FAANG Mock Coding Interviews Real Technical Interview Practice – intervu.dev

Developers preparing for technical interviews at top tech companies face a gap that traditional practice platforms cannot close. LeetCode and similar tools train problem-solving in isolation, but real interviews at FAANG-level companies test something broader: the ability to think out loud, clarify requirements, defend trade-offs, and write working code under pressure — all at the same time. intervu.dev exists to close that gap. The platform delivers AI-driven mock coding interviews that simulate the full structure of a real technical interview, not just the algorithmic puzzle at its center. A session follows the same flow candidates encounter in actual FAANG interviews: clarify the problem, design an approach, write and test code, then field follow-up questions from an AI interviewer that probes how the candidate thinks rather than just whether they reached the correct answer. The four-stage structure — Start, Think Out Loud, Code and Test, Get Feedback — mirrors what top companies actually evaluate. The AI interviewer is designed to behave like a real human interviewer, asking probing follow-up questions that pressure-test the reasoning behind a candidate's decisions. This trains the communication muscle memory that LeetCode-style grinding does not develop: explaining an approach before writing a single line of code, justifying architectural trade-offs, and narrating decisions as they happen. Candidates write, run, and debug code in a live environment while simultaneously explaining their thinking — exactly the dual-channel performance that technical interviews demand. Feedback at the end of each session goes beyond correctness. The evaluation covers clarity of communication, quality of trade-off analysis, and code quality, giving candidates signal-focused insight into how they would actually be perceived by an interviewer. This mirrors the "signal-based evaluation" that real hiring committees use when deciding whether a candidate advances. Multi-language support means candidates can practice in the language they plan to use on the day of their interview. Python, JavaScript, Java, C++, and additional languages are available, so preparation aligns with the actual interview conditions rather than forcing candidates into an unfamiliar environment. intervu.dev is built for software engineers who already have a foundation in problem-solving and need to develop the interview performance layer on top of it. It targets candidates who understand that getting an offer at a top tech company requires not just solving hard problems, but solving them visibly — with clear narration, structured thinking, and confident communication under time pressure. A free tier is available, allowing candidates to try the experience before committing further. For anyone preparing for a technical interview where the evaluator cares as much about how you think as what you produce, intervu.dev provides a practice environment engineered around that reality.