
Next, install the Express framework:
npm install express
2. Server Code
Create a file named server.js and add the following code:
server.js
const express = require('express');
// Create an instance of the express application
const app = express();
// Define the port the server will listen on
const port = 3000;
// Define a route handler for the root URL ("/")
app.get('/', (req, res) => {
// Send a simple text response
res.send('Hello World from the Express Server!');
});
// Start the server and listen for requests
app.listen(port, () => {
console.log(`Express server is running at http://localhost:${port}`);
});
3. Running the Server
You can run your server directly using Node.js:
node server.js
You should see the output in your terminal:
Express server is running at http://localhost:3000
4. Testing the Server
Open your web browser or use a tool like curl to access the server:
Navigate to: http://localhost:3000
You will see the response: Hello World from the Express Server!
Summary of Steps:
1. Install Dependencies: npm install express
2. Create File: Create server.js with the code above.
3. Run Server: node server.js
4. Access: Open your browser to http://localhost:3000
Conclusion: The Shift Has Already Started
Replacing Codex entirely?
Not yet.
But replacing a significant part of your workflow?
Absolutely.
What this experiment shows is simple:
You no longer need to rely entirely on cloud AI to build real software.
With Gemma 4 and Ollama , you can now:
- Run AI models locally
- Build and understand code
- Create real development workflows
- Maintain full control over your environment
All without sending a single line of code to an external API.
The Reality
Local AI still has limitations:
- Smaller models like E2B aren’t as powerful as top cloud models
- Complex reasoning and large-scale tasks can struggle
- Tooling is still evolving
But here’s the key:
The gap is shrinking — fast.
What This Means for Developers
We’re moving into a new phase of software development:
- AI is no longer just an API
- It’s becoming part of your local stack
- Agents are becoming customizable tools
Just like:
- Git changed version control
- Docker changed deployment
Local AI is changing how we build software.
Final Thought
This setup —
Gemma 4 + Ollama + Claude Code
is more than just a demo.
It’s a glimpse into a future where:
Every developer has their own AI system running locally,
fully customizable, private, and always available.
And the best part?
That future is already here — running on your machine.
Thank you so much for reading
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