Why Most Developers Shouldn't Write Their Own Algorithms (And Why You Probably Think You're the Exception)

Look, I get it. You’ve been coding for a while, you understand Big O notation, and you’re pretty confident you could whip up a sorting algorithm that would make Knuth himself shed a proud tear. That binary search tree you implemented in college? Chef’s kiss. Surely you’re ready to tackle the big leagues and craft some custom algorithms for production, right? Well, hold your horses there, Algorithm Annie. Before you start reinventing the wheel (or worse, the square wheel), let’s have a heart-to-heart about why most of us should probably stick to using the battle-tested algorithms that smarter people have already perfected.

The Seductive Whisper of “I Can Do It Better”

Every developer has felt it—that intoxicating moment when you think, “This existing solution is almost perfect, but if I just tweaked it a little…” or “I bet I could write something more efficient for our specific use case.” It’s like looking at a recipe and thinking you can improve it by adding pineapple. Sometimes you create Hawaiian pizza; more often, you create an abomination. The temptation is real, and it comes from several places: The NIH Syndrome (Not Invented Here): We developers have egos. Shocking, I know. We see an algorithm and think, “Pfft, I could write that in my sleep.” Sure, you probably could write a basic sorting algorithm, but can you write one that handles edge cases, performs well across different data distributions, and doesn’t break when Jerry from accounting feeds it malformed data? The Optimization Obsession: “This library function is too general. I only need to sort integers between 1 and 100. I could write something way faster!” And you might be right—for that specific case. But what happens when requirements change and suddenly you need to sort strings? Or floating-point numbers? Or custom objects? The Learning Trap: “I want to understand how it works, so I’ll implement it myself.” This is actually noble, but there’s a difference between implementing something to learn and implementing something for production use.

The Hidden Complexity Monster

Here’s the thing that’ll make you wake up in cold sweats: algorithms that seem simple on the surface are usually icebergs. What you see in that Wikipedia article or computer science textbook is just the tip. The real complexity lurks beneath, waiting to torpedo your confidence and your release schedule. Let’s take a seemingly innocent example—string matching. How hard could it be to find if one string contains another?

# Naive approach - looks simple enough, right?
def naive_string_search(text, pattern):
    for i in range(len(text) - len(pattern) + 1):
        if text[i:i+len(pattern)] == pattern:
            return i
    return -1
# Test it
text = "The quick brown fox jumps over the lazy dog"
pattern = "fox"
print(naive_string_search(text, pattern))  # Works fine!

This looks reasonable, and for most cases, it’ll work just fine. But here’s where things get spicy:

1. Performance: This has O(n*m) time complexity. For large texts and patterns, it’s glacially slow.
2. Unicode handling: What about accented characters? Emojis? Right-to-left text?
3. Case sensitivity: Should “FOX” match “fox”?
4. Locale-specific rules: In Turkish, uppercase ‘i’ becomes ‘İ’, not ‘I’. Suddenly, your “simple” string search needs to handle internationalization, performance optimization, and a bunch of edge cases you never considered. Meanwhile, the standard library’s implementation has been battle-tested by millions of developers and handles all this complexity for you.

The Security Nightmare

This is where things get really scary. When you roll your own algorithms, especially anything related to security, you’re essentially playing Russian roulette with five bullets loaded. Consider this “clever” custom encryption someone might write:

def custom_encrypt(data, key):
    """
    Super secure encryption! 
    Nobody will figure this out!
    """
    result = ""
    for i, char in enumerate(data):
        # XOR with key, add position for extra security
        encrypted_byte = ord(char) ^ ord(key[i % len(key)]) ^ i
        result += chr(encrypted_byte % 256)
    return result
def custom_decrypt(data, key):
    result = ""
    for i, char in enumerate(data):
        decrypted_byte = ord(char) ^ ord(key[i % len(key)]) ^ i
        result += chr(decrypted_byte % 256)
    return result
# Look, it works!
secret = "Attack at dawn"
key = "mysecretkey"
encrypted = custom_encrypt(secret, key)
decrypted = custom_decrypt(encrypted, key)
print(f"Original: {secret}")
print(f"Decrypted: {decrypted}")

This might look clever to the untrained eye, but it’s cryptographically worthless. The XOR cipher is trivially breakable, the position-based modification creates patterns, and the whole thing falls apart under basic cryptanalysis. A professional cryptographer would crack this faster than you can say “security through obscurity.” Real encryption algorithms like AES have been scrutinized by the world’s best cryptographers for decades. They’ve survived countless attack attempts and peer reviews. Your weekend project? Not so much.

When Existing Solutions Fail You (Spoiler: It’s Rarer Than You Think)

Now, I’m not saying there’s never a time to write your own algorithm. But these situations are rarer than finding a bug-free codebase on the first try. Here are the legitimate cases: Novel Problem Domains: You’re working on something genuinely new where existing algorithms don’t apply. Maybe you’re doing cutting-edge research in machine learning or working on a problem that’s never been solved before. Extreme Performance Requirements: You’ve profiled extensively, identified a specific bottleneck, and determined that the existing solutions genuinely can’t meet your performance needs. Note: “I think it might be faster” doesn’t count. Highly Specialized Constraints: You’re working on embedded systems with severe memory constraints, real-time systems with hard deadlines, or other specialized environments where general-purpose algorithms don’t fit. But even in these cases, the right approach is usually to start with existing algorithms and modify them, not to build from scratch.

