House Robber
Five houses, one rule: no two neighbors. Grabbing the biggest house first gets $12; the best plan gets $15. Build dp[i] from nothing, then shrink it to two variables.
Visual algorithms & problem-solving patterns
Applied Coding
Stop memorizing algorithms.
Start visualizing them.
You know how to code. Now learn how to think. Every guide starts with a problem, lets the obvious solution fail, and builds the pattern in pictures before a single line of Python.
On YouTube as @AppliedCodingHQ
Guides
One problem, told as a story
Each guide is a video and a written walkthrough of the same idea: the problem, the failure, the observation, the code.
Sudoku Solver
Fill the first empty cell with the first digit that fits and keep going. When a cell has nothing left, walk back to the last choice that can still change. The whole technique in one row of a puzzle — then the code, and its real run: 4,208 placements.
Sudoku Solver, part 2: choose smart
Same backtracking, one better decision: fill the square with the fewest options first, and remember what every row, column and box already uses. 4,208 placements become 51 — not one wrong guess.
Combination Sum
A machine wants exactly $7, and you have all the $2, $3, $6 and $7 coins you like. Choose, explore, undo — plus the one rule that keeps the same answer from showing up twice.
Two Sum II
Seven sorted price tags and $18 to spend. Brute force checks pair after pair; two pointers walking in from both ends find the answer in three moves — and each move rules out a whole row of pairs.
Maximum Average of k in a Row
The biggest average of three numbers next to each other. Adding up every range again repeats the same numbers; a window that slides — one leaves, one comes in — does the whole row in one pass. Plus the off-by-one that quietly gives the wrong answer.
How it works
An algorithm should feel necessary, not arbitrary
Nothing here opens with a definition. Every guide follows the same four beats.
The problem comes first
No “today we learn binary search”. You meet the situation that makes the technique worth inventing.
Brute force gets its turn
We try the obvious solution and watch exactly where it breaks, so the cost is something you can see.
One observation changes it
What are we repeating? What can we skip? The pattern is built in pictures first and named afterwards.
Then code, then practice
Python that follows the pictures line by line, time and space explained, and problems to solve on your own.
Patterns
The question that gives each pattern away
The hard part of a new problem is recognizing what it is. These are the patterns the guides cover, with the question to ask yourself for each.
Two Pointers1 guide
Am I looking at the same elements twice?
Backtracking3 guides
Can I choose, explore, and undo?
Dynamic Programming1 guide
Do the same subproblems keep coming back?
HashingSoon
Have I seen this before?
Sliding Window1 guide
Am I recalculating the same range?
Prefix SumSoon
Am I adding the same numbers again?
StacksSoon
Is something waiting for its future?
Binary SearchSoon
Can I cut the search space in half?
Linked ListsSoon
What if two pointers move at different speeds?
TreesSoon
Do I go deep first, or level by level?
HeapsSoon
Do I keep asking for the smallest one?
GraphsSoon
Are these things connected to each other?
GreedySoon
Do I really need to look ahead?
The checklist
Twelve questions for a problem you have never seen
This is where every guide is heading: not fifty memorized algorithms, but a way to start thinking.
- What are the constraints?
- What would brute force do?
- Why is brute force too slow?
- What work is repeated?
- Is the data sorted?
- Is there a contiguous range?
- Do I need to remember previous information?
- Are there relationships between objects?
- Am I repeatedly asking for the min or the max?
- Are there overlapping subproblems?
- Can I trade memory for speed?
- What is my complexity?
About
For people who studied the algorithms and still freeze on the problem
Applied Coding is for recent graduates and junior developers getting ready for coding interviews. You have met arrays, hash maps, recursion and dynamic programming. What nobody taught is how to look at an unfamiliar problem and tell which of them it is asking for.
That gap between knowing a technique and recognizing when to use it is the only thing this site works on. The guides are short animated stories with one narrator, real interview-style problems, and code you can run.
- Not a syntax tutorial.
- Not an encyclopedia of algorithms to memorize.
- Not solutions to copy — every guide ends with problems to try first.
“I know the algorithms, but I don’t know which one to use.”
“Give me a problem I’ve never seen, and I know how to start thinking.”
The next guide is a video first.
New guides go up on YouTube, and the written version with the full code lands here.