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Study Tipsby FlashRecall Team

Anki LeetCode Study System: 7 Powerful Flashcard Tricks To Finally Crack Coding Interviews Fast – Stop forgetting patterns and turn every LeetCode problem into long‑term muscle memory.

anki leetcode sounds great, but what you really want is a system to spot patterns, remember edge cases, and review LeetCode with spaced repetition that doesn...

How Flashrecall app helps you remember faster. It's free

FlashRecall anki leetcode flashcard app screenshot showing study tips study interface with spaced repetition reminders and active recall practice
FlashRecall anki leetcode study app interface demonstrating study tips flashcards with AI-powered card creation and review scheduling
FlashRecall anki leetcode flashcard maker app displaying study tips learning features including card creation, review sessions, and progress tracking
FlashRecall anki leetcode study app screenshot with study tips flashcards showing review interface, spaced repetition algorithm, and memory retention tools

Forget Anki + LeetCode For A Second… Here’s The Real Goal

You don’t actually want “Anki for LeetCode”.

You want this:

  • See a problem and instantly recognize the pattern
  • Remember edge cases without re-reading solutions 10 times
  • Stop re-solving the same easy/mediums because you forgot the trick
  • Walk into interviews feeling, “Yeah, I’ve seen this before”

That’s exactly where a good flashcard system shines. And instead of wrestling with clunky decks, you can use Flashrecall to do this in a way that actually fits how you code and review:

👉 Flashrecall app: https://apps.apple.com/us/app/flashrecall-study-flashcards/id6746757085

It’s like Anki’s brain, but:

  • Way faster to make cards (from text, screenshots, PDFs, YouTube, whatever)
  • Built-in spaced repetition and active recall
  • Modern, simple UI that doesn’t feel like homework
  • Free to start, works on iPhone and iPad, and even works offline

Let’s break down how to turn LeetCode into flashcards properly, what people get wrong with Anki, and how to do it better with Flashrecall.

Anki + LeetCode: Why It Works… And Why It Often Fails

Anki is famous in med school and language learning, so people think:

“Cool, I’ll just throw LeetCode into Anki and become cracked at interviews.”

Then reality hits:

  • Making cards takes forever
  • You end up with 500+ messy cards you don’t want to review
  • Cards are too detailed (“entire solution pasted in”)
  • You stop using it after 2 weeks

The idea is right though:

The problem is implementation.

That’s where Flashrecall makes this whole thing actually doable:

  • You can snap a screenshot of a solution or pattern and auto-generate cards
  • Paste in a LeetCode solution or explanation and Flashrecall makes the flashcards for you
  • You can even chat with your flashcards if you’re unsure about a concept and want a deeper explanation

So instead of spending more time “organizing your system” than coding, you just… study.

Step 1: What You Should Actually Turn Into Flashcards

Most people do this wrong:

They make a card for every single problem.

That’s overkill.

You don’t need to memorize 500 problems.

You need to memorize patterns, templates, and gotchas.

Here’s what does deserve a flashcard:

1. Core patterns

  • Sliding window
  • Two pointers
  • Binary search on answer
  • Backtracking
  • Topological sort
  • DP patterns (knapsack, subsequences, intervals, grids, etc.)

2. Key templates

  • “When I see X, I usually try Y first”
  • Generic code templates (like binary search, BFS, DFS, prefix sums)

3. Mistakes you made

  • Off-by-one errors
  • Missed edge cases
  • Wrong time complexity assumptions

4. Concepts you keep Googling

  • “What exactly is monotonic stack again?”
  • “How do I detect a cycle in a directed graph?”
  • “When do I use union-find vs DFS?”

With Flashrecall, you can:

  • Copy-paste a LeetCode explanation or your own notes
  • Let the app auto-generate flashcards from that text
  • Then tweak or add your own cards on top

So you focus on solving problems, not formatting decks.

Step 2: How To Write Good LeetCode Flashcards (Anki-Style, But Easier)

Whether you use Anki or Flashrecall, good card design is everything.

1. Use Simple Q&A For Patterns

“When should you use the sliding window pattern?”

“When you’re dealing with subarrays/substrings and need to track a window over a sequence, often with constraints like ‘longest’, ‘shortest’, ‘count of…’ within a range.”

