Start with just one solution—understand it fully instead of guessing
⚠️ Friendly Disclaimer: This framework is NOT for people who solve hards under 5 min. This is for us—the regular, normal people—who spent a lot of time brainrotting and now need 15+ minutes to write down a decent brute force solution. If you're some leetcode genius, this article will bore you.
If your brain is rotting—from doom-scrolling, context-switching, or general mental entropy—attempting to solve LeetCode problems from scratch is training your brain on failure patterns, not solution patterns.
Core insight: Like a machine learning model needs training data to recognize patterns, your brain needs exposure to solutions before it can generate good ones. When you have zero training data, you're not problem-solving—you're speculating. And speculation with broken heuristics produces garbage.
The traditional advice is "struggle is good for learning." That's true when your cognitive machinery is functioning. But when your brain is mush, struggle reinforces the mush. You're practicing being bad at problem-solving.
Use binary search on your capacity to solve a medium problem in under 15 minutes. Based on your level of brainrot, here's how many solutions you should consume before attempting to solve problems independently:
Low Brainrot (Solve medium in <15 min)
Your problem-solving scaffolding is intact. Consume 2-3 solutions per pattern, then start solving.
Medium Brainrot (15-30 min for medium)
You need more training data. Consume 7-10 solutions per pattern with spaced repetition before attempting problems.
High Brainrot (30+ min or can't solve medium)
Significant rewiring needed. Consume 15-20 solutions per pattern with active reviews at Day 1, 2, 4, 7 before solving.
Extreme Brainrot (Can't conceptualize brute force)
Neural pathways need complete reconstruction. Consume 25-30+ solutions per pattern. Don't attempt solving for at least 3-4 weeks of active review.
How to assess yourself using binary search:
Let's look at a concrete example: LeetCode 286 - Walls and Gates
You are given an m×n grid where:
-1 represents a wall or obstacle0 represents a gateINF represents an empty room (use 2147483647)Fill each empty room with the distance to its nearest gate. If impossible to reach a gate, leave it as INF.
Input: INF -1 0 INF INF INF INF -1 INF -1 INF -1 0 -1 INF INF Output: 3 -1 0 1 2 2 1 -1 1 -1 2 -1 0 -1 3 4
You stare at this problem. Your brain, with zero training data on graph traversal, generates this thought process:
"Okay, so I need to find distances... maybe I can loop through each empty room, and for each room, search outward until I hit a gate?"
The Brute Force Code:
def wallsAndGates(rooms):
if not rooms:
return
m, n = len(rooms), len(rooms[0])
INF = 2147483647
# For each empty room, find nearest gate
for i in range(m):
for j in range(n):
if rooms[i][j] == INF: # Empty room
# BFS from this room to find nearest gate
queue = [(i, j, 0)] # (row, col, distance)
visited = set()
visited.add((i, j))
min_dist = INF
while queue:
x, y, dist = queue.pop(0)
# Found a gate
if rooms[x][y] == 0:
min_dist = min(min_dist, dist)
break # BFS guarantees shortest path
# Explore neighbors
for dx, dy in [(0,1), (1,0), (0,-1), (-1,0)]:
nx, ny = x + dx, y + dy
if (0 <= nx < m and 0 <= ny < n and
(nx, ny) not in visited and
rooms[nx][ny] != -1): # Not wall
visited.add((nx, ny))
queue.append((nx, ny, dist + 1))
rooms[i][j] = min_distYou came up with this yourself? You're not as brainrotted as you think. You have basic problem-solving scaffolding. Now go read the optimal solution and see the insight you missed.
What's wrong with this:
"Maybe I need to optimize... add memoization? Try different pruning?"
What just happened:
🚨 Ultimate Brainrot Check:
If you can't even conceptualize the brute force approach above—if you can't think through "loop through empty rooms, BFS from each to find nearest gate"—that's ultimate brainrot. You're not even at the level of generating bad solutions. You're at zero training data, no problem-solving scaffolding. This is precisely when you should stop trying and consume solutions instead.
Instead of guessing, you go straight to a high-quality solution. Here's what you consume:
Key Insight: Multi-Source BFS
Instead of starting BFS from each empty room, start BFS from all gates simultaneously. This way, you only traverse the grid once.
Algorithm:
Time Complexity: O(mn)
Each cell is visited at most once.
Code:
from collections import deque
def wallsAndGates(rooms):
if not rooms:
return
m, n = len(rooms), len(rooms[0])
queue = deque()
# Step 1: Add all gates to queue
for i in range(m):
for j in range(n):
if rooms[i][j] == 0:
queue.append((i, j))
# Step 2: Multi-source BFS
directions = [(0,1), (1,0), (0,-1), (-1,0)]
while queue:
x, y = queue.popleft() # O(1) instead of pop(0)
for dx, dy in directions:
nx, ny = x + dx, y + dy
# Check bounds and if it's an empty room
if 0 <= nx < m and 0 <= ny < n and rooms[nx][ny] == INF:
rooms[nx][ny] = rooms[x][y] + 1
queue.append((nx, ny))What you just learned:
Questions You MUST Ask While Understanding This Solution:
1. What is BFS? (For the ultimate brainrotted)
2. Why BFS and not DFS?
3. Why use a queue? Why not a deque?
4. What data do I need to persist as I'm solving?
5. Why start from gates instead of empty rooms?
6. How does the distance calculation work?
Next Steps:
The Difference:
Scenario A: You spent 45 minutes reinforcing bad problem-solving habits. You learned nothing about BFS, nothing about multi-source search, nothing transferable.
