✍️ GPT-4o in under 3 minutes (#20)

- Created new blog post entry "✍️ GPT-4o in under 3 minutes"
- Fixed youtube video missing base url

src/_includes/blog.html

@@ -19,8 +19,8 @@ <h3 class="text-4xl lg:text-6xl font-bold blog-title">{{ title }}</h3>
 
     <div class="blog-content">
         {% if video %}
-        <iframe width="560" height="315" src="{{ video }}" title="YouTube video player" frameborder="0"
-            allow="autoplay; clipboard-write; encrypted-media; picture-in-picture"
+        <iframe width="560" height="315" src="https://www.youtube.com/embed/{{ video }}" title="YouTube video player"
+            frameborder="0" allow="autoplay; clipboard-write; encrypted-media; picture-in-picture"
             referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
         {% endif %}
 

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.