I believe this is a question on everyone’s mind. The Codex team even asked it at the very start of the product: “Why not use AI interview tools?” This article explains what Codex Interview is and why we decided to build it.
If you've been researching AI-powered coding interview tools, you've almost certainly come across Interview Coder — the app built by Columbia University student Roy Lee that went viral on GitHub and Reddit. It promises to solve coding problems during live technical interviews using AI, running invisibly in the background.
ViewIf you've spent any time searching for a coding interview assistant, you've probably found Interview Coder — the AI tool created by Roy Lee, a Columbia University student, that went viral on GitHub and Reddit. The idea is simple: an invisible AI app that solves coding problems during live technical interviews. But here's what the marketing doesn't tell you: AI interview assistants like Interview Coder have a fundamental flaw. They hallucinate. They lag. They get detected. And when they fail, you're the one who gets blacklisted — not the tool. In this review, we'll break down exactly why Interview Coder falls short, what makes it detectable on platforms like CoderPad, HackerRank, and CodeSignal, and why Codex Interview has become the go-to Interview Coder alternative for developers targeting roles at Google, Meta, Amazon, and other top tech companies.
ViewAs a Software Development Engineer (SDE), you're no stranger to challenging coding interviews. However, when it comes to the tech giant Meta (formerly Facebook), the interview process can be particularly daunting. In this article, we'll delve into the world of Meta interviews, providing you with valuable insights, tips, and strategies to help you prepare and succeed.
ViewThis is a question on everyone's mind: Why do AI interview tools keep failing candidates?
The Codex team asked this very question at the start of building the product. Before writing a single line of code, we wanted to understand — from an AI research perspective — why tools like Interview Coder, Cluely, and other AI-powered interview assistants produce such unreliable results. And more importantly: what would actually work?
This article explains what we found, why we built Codex Interview as a non-AI solution, and why human collaboration beats AI for coding interviews — every single time.
At the end of 2024, while preparing for a job switch, I came across one of the first AI interview tools on the market: Offerin. After several rounds of mock interview testing, the experience was terrible — for three specific reasons:
1. The phone-on-screen setup is impractical. You're required to place your phone in front of the screen, which is highly inconvenient during a coding interview. Scrolling through code, flipping between tabs, and keeping a phone propped up without it falling over — all while an interviewer is watching — is a recipe for disaster.
2. Unnatural eye movements raise suspicion. Glancing back and forth between your screen and a second device creates an obvious behavioral pattern. Any experienced interviewer will notice — and many are specifically trained to look for it.
3. AI can't handle follow-up questions. This is the deepest flaw. As the interview progresses — especially with follow-up questions about edge cases, time complexity, or design tradeoffs — AI fails to understand the real context, intent, or depth of the conversation. It generates answers to the literal prompt, not to the actual question being asked.
Points 1 and 2 have seen improvements. The rapid rise of Interview Coder — built by Columbia University student Roy Lee — brought genuinely impressive marketing and a more polished technical implementation. Their invisible overlay approach eliminated the need for a physical phone.
But has the core problem actually been solved?
No. And here's why it can't be — at least not with current AI.
As those who've worked with me on research know, I've served as a NeurIPS reviewer in both 2024 and 2025, and I have a deep, technical understanding of how these AI systems work — and where they break down.
Most AI interview assistants — whether it's Interview Coder, Cluely, Offerin, or any of the newer alternatives — follow the same basic architecture:
The marketing makes this sound seamless. The reality is far more fragile.
The real failure starts at Step 1: voice input and recognition. In a 40–60 minute technical interview, the AI must:
Can Retrieval-Augmented Generation (RAG) solve this? No. Long-form conversational understanding remains an unsolved challenge in AI research. Papers at top conferences like NeurIPS, ICML, and ICLR continue to tackle this problem — it's nowhere near production-ready for real-time interview scenarios.
Even OpenAI acknowledges this directly. Their own research paper explains that language models hallucinate because standard training procedures reward guessing over acknowledging uncertainty. When a model doesn't know the answer, saying "I don't know" scores zero on benchmarks — so models are incentivized to guess.
For everyday ChatGPT conversations, a hallucination is mildly annoying. For a live coding interview, a hallucination means:
A 2026 study found that even the best AI models still hallucinate at a rate of at least 0.7% of responses — and that's for straightforward factual questions. For complex, multi-step coding problems with ambiguous constraints? The rate is dramatically higher.