Scenario B: You spent 15 minutes absorbing a high-quality solution. You now have training data. Next time you see "multiple sources, shortest path," your brain will fire: "multi-source BFS." You didn't guess—you learned.
Here's the process:
Understand what it's asking. Notice the constraints, the input/output format. Don't try to solve it.
Find a well-explained solution (NeetCode, LeetCode Discuss, etc.). Read it fully.
Ask yourself:
Type it out from memory. If you forget, look back. Repeat until you can implement it without looking.
Keep a note:
Don't jump to a different pattern yet. Find 2-3 more problems with the same pattern. Consume their solutions. Reinforce the training data.
This is where most people fail. Consuming solutions once is not enough. Your brain will forget. You need active review with spaced repetition.
The Schedule:
What is Active Review?
✓ Active Review (Good):
✗ Passive Review (Useless):
Why This Works:
⚠️ Without Spaced Active Review:
You'll consume 50 solutions, feel like you're learning, and then forget everything within 2 weeks. You'll see the same problem type in an interview and draw a blank. "I know I've seen this before, but..." → That's passive consumption without active review. Your brain never actually learned the pattern—it just recognized it temporarily.
Practical Implementation:
When to Start Solving:
After you've consumed 5-10 solutions of the same pattern and done spaced active reviews on the first 3-5, then try solving a new problem with that pattern. Your brain now has training data that's actually consolidated in long-term memory. You'll recognize: "Oh, this is multi-source BFS" and implement it. Before, you were guessing. Now, you're applying learned patterns that you've actively rehearsed.
Consuming solutions is powerful, but it has a dangerous failure mode: confusing similar-looking solutions when you don't actually understand the underlying problem structure.
Example: Walls and Gates vs Longest Increasing Path in a Matrix
Both are grid problems. Both use DFS/BFS. Both involve exploring neighbors. But they solve fundamentally different problems:
Walls and Gates (Multi-Source BFS)
Longest Increasing Path in a Matrix (DFS + Memoization)
What Goes Wrong Without Understanding:
How to Consume Solutions Correctly:
Side-by-Side Code Comparison:
Walls and Gates (Multi-Source BFS)
def wallsAndGates(rooms):
if not rooms:
return
m, n = len(rooms), len(rooms[0])
queue = deque()
# Initialize: Add ALL gates to queue
for i in range(m):
for j in range(n):
if rooms[i][j] == 0: # Gate
queue.append((i, j))
directions = [(0,1), (1,0), (0,-1), (-1,0)]
# BFS from all gates simultaneously
while queue:
x, y = queue.popleft()
for dx, dy in directions:
nx, ny = x + dx, y + dy
# Only visit empty rooms (INF)
if 0 <= nx < m and 0 <= ny < n and rooms[nx][ny] == INF:
rooms[nx][ny] = rooms[x][y] + 1 # Distance from gate
queue.append((nx, ny))
# Key: Start from sources (gates), propagate outward once
# Result: Each room gets shortest distance to nearest gateLongest Increasing Path (DFS + Memo)
def longestIncreasingPath(matrix):
if not matrix:
return 0
m, n = len(matrix), len(matrix[0])
memo = {}
def dfs(x, y):
# Return cached result if exists
if (x, y) in memo:
return memo[(x, y)]
max_length = 1 # Current cell counts as length 1
directions = [(0,1), (1,0), (0,-1), (-1,0)]
# Try all 4 directions
for dx, dy in directions:
nx, ny = x + dx, y + dy
# Only visit if in bounds AND strictly increasing
if (0 <= nx < m and 0 <= ny < n and
matrix[nx][ny] > matrix[x][y]):
length = 1 + dfs(nx, ny) # Recursively explore
max_length = max(max_length, length)
memo[(x, y)] = max_length # Cache result
return max_length
# Must try DFS from EVERY cell to find global max
result = 0
for i in range(m):
for j in range(n):
result = max(result, dfs(i, j))
return result
# Key: Start from every cell, explore all paths, cache results
# Result: Find the single longest increasing path in entire gridCritical Differences:
| Aspect | Walls and Gates | Longest Increasing Path |
|---|---|---|
| Starting points | All gates at once (multi-source) | Every cell individually |
| Traversal | BFS (queue, iterative) | DFS (recursion stack) |
| Visit condition | Visit unvisited rooms (INF) | Visit if value is increasing |
| Goal | Find shortest distance (minimize) | Find longest path (maximize) |
| Data structure | Queue (deque) | Recursion + memo dict |
| Visits per cell | Once (BFS guarantees shortest) | Multiple times until memoized |
| Result storage | Modify grid in-place | Separate memo dictionary |
| Why this approach? | BFS explores level-by-level = shortest path naturally | DFS explores all paths + memo avoids recomputation |
Why You Can't Swap Them:
The Pattern Library Should Store DISTINCTIONS:
⚠️ The Real Danger:
If you consume solutions without understanding the problem structure and why this solution fits this problem, you'll build a library of superficially similar patterns that you'll misapply. You become the person who says "I know BFS!" but then tries to use it for every grid problem, even when DFS+memo or backtracking is correct. This is worse than having zero training data—you have corrupted training data.
Final Word:
Don't attempt to solve problems with zero training data. Your brain needs examples before it can generalize. Start with just one solution—understand it fully, deeply, completely. Then do another. And another. Build your pattern library. Then solve. This isn't cheating. This is how learning actually works.