Summary of "Coding Interviews in 2026"

Concise summary — main ideas and lessons

Big picture (2026)

AI now writes a large portion of code in practice, but hiring and the reality of coding work remain complex. There are conflicting signals: many companies still expect strong human technical skills even as AI tools become widespread.

Contradiction in hiring practices

Major AI firms (Anthropic, OpenAI) and many others continue to use traditional DSA (data structures & algorithms) interview questions.
Some firms (e.g., Meta) are experimenting with AI-assisted interviews — these are not necessarily easier and often raise expectations.

AI-assisted interviews

Having an AI tool available typically raises the bar. Interviewers expect candidates to:

Understand and validate AI-generated code.
Explain design choices and trade-offs.
Write or reason about tests and correctness.
Consider performance, security, and maintainability.

Core skills that remain essential

Reading and understanding code, ensuring correctness, thinking about edge cases, communicating reasoning, and choosing/justifying trade-offs are still critical — arguably more important now because candidates might be tempted to copy AI output without understanding it.

Hiring reality

Hiring remains hard. Anecdotes show engineers producing PRs via AI without reading or understanding the changes, resulting in messy or incorrect code. Employers must still filter out false positives and find people who can reason about systems.

Interview prep guidance

If a target company still uses DSA-style interviews (common across levels), prepare for DSA.
If a company explicitly does not use DSA, tailor preparation to practical coding, system knowledge, or domain-specific tasks.
Do not assume DSA is obsolete.

Tools & prep resources

The presenter recommends using NeetCode for structured preparation:

Pattern-based learning with curated problem sets.
Video and written explanations, multi-language solutions, and complexity analysis.
Fast code execution, hints/AI suggestions, and personalized study plans that prioritize high-probability topics for a given company/time budget.

Mental model and developer habits

Maintain or (re)learn a developer checklist when using AI:

Verify the solution solves the correct problem.
Ensure correctness with tests and edge-case reasoning.
Evaluate performance where relevant.
Consider security and maintainability.
Understand pros and cons and alternatives.
Be able to explain what you did and why.

Detailed actionable methodology and checklists

1) Interview / coding-job mental checklist (use every time you produce or review code)

Are we solving the correct problem?
- Confirm requirements and output format.
Is the solution correct?
- Use unit tests and reason about edge cases.
Are there edge cases I missed?
Is performance acceptable for expected input sizes?
- Know when performance matters and when it doesn’t.
Are there security concerns?
Is the code maintainable?
- Readability, separation of concerns, comments.
What are the pros and cons of this approach? What alternatives exist and why pick this one?
Can I explain each significant decision or line of code to an interviewer or teammate?

2) How to behave in AI-assisted interviews / when using AI tooling

Use AI to accelerate work but never blindly copy-paste: always read and test the output.
Resolve merge conflicts and code changes manually when needed; do not let AI blindly transform codebase structure.
Run and author unit tests yourself; confirm correctness and behavior on edge cases.
Be prepared to justify why you used a particular AI suggestion and why you accepted or modified it.
Treat AI review tools as aids, not absolute truth — validate their recommendations.

3) Preparing for interviews in 2026 (practical study strategy)

First, check the target company’s current interview format. Prioritize DSA if they still use it.
If the company uses AI-assisted interviews or skips DSA, focus on practical coding, system knowledge, or domain-specific tasks.
Use pattern-based practice (e.g., learn ~150 core problems, then expand into related patterns you’re weak on).
Practice explaining solutions out loud and writing tests; simulate interviews that require reasoning, not only implementation.
When time-constrained, prioritize topics by frequency at your target company (e.g., deprioritize rare topics like DP if it’s uncommon at that company).

4) How to use a resource like NeetCode (features and recommended workflow)

Follow pattern-based lists (start with curated sets such as a “150” problems list).
When stuck, use layered help: hints → relevant code snippets → written solution → video walkthrough.
Use fast code execution to iterate quickly on tests and spot bugs (e.g., off-by-one errors).
Use AI-suggested fixes as learning aids: review suggested changes, accept/reject, and reason why they work.
Opt into personalized study plans: provide available time and target companies to get a prioritized and feasible practice list.

Key lessons and recommendations

Don’t assume coding fundamentals are obsolete because of AI. You still must be able to read, reason about, test, and explain code.
AI increases throughput but makes critical thinking and judgment more important.
Prepare according to the employer — many organizations still use DSA interviews and those are unlikely to disappear overnight.
Use AI as an amplifier for learning and productivity, but cultivate mental habits to validate and understand what AI produces.