Ai

Initially I was impressed by GPT-4o-Mini's ability to read text from images, but I quickly realized that getting consistent, reliable results required some trickery.

This blog post will delve deep into this critical concept, exploring why the 80/20 rule of project completion is more relevant than ever in the age of AI. We will uncover the inherent limitations of even the most advanced AI when it comes to the nuanced, subjective, and truly innovative aspects of human work, and illustrate why the human touch remains indispensable for transforming mere output into genuine impact. By embracing AI as a powerful co-pilot, not an autonomous driver, you can unlock unprecedented productivity without sacrificing the quality, authenticity, and ultimate success of your endeavors.

AI image generation has made huge strides, but generated images often have subtle flaws that make them unsuitable for professional use.

Vibe-coding represents a shift in how we approach programming. This article explores the good, the bad, and the ugly aspects of this emerging trend.

Imagine this - a user uploads a photo of a handwritten family recipe or a snapshot of their dinner plate. Within seconds, that image is transformed into a fully structured recipe—complete with ingredients, instructions, grocery categories, nutritional data, and suggested tags.

In this post, I'll walk through a system I built that orchestrates various AI models to generate location-specific content with a focus on weather forecasts and local information.

I have been working with large language models (LLMs) for the past year. During this time, I have built a couple of chatbots and most recently added a text generation feature to my personal weather application.