Why People Say “F*** LeetCode”: Difficulty, Fairness, Real-World Value — and a Better Way

Source: Dev.to

Why so many smart engineers feel this way?

The frustration isn’t just venting; it’s a pattern that emerges when preparation loops and interview evaluation are misaligned.

1. Metric mismatch – You track progress by solved counts and streaks, while interviewers care about clarity, adaptability, and edge‑case handling under time pressure. When the two diverge, weeks of grind feel like treading water.
2. Memory decay – Solving a problem on Tuesday and forgetting it by Friday isn’t a character flaw; it’s how the brain works without structured notes and spaced review.
3. Social nature of interviews – You must narrate constraints, justify trade‑offs, own mistakes, and steer the conversation. Solo grinding doesn’t train that muscle.
4. Context‑switch overload – Copy‑pasting prompts, shuffling logs, and jumping between tabs drain working memory, evicting the very details you need.
5. Misplaced self‑doubt – Shipping reliable systems yet stumbling on a contrived DP twist can make you think you’re “not cut out for this,” when the real gap is a prep loop that preserves struggle while increasing feedback.

Perceived difficulty: a simple formula

Difficulty = Novelty Load + Time Pressure + Feedback Delay

Reducing novelty (by cueing patterns), controlling the clock (gentle timeboxes), and shortening feedback loops (generating adversarial tests early) can make the same problem feel roughly twice as easy—without dumbing it down.

Fairness and usefulness of algorithm rounds

When done well, algorithm rounds produce a portable signal: the ability to represent state cleanly, maintain invariants, and reason about trade‑offs under constraints. That shows up at work as rate‑limiters, schedulers, stream windows, dependency graphs, and bounded caches. When done poorly, they become trivia, gotchas, and a cottage industry of “memorize 500 mediums” advice.

Real‑world value of common pattern families

Even if you never code “edit distance” at work, the mental move—define state, keep invariants, test edges early—is the difference between “works on dev” and “survives prod.”

A better learning system – the FAITH loop

A five‑part daily routine that keeps the struggle (where learning happens) while reducing wasted friction. Spend 60–90 minutes each day.

F — Find the family

Identify the pattern family and get a strategy‑level hint (no code).
Examples: “growing/shrinking window?”, “BFS over levels?”, “binary search on answer space?”

A — Articulate the invariant

State the core invariant before writing code.
Examples: “window has unique chars”, “heap holds current k candidates”, “dp[i] = best up to i with …”.

I — Implement under a kind timer

• 15 min: framing / first pass.
• If stuck, take one structure hint (scaffold, not full syntax).
• 45–50 min total; then switch to learning mode.

T — Test by trying to break it

Generate 3–5 adversarial inputs (duplicates, empty, extremes, skewed). Run them in batch, fix the first failure, and log why it failed.

H — Hold the learning

Spend 2 min writing a micro‑note:

Problem: 
Approach: 
Invariant: 
Failure mode + fix: 

Let the note cool for 10 minutes before reviewing.

Add a 30‑minute mock once a week (one medium, one easy). The goal isn’t to “win” but to surface your weak link (clarity, pacing, edge handling) and feed it back into the next week’s plan.

Using AI effectively

Good uses

• Progressive hints – strategy → structure → checkpoint questions; no code unless you’re in “post‑mortem mode.”
• Edge pressure – generate tricky inputs and run them in one batch so bugs surface early.
• Visualization – 30‑second call‑stack or pointer timeline when text fails.
• Recall – auto‑create micro‑notes and schedule resurfacing so today’s effort survives to next week.
• Performance practice – mock interviews with follow‑ups and a score breakdown (clarity, approach, correctness).

Bad uses

• Direct code requests during practice attempts.
• Endless chat that doesn’t act (no runs, no tests, no visualizations).
• Notes so long you’ll never reread them.

Rule of thumb: ask AI to make feedback cheap, not thinking optional.

Sample weekly rhythm (FAITH‑focused)

Stick to the FAITH loop each day, use AI as a feedback scaffold, and you’ll turn the “F*** LeetCode” frustration into a sustainable, high‑impact learning habit.