In early 2025 during a remote coding interview, I found myself on autopilot, solving algorithms in shared digital canvases that felt more like robot puzzles, timing candidates like runners at the finish line, and ticking off test cases like shopping list items. It felt less like finding teammates and more like grading performance art. This change matters because each new hire shapes our culture: how we debate design, handle ambiguity, and, yes, celebrate a clever workaround when it happens.

LeetCode interviews always felt overhyped; they were shiny brainteasers divorced from the daily grind of production code. Their predictable green ticks felt comforting until chatbots started acing every question without breaking a sweat. Suddenly the process measured who could coax an AI instead of who could think on the fly.

When AI masters the rote, interviews should spotlight what still trips up machines: spotting the hidden dependencies in a legacy system, yanking apart tangled code to draft a resilient design, or poking at AI-generated snippets until they hold up under real-world conditions. And let’s not forget explaining complex trade-offs without drowning people in jargon.

I’ll admit: sometimes I miss the neat world of a single algorithm question. But that neatness is hollow without context. A good interview feels contradictory, structured yet open-ended, objective yet subjective, challenging yet fair.

Interview Formats That Pull Their Weight #

Take-Home Challenges (Then Talk It Through) #

Imagine handing someone a half-finished API spec or a dataset that’s part-cleaned and part-shoved-into-a-spreadsheet. After tinkering, I skip the slides and dive straight into an honest walkthrough. No whiteboard smoke and mirrors, just human conversation that surfaces curiosity, ownership, and the inevitable “oops, I missed that” moments.

Live Pair Programming #

In a live session, spoon-fed puzzles give way to genuine collaboration. Candidates share their screens, talk us through code tweaks when a surprise constraint pops up, and decide whether to trust or challenge an AI’s suggestion. It’s raw, it’s unpredictable, and it reveals how people think out loud, something no AI cheat sheet can fake.

System Design Debates #

System design interviews morph into a playground for nuanced debate. We hash out trade-offs, cost versus latency, governance versus speed, and layer in data privacy or ethical guardrails when AI is part of the equation. Next come rapid-fire “what-ifs,” like traffic spiking tenfold or a critical index vanishing overnight. These conversations expose real expertise, not rehearsed patterns.

Behavioral Storytelling #

Stories beat scripts every time. When we ask about a rollout that went sideways, we don’t want a tidy narrative; we want the candid version: how they owned the mistake, dug out of the hole, and what they’d do differently with a time machine in hand. Those tales reveal resilience, accountability, and empathy in a way no AI-generated case study could match.

Portfolio & Code Autopsy #

Your code repo is your autobiography. I dive into your repo to see where you riffed on inherited code, why you chose a particular library or pattern, and which parts you’d refactor six months down the line. Those insights are priceless—and unmistakably human.

A Practical, Contradictory Reflection #

The AI era is here, but that doesn’t mean losing judgement. Clinging to brainteasers or checklists brings in signal-bots, not true collaborators. Better to embrace interview formats that feel paradoxical: rigorous yet human, practical yet philosophical, fast yet thoughtful.

In the end, interviews shouldn’t be about catching someone mid-mistake; they should be about exchanging real experiences. If you can’t poke holes in an AI answer or crack a joke over a near-miss, how much fun will you have when the real work begins? Let’s hire for that nuance because practical engineers who are a blast at dinner parties are the ones who move mountains.