If there is one thing that is worth chasing after, let it be the understanding of knowledge and reality.

Learning to program and about artifical intelligence have been intriguing. It is not just about the synax but looking things from a different perspectives and being aware of the code written. Becoming proficient in programming requires a curious mindset and the willingness to dig deeper, even in the era of easily accessible tools. The rise of 'low code' platforms has led to a tendency to overlook the underlying mechanisms. For instance, using a transformer model with a machine learning library is straightforward. While this convenience aids quick prototyping for various applications, it's crucial not to underestimate the significance of exploring the code for a thorough understanding.

Constructing Mental Models

Personally, I resonate a lot with Seymour Papert's Constructionism. It states that individuals construct their understanding of the world via active hands-on experience. It emphasizes on asking questions and finding solutions through trial and error. While this does not sound like the most efficient way of learning, I find that it helps in gaining a better understanding of the subject matter than simply a spoon feeding approach.