As an Information Technology student, I have always been interested in learning technology by actually building things. Instead of limiting my learning to classrooms and theory, I have been exploring AI, cloud technologies, IoT, software development, and hackathons to understand how technology can be applied to real-world problems.
Over the past few semesters, I have had the opportunity to work on different projects, participate in hackathons, attend technical workshops, and experiment with AI-powered solutions. Each experience has helped me understand not only how to build a solution, but also how to think about the problem behind it.
Hackathons have been one of the most important parts of my technical journey. They pushed me to work with a team, understand problem statements quickly, divide responsibilities, and build solutions within limited time.
One of my memorable experiences was working with my team on a real-time campus bus tracking solution. The idea was to make transportation tracking more accessible by combining hardware, real-time data, and software.
Working on this project gave me practical exposure to connecting different components of a system and understanding how data moves from the physical world to a software application. More importantly, I learned how important teamwork and communication are when building a solution under time constraints.
My interest gradually moved from traditional software development toward Artificial Intelligence and Generative AI.
I started exploring tools and platforms such as Google AI Studio, Gemini, Vertex AI, and other AI/ML technologies. What interested me most about Generative AI was its ability to turn natural-language interaction into something useful for real users.
Instead of thinking about AI only as a chatbot, I started looking at how AI could become part of larger applications — helping users understand information, automate repetitive tasks, search through knowledge, and make technology easier to use.
This changed the way I approach projects. I now try to ask not only “Can AI do this?” but also “Will AI actually make this solution better for the user?”
One of the areas I have been exploring is the development of AI-powered assistants and intelligent applications.
I have worked with technologies such as Python, machine learning, cloud platforms, databases, and AI services while experimenting with different project ideas. These experiences helped me understand the complete journey of an AI application — from collecting data and processing it to creating an interface through which users can interact with the solution.
I have also explored the idea of using AI to make technical information easier to access. This includes experimenting with AI assistants that can understand information and provide useful responses instead of forcing users to manually search through large amounts of content.
My learning journey has also included cloud and data technologies.
Through hands-on workshops and projects, I explored Snowflake, cloud data warehousing, databases, and data processing. Working with data made me realize that a good AI solution is not just about the model. The quality, organization, and accessibility of data are equally important.
I also became interested in technologies such as BigQuery ML and Vertex AI, which showed me how data engineering, machine learning, and cloud computing can come together to build practical applications.
This helped me see AI as part of a larger technology ecosystem rather than something that works independently.
Another part of my journey has been working with hardware and IoT.
I have experimented with devices such as ESP32 and Raspberry Pi Pico W, along with sensors and GPS modules. These projects taught me how software can interact with the physical world.
Combining IoT with AI became particularly interesting to me because sensors can continuously generate real-world data, while AI can help interpret that data and provide meaningful insights.
This opened up possibilities for applications in areas such as transportation, agriculture, safety, and environmental monitoring.
Participating in hackathons taught me lessons that cannot be learned from coding alone.
I learned how to:
Sometimes the first solution is not the best solution. Hackathons taught me to test, improve, and keep moving instead of getting stuck trying to make the first idea perfect.
I am currently continuing to explore Generative AI, cloud computing, AI agents, and intelligent applications.
I am particularly interested in how AI can move beyond simple question-and-answer systems and become a useful part of real applications. Tools such as Gemini and Google AI platforms have made it easier to experiment with these ideas and turn concepts into working prototypes.
My goal is to keep building projects that combine different technologies rather than learning each technology in isolation.
I am still at the beginning of my journey in technology, and I don't have everything figured out yet. But every project, hackathon, workshop, and experiment has taught me something new.
For me, being a builder means being willing to learn, experiment, fail, improve, and try again. AI is changing how we build technology, and I want to be part of that change — not just by using AI tools, but by understanding them and using them to create solutions that can make a real difference.
This journey is still being built, one project at a time.