OpenAI’s Codex: The Ultimate Vibe Coder Sidekick or Just a Gimmick?

Publié : 25 November 2025
Actualisé : 8 hours ago
Fiabilité : ✓ Sources vérifiées
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Codex vs. ChatGPT: The Coder’s Showdown 🤖

We’ve been hearing a lot about AI lately, especially ChatGPT. It’s everywhere, capable of generating text on the fly, answering complex questions, and yes, even writing code. But if you’re a developer and the idea of an AI co-pilot for your projects excites you, then forget ChatGPT for a moment. There’s a new sheriff in town, specifically designed for lines of code: OpenAI Codex.

Codex isn’t just a chatbot that can code. It’s a true AI agent, tailored for professionals who want to transform their way of working. Think of “vibe coders,” those augmented developers looking to maximize their productivity and creativity. Companies like Temporal are already talking about it with enthusiasm. It’s OpenAI’s answer to formidable competitors like Anthropic’s Claude Code or Mistral’s Codestral.

Secure First Steps with Codex 🔒

So, ready to dive in? Using Codex is quite straightforward, whether in your favorite editor like VS Code or via its web version. For the curious who want to test it directly in the browser, you’ll need to activate the GitHub connection and add a layer of security to your OpenAI account with two-factor authentication. This is crucial to prevent hackers from generating malware or accessing your projects through your account.

And here, a word of caution! Security is not optional. We all remember security breaches, like GitLab Duo’s, which exposed private code. To avoid any unpleasant surprises, the best practice is to create a new GitHub project dedicated to your Codex experiments. Grant it access only to this specific project. It’s a simple yet effective barrier against unwanted access to your sensitive repositories.

Alright, let’s be honest, the interface has its little quirks. OpenAI’s automatic translations can sometimes be amusing (a “forfait” instead of a “plan,” a “référentiel” for a GitHub “repository”…). But let’s move on, these are minor details compared to what this generative AI can bring to software developers!

My Experiment: Coding an AI… with AI! 🧠

But let’s get serious. I wanted to push Codex to its limits, not for a mundane script, but for a true exploration. My challenge? To use Codex to learn the basics of deep learning by creating a neural network in Python. It’s so much more impactful to see a model come to life than to read hundreds of pages of theory.

I opted for a use case that was both complex in essence and simple in appearance: a network that takes a number below 255 and returns that same number + 1. No need for a pre-built dataset, just randomly generated numbers. My first question to Codex, in “planning” mode, was technical:

I want to create an application that demoes the training of a basic deep neural network. The entry of the network is a number below 255. The expected output is the same number, + 1. So we don’t need to prebuild a dataset, just generate random numbers, and then the expected result should be “input +1”.

– Should I use scikit-learn or tensorflow?

– What should be the input size of the network (for instance for numbers between 0 and 255 ?) ?

– How many data point do I need?

– What’s the most basic architecture I can test, I guess a fully connected single layer network?

And that’s where the magic happens. Codex doesn’t just answer; it confirms the technical choices (TensorFlow, of course) and offers me a valuable idea I hadn’t considered: normalizing values between 0 and 1. An obvious step for pros, but a revelation for the learner. A few dozen values would suffice for the test. Brilliant! Less computational power needed.

From Theory to Code: AI in Action 🛠️

Armed with these recommendations, I asked Codex to put them into action. The goal was clear: a single Python script that implements the recommended architecture, trains over a few dozen values, and then computes the error for a test set. Simple, direct, effective.

Ok, implement a script that demoes the recommended architecture, trains over a few dozens values, and then computes the error for a test set of a few dozens values. Write it in a single Python script. We will use uv project manager for installing dependencies.

The generated code was not only functional but also clean and well-structured. What’s reassuring is that Codex also considers security: internet access is disabled by default to prevent prompt injections, the number one vulnerability for LLMs according to the OWASP ranking. It’s a detail that shows the tool is designed for professional and secure use.

Learning a new technology, especially deep learning, often feels like climbing a mountain of books and theories. But with a tool like Codex, you jump straight into practice, you experiment, you watch the code come alive. That’s ‘vibe coding’: no longer drowning in theory, but creating to understand.

Quick Comparison: Codex vs. ChatGPT for Code

So, Codex, a mere assistant or a revolution? For me, it’s clearly a giant leap towards a new era of development. AI is no longer just a tool to automate repetitive tasks; it becomes a true intellectual partner, capable of accelerating learning, suggesting elegant architectures, and ensuring a certain level of security. “Vibe coding” is no longer a distant dream but a tangible reality, and Codex is undoubtedly one of its brightest ambassadors.

À propos de Rigaud Mickaël 551 articles

Webmaster • Bretagne, France

🎯 LLM, No Code Low Code, Intelligence Artificielle • 3 ans d'expérience

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