This blog is where I think out loud as an applied AI engineer — working through research papers, building with new tools, and forming views on where AI is actually going.
What you’ll find here
paper-review — Notes and breakdowns of research papers and technical white papers I’m reading. Not summaries for their own sake, but the parts that matter for someone building real systems. When I implement something from a paper, I link to the code.
code-experiments — Notebooks, prototypes, and tools I build to test ideas. Everything is linked to my GitHub. If the experiment taught me something useful, it ends up here.
industry-thoughts — Takes on the current state of AI: what’s overhyped, what’s underrated, and what actually matters for engineers building in enterprise environments.
Who I am
I’m Hamza — an applied AI engineer focused on making LLMs and AI systems work reliably outside of demos. My day-to-day sits at the intersection of research and production: reading what’s new, figuring out what’s practical, and building things that hold up under real constraints.
The bias here is always toward practical. What can you actually use in a personal project? What scales to an enterprise environment? That’s the filter everything gets run through.