I want to start with something that doesn’t get said enough: coding interviews are a terrible proxy for engineering ability. They test your recall of specific algorithm patterns under artificial time pressure, with a stranger watching you type. Most of what makes someone a good engineer, debugging instinct, system thinking, code review judgment, almost none of that shows up in a LeetCode round.
That said, they’re still the dominant hiring filter at most tech companies. So here’s what I’ve actually found useful about AI coding interview assistants, and where they fall flat.
What these tools actually do
An AI coding interview assistant is (usually) a desktop app that captures your screen or audio during a live coding session and surfaces suggestions in an overlay only you can see. Some focus on giving you algorithmic hints when you’re stuck. Others help you narrate your approach out loud by suggesting talking points as you work through a problem.
The key technical piece is the overlay. Good tools use OS-level rendering (similar to how game overlays work) so the assistant window doesn’t appear when you share your screen in Zoom or Google Meet. Browser extensions can’t do this reliably. If you’re evaluating tools, test the screen-share invisibility before you’re in an actual interview.
Craqly does this for both interviews and sales calls. It captures system audio, transcribes the interviewer’s question, and surfaces relevant context in an overlay. You don’t have to copy-paste the problem text anywhere.
the edge case problem
Here’s one thing these tools genuinely help with: edge cases.
You write a function that handles the happy path. The interviewer asks “what about empty input?” or “what if the array has duplicates?” And your brain, already burning CPU on the main problem, just stalls for a second.
An AI assistant watching the problem will often surface those edge cases before the interviewer brings them up. That’s not cheating the interview, that’s doing what a good code review would do. Catching your own blindspots before someone else does is the actual skill.
The same logic applies to syntax. If you know Python but you’re blanking on the exact method name for something, a quick suggestion saves you 90 seconds of visible fumbling that reads to an interviewer as uncertainty. It’s not that you don’t know it, it’s that high-pressure recall is different from normal recall.
Where it won’t help you
If you don’t understand the core pattern a problem is testing, an AI hint will confuse more than clarify. “Use dynamic programming” as a suggestion is useless if you don’t have mental models for when DP applies. The assistant can’t build that foundation for you.
The 2024 Stack Overflow Developer Survey found that 62% of professional developers use AI tools weekly in their actual jobs. The point isn’t that AI makes them better programmers, it’s that working with AI tools is itself a skill now. But there’s no substitute for knowing what a hash map is, or why O(n log n) matters.
Also: if your internet drops, the assistant app crashes, or you’re suddenly in a phone screen with a live coding environment you didn’t expect, you’re on your own. Your foundation still has to be there.
How to actually practice with it
Most people use AI interview tools wrong. They turn on the assistant during practice and immediately look at every suggestion. That’s not practice, that’s reading answers.
Here’s a more useful approach:
- Set a timer. Work the problem for 8 minutes with no AI input at all.
- If you’re stuck after 8 minutes, allow yourself one hint. Not the full solution, just one nudge.
- After you finish, compare your approach to what the AI would have suggested from the start. That gap is your study material.
- Run mock sessions where you talk through your solution out loud, exactly as you would in a real interview. The AI can help you catch places where your verbal explanation breaks down.
The ratio I’d suggest: roughly 80% of your prep time on old-fashioned problem-solving without assistance, 20% on practicing with the tool so you’re comfortable using it quickly under pressure. Trying to use an AI assistant for the first time in an actual interview is its own source of stress.
A note on ethics
Some people treat this as settled and obvious (“using any tool is cheating”). Others treat it as settled in the other direction (“companies use AI tools, so interviews should too”). I think the honest answer is: it depends on the company’s rules and the type of interview.
Some companies explicitly allow AI tools in their technical screens, particularly for senior roles. Many don’t say either way. Take-home assessments are generally in a different category than synchronous screen-share coding rounds. When in doubt, ask your recruiter. “Is the use of AI assistance allowed during this assessment?” is a reasonable professional question, not a red flag.
What’s not debatable: if an AI assistant writes your answers and you have no idea what they mean, you’re going to fail the next round anyway. The goal is to pass the interview, not just the screening.
The 2024 GitHub Octoverse report noted that AI-assisted coding tools are now part of most professional development workflows. Learning to work alongside these tools well, including knowing when not to lean on them, is genuinely a current skill. Coding interviews will probably catch up to that reality eventually.
Until then, the Blind 75 still exists and someone’s asking you about binary trees on a Tuesday morning.