This is off-topic from my usual content, but recent geopolitical shifts have fundamentally altered how I engage with technology. It's no longer just about convenience versus principles; It feels like we're reaching an inflection point in digital sovereignty.

We've all seen charts like this where models like DeepSeek challenge OpenAI's latest offerings. These comparisons rely on standardized tests to demonstrate capabilities.

After months of coding with LLMs, I've finally gathered enough experience to share my thoughts on how these tools are changing our field.

The initial excitement of AI-assisted development is remarkable. Code scaffolding happens at lightning speed, and applications seem to materialize effortlessly.

Yet, as your codebase expands, you'll hit what developers call the "pit of death" - that critical point where AI-generated code becomes unreliable and troubleshooting grows increasingly complex. This transition from rapid development to maintenance challenges represents a fundamental shift in how we must approach AI-assisted coding.

I decided to dive back into machine learning through a hobby project, leveraging AI tools for development and deployment. Here's what I learned along the way.