You can make these in Flashrecall manually, or:

  • Paste a blog post / discussion explanation
  • Let Flashrecall auto-extract key Q&A cards

2. Turn Code Into Concept, Not Just Code

Bad card:

> Q: “What’s the solution to LeetCode 3 (Longest Substring Without Repeating Characters)?”

> A: [entire code dump]

You’ll just memorize the code, not the idea.

Better:

  • Front:

“Core idea behind ‘Longest Substring Without Repeating Characters’?”

  • Back:

Flashrecall automatically keeps track and reminds you of the cards you don't remember well so you remember faster. Like this :

Flashrecall spaced repetition study reminders notification showing when to review flashcards for better memory retention

“Use sliding window with a hash map/set to track characters in the current window. Expand right, and when you hit a duplicate, shrink left until valid again. Track max window size.”

  • Front:

“Common pitfall in implementing Longest Substring Without Repeating Characters?”

  • Back:

“Not updating left pointer correctly when duplicate appears earlier than current left, or forgetting to update max length after each expansion.”

You can also:

  • Paste your solution into Flashrecall
  • Ask it (via chat) to explain the core idea and pitfalls, then turn that into cards

3. Use “Fill in the Blank” for Templates

Example: Binary search template.

“Binary search template (sorted array) – fill in the missing parts:

`while (_____ <= _____) {

int mid = left + (right - left) / 2;

if (nums[mid] == target) return mid;

else if (nums[mid] < target) left = _______;

else right = _______;

}`”

“`left`, `right`, `mid + 1`, `mid - 1`”

Flashrecall supports this kind of structured Q&A easily, and you can generate these from code snippets by pasting them in and letting the app help you.

Step 3: Use Spaced Repetition Properly (Without Babysitting It)

Manually managing review schedules in Anki is… a lot.

Flashrecall just does it for you:

  • Built-in spaced repetition
  • Auto reminders so you don’t forget to review
  • Works offline, so you can review on the train, in bed, whatever

Here’s a simple system:

1. Warm-up: 10–15 minutes of Flashrecall reviews

2. Then do new LeetCode problems (1–3, depending on level)

3. After each problem:

  • If it was new or tricky → make 2–5 flashcards
  • If it used a known pattern → check if you already have cards for that pattern; if not, add 1–2

Over time:

  • Old patterns show up less often
  • Weak topics get pushed to you more
  • You build a dense “pattern brain” without burning out

Step 4: Example – Turning One LeetCode Problem Into Cards

Let’s say you solved LeetCode: Course Schedule (Topological Sort) and it was confusing.

Step A: Capture the Explanation Fast

You can:

  • Copy the LeetCode discussion solution
  • Or grab a screenshot of a YouTube explanation / notes

Then in Flashrecall:

  • Paste the text or drop the screenshot
  • Let it auto-generate flashcards from the content

Step B: Refine Into 3–6 High-Value Cards

Example cards:

  • Front:

“What pattern is used to solve Course Schedule (detect if you can finish all courses)?”

  • Back:

“Graph + cycle detection using topological sort (Kahn’s algorithm with in-degree) or DFS with visited states.”

  • Front:

“Steps of Kahn’s algorithm for Course Schedule?”

  • Back:

“1) Build adjacency list + in-degree array

2) Push all nodes with in-degree 0 into queue

3) Pop from queue, decrement in-degree of neighbors

4) If neighbor’s in-degree hits 0, push to queue

5) Count processed nodes; if count == total courses → possible, else cycle.”

  • Front:

“Key condition that tells you there’s a cycle in Course Schedule?”

  • Back:

“If the number of processed nodes in Kahn’s algorithm is less than total courses, there’s a cycle.”

  • Front:

“How does DFS detect a cycle in Course Schedule?”

  • Back:

“Use 3 states: 0 = unvisited, 1 = visiting, 2 = visited. If you reach a node with state ‘visiting’ during DFS, there’s a cycle.”

Now, instead of hoping you “remember it next time”, you’ve locked it into your spaced repetition system.

Step 5: Why Use Flashrecall Instead of Anki for LeetCode?

