Ah, the joys of algorithm and data structure interviews. Or as I like to call them, the “let’s weed out anyone who isn’t a robot” interviews. You see, these code challenge interviews are touted as the pinnacle of assessing a developer’s true potential. Because nothing says “I can build scalable, maintainable software” like solving a problem about reversing a linked list on a whiteboard, right?
Let me break down the astounding advantages of these interviews for you.
Narrowing Your Candidate Pool
First and foremost, if you want to reduce the number of applicants faster than a bad odor clears a room, code challenge interviews are the way to go. These challenges are a fantastic method for eliminating a huge percentage of your candidate pool. Who cares about that candidate with 10 years of solid, hands-on experience in system design and architecture? If they can’t recall the exact time complexity of bubble sort, they’re clearly not worth your time. Because everyone knows that bubble sort is essential knowledge in the real-world scenarios you’ll encounter daily. Sure, buddy.
Programming Problems & Solutions : “Exploring IP Address Ranges in Go”. The introduction to this series is here and includes all links to every post in the series. This is the 8th of a dozen programming challenges I’m doing to setup for an eventual blog post on AI coding. The intent is to setup these programming challenges, get a solution, find a good refactoring, and then see how the AI tooling performs going through the same thing or refactoring what is in place. If you’re interested in how AI performs, and checking out these experiments and tests, subscribe to the blog to have the articles delivered directly to your email!
Hey there, fellow code adventurers! Today, we’re diving into the world of IP addresses and ranges. We’ve got an exciting coding challenge on our hands, and we’ll be tackling it using the Go programming language. So, grab your favorite beverage, and let’s get started!
The Challenge
Our mission, should we choose to accept it, is to implement a function that takes two IPv4 addresses as input and returns the number of addresses between them (including the first one, but excluding the last one). We’ll be working with valid IPv4 addresses in the form of strings, and the last address will always be greater than the first one.
Programming Problems & Solutions : “Transforming 2D Arrays to CSV Format in Go”. The introduction to this series is here and includes all links to every post in the series. This is the 7th of a dozen programming challenges I’m doing to setup for an eventual blog post on AI coding. The intent is to setup these programming challenges, get a solution, find a good refactoring, and then see how the AI tooling performs going through the same thing or refactoring what is in place. If you’re interested in how AI performs, and checking out these experiments and tests, subscribe to the blog to have the articles delivered directly to your email!
Hey there, coding enthusiasts! Today, I’m diving into an interesting problem that involves converting a two-dimensional numeric array into its CSV (Comma-Separated Values) representation. This is a common task when working with data in various formats, and I’ll explore how to tackle it using Go.
The Problem: Imagine you have a 2D array filled with numbers, and you need to convert it into a CSV string. Each row of the array should be represented as a line in the CSV, with the elements separated by commas. For example, consider the following input array:
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
A common practice in any coding is to get a time stamp for the start and stop of a process, and probably calc the elapsed time since it started (ya know, because that means we don’t have to mentally calc it ourselves the zillion times the code will run!). The following is usually an example of how I do this particular activity:
start_time = datetime.datetime.now()
print(f"Process started at: {start_time.strftime('%Y-%m-%d %H:%M:%S')}")
# ...do the process and all here that is being timed...
end_time = datetime.datetime.now()
print(f"Process ended at: {end_time.strftime('%Y-%m-%d %H:%M:%S')}")
time_elapsed = end_time - start_time
print(f"Time elapsed: {time_elapsed}")
Now, my question is, how do YOU do this in Python? Are there other tricks, cleaner ways to do it?
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