feat: enhance command palette with new "uses" feature and update chat types

- Added "View setup" command to the command palette in English, French, and Spanish.
- Removed "Tech Stack" command from the command palette.
- Updated MessageContainer to handle new "uses" message type.
- Refactored chat.ts to use a new ChatMessages function for better organization.
- Created new Uses.vue component to display a list of software and gadgets.
- Added Item.vue and List.vue components for rendering individual items and categories.
- Updated content configuration to include new skills and uses categories.
- Added new JSON files for programming languages, frontend, backend, devops, and python frameworks.
- Updated existing JSON files for homelab items with improved descriptions.
- Removed obsolete stack JSON files.

content/writings/how-my-website-works.md

@@ -60,7 +60,7 @@ Tailwind provides everything I need out of the box, but I've gradually added a b
 
 #### Nuxt UI
 
-Nuxt UI is a new tool I've been using since its release to enhance and streamline my Nuxt projects. It’s a module that offers a collection of Vue components and composables built with Tailwind CSS and Headless UI, designed to help you create beautiful and accessible user interfaces.
+Nuxt UI is a new tool I've been using since its release to enhance and streamline my Nuxt projects. It's a module that offers a collection of Vue components and composables built with Tailwind CSS and Headless UI, designed to help you create beautiful and accessible user interfaces.
 
 Nuxt UI aims to provide everything you need for the UI when building a Nuxt app, including components, icons, colors, dark mode, and keyboard shortcuts. It's an excellent tool for both beginners and experienced developers.
 

content/writings/log-lin-reg.md

@@ -144,7 +144,7 @@ Interpreting the results in logistic regression differs from linear regression d
 
 ### **Understanding Odds Ratios**
 
-In logistic regression, odds ratios provide insights into the relationship between the independent variables and the likelihood of the outcome variable belonging to the positive class. An odds ratio greater than 1 indicates that an increase in the independent variable’s value leads to higher odds of the positive outcome, while an odds ratio less than 1 suggests a decrease in the odds of the positive outcome. Additionally, odds ratios close to 1 indicate a weaker or negligible impact of the independent variable on the outcome.
+In logistic regression, odds ratios provide insights into the relationship between the independent variables and the likelihood of the outcome variable belonging to the positive class. An odds ratio greater than 1 indicates that an increase in the independent variable's value leads to higher odds of the positive outcome, while an odds ratio less than 1 suggests a decrease in the odds of the positive outcome. Additionally, odds ratios close to 1 indicate a weaker or negligible impact of the independent variable on the outcome.
 
 ### **Confidence Intervals and Significance**
 

content/writings/rag-ai-agents.md

@@ -27,7 +27,7 @@ A **Large Language Model (LLM)** is a machine learning model trained on vast amo
 
 LLMs work by predicting the next token (word or part of a word) based on the input they receive. This ability allows them to generate text, summarize documents, answer questions, and even carry on conversations that seem remarkably human.
 
-However, LLMs have their limitations. They can sometimes generate **hallucinations** (incorrect or fabricated information), and their knowledge is **static**, meaning they can become outdated as they don’t automatically update from the web.
+However, LLMs have their limitations. They can sometimes generate **hallucinations** (incorrect or fabricated information), and their knowledge is **static**, meaning they can become outdated as they don't automatically update from the web.
 
 ## 3 - Messages and Tokens
 
@@ -101,7 +101,7 @@ Here's how it works:
 
 RAG solves a major problem with LLMs: the **outdated or incomplete information** they may have. By pulling in real-time data, RAG ensures that the generated content is relevant and grounded in current knowledge.
 
-A classic example of RAG is when you ask an AI to summarize the latest research on a particular topic. Instead of relying on the model’s static knowledge base, the model can retrieve relevant papers or articles and generate an accurate summary.
+A classic example of RAG is when you ask an AI to summarize the latest research on a particular topic. Instead of relying on the model's static knowledge base, the model can retrieve relevant papers or articles and generate an accurate summary.
 
 ## 7 - Synergy Between RAG and AI Agents
 
@@ -112,7 +112,7 @@ Here's how they complement each other:
 - **RAG** acts as an external memory or knowledge source for AI agents, providing them with up-to-date information to improve their decision-making and outputs.
 - **AI agents**, powered by LLMs, can process this information and take actions based on it, whether it's generating a response, making a decision, or interacting with other systems.
 
-For example, imagine an AI agent that's tasked with assisting a business in handling customer inquiries. It could use RAG to retrieve relevant customer information and FAQs, then generate a response based on that data. It might then take action by sending an email or updating a CRM system based on the customer’s query.
+For example, imagine an AI agent that's tasked with assisting a business in handling customer inquiries. It could use RAG to retrieve relevant customer information and FAQs, then generate a response based on that data. It might then take action by sending an email or updating a CRM system based on the customer's query.
 
 This synergy leads to **autonomous, efficient systems** that can process, reason, and act in a dynamic environment.
 

content/writings/what-is-machine-learning.md

@@ -43,7 +43,7 @@ caption: The different types of machine learning models
 ---
 ::
 
-With this overview of ML types, let’s now focus on supervised learning, the most widely used approach, and explore how to choose the right model.
+With this overview of ML types, let's now focus on supervised learning, the most widely used approach, and explore how to choose the right model.
 
 ## 3 - Three Considerations for Choosing a Supervised Learning Model
 
@@ -59,7 +59,7 @@ Selecting the right supervised learning model is critical and depends on several
 
 3. **Algorithmic Approach**
    - Should you choose a feature-based or similarity-based model?
-   - **Key Point**: The choice of the model (e.g., linear regressions vs k-NN) depends on the dataset’s size and complexity.
+   - **Key Point**: The choice of the model (e.g., linear regressions vs k-NN) depends on the dataset's size and complexity.
 
 Once the model type is defined, the next step is to delve into the full workflow of developing an ML model.
 
@@ -89,12 +89,12 @@ Evaluation is a crucial step to verify the performance of a model. For regressio
 
 ## 5 - Evaluating Models: The R² Score
 
-For regression problems, the **R² score** measures the proportion of the target’s variance explained by the model:
+For regression problems, the **R² score** measures the proportion of the target's variance explained by the model:
 
 $$R^2 = 1 - \frac{\text{SS}_{\text{residual}}}{\text{SS}_{\text{total}}}$$ where:
 
 - $$\text{SS}_{\text{residual}}$$ : Sum of squared residuals between actual and predicted values.
-- $$\text{SS}_{\text{total}}$$ : Total sum of squares relative to the target’s mean.
+- $$\text{SS}_{\text{total}}$$ : Total sum of squares relative to the target's mean.
 
 A $$R^2$$ close to 1 indicates a good fit.