The Blind 75 is a curated set of LeetCode problems designed to cover the patterns that show up again and again in interviews. If you work it like a system—not like a checklist—you’ll build speed, correctness, and confidence.
This guide teaches you how to practice the Blind 75 end-to-end: how to pick the right learning order, how to rehearse solutions without memorizing, and how to troubleshoot when your approach breaks. You’ll also get concrete schedules, a scoring method, and a “what to do next” playbook.
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Whether you’re preparing for your first technical interview or leveling up for a timed round, treat this as your reference manual. Follow the steps, measure your results, and iterate.
What the Blind 75 Is (and Why It Works)
The most popular version of the Blind 75 is a list of 75 LeetCode problems spanning core topics like arrays, strings, hash maps, two pointers, binary search, trees, heaps, dynamic programming, and graphs. People call it “blind” because the problems are representative even if you don’t know the exact interview questions in advance.
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What makes it effective isn’t that it guarantees your interviewer will pick one of the same prompts. It’s that these problems collectively force you to master the underlying templates: how to shrink search space, how to detect invariants, how to reason about states, and how to implement standard data-structure techniques reliably.
Prerequisites Before You Start
1) Pick a single language and stick to it
LeetCode practice goes fastest when your syntax, data structures, and standard library habits are consistent. Choose one language for the entire cycle.
- Recommended for speed: Python, Java, or JavaScript for rapid iteration.
- Recommended for structured practice: Java or C++ if you want strict types and strong debugging signals.
- If you use JavaScript: keep an eye on performance pitfalls with arrays, sorting, and frequent string concatenations.
2) Know your baseline
Before solving anything, run a quick diagnostic. Pick 6 problems (easy/medium spread) from the areas you’re weakest in and time yourself.
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- Track time to correct submission.
- Write down the failure mode: wrong assumption, missing edge case, bug in data structure usage, or algorithm not fully formed.
3) Set a realistic target
Most candidates don’t need “solve all 75 perfectly.” They need “solve the majority confidently, with repeatable techniques.”
A practical goal for a typical 6–10 week prep cycle:
- Target 45–60 problems to full independent solutions.
- Target an additional 15–25 problems to partial readiness (you can complete with hints).
- Be able to explain core patterns in plain language (not just code).
How to Use the Blind 75 Correctly
Common practice error: reading a solution too early, then rewriting it until it works. That gives you short-term code output, not interview-ready problem-solving.
The correct usage is closer to deliberate practice: attempt first, struggle productively, then review, then retrain your muscle memory with variants.
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Step 1: Categorize each problem by pattern
Don’t treat the list as 75 random questions. For each problem, label the primary skill:
- Two pointers
- Sliding window
- Binary search (and variants)
- DFS/BFS (trees/graphs)
- Heap / priority queue
- Hash map frequency counting
- Dynamic programming (1D/2D; knapsack-like; subsequences)
- Tree traversal + recursion invariants
Step 2: Define your “minimum acceptable” solution
Before looking at the editorial, decide what you want to achieve. A minimum acceptable solution is one that is logically correct, handles constraints, and uses an appropriate approach.
Example minimums:
- For sliding window problems: you can explain window validity and update rules.
- For binary search: you can state the monotonic property and your boundary choice.
- For DP: you can name the state definition and recurrence.
Step 3: Review only after a timed attempt
Use a strict attempt window. If you’re always reading solutions within minutes, your brain never builds the solution path.
A good rule:
- Easy: 15–25 minutes before checking.
- Medium: 25–40 minutes.
- Hard (if included): 40–60 minutes, then use editorial to recover.
A Repeatable Practice System (From Reading to Submission)
If you want consistent improvement, practice with the same workflow every time. It reduces cognitive overhead and prevents “randomness” from dominating outcomes.
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Workflow: Attempt → Skeleton → Verify → Code → Review
- Attempt: Write a brief plan in 5–10 bullet points. Don’t write full code yet.
- Skeleton: Draft function signature and the core loop/recurrence structure. Leave tricky pieces blank if needed.
- Edge cases: List 3–5 edge cases specific to the prompt (empty input, duplicates, extremes, off-by-one).
- Verify logic: Run a mental test on 2 examples (one typical, one edge).
- Code: Implement, then immediately re-read invariants (window bounds, pointer updates, DP transitions).
- Dry run: Step through the code with one small case where you know the answer.
- Post-mortem: If it fails, classify the error: wrong algorithm, wrong boundary, incorrect update rule, or bug in implementation.
