Why So Many People Say “Fuck LeetCode” — And What to Do About It

Source: Dev.to

Originally published on LeetCopilot Blog

Why LeetCode makes smart people miserable

A lot of smart, capable engineers end up hating interview prep. The reasons are less about intelligence and more about systems.

1. The metric doesn’t match the job
   Most people measure progress by problem count or daily streaks. Interviewers measure clarity of thought, adaptability, and edge‑case instincts. When your metric diverges from the real signal, you can grind for weeks and feel like you’re not moving.

2. You’re fighting memory, not just difficulty
   Solving a problem once is the easy part; remembering its invariant and why the approach works—two weeks later, under pressure—is the hard part. Without structured notes and spaced review, your brain quietly throws work away.

3. Silent practice for a loud exam
   Interviews are social. You’ll be asked to explain, defend, and course‑correct in real time. Solo grinding doesn’t train those muscles. The first time you narrate under a clock, everything feels harder than it is.

4. Friction kills focus
   Copy‑pasting prompts, switching tabs, and juggling logs each tax working memory. By the time you get help, you’ve lost the mental stack you were trying to preserve.

5. The “hidden curriculum”
   There’s an unwritten set of tricks—how to timebox, when to ask for a hint, how to design test inputs to break your own code, how to narrate trade‑offs. If no one teaches you this, you assume the problem is you. It’s not.

Are LeetCode problems just too hard?

Sometimes. But mostly, they’re targeted. The bulk of interview questions live in a band where:

• Easy/Medium: canonical patterns (sliding window, two pointers, BFS/DFS, topological sort, heap, monotonic stack, binary search—in arrays and on the answer space).
• Hard: either an unusual twist on a known pattern or a composition of patterns (e.g., sweep‑line + heap, or DP + reconstruction).

What feels “too hard” often masks two issues:

• Pattern identification latency – you know the technique but recognize it too late.
• Invariant articulation – you can write code but can’t state the condition that must hold (the thing that keeps your window/stack/DP state honest).

The antidote isn’t “do 300 more problems.” It’s better reps on the same problems: progressive hints (non‑spoiler), early edge‑case pressure, quick visualization when your brain fogs, and tiny notes you’ll actually review.

Do data structures & algorithms matter on the job?

Short answer: Yes, but not always the way interview problems frame them.

• Arrays / Maps / Sets / Heaps – telemetry aggregation, rate limiting, ranking feeds, priority scheduling.
• Graphs – dependency resolution, network/service routing, permissions, recommendations.
• Sliding window / two pointers – streaming analytics, back‑pressure management, log windows.
• Dynamic programming – optimization, pricing, recommendation, compiler/analysis tooling; the core idea of state and transition is broadly useful.
• Binary search on answers – tuning configs, autoscaling thresholds, SLO budget searches.

The practicality isn’t that you’ll implement “Longest Substring” every Tuesday. It’s that you’ll design representations, maintain invariants, and reason about trade‑offs under constraints. The interview is an imperfect proxy, but the mental models transfer.

So why does it still feel bad?

Because process beats intent. If your loop rewards streaks, punishes asking for help, and saves nothing for future you, it will grind you down even if you “believe” in DS&A. A sustainable loop needs to:

• Teach you to ask for the right hint at the right time.
• Put edge‑case pressure on your code early.
• Visualize when text stops helping.
• Turn today’s reps into tomorrow’s recall with tiny notes.
• Train communication every week, not only after “I’m ready.”

Let’s build that.

A sane learning loop you can actually sustain

Think of this as a five‑step cycle. I call it FRAME:

1. Find the family (pattern) with one strategy‑level hint if needed.
2. Represent the state & invariant before you code (“what must always be true?”).
3. Attempt a first pass under a kind timebox (15–20 minutes).
4. Measure by trying to break your own code (edge‑case batch runs).
5. Encode the insight in a two‑minute note (for Day‑3/7/30 review).

Rinse, repeat.

1) Strategy hints (not spoilers)

Ask for a nudge toward the family: “growing/shrinking window,” “BFS over levels,” “binary search the answer space.” Avoid code. If you need more, escalate once: outline moving parts, not exact updates. Final step: checkpoint questions (“when duplicates collide at r, where does l jump?”).

2) Represent the invariant

Write one sentence that must hold—e.g., “window has unique chars,” “heap holds current k candidates,” “dp[i] means best up to i with …”—and one reason it could break.

3) Attempt under a kind timer

Fifteen minutes to frame/try; if stuck, one strategy hint; ten more minutes to adapt; stop by ~45–50 minutes and switch to learning mode. (Tomorrow‑you will finish faster than tonight‑you continue frustrated.)

4) Measure by breaking your own code

Generate 3–5 inputs that would embarrass your solution (empty/corner, duplicates, skewed trees, extremes). Batch‑run them. Fix one failure and log the cause in a single line.

5) Encode for future you

Two‑minute note template:

• Problem in one sentence
• Approach in two
• Invariant in one
• One failure mode + fix

Tag it (#array #window, #graph #bfs, #dp #1d). Schedule Day‑3/7/30. Before each review, try the problem cold for 10 minutes; only then peek.

Can AI actually help with algorithms—without ruining learning?

Yes—if you constrain it. AI increases leverage; unconstrained, it collapses the very struggle that builds skill. Use it as scaffolding, not a shortcut:

• Progressive hints only—strategy → structure → checkpoint; no code.
• Act, don’t just talk—have it generate tricky inputs and batch‑run them; surface the bug faster.
• Visualize execution for recursion and pointer‑heavy flows; pictures beat walls of text when memory is taxed.
• Capture insights the moment they land; the tool should let you save a micro‑note without leaving the editor.
• Practice performance with mock interviews: follow‑ups, light scoring on clarity/approach/correctness.

Used this way, AI reduces friction and amplifies reps while leaving the hard (useful) thinking intact.

Where LeetCopilot fits (lightly, on your terms)

You can implement FRAME with pen and paper. If you want the loop to live inside the Leet… (the original article continues with a deeper dive into LeetCopilot integration).