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Achievements

Achievement: YOLOAchievement: Pull Sharkx2Achievement: Quickdraw

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tauimonen/README.md

I build data-driven applications with a focus on reliability, structure, and real-world usability.

My technical stack includes Java, Spring Boot, Hibernate, Maven, JUnit, Mockito, REST APIs, PostgreSQL, SQL, Docker, Git, and cloud platforms such as Azure. I also work with Python and frontend technologies including React, HTML, and CSS when needed.

I use AI-assisted development tools (Codex, Cursor, modern LLMs) together with specification-driven development and TDD to build structured and maintainable systems. I am particularly interested in how explicit workflows, validation, and orchestration can improve the reliability of AI-assisted software development.

In addition to software development, I bring domain knowledge in automation technology, process industry, and logistics, allowing me to connect software systems with real-world applications.

Education: B.Sc. in computer science & mec. eng. studies
Blogs: https://medium.com/@tauimonen https://toniuimonen.hashnode.dev/

Java Spring Spring Boot Maven Hibernate JUnit Lombok Mockito IntelliJ MySQL AWS Java Java Java Java Java
Java Java Java Java Java Java Java Java Java Java
Java Java Java Java Java Java Java Java Java

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  1. industrial-IoT-streaming industrial-IoT-streaming Public

    This project implements a industrial IoT streaming pipeline for monitoring the health and performance of a production line. Using Azure Databricks, Delta Live Tables (DLT), and the Medallion Archit…

    Jupyter Notebook

  2. ML-NBA-dataset-linear-regression ML-NBA-dataset-linear-regression Public

    This project builds and evaluates a linear regression model to predict NBA teams’ win percentages (W_PCT) based on traditional per-game statistics from the NBA Stats API.

    Jupyter Notebook

  3. phishing_ML phishing_ML Public

    This Python package provides two methods for detecting phishing websites using the phishing dataset and FastAPI REST API for phishing site detection.

    Jupyter Notebook

  4. ML-book-recommendation ML-book-recommendation Public

    The project's objective is to build a book recommendation system that utilizes the Surprise library to implement a user-specific recommendation model. The system aims to predict what kinds of books…

    Jupyter Notebook

  5. local-mini-ai-agent local-mini-ai-agent Public

    This project is a lightweight ReAct-style AI agent that uses a fully local LLM through Ollama (e.g., llama3.2:3b).

    Python

  6. Data-science Data-science Public

    One of my data science & HPC workspaces - ML experiments and various computational projects. A mix of learning, testing, and building.

    Jupyter Notebook