diff --git a/src/_includes/blog.html b/src/_includes/blog.html
index 173088a..30983b4 100644
--- a/src/_includes/blog.html
+++ b/src/_includes/blog.html
@@ -19,8 +19,8 @@ {{ title }}
{% if video %}
-
{% endif %}
diff --git a/src/blog/2024-05-16-a-quick-tutorial-on-gpt-4o.md b/src/blog/2024-05-16-a-quick-tutorial-on-gpt-4o.md
new file mode 100644
index 0000000..c4efd6c
--- /dev/null
+++ b/src/blog/2024-05-16-a-quick-tutorial-on-gpt-4o.md
@@ -0,0 +1,96 @@
+---
+title: "GPT-4o in under 3 minutes"
+date: 2024-05-16
+description: |
+ Quick introduction into how to use the newly released model GPT-4o.
+tags:
+ - openai
+ - ai
+video: "4GPXwaWyxX8?si=m60rvMjzd7D9Qq5o"
+---
+Now that [GPT-4o](https://openai.com/index/hello-gpt-4o/) has been released, let’s look into how we can implement our custom GPT-4o assistant.
+
+To start, go to an empty directory and create a `npm` project and install the dependencies:
+- `npm init -y`
+- `npm install openai`
+
+Because I’m running the code directly, I’m adding the `type: "module"` to my package.json:
+```json
+{
+ "name": "tutorial-gpt-4o",
+ "type": "module",
+ "version": "1.0.0",
+ ...
+}
+```
+
+Now that we have all of our preparation, it is time to create our script.
+
+```js
+// We import the OpenAI library
+import { OpenAI } from "openai";
+
+// We set our key in a variable
+// Pssss.. you should use an environment variable
+const key = "YOUR-API-KEY";
+
+async function main() {
+ // The OpenAI constructor accepts an `apiKey` string parameter
+ const openai = new OpenAI({ apiKey: key });
+
+ console.log("Writing message");
+
+ // We need to call a chained function
+ const chat = await openai.chat.completions.create({
+ // This is where we assign GPT-4o
+ model: "gpt-4o",
+ messages: [{
+ // Every message has a `role` and `content`
+ role: "user", content: "What color is the sky?"
+ }]
+ });
+
+ // Finally we log our response
+ console.log("Response:", chat.choices[0].message.content);
+}
+
+// Don't forget to call your function!
+main();
+
+```
+
+If you run `node index.js`, you’ll have an output similar to:
+> Writing message
+> Response: Hello! I'm just a computer program, so I don't have feelings, but thanks for asking. How can I assist you today?
+
+## Deep diving into the types
+Now let’s analyze the code in detail, as some parts may be a little confusing:
+```js
+messages: [{
+ role: "user", content: "What color is the sky?"
+}]
+```
+
+Every `message` object in the array has two values, the first one is `role`, which represents _who_ is sending the message.
+We are using `user`, which indicates the model that it has to reply. We can also use `system`, which will be the core instructions for the model, but it will still be waiting for a second message coming from `user`.
+
+A working example would be the following:
+```js
+messages: [
+ { role: "system", content: "You are a meteorologist"}
+ { role: "user", content: "Why does it rain?"}
+]
+```
+
+By setting the `system` instructions, the model now will know that, to every query it receives, it needs to remember that it _is a meterologist_.
+
+The other confusing element could be the value that we receive:
+```js
+chat.choices[0].message.content
+```
+
+Every time we call the completion endpoint it will return an array of choices, by default, **we will always receive a single choice**, so, unless that you manually change this (because you want to compare results), you _should always obtain the element 0 in the array_.
+
+`message` is a wrapper with 3 values, `content`, which is the answer, `role`, which we just saw before. In this case, it would be `assistant`, and finally `tool_calls`, but we won’t see that here (you can see it in [the documentation](https://platform.openai.com/docs/guides/function-calling)).
+
+And that’s it! You can now start experimenting with your model.