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. ...more

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. ...more

Hey everyone! I recently came across an intriguing study titled "Limits for Learning with Language Models" by Nicholas Asher and colleagues, and I couldn't help but share some insights. This paper dives into the capabilities and limitations of large language models (LLMs) in understanding linguistic meaning—an area that’s crucial as we increasingly rely on these models for various applications. ...more

AI and language models have exploded onto the scene, offering solutions for all sorts of tasks. But here's the thing - they're not perfect. These models can hallucinate or make stuff up, which is a problem. So how do we figure out if they're actually useful for our needs? That's where LLM testing comes in. ...more

The Nobel Prize in Physics was recently awarded to pioneers in AI, sparking diverse reactions online. Some have celebrated it as a historic step, while others question whether AI even belongs in the realm of physics. This debate touches on the boundaries between scientific disciplines and how foundational research is evolving. ...more