The Smart Developer’s Algorithm Strategy

So what should you do instead? Here’s your battle plan:

Step 1: Reach for the Standard Library

Most programming languages come with excellent standard libraries that include well-implemented, thoroughly tested algorithms. Python’s sorted(), Java’s Collections.sort(), and C++’s std::sort are marvels of engineering that handle edge cases you haven’t even thought of.

# Instead of rolling your own sorting
def my_quicksort(arr):  # Don't do this
    # 50 lines of code with subtle bugs
    pass
# Just use the standard library
data = [3, 1, 4, 1, 5, 9, 2, 6, 5]
sorted_data = sorted(data)  # That's it. You're done.

Step 2: Look for Established Libraries

If the standard library doesn’t have what you need, there’s probably a well-maintained library that does. NumPy for numerical computing, requests for HTTP, pandas for data manipulation—these libraries exist because smart people solved hard problems and shared their solutions.

Step 3: Understand Before You Replace

If you absolutely must implement something custom, start by deeply understanding the existing solutions. What trade-offs did they make? What edge cases do they handle? What can you learn from their approach?

Step 4: Measure Everything

If you think you can do better, prove it. Write benchmarks, compare performance across different inputs, and test edge cases. You might discover that your “optimization” only works for very specific scenarios.

import time
import random
def benchmark_sorting():
    # Test different data sizes and patterns
    sizes = [100, 1000, 10000]
    patterns = ["random", "sorted", "reverse", "mostly_sorted"]
    for size in sizes:
        for pattern in patterns:
            if pattern == "random":
                data = [random.randint(1, 1000) for _ in range(size)]
            elif pattern == "sorted":
                data = list(range(size))
            # ... other patterns
            # Time your custom implementation vs standard library
            start = time.time()
            sorted(data.copy())
            standard_time = time.time() - start
            # Compare with your implementation
            # (Spoiler: the standard library will probably win)

The Maintenance Burden Nobody Talks About

Here’s something they don’t teach you in algorithms class: every custom algorithm you write is a pet you have to feed for the rest of its life. Unlike that Tamagotchi you killed in middle school, this one doesn’t just disappear when you forget about it—it becomes a source of bugs, confusion, and late-night debugging sessions. When you use a standard library algorithm, bugs get fixed by people much smarter than us. When you write your own, guess who’s on call when it breaks? That’s right—future you, who will curse past you’s hubris while trying to remember why you thought that clever optimization was a good idea at 2 AM on a Friday.

The Learning Paradox

“But how will I learn if I don’t implement things myself?” I hear you cry. And you’re not wrong—implementing algorithms is a great way to understand them. But there’s a crucial distinction between learning implementations and production implementations. Write that red-black tree to understand how it works. Implement quicksort to appreciate the elegance of divide-and-conquer. Build a hash table to understand collision resolution strategies. But do it in a separate project, with the understanding that you’re learning, not building production code.

Real Talk: Your Algorithm Probably Sucks

I know this sounds harsh, but somebody needs to say it: your first attempt at implementing a complex algorithm probably has bugs. Maybe subtle ones that only show up with weird data. Maybe performance issues that only matter at scale. Maybe security vulnerabilities that won’t be discovered until it’s too late. The algorithms in standard libraries have been used by millions of developers, tested with billions of inputs, and refined over decades. They’ve been through more code reviews than a junior developer’s first pull request. Your weekend implementation, no matter how clever, probably hasn’t.

When You Absolutely Must Go Custom

Alright, alright. Sometimes you really do need to write a custom algorithm. Maybe you’re working on something genuinely novel, or you’ve exhausted all existing options. If that’s the case, here’s how to do it without shooting yourself in the foot:

1. Start Small: Implement the simplest version that could possibly work.
2. Test Extensively: Write more test cases than you think you need. Test edge cases, invalid inputs, and boundary conditions.
3. Benchmark Early: Compare your implementation against alternatives from the start.
4. Document Everything: Future you will thank present you for explaining why you made certain choices.
5. Get Code Reviews: Have other developers look at your implementation. Fresh eyes catch bugs that you’ll miss.
6. Plan for Replacement: Design your code so that your custom algorithm can be easily swapped out when you inevitably find something better.

The Bottom Line

Here’s the uncomfortable truth: most of us aren’t algorithm researchers. We’re business logic developers, web app builders, and mobile app creators. Our value isn’t in crafting the perfect sorting algorithm—it’s in solving real problems for real users. Every hour you spend debugging your custom hash function is an hour you’re not spending on features that actually matter to your users. Every bug in your homegrown encryption is a potential security breach. Every performance issue in your custom data structure is a user waiting longer for their results. The world doesn’t need another sorting algorithm (unless you’re doing legitimate research). It needs developers who can choose the right tools, implement clean solutions, and ship reliable software. So next time you’re tempted to write your own algorithm, take a step back. Ask yourself: “Am I solving a genuinely novel problem, or am I just stroking my ego?” If it’s the latter, do yourself (and your future debugging self) a favor—use the library. Your users won’t care that you used std::sort instead of rolling your own. They’ll care that your app works reliably, performs well, and doesn’t lose their data. And isn’t that what really matters? Now, who’s ready to argue with me in the comments about why their custom algorithm is definitely the exception to this rule? 😉