Un ordenador no puede entender texto ni relaciones semánticas o significados entre palabras. Solo puede entender números. Este problema lo resolvemos mediante el uso de embeddings.

Un embedding es la representación de texto (en forma de números) en un espacio vectorial. Esto permite a los modelos de IA comparar y operar sobre el significado de las palabras.

flowchart TD
    A["perro"] --> B{{Modelo de embedding}}
    B --> C["[-0.003, 0.043, ..., -0.01]"]
    
    N1["(texto que queremos convertir)"]:::note --> A
    N2["(vectores con contenido semántico)"]:::note --> C
    
    classDef note fill:none,stroke:none,color:#777;    

Los vectores de cada palabra o documento capturan el significado semántico del texto.

• perro estará cerca de mascota
• contrato estará lejos de playa

Vector vs SQL databases

El problema con las bases de datos típicas es que solo buscan matches exactos. Si yo busco por coche solo me sacará las entradas que contengan coche.

En cambio las BBDD vectoriales pueden interpretar la semántica de las palabras mediante vectores. Si busco por coche puede sacarme valores como sedán, SUV, Land Rover, etc.

Las BBDD vectoriales son muy buenas cuando necesitamos buscar items similares por proximidad uno respecto al otro.

Algunos ejemplos de uso son:

• buscar películas parecidas (Netflix)
• Recomendadores de items parecidos en tiendas online (Amazon)
• buscar canciones parecidas (Spotify)

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Rest levels

Model of restful maturity used to help explain the specific properties of a web-style system.

Level 0

The starting point for the model is using HTTP as a transport system for remote interactions, but without using any of the mechanisms of the web.
We publish a document on how to use our API. We declare only one endpoint and do all the communication through this endpoint.

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Template

This is all needed to test.

package main

func main() {

}

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Check installation

go version

Environment variables

GOROOT
Folder where go was installated. It must only be set when installing to a custom location.

GOPATH
Place to get, build and install packages outside the standard Go Tree.

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Liquid is a template engine for HTML. It’s used by Jekyll.

Variables Usage

1. Declaration in a config.yml file with home_sidebar: Home
2. Usage with liquid in file.html as {{ site.home_sidebar }}

Functions

Show liquid code snippets

When writing liquid code snippets, jekyll process this code instead of showing it. To solve this, wrap the code snippet with the tags

{percent raw percent}
{percent endraw percent}

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Implementation of several patterns in Java, which may be used as future example on how to technically implement them.

Database

DAO & DTO

Data Access Object & Data Transfer Object. DAO - Design pattern, used to encapsulate the access to a persistence resource (e.g a database) and eliminate dependencies which come with the implementation of the code. DTO is the object which representates an entity of the database, with all its own properties to be manipulated.

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Since the beginning of my programming career, I’ve been writing small notes and how-to’s in Markdown for my personal use. This approach helps me avoid relearning the same thing twice and saves me time from repeatedly searching solutions to obscure problems.

Over time, this collection grew significantly, and I needed a more efficient way to index, access, and search through all my information. Building a website that also serves as a portfolio seemed like the best long-term solution.

Technology Stack v2.2 (now)

After doing a POC with Statiq I found it too niche for me (I may be wrong), but the learning curve seems too step. I find the documentation sparse and I’m not able to find lots of examples, themes or customisation online. I prefer to build something more visual and all the Statiq examples I find look exactly like the v1 I had with Jekyll.

I decided instead to move to Blazor WASM as it seems like a balance to get something more visual yet still a static page that doesn’t cost me lots of money.

• Azure Static Web Apps - azure hosting (free tier)
• .NET / C# - code and language
• Blazor WebAssembly - With WASM I build a static site so I’m still able to host it as a static page
• Markdown - still used to write my notes. It doubles down nowadays as I can just paste notes straight to AI
• Github (CI/CD) - code repo. automatically deploy when pushing new notes through Github Actions.
• HTML & CSS

Technology Stack v2.1 (POC 2026)

• Azure Static Web Apps - azure hosting (free tier)
• .NET / C# - code and language
• Statiq - static website generator (framework) to build websites from markdown using Razor
• Markdown - still used to write my notes. It doubles down nowadays as I can just paste notes straight to AI
• Github (CI/CD) - code repo. automatically deploy when pushing new notes through Github Actions.
• HTML & CSS

Improvements v1.1 - v1.4 (2026)

After 7 years a migration and some improvements are long due. I still use this site 2 to 3 times a week to check my notes and a website still seems like the easiest way to access it anywhere (and be able to show a portfolio from time to time) and I want to keep my knowledge base in markdown.

After all this time I now work mainly in Microsoft’s environment as an AI .NET developer with Azure and I want to gather and join all the resources I use to reflect more on my daily workload and abilities.

Sadly I don’t have the time yet to fully move it to Microsoft’s environment and move out of Jekyll, so I’ll roll it over it slowly over this year.

Changes in the stack

• Moved from Gitlab to Github - I mainly use Github for all my projects and this was the only one still in Gitlab (I wanted to test its CI/CD before Github’s got that good).
  • I have redone all CI/CD to automate checks and automatically deploy from Github to Azure
  • Before deploying it checks for dead links (403, 404 and 5xx) and sends me an email in case of problems
  • It also checks for dead image links
• Moved from GCP to Azure. I deleted the project in Firebase and created a new Static Web App in Azure.

Minor improvements

• I’ve changed Jekyll’s theme
• I’ve set up a dark mode

Initial Stack v1 (2019-2026)

• Firebase - hosting and serve through Google’s cloud platform
• Jekyll - transforms markdown notes into static websites. Deployable through Github Pages or other services (I use Firebase)
• Ruby - All examples for Jekyll I found used ruby. Useful as I wanted to test it at the time, but I hate it now and I want to focus in .NET
• Liquid - template language to improve on Ruby
• Markdown - used to write my notes to avoid the need to write code
• Gitlab (CI/CD) - code repo host. automatically deploy when pushing new notes
• HTML, CSS, vanilla JS - self-explanatory