A LeetCode Alternative for People Who Forget Solutions

The actual problem

Most engineers who struggle in coding interviews have a very specific problem: they have seen the solutions, but they cannot reproduce them.

This is a different problem than "I do not know enough algorithms." If the gap were knowledge, you would fix it by watching more explanations or solving more problems. But engineers who have ground through hundreds of LeetCode problems and watched full NeetCode roadmap videos still blank when the interviewer is watching. More exposure to solutions does not fix this.

The reason is that watching solutions trains recognition, not recall. You can recognize a sliding window problem when you see it. You may not be able to write the sliding window template from scratch when you are nervous and the clock is ticking.

What LeetCode is built for

LeetCode is designed around a specific feedback loop: you attempt a problem, you get a result from the judge, you read the solution or discussion, you learn from the comparison. That loop is useful. It gives you immediate feedback on whether your code is correct.

What it does not do is force you to retrieve knowledge before showing you the answer. You can look at the solution anytime. You can read others' approaches before writing your own. The platform is optimized for problem volume and code correctness, not for building the kind of durable recall that holds up in an interview.

What LeetCode is not built for

LeetCode does not track which parts of your solutions you consistently get wrong. It does not distinguish between the three lines in a sliding window solution that everyone gets right and the two lines that most people blank on under pressure. It treats every accepted submission equally, which means you do not know what your specific weak points are.

It also does not enforce recall before reference. Nothing stops you from reading the solution before writing a single line. The discipline to attempt genuine reconstruction is entirely on you, and most people do not maintain it consistently when they are tired or under time pressure.

A different prep model

The cognitive science behind this is straightforward: retrieval practice strengthens memory more than passive review. Attempting to produce information before seeing it forces your brain to engage differently than reading or watching does. The act of retrieval itself, even when partially wrong, builds the memory trace that makes future retrieval easier.

Applied to coding interview prep, this means the right practice model looks like: learn the pattern (explanation layer), attempt to reconstruct it without reference (retrieval layer), check what you missed (feedback layer), drill the specific gaps (targeted repetition). LeetCode's model skips the retrieval layer. You can go straight from explanation to checking solutions with no forced attempt in between.

How AlgoDrill works

AlgoDrill is built around the retrieval layer. Each problem connects to a pattern guide explaining the approach. Then instead of showing you the full solution to copy or study, it removes the critical lines and asks you to reconstruct them. You cannot skip to the answer. You have to attempt the retrieval first.

The system records which lines you consistently miss. Across multiple sessions, you build a picture of your specific weak points. The drill sessions focus on those points rather than giving equal time to things you already know solidly.

This is not a replacement for LeetCode's problem volume. LeetCode is useful for testing your code against a judge and for company specific preparation. AlgoDrill is the layer that converts pattern understanding into durable recall, which is what the interview actually tests.

Who this is for

AlgoDrill is most useful for engineers who:

• Have done significant LeetCode or NeetCode prep but still struggle in actual interviews
• Can recognize the right approach when they see a problem but cannot write the code reliably from scratch
• Want to know specifically which parts of which patterns they are weak on, not just a general sense of needing more practice
• Are in the final weeks before an interview and need to convert existing pattern knowledge into reliable performance

If you are just starting and have never studied algorithms, begin with a structured explanation resource (the AlgoDrill pattern guides cover the core patterns). Once you have the conceptual foundation, the recall drills become the most valuable part of your practice.

Browse the practice problems to see the drill format in action, or read through a pattern guide to understand how explanation connects to retrieval practice.