🧠 Master these 6 advanced algorithms:

🌳 Segment Tree
https://rulcode.com/problem/segment-tree

📈 Fenwick Tree
https://rulcode.com/problem/fenwick-tree

⚡ Sparse Table
https://rulcode.com/problem/sparse-table

🔍 KMP
https://rulcode.com/problem/kmp

🧩 Rabin-Karp
https://rulcode.com/problem/rabin-karp

🔗 Union by Rank
https://rulcode.com/problem/union-by-rank

Most developers never go beyond the basics.

These are the concepts that separate good problem solvers from great ones.

💡 Build intuition.
🚀 Crack interviews.

Start thinking.
Start visualizing.

#DSA #Algorithms #LeetCode #Programming #CompetitiveProgramming #Rulcode

Many candidates jump straight into coding.

The strongest candidates usually spend more time understanding the problem than typing code.

That's why we're building Rulcode around a simple learning cycle:

1. Read
2. Visualize
3. Think
4. Plan
5. Code
6. Review

The goal isn't to solve today's problem.

The goal is to recognize tomorrow's problem.

Most developers read tutorials.

The best developers read how real products are built.

Here are some engineering blogs worth bookmarking:

🔴 Netflix Tech Blog
https://netflixtechblog.com

🟠 Cloudflare Blog
https://blog.cloudflare.com

🔵 Stripe Engineering
https://stripe.com/blog/engineering

⚫ GitHub Engineering
https://github.blog/category/engineering

🟢 Vercel Blog
https://vercel.com/blog

🏠 Airbnb Engineering
https://medium.com/airbnb-engineering

🚕 Uber Engineering
https://www.uber.com/en-IN/blog/engineering

📊 PostHog Blog
https://posthog.com/blog

🤖 Anthropic Engineering
https://www.anthropic.com/engineering

♾️ Meta Engineering
https://engineering.fb.com

Why read them?

✅ System Design
✅ Scalability
✅ Performance Optimization
✅ Architecture Decisions
✅ Engineering Culture
✅ Real-world Problem Solving

Learn from teams building products used by millions (and sometimes billions) of users.

Which engineering blog do you read regularly? 👇

#SoftwareEngineering #Programming #SystemDesign #WebDevelopment #Engineering #Developers #BuildInPublic #Rulcode

🟢 Next.js
🟢 TypeScript
🟢 Supabase
🟢 Google Cloud Run
🟢 Judge0
🟢 PostHog

A modern stack for modern developers. 🚀

#Rulcode #TechStack #SoftwareEngineering

👨 Day 1:
"I just need to solve more problems."

👨 Day 30:
"Why does everyone keep talking about patterns?"

👨 Day 60:
"Wait... this looks similar to another problem."

👨 Day 90:
"I know when to use Hash Maps, Two Pointers, and Sliding Window."

👨 Day 120:
"I read constraints before writing code."

👨 Day 180:
"I think about complexity before implementation."

👨 Day 365:
"I don't solve problems anymore.

I solve patterns."

🚀 That's the real DSA journey.

#Rulcode #DSA #CodingInterview #LeetCode #SoftwareEngineer #FAANG

Not just to solve problems, but to understand them through visualization.

Thanks for the feedback, Ketan Raul!

🔗 https://rulcode.com/problem/dutch-national-flag

#Rulcode #CodingInterview #ProblemSolving

Rotate Array using O(1) Extra Space

The elegant solution uses 3 reversals:

1. Reverse the entire array
2. Reverse the first k elements
3. Reverse the remaining elements

Example:

[1,2,3,4,5,6,7]

→ [7,6,5,4,3,2,1]

→ [5,6,7,4,3,2,1]

→ [5,6,7,1,2,3,4]

Time: O(n)

Space: O(1)

Challenge:

https://rulcode.com/problem/rotate-array

It was great to understand visually if stuck in Problems, it look kind of simple at first but it takes some kind of logic building here, but good problem

Try here
Dutch National Flag Visualizer & Solution | Rulcode

It's building the feed.

Design Twitter is a great interview problem because it combines:

• HashMap
• HashSet
• Max Heap

to generate the most recent tweets efficiently.

Visualized step-by-step:

https://rulcode.com/problem/design-twitter

The first solution most people think of is:

• store rows in a set
• store columns in a set

and then update the matrix.

Works fine.

But the optimal solution is much more interesting.