Anki is powerful, but for LeetCode specifically, Flashrecall has some big advantages:

  • Way faster card creation
  • Make flashcards instantly from:
  • Text (copy-paste from LeetCode/discussions)
  • Images (screenshots of whiteboard, notes, slides)
  • PDFs (interview prep books, cheat sheets)
  • YouTube links (algorithm tutorials)
  • Typed prompts or manual entry
  • Built-in active recall + spaced repetition
  • You just answer cards and rate how hard they were
  • Flashrecall handles the schedule and reminders
  • Chat with your flashcards
  • Stuck on a concept?
  • Ask in the app: “Explain this DP card again with a simpler example”
  • Works offline
  • Perfect for commute or dead Wi-Fi zones
  • Modern, clean, fast
  • No confusing settings jungle
  • Free to start
  • You can test your whole LeetCode workflow without paying upfront

Grab it here and start turning your LeetCode grind into actual long-term memory:

👉 https://apps.apple.com/us/app/flashrecall-study-flashcards/id6746757085

A Simple LeetCode + Flashcard Plan You Can Start Today

Here’s a no-nonsense routine you can adopt immediately:

Daily (30–60 minutes total)

1. 10–15 minutes – Flashrecall reviews

  • Only review what’s due
  • Don’t add new cards yet

2. 1–3 LeetCode problems

  • Mix of:
  • 70–80% problems in your current comfort range (easy/medium)
  • 20–30% slightly harder ones

3. After each problem, add flashcards:

  • 1–2 cards for the pattern
  • 1 card for any mistake you made
  • 1 card for any concept you had to look up

Weekly

  • Look at which topics you’re missing frequently:
  • Graphs? DP? Strings?
  • For that topic:
  • Watch 1 good YouTube explanation
  • Paste the notes / transcript into Flashrecall
  • Auto-generate a small set of cards and refine them

Do this for a few weeks and you’ll notice:

  • Problems start to “look familiar”
  • You spend less time re-learning old ideas
  • Interview-style questions feel more manageable

Final Thoughts

You don’t need some insane, over-engineered “Anki + LeetCode master system”.

You just need:

  • Good problems
  • Good patterns
  • A way to not forget what you’ve already learned

Flashcards + spaced repetition are perfect for that — and Flashrecall makes the whole process actually pleasant instead of a chore.

If you’re serious about cracking coding interviews and want your LeetCode grind to stick:

➡️ Download Flashrecall and turn your LeetCode sessions into long-term memory:

https://apps.apple.com/us/app/flashrecall-study-flashcards/id6746757085

Frequently Asked Questions

Is Anki good for studying?

Anki is powerful but requires manual card creation and has a steep learning curve. Flashrecall offers AI-powered card generation from your notes, images, PDFs, and videos, making it faster and easier to create effective flashcards.

What's the fastest way to create flashcards?

Manually typing cards works but takes time. Many students now use AI generators that turn notes into flashcards instantly. Flashrecall does this automatically from text, images, or PDFs.

How do I start spaced repetition?

You can manually schedule your reviews, but most people use apps that automate this. Flashrecall uses built-in spaced repetition so you review cards at the perfect time.

What is active recall and how does it work?

Active recall is the process of actively retrieving information from memory rather than passively reviewing it. Flashrecall forces proper active recall by making you think before revealing answers, then uses spaced repetition to optimize your review schedule.

Related Articles

Research References

The information in this article is based on peer-reviewed research and established studies in cognitive psychology and learning science.

Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380

Meta-analysis showing spaced repetition significantly improves long-term retention compared to massed practice

Carpenter, S. K., Cepeda, N. J., Rohrer, D., Kang, S. H., & Pashler, H. (2012). Using spacing to enhance diverse forms of learning: Review of recent research and implications for instruction. Educational Psychology Review, 24(3), 369-378

Review showing spacing effects work across different types of learning materials and contexts

Kang, S. H. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12-19

Policy review advocating for spaced repetition in educational settings based on extensive research evidence

Karpicke, J. D., & Roediger, H. L. (2008). The critical importance of retrieval for learning. Science, 319(5865), 966-968

Research demonstrating that active recall (retrieval practice) is more effective than re-reading for long-term learning

Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20-27

Review of research showing retrieval practice (active recall) as one of the most effective learning strategies

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-58

Comprehensive review ranking learning techniques, with practice testing and distributed practice rated as highly effective

Ebbinghaus, H. (1885). Memory: A Contribution to Experimental Psychology. New York: Dover

Pioneering research on the forgetting curve and memory retention over time

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FlashRecall Team

FlashRecall Development Team

The FlashRecall Team is a group of working professionals and developers who are passionate about making effective study methods more accessible to students. We believe that evidence-based learning tec...

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