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Instead of jumping straight into difficult LeetCode problems, Cathy Lai built confidence with easier exercises and a repeatable way to reason through each prompt: clarify assumptions, trace an example, explain the logic, then code and test it incrementally. Her September 16, 2026 DEV Community post describes a personal practice routine, not a proven formula for passing interviews.
Why start with easier questions?
Lai’s starting point was the anxiety behind a common question: “Should I start cramming LeetCode problems?” Her answer was to avoid beginning with problems so difficult that they undermine confidence. She started with easy exercises generated with AI and increased the difficulty gradually.
She aimed for two to three problems a day, adjusting for difficulty. That was her own target, not a universal benchmark or evidence-based requirement. The practical principle is to choose a challenge that lets you practice a complete solution process, then raise the difficulty as that process becomes more familiar.
What to do before writing code
Clarify assumptions
Before solving, identify what the prompt does and does not specify. Write down assumptions that affect the answer, such as how to handle empty input, repeated values, or boundary cases. In an interview, state important assumptions aloud so the interviewer can correct them before they shape the implementation.
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Check the coding setup
Lai first tested the environment with a small dummy function and checked its output. This separates setup problems from algorithm problems: if a basic function does not run, resolve that before diagnosing a more complex solution.
Trace a sample by hand
Walk through the example input one step at a time and record how relevant values change. Lai’s formulation is: “Trace the algorithm manually: Walk through the example input step-by-step to identify every variable needed across iterations.” The point is to discover what state the algorithm needs before committing to a data structure or implementation.
How to make your reasoning visible
When you get stuck, describe the uncertainty instead of going silent or guessing. For example, say that you are deciding whether the solution needs a flag, a running total, or a value maintained separately for each group. Naming the question makes the next step clearer and gives the interviewer something specific to respond to.
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Once you have a candidate approach, run the sample input through it again. Check whether each value should be set, reset, or accumulated at each step. Use pseudocode or a small state trace if that makes the logic easier to inspect. Lai’s advice is to start implementation once the logic is clear, rather than trying to solve the algorithm and syntax problems at the same time.
Implement, test, and debug incrementally
- Write the smallest useful piece. Translate the established logic into code in manageable steps.
- Run a test early. Check the sample or a small case before adding more complexity.
- Inspect the data. Simple print debugging can reveal whether a variable or data structure contains what you expect.
- Reconcile output with the trace. If actual output differs, compare the program’s state at each step with the hand-worked example and look for a missed reset, incorrect accumulation, or unset value.
Unexpected output is a normal debugging problem, not proof that the entire approach has failed. Lai recommends recovering calmly and checking the logic and state rather than rushing into a rewrite.
Practice communication as well as problem solving
Lai recorded some practice sessions and reviewed her pacing, explanations, and overall presence. Recording can make it easier to notice habits you miss while solving, but her account does not establish that recording improves interview outcomes. It is an optional way to review how clearly you explain your work.
A commenter recommended practicing with someone experienced in hiring, who can observe both technical and behavioral interviews. Another described solving Codewars challenges and then reading and explaining other people’s solutions aloud. These are suggestions from readers in the discussion, not findings from Lai’s original post. A human practice partner can offer feedback that a solo recording cannot.
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Using AI as a practice aid
In a reply, Lai described organizing questions in a project, starting a separate conversation for each coding problem, pasting her solution into ChatGPT for critique, and specifying the desired difficulty. This is her reported workflow; it does not show that AI reliably calibrates difficulty or gives every learner accurate instruction.
Best Value
If you use AI feedback, treat it as a prompt for review rather than a verdict. Check any suggested approach against the problem’s requirements, test it with examples, and make sure you can explain why it works. The same applies to solutions found elsewhere: understanding and reconstructing the reasoning is more useful practice than simply collecting answers.
What this method does—and does not—establish
Lai’s post offers a concrete account of how she practiced: start at a manageable level, reason through examples, make uncertainty explicit, and implement and debug in stages. It does not report measured improvement, interview success rates, or a comparison of AI practice with human coaching. It is best read as a practical routine to adapt, not a guarantee of hiring success.
Source: Cathy Lai’s DEV Community post, published September 16, 2026.
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