Working Notes

Exploring AI, Machine Learning, and Graphs — with Limited Resources

This blog is a technical notebook to explore topics I’m curious about and bridge the gap between theory and practice through hands-on experiments. Everything here—code, data, and analysis—is hosted on GitHub to ensure others can inspect, reproduce, or build upon my work.

Current Interests & Research Ideas

These are topics I am thinking about:

• AI & Machine Learning: LLM fine-tuning, data poisoning, and predictive modeling (academic challenges/real-world data).
• Graph Theory & Network Science: Analyzing political networks (parliamentary voting behavior) and modeling epidemic spreads.
• Quantum Computing & Cryptography: Exploring the fundamentals of quantum information—such as running single-qubit circuits via the Felis framework and Alice & Bob’s cat qubit technology.
• Decision Systems & Strategy: Game theory applied to sports (tennis) or board games, and reinforcement learning.
• Reflections on AI & Sovereignty: Analyzing how Large Language Models shape public opinion and impact democratic stability. I’m particularly interested in the dominance of a few tech giants and how states leverage these tools to project influence and challenge national autonomy.

The “Low-Tech” Constraint

I run my experiments on a 2017 MacBook Air (8GB RAM, Dual-Core i5) without paid cloud services. This constraint encourages me to focus on small LLMs and lightweight techniques like LoRA. For me, it’s about deep understanding and efficiency rather than massive scale.

Closing Note

This is a space for curiosity and open-source collaboration. Everything shared here is open for you to reuse, adapt, or critique. If a project sparks your interest, feel free to dive in and share your findings.

Photo by Pawel Czerwinski on Unsplash