What “done” means for each problem
Only mark a problem as complete when you can do one of these without looking:
- Reconstruct the approach from the problem statement.
- Explain the invariant/state in words.
- Produce correct code within a reasonable time window (often 20–40 minutes for mediums, depending on your skill).
Category-First Strategy: Map Problems to Patterns
Blind 75 works best when you practice pattern recognition. When you see a new prompt, you shouldn’t start from scratch. You should recognize it as a known template and then adjust details.
Arrays and strings: focus on invariants
Many array/string problems become easy when you identify what must remain true while scanning. Examples include sortedness checks, frequency constraints, and “window validity.”
- Two pointers problems become manageable when you track where pointers move and why.
- Hash map problems become stable when you distinguish presence vs counts.
- String problems often hinge on off-by-one boundaries and character indexing.
Binary search: treat boundaries like part of the solution
Binary search mistakes are rarely about comparisons. They’re about boundary choices: what you return, what your loop condition is, and whether you’re searching for first/last occurrences.
When you practice, always write down:
- The monotonic condition (why the answer space is ordered).
- The invariant for low and high.
- The termination rule and what you return.
Trees and graphs: master traversal contracts
Trees are mostly about traversal + state. Graph problems often reduce to reachability (BFS/DFS), shortest path (Dijkstra/BFS), or connected components.
- For DFS: define what your recursion returns (boolean? subtree size? max depth?).
- For BFS: define the layer meaning (distance, steps, or “wavefront” traversal).
- For invariants: keep track of visited logic and what counts as “processed.”
Heaps and priority queues: use them for “best candidate so far”
If your problem asks for top-k, merging, scheduling, or picking the smallest/largest repeatedly, you’re often looking at a heap approach. Practice the push/pop pattern until it’s automatic.
Dynamic programming: state definitions beat memorization
DP fails when candidates memorize recurrences without understanding state. For each DP problem, write the state in one sentence:
- dp[i] means what exactly for prefix/suffix?
- What transition moves from earlier to later states?
- What is the objective: maximize, minimize, or boolean feasibility?
If you can answer those questions, coding becomes mechanical.
Sample Study Plans (Pick One and Stick to It)
Use a plan that matches your timeline and availability. The schedule matters less than execution quality: attempt first, review, practice variants, and track outcomes.
Plan A: 6-week acceleration (daily practice, moderate volume)
Best when you can do 60–90 minutes per day.
- Week 1–2: Arrays, strings, hash maps, two pointers (25 problems).
- Week 3: Sliding window + binary search variants (15 problems).
- Week 4–5: Trees + BFS/DFS + heaps (20 problems).
- Week 6: DP fundamentals + review your weakest 10 patterns (15 problems).
Rule: For each day, pick 1 main problem and 1 follow-up practice problem (a close neighbor pattern).
Plan B: 10-week interview prep (less pressure, deeper retention)
Best when you can do 45–75 minutes per day or have busy weekdays.
- Weeks 1–3: 20 problems (arrays/strings/hash maps/two pointers).
- Weeks 4–6: 30 problems (binary search + sliding window + heaps).
- Weeks 7–9: 25 problems (trees/graphs/advanced traversal).
- Week 10: Repeat the hardest 12 problems and re-solve without looking.
Rule: Revisit each solved pattern after 7–10 days with one new problem or a timed re-attempt.
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Best for candidates who already know basic DS&A and need pattern speed.
- Day 1–8: 20 fast mediums (focus on binary search, two pointers, sliding window).
- Day 9–16: 20 tree/graph problems with templates (DFS/BFS/visited).
- Day 17–21: 15 DP + re-solve the worst mistakes under time limits.
Rule: You’ll accept partial completeness; don’t try to “perfect” everything.
How to Write Better Solutions Under Time Pressure
Interview performance is half algorithm, half engineering discipline. If your code is messy, you’ll lose time debugging instead of thinking.
Use an interview-friendly structure
- Read constraints and restate them (in code comments or mentally).
- Name variables with intent:
left,right,windowCount,visited. - Isolate tricky logic into small blocks (helper functions for DFS, recurrence functions for DP).
Practice “no-look coding” for your top patterns
You don’t need to memorize every solution. You need to be able to reproduce the template quickly. For example:
- Binary search template: boundaries, mid update, and return strategy.
- Sliding window template: expand right, validate, shrink left.
- Tree DFS template: return value definition and base cases.
Respect time limits with a triage mindset
At real interviews, time management matters. Use this triage:
- If you don’t have an approach after ~30% of the time: reduce scope—search for a known pattern (two pointers, binary search, BFS).
