Hello fellow coders, AI nerdists (ok, IYKYK, “machine learning LLMs nerdists), and language enthusiasts! Adron here, ready to dive into an exciting new series that’s all about pushing the boundaries of our programming prowess. If you’ve followed my work, you know I’m often delving into exploring the depths of code (or systems in general), optimizing it, and sharing those insights with all of you. This time, we’re taking things up a notch by blending the art of manual code slinging and then shifting over and giving the power of artificial intelligence (AI) a try. Ok, my nitpick has already struck. This isn’t really AI but is really just the power of LLMs and machine learning, the ole’ Chat GPT and CoPilot and Claude and whatever else, that I’ll be diving into. Let’s go and put em’ to the test.
The Journey
Our journey starts with tackling programming problems the old-fashioned way: rolling up our sleeves and solving them manually. This is where we lay the groundwork, understand the problem’s intricacies, and come up with a solution that, while not perfect, gets the job done. Then, we refactor our code, streamlining and optimizing it to make it more efficient and elegant.
Enter the AI
Once we’ve got a solid foundation, it’s time to bring in the big guns – artificial intelligence. We’ll explore various AI tools, from ChatGPT-4 to Claude and beyond, to see how they tackle the same problems. Each AI brings its unique approach and strengths to the table, and we’ll break down their solutions, comparing them to our manual efforts.
Why This Matters
In today’s fast-paced tech world, understanding how to leverage AI in our coding practices is crucial. This series isn’t just about showcasing cool AI tools; it’s about understanding their capabilities, limitations, and how they can augment our problem-solving skills. By the end of this journey, you’ll have a deeper appreciation for both human ingenuity and AI’s potential.
What to Expect
In each post, you’ll find:
- The Hand Crafted Solution: A detailed walkthrough of solving the problem by hand.
- Refactoring: Steps to optimize and clean up the manual solution.
- AI Solution: A deep dive into how different AI tools approach the problem.
- Comparative Analysis: A side-by-side comparison of manual and AI-generated solutions, highlighting strengths, weaknesses, and key takeaways.
- The links to the code repository and respective before and after of each code base commits.
- A video walk through of the code and process I went through to get to the solution presented in each video & blog post.
- A ordered list of the posts as I complete them at the bottom of this post. For starters, as this post has gone live the first post of the series is also live now.
So, get those keyboards and processors ready, grab your favorite code editor, overclock your proc, and join me on this ride through the world of coding with AI. Let’s push the boundaries of what’s possible, one problem at a time.
Stay tuned and happy thrashing coding!
The Ordered List of Coding with AI: A Comparative Series Posts
- Finding the Maximum Sum Path in a Binary Tree in C#
- C# Array to Phone Number String Conversion & Testing with NUnit
- Converting Numbers into Roman Numerals with C#: A Classical Coding Exercise
- Simplifying Time: Humanizing Duration in Programming in C#
- Conquering the Top Words Challenge in C#: A Tale of Regular Expression and LINQ Magic
- How to Convert an IPv4 Address to a 32-bit Integer in C#: A Step-by-Step Guide
- Converting 2D Arrays to CSV in Go: Problem-Solving and Testing
- Calculating IP Address Ranges in Go: Learn IPv4 Range Between Addresses
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