Funso Oyedele

I've recently decided to go back to the basics and strengthen my understanding of Computer Science fundamentals. Over the next few weeks, I'll be sharing what I learn about Data Structures & Algorithms (DSA), from Arrays and Linked Lists to Trees, Graphs, and beyond.

@nivi Well said! The best prompts look a lot like good software requirements: clear, specific, and unambiguous.

Prompting is a microcosm of taste, agency and clarity.

@natemcgrady That's the reason why I love using Google products😌

hire people who use your product team/role doesn’t matter users have a vested interest in making the product better it’s why I loved working at twitter/x and why I love working at vercel

@marclou I'd add one thing: want intentionally, not necessarily less. Ambitious goals are fine. The real gap is between intention and consistent execution. Daily actions compound; wishes don't.

The quality of your life is determined by the distance between what you want and what you do. Want less. Do more.

A junior developer writes code that works. A great engineer writes code that still works when the data grows.

📈 From Best to Worst O(1) ↓ O(log n) ↓ O(n) ↓ O(n log n) ↓ O(n²) ↓ O(2ⁿ) ↓ O(n!) The goal of every software engineer isn't just to make code work. It's to make code work efficiently when 10 users become 10 million users. That's why Big O matters.

7. O(n!) — Factorial Time The final boss. You try every possible arrangement. Example: How many ways can 10 people sit in 10 chairs? 10! = 3,628,800 possibilities 15! = 1.3 trillion possibilities This can make even powerful computers struggle.

6. O(2ⁿ) — Exponential Time Every new input doubles the work. 5 inputs → 32 possibilities 10 inputs → 1,024 possibilities 20 inputs → 1,048,576 possibilities 30 inputs → 1+ billion possibilities This gets out of control very fast.

100 items ≈ 10,000 operations 1,000 items ≈ 1,000,000 operations This is where performance starts becoming a problem.

5. O(n²) — Quadratic Time Every item interacts with every other item. Example: You have 100 students. Each student must shake hands with every other student. That's a lot of handshakes. for (...) { for (...) {} }

@sridharfyi Uiltra - a 3-in-1 multi-tenant platform for blog, web design and e-commerce uiltra.com

building something great that people haven’t discovered yet? share your startup below.

4. O(n log n) — Linearithmic Time A little more work than O(n), but much better than O(n²). Most efficient sorting algorithms live here. Examples: • Merge Sort • Quick Sort (average case) This is why large applications can sort millions of records quickly.

3. O(n) — Linear Time You check items one by one. Example: Looking for "John" in your contacts. Worst case? You scan every contact. 10 contacts → 10 checks 1,000 contacts → 1,000 checks for (const item of arr) {}

This is why Binary Search is so fast. Even with 1,000,000 items, you only need about 20 checks.

2. O(log n) — Logarithmic Time You keep cutting the problem in half. Imagine guessing a number between 1 and 1,000. You don't start from 1. You ask: "Is it greater than 500?" Then 750. Then 625. Each guess removes half the possibilities.

Example: You have a book with 10 pages or 10 million pages. Someone asks: "What's on page 1?" You open page 1 immediately. 1 step. Always. arr[0]

Common Types of Big O (From Fastest to Slowest) Understanding these will help you quickly identify whether your code will scale well or become a bottleneck as data grows. 1. O(1) — Constant Time No matter how much data you have, the work stays the same.

@euniceajim Bravo👏🏿

I'm 30. I'm black. I'm African. I'm a woman. I manage an early-stage VC fund focused on Africa. This is my experience.

@x_jinal Just me😂

GitHub thinks I'm a senior engineer. I think I'm lucky.

@vivoplt I missed copying codes from Stack overflow🥺

There was a time when we used to copy paste code from a website.

It asks: ✅ "What happens when we go from 10 users to 10 million users?" That's why companies like Google, Netflix, and Amazon care so much about it. Big O is basically a future-proofing score for your code.

Big O ignores the small details (3 and 5) and focuses on what matters most as the data grows: O(n) Because when the list gets huge, the n is what makes the biggest difference. Think of it this way Big O doesn't ask: ❌ "How fast is it on my laptop?"

🔹Big O Notation Big O is simply a way of measuring how much harder a program has to work as the amount of data increases. Imagine you have: i. 10 names to search through ii. 1,000 names iii. 1,000,000 names Big O helps us answer: "Will this program still be fast when the data