- If you have an approach but keep failing: shift to debugging—check boundaries, loop conditions, and update order.
Tracking Progress Like a Serious Candidate
Blind 75 practice becomes dramatically more effective when you track what’s actually going wrong. “I solved it” isn’t enough.
A simple scoring model
For each problem, record:
| Field | Values to log |
|---|---|
| Attempt time | minutes until you had a full plan |
| Fix time | minutes to correct code after first attempt |
| Outcome | Accepted / Wrong Answer / Runtime Error / Time Limit |
| Error type | Algorithm / Boundary / State definition / Implementation bug |
| Pattern label | sliding window, DP, BFS, etc. |
Review your log weekly and choose the pattern with the most repeated error type. That becomes your focus area.
Decide when to revisit vs move on
A good policy:
- If you solved without looking: schedule a revisit in 7–14 days.
- If you needed editorial help but understood it: revisit in 3–7 days.
- If you failed due to boundaries/bugs: revisit immediately after you fix your mistake.
Troubleshooting When You Get Stuck
Stuck is normal. What matters is your recovery strategy. Here are targeted fixes that work across most problem types.
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Do a structured debug instead of random changes.
- Re-run with the smallest counterexample you can find (often length 0–3 input).
- Check invariants after each loop iteration (window bounds, pointer movement, visited marking).
- Validate boundary conditions:
i == 0,i == n-1, empty arrays, single element. - Confirm the return value matches the problem requirement (first/last/closest/min/max).
Problem fails with Runtime Error
Most runtime errors in practice are index issues, recursion depth, or data-structure misuse.
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- Verify array bounds when you access
i-1ori+1. - Check recursion base cases for trees/DP.
- Ensure heap comparator logic is correct (min-heap vs max-heap expectations).
Problem fails with Time Limit Exceeded (TLE)
Time issues usually mean your approach is correct but not efficient, or you wrote an accidental O(n^2) in a loop.
- Estimate your complexity from the loops (count nested loops first).
- Replace repeated scanning with precomputed maps/arrays.
- For DP: confirm you’re using memoization or tabulation, not recomputing states.
- For strings: avoid costly concatenation inside loops when possible.
Your approach is wrong from the start
When your logic doesn’t fit the constraints, don’t brute-force code harder. Switch patterns.
Try this decision checklist:
- Do you need the “first/last position” of something? → binary search variant.
- Do you need a condition over a contiguous range? → sliding window / two pointers.
- Do you need to visit states/paths? → BFS/DFS.
- Do you need best value with overlapping subproblems? → DP.
Common Mistakes That Prevent Improvement
Most candidates plateau because of a few repeat errors. Here are the big ones to actively avoid.
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Speed comes from building solution paths in your head. If you skip the attempt window, your future self can’t reproduce the approach.
2) Never re-solving
Blind 75 is not a “solve once” list. You must re-solve key patterns to make them second nature.
3) No explicit edge case list
If you don’t list edge cases before coding, you’ll “discover” them through bugs and rework. That slows you down every cycle.
4) Confusing similar templates
Examples:
- Binary search vs two pointers: binary search needs monotonicity; two pointers works with ordering/invariants along the array.
- Sliding window vs two pointers: sliding window typically moves one boundary each step while maintaining validity.
- DFS recursion vs iterative BFS: trees and graphs often need different contracts for correctness.
5) Not measuring time to approach
Interview improvement requires reducing time to a correct plan. If you only measure acceptance, you’ll miss the real bottleneck.
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Blind 75 vs Alternatives (When You Should Expand)
The Blind 75 is great for core coverage, but real interviews can push beyond it. Use expansion strategically.
When to expand to additional problems
- You’re consistently TLE’ing: add more practice on complexity patterns and DP optimization.
- Your failures are boundary-heavy: add more problems from the same pattern cluster, not random topics.
- Your interview format includes system-style constraints: add graph shortest path / union-find and more rigorous DP.
How to expand without losing focus
Don’t add 200 problems. Add 10–20 targeted ones that match your weak patterns, then rework the hardest 10 Blind 75 problems until they’re stable.
Final Thoughts
Mastering the Blind 75 is less about finishing 75 problems and more about building a repeatable problem-solving workflow: attempt first, label patterns, implement carefully, and debug with invariants. When you track your errors by type, you’ll improve faster than anyone who just keeps grinding.
Pick a schedule, practice with a consistent system, and revisit your hardest patterns. Do that for a few weeks and the list stops feeling like a mountain—and starts feeling like a set of tools you can apply under interview pressure.
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