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Achievement: YOLOAchievement: Pull Sharkx2Achievement: Quickdraw

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S4nxez/README.md

Hi, I'm Dani

Backend Developer · Java / Kotlin · Systems & Architecture

About me

Backend developer with strong foundations in systems programming, software architecture and testing.
Currently studying at Campus 42 Madrid and working professionally with Java & Spring Boot.

  • Currently working on Campus 42 projects
  • Professional experience building production-ready backend systems
  • Strong focus on clean architecture, performance and testability
  • Ask me about Java, Kotlin, C, Python, Spring, testing and concurrency
  • Contact: s4nxez@gmail.com

Languages & Tools

GitHub Stats

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  1. MensajeriaSecreta MensajeriaSecreta Public

    Mensajería encriptada con Spring Boot. Almacenamiento de usuarios y mensajes cifrado en keystore, hilos, Railway Programming y tests JUnit,. CI/CD con GitHub Actions para testear y compilar.

    Java 4

  2. MedCenter-CRUD MedCenter-CRUD Public

    Developed using Java Spring Boot with MySQL as the database. The application includes multiple profiles to demonstrate proficiency in JDBC and Spring. It manages patient data and medical records ef…

    Java

  3. CRUD-Contactos CRUD-Contactos Public

    App that consumes the JSONPlaceholder API using MVVM, Flow for state management, the Navigation Component, navigation via TopAppBar and BottomAppBar, search in the TopAppBar, Snackbars, and a custo…

    Kotlin

  4. campus42 campus42 Public

    A collection of projects completed as part of the curriculum at Campus42. From implementing fundamental algorithms to building complete applications, these projects showcase the progress and master…

    C

  5. BarTunesVote BarTunesVote Public

    Application for managing song voting in bars, where users vote from their mobile devices. The winning song is automatically played in the bar via Spotify. Developed using Spring Boot, OAuth, WebSoc…

    Java 2

  6. exoplanet-hybrid-classifier exoplanet-hybrid-classifier Public

    🔭 Hybrid machine learning system for exoplanet classification using TensorFlow + scikit-learn Combines RandomForest, GradientBoosting, and Neural Networks to achieve 85.42% accuracy on Kepler space…

    Python 2