I use Python to build scalable backends, automate workflows with machine learning, and streamline data processing across web apps and real-time systems.

I use TypeScript to build maintainable, type-safe frontends and internal tools, often pairing it with modern frameworks like Vue and Svelte for responsive and scalable UI development.

I use JavaScript to develop dynamic user interfaces and interactive dashboards, integrating with backend APIs to deliver responsive, data-driven web applications.

I use HTML and CSS to craft clean, accessible, and responsive user interfaces, ensuring a smooth user experience across web applications.

I learned to program using C# and built multiple applications during my studies, gaining a strong foundation in object-oriented programming and software development principles.

I use Linux as my primary development environment, managing servers, deploying applications, and troubleshooting systems using command-line tools and shell scripting.

I use Docker to containerize applications, streamline local development, and ensure consistent deployments across environments in both development and production.

I use GitHub Actions to automate everything from CI/CD pipelines to infrastructure workflows, like deploying Kubernetes clusters for Araelec’s IoT platform or running flight deal tests at Jack’s Flight Club, cutting manual steps and ensuring consistent releases.

I use Git for version control, managing code changes, collaborating across teams, and maintaining clean, trackable development workflows through branches, commits, and pull requests.

I use FastAPI to build high-performance APIs with Python, enabling fast development of scalable backend services and seamless integration with machine learning models and real-time data pipelines.

I used Flask in school projects to build lightweight web applications, gaining hands-on experience with routing, templating, and integrating Python backends with web interfaces.

At Araelec, I used Django and Django Rest Framework to develop robust, scalable backend services, enabling secure APIs and efficient data handling for IoT applications.

I’ve used Redis to implement caching and real-time data storage, as well as to integrate task queues for handling background jobs and improving system responsiveness under load.

I've used PostgreSQL in Jack's Flight Club as a relational database to store and manage structured data,while optimizing queries and integrating it with Python backends (FastAPI, SQLAlchemy) for efficient data processing in your full-stack applications.

I use MySQL as a relational database to manage structured data, ensuring efficient querying, scalability, and seamless integration with Python backends.

I use SQLAlchemy as my Python ORM and query builder to interface with relational databases (like MySQL and PostgreSQL), abstracting raw SQL while maintaining performance for data-heavy applications such as flight tracking platforms.

I use Pandas to clean, analyze, and transform data in my Python projects. It helps me work efficiently with spreadsheets, sensor readings from IoT devices, and flight deal datasets, preparing them for machine learning models or business reports.

I use Scikit-learn to build practical machine learning models that solve real problems. For example, I developed a system to identify the best flight deals by analyzing pricing patterns, and I've worked on AutoML research to automate model selection.

I use Pytest to write reliable tests for my Python code, ensuring my applications (like flight tracking systems and IoT platforms) work correctly before deployment. It helps me catch bugs early and maintain clean, stable code.

I use Playwright to test websites and scrape data.

I use Svelte to build fast, interactive dashboards - like the flight deal tracking system at Jack's Flight Club - where real-time updates and smooth user experience matter most.

I use Vue.js to build responsive frontend interfaces for data-heavy applications, like the IoT device dashboard at Araelec, where users monitor and control smart lighting systems in real-time.

I use Tailwind CSS to rapidly build clean, responsive interfaces for my web apps, like the IoT control dashboard at Araelec, where I need to ship polished UIs without getting bogged down in custom stylesheets.

I use Plotly to create interactive data visualizations fast, like displaying flight deal trends at Jack's Flight Club or energy usage patterns in the Araelec IoT system, helping users understand complex data at a glance.

I use TensorFlow to implement and experiment with machine learning models in research projects, like improving AutoML systems during my academic work, where I needed flexible tools to test novel architectures and optimize model performance.

I use NLTK for text processing and language analysis in research projects, like developing psychological assessment tools at Deepdatatech, where I applied Carl Jung's theories to analyze personality test responses.

I use spaCy for advanced NLP tasks like entity recognition and text processing, such as recognizing negation in Spanish written texts and determining its scope

I use Keras to quickly prototype and experiment with neural networks, like during my AutoML research at the University of Havana, where its intuitive design lets me focus on model architecture and hyperparameter tuning rather than low-level code.

I use Jinja2 to quickly build early versions of web apps, like prototyping dashboards for school projects or mocking up interfaces before switching to full JavaScript frameworks. It lets me focus on functionality first, with clean templates that blend Python data and HTML.

I use Streamlit to rapidly prototype data apps, like testing flight deal algorithms at Jack’s Flight Club or sharing AutoML research findings, where I need to turn Python scripts into interactive dashboards without frontend overhead.

I use NumPy as the foundation for numerical work in my projects, whether processing sensor data for IoT systems at Araelec, analyzing flight pricing trends, or accelerating machine learning research at university. Its arrays and vectorized operations let me handle large datasets efficiently in Python.

I use Selenium to automate browser testing for web applications, like verifying flight deal dashboards at Jack's Flight Club, and for practical scraping tasks, such as collecting real-time pricing data when APIs are unavailable.

I use Kubernetes to orchestrate containerized applications. like deploying the IoT control system at Araelec or scaling Jack’s Flight Club’s deal-processing microservices, where I need self-healing clusters and efficient resource management.

I use Terraform to automate cloud infrastructure deployment, like setting up AWS environments for Arealec different clients, where I need reproducible, version-controlled infrastructure as code

I use AWS to deploy and scale cloud-based applications, like hosting Araelec’s IoT platform, leveraging services like EC2, RDS, and Lambda to balance performance, reliability, and cost.

I use Heroku to quickly deploy personal projects and prototypes, like experimental web apps or machine learning demos, where I need a hassle-free cloud platform to share ideas without managing infrastructure.

I used DigitalOcean droplets to create lightweight, cost-effective testing environments for Araelec’s IoT platform, spinning up disposable Linux instances to validate device communication and backend updates before pushing to production.