Automation Has Made Programming Less Fun.
The screen blinked back at me, a silent acknowledgment of the journey I was embarking upon. Over the past 41 years, coding has been more than just a profession; it's been a passion fueled by curiosity, creativity, and the thrill of solving complex problems. But recently, I had an experience that made me question the evolving landscape of programming and its impact on the joy it once brought me.
A few weeks ago, I decided to create a simple ASCII-art 4X space game—a project that, in the past, would have been a delightful challenge filled with hours of brainstorming, debugging, and incremental victories. This time, however, I turned to a Large Language Model (LLM) to assist me. In just about two hours, the game was complete. No hurdles, no late-night problem-solving sessions, no trial-and-error. And yet, instead of feeling accomplished, I was... bored.
Coding has always been akin to solving a intricate puzzle. Each bug fixed and each function optimized brings a sense of achievement that's hard to replicate. The process demands patience, logical thinking, and creativity. It's not just about the end product; it's about the journey—the countless trials and errors that lead to the final result.
When an LLM can generate code in a fraction of the time, it strips away the challenges that make coding rewarding. The automation of problem-solving turns an engaging process into a mechanical one. The excitement of unraveling a complex issue diminishes when the solution is handed to you on a silver platter.
As an educator, I see the same patterns emerging among my students who are learning to code. The allure of quick solutions is tempting, but it deprives them of the fundamental experiences that build proficiency and confidence. Struggling with code isn't a setback; it's a crucial part of the learning curve. It's through debugging and iterative problem-solving that students develop a deeper understanding of programming concepts.
When students rely too heavily on AI-generated code, they miss out on the opportunity to think critically and develop their problem-solving skills. The "eureka" moments that come after hours of hard work are invaluable. They not only reinforce learning but also build resilience and a growth mindset.
This isn't to say that LLMs and AI tools have no place in programming—they undoubtedly increase efficiency and can handle repetitive tasks with ease. However, it's essential to strike a balance. For seasoned programmers like myself, perhaps it's about using these tools to handle mundane aspects while reserving the more challenging problems for manual coding. For students, it might mean using AI as a learning aid rather than a crutch.
Coding is changing rapidly with the advent of AI and automation. While these tools offer incredible benefits, they also pose questions about the future of programming as a fulfilling craft. For those of us who find joy in the challenges of coding, it's important to remember why we started in the first place. And for the new generation of coders, embracing the hard work and the hurdles isn't just beneficial—it's essential.
The next time you sit down to code, consider taking the longer path. Embrace the difficulties, relish in the trial-and-error, and remember that sometimes, the struggle is where the real fun lies.
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That musical thing
Jaxon's finger hammered lightly and quickly on the drum. His eyes were closed, and he seemed consumed entirely by the music he was playing. A slight smile. The bassist was strumming, the pianist was finding the right note at the right time, and the crowd was with them. Maybe the darkness, perhaps the small space, but at a point during the show, a discernible "togtherness" happened. Jaxon lifted his head up. The cadence of the music flowed, started to become softer and then ended.
Jaxon exhaled softly, the stress his shoulders visibly relaxing, and focused on the crowd. All of them were on their feet applauding and gesturing in appreciation of the music. His band mates all seemed to be coming out from the act of creation and appreciating the appreciation. The lights slowly became brighter.
After some time, Jaxon stood up, waved to the crowd and waled off the stage. He took a glass of water and broke into a laugh "ok, THAT was fine creation, mates!", his fellow band members laughed and nodded in agreement "a fine set" said Silian, matter of factly. A few minutes later some eager members of the audience insisted on continuing their gratitude for an incredible live show.
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AI Is a Language Microwave
The article, written by Stephen Marche, addresses a few points about chatGPT in contemporary education. A quote in the article underlined some ideas I appreciated
The existence of AI will change what the world values in language. “The education system’s emphasis on [cumulative grade point average] over actual knowledge and understanding, combined with the lack of live monitoring, increases the likelihood of using ChatGPT,” the study on student use says. Rote linguistic tasks, even at the highest skill level, just won’t be as impressive as they once were. Once upon a time, it might have seemed notable if a student spelled onomatopoeia correctly in a paper; by the 2000s, it just meant they had access to spell-check.
Herein, I think, lay the thing; that we [professional educators] should return to a more basic, foundational practice; that our adaptation to chatGPT, especially as it applies to assessment of student work, must be more basic. How do we know our students know? How do we know our students understand is within the context of varying levels of knowing, so it's not just just drill-and-kill, but rather a more nuanced approach to assessment, Bloom's taxonomy is always a helpful framework to understand how we think about learning. . That chatGPT (and its ilk) requires giving students assignments which tease out what they really think and know.
The article is well put-together and I'd say an important part of the road to our understanding of LLM's and learning.
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The changing nature of conflict in an era of drones
Here is a local copy of the analysis and here is the external link to Institute for the Study of War
The analysis begins with a comparison of the Spanish Civil War and WW2; that lessons from the first greatly informed the execution of the later. A major thesis is that "the challenge of restoring operational maneuver to war remains the central problem for both sides [Ukranian and Russian] in this conflict".
The problem with positional warfare is that it leads to stalemate and attrition; it often leads to a prolonged stalemate, where neither side can gain a decisive advantage. This can result in a war of attrition, where victory is determined by which side can sustain losses longer. Such warfare is costly in terms of human lives, resources, and morale.
There is so much more in this article, as a historian, I appreciate the parallels drawn between past conflicts (the section about the battle of the bulge was especially interesting) and this current one. I recommend the study of this analysis.
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