Instead of creating extra storage, you use:

• first row as column markers
• first column as row markers

The matrix effectively stores its own metadata.

That's the key insight that makes the solution O(1) extra space.

We added a step-by-step visualization on Rulcode showing:
• where markers are placed
• how rows are identified
• how columns are identified
• how the final matrix is built

Problem:
https://rulcode.com/problem/set-matrix-zeroes

One of my favorite matrix interview problems because the trick is simple but memorable.

Most people memorize DSA solutions.

Top engineers understand:
✅ Why it works
✅ Which pattern applies
✅ How to derive it from scratch

We'll teach the thinking process, not just the code.

🎥 First video below.

Practice: https://rulcode.com

#DSA #LeetCode #CodingInterview #Programming #SoftwareEngineer

Most people learn Binary Search once and think they're done.

The real challenge is recognizing Binary Search when the problem doesn't look like Binary Search.

This weekend, try solving:

• Search in Rotated Sorted Array
• Missing Number
• Longest Increasing Subsequence
• Time-Based Key Value Store

Together they teach:

• Modified Binary Search
• Search Space Reduction
• Binary Search on Time
• Binary Search + DP Optimization

We've also added visualizations on Rulcode so you can see exactly how the algorithms work step-by-step.

Practice here:

https://rulcode.com/dsa/query?topic=Binary%20Search

I think Last Stone Weight is a great beginner heap problem because the intuition is easy to understand.

The process is:

• Take the two heaviest stones
• Smash them together
• If one survives, put it back
• Repeat

The challenge is efficiently finding the largest stones every time.

That's where a Max Heap becomes the perfect data structure.

We added a full visualization on Rulcode showing:

• heap state after every operation
• stone collisions
• insertion of remaining weights
• complete algorithm flow

Problem:
https://rulcode.com/problem/last-stone-weight

It's much easier to understand once you can watch the heap evolve step-by-step.

This problem looks intimidating at first because of pointers, but the core idea is actually very clean.
You simply:
compare nodes from both lists
attach the smaller one
move forward
repeat
The interesting part is learning how pointers connect nodes dynamically.
We added a full visualization on Rulcode showing:
• pointer movement
• node connections
• dummy node usage
• merge progression step-by-step
Problem:
https://rulcode.com/problem/merge-two-sorted-lists

I recently added a visualization for the Sieve of Eratosthenes algorithm on Rulcode and honestly it’s one of the coolest algorithms to watch step-by-step.

Instead of checking every number manually for primality:

• start from 2
• mark all multiples as non-prime
• repeat with the next available prime

The visual flow makes the pattern instantly intuitive.

You can literally see:
• multiples getting eliminated
• prime numbers surviving
• why the algorithm is efficient
• why we stop at √n

Visualization:
https://rulcode.com/problem/sieve-eratosthenes

Really good algorithm for beginners learning number theory + optimization.

I think House Robber is one of the few DP problems where the pattern suddenly becomes intuitive.

Most people try memorizing DP formulas.

But this problem is really just:

• rob this house
• or skip it

The interesting part is how that decision propagates through the array.

We added full visualizations on Rulcode showing:
• rob vs skip decisions
• rolling DP variables
• state transitions
• optimized DP flow
• Python walkthrough

Problem:
https://rulcode.com/problem/house-robber

Honestly feels much easier to understand visually than through traditional explanations.

I used to memorize Big-O formulas without actually understanding what they meant.

What helped me was thinking in terms of growth instead of notation.

Quick mental model:

• O(1) → no matter how big input gets, runtime stays almost constant
• O(log n) → grows very slowly (Binary Search)
• O(n) → grows directly with input size
• O(n log n) → efficient sorting algorithms
• O(n²) → nested loops start becoming painful
• O(2ⁿ) → brute force becomes impossible very quickly

Real examples:

O(1)

• HashMap lookup
• Stack push/pop

O(log n)

• Binary Search
• Heap operations

O(n)

• Traversing arrays
• Linear Search

O(n log n)

• Merge Sort
• Quick Sort average case

O(n²)

• Bubble Sort
• Comparing every pair

O(2ⁿ)

• Recursive Fibonacci
• Backtracking brute force

The biggest shift for me:
Big-O is not just interview prep.

It directly impacts:

• API latency
• scaling cost
• database performance
• frontend rendering
• cloud bills

Curious:
What concept made Big-O finally “click” for you?