This is why AI interview assistants fail to address real user pain points — and worse, they provide misleading information that actively harms the candidate's performance.
Let's get concrete about how these theoretical AI limitations play out in Interview Coder specifically.
Independent reviews consistently report that Interview Coder's generated solutions fail the majority of test cases. One detailed analysis found that 3 out of 5 solutions pass fewer than 30% of test cases. For system design questions — a critical component of senior-level interviews — the tool is essentially non-functional because its AI model wasn't trained for architectural reasoning.
Users report 20+ second response times for medium-difficulty LeetCode problems. In a live interview, 20 seconds of silence while you wait for AI-generated code is an enormous red flag. Experienced interviewers know what that pause looks like.
Enterprise CoderPad now includes Interview Coder-specific detection flags. Multiple hiring managers on Blind have confirmed catching candidates using the tool — and permanently blacklisting them. HackerRank and CodeSignal employ similar proctoring that monitors browser activity, tab switching, and application overlay patterns.
Here's the scenario every Interview Coder user fears: the AI gives you a solution, you type it in, and then the interviewer asks "Can you walk me through why you chose a hash map here instead of a binary search tree?"
You didn't write the code. You may not understand the tradeoffs. And Interview Coder can't help you explain it — because the AI doesn't model conversational follow-ups in the context of its previous answers.
This isn't a minor gap. Follow-up questions are how FAANG interviewers separate strong candidates from weak ones. If you can't defend your solution, the code itself is worthless.
Before AI interview tools, there was a simpler approach that actually worked: having a human expert help you in real time.
The setup was straightforward: open two CoderPad sessions — one with the interviewer, and another with a friend or tutor who provides real-time solutions and behavioral insights drawn from their own interview experience.
This approach solved the accuracy problem completely. A human expert doesn't hallucinate. They understand context. They can adapt to follow-up questions. They can explain why a particular approach is correct.
But it had a critical flaw: unnatural eye movement. Looking at a second screen, a second monitor, or a phone creates visible behavioral patterns that interviewers are trained to detect. Google CEO Sundar Pichai has specifically addressed the rise of interview cheating tools, and companies are investing heavily in behavioral analysis.
This is exactly the gap that Codex Interview was designed to fill.
Codex Interview is a collaborative remote code editor designed specifically for technical interviews. It's not an AI tool — it's a human-powered collaboration platform with a kernel-level invisible overlay.
Here's what sets Codex apart from every AI interview assistant on the market:
1. Real human answers — zero hallucinations. By sharing a Markdown editor with your friends, classmates, or colleagues, they can remotely support you with algorithm strategies, code solutions, and behavioral question guidance. A human expert understands context, adapts to follow-up questions, and never generates code that doesn't compile.
2. Kernel-level undetectability — invisible to every platform. Codex Interview's desktop app operates at the kernel level, making it completely invisible to screen sharing, screen recording, and platform proctoring systems. Unlike Interview Coder — which has been specifically flagged on enterprise CoderPad — Codex has never been detected on any platform.
The overlay panel displays assistance discreetly on your screen, positioned directly over your code editor. This means:
3. Natural eye contact maintained. Because the Codex overlay sits directly on top of your code editor, your eyes never wander to a second screen, a phone, or a different application. To the interviewer, it looks exactly like you're reading and thinking about your code — because you are.
4. Works on every major platform. Codex has been thoroughly tested and verified on:
Every interaction is controlled via global shortcuts — no mouse clicks, no visible UI interactions:
| Action | Shortcut |
|---|---|
| Hide/Show overlay | ⌘ + B |
| Move window | ⌘ + Arrow keys |
| Refresh | ⌘ + R |
| Quit | ⌘ + Q |
Unlike Interview Coder — which offers no verification system — Codex provides a built-in undetectability test page at /undetectable-test. Before your real interview, you can verify that:
This test environment means you never go into an interview wondering "will this actually work?" — you know it works because you've already verified it on your exact setup.
| Problem | AI Tools (Interview Coder, Cluely, etc.) | Codex Interview |
|---|---|---|
| Hallucinations | Frequent — AI generates wrong code | None — humans don't hallucinate |
| Follow-up questions | Can't maintain conversational context | Human collaborator adapts naturally |
| System design | Not trained for architectural reasoning | Human experts handle any question type |
| Response time | 20+ seconds lag | Real-time, as fast as typing |
| Voice recognition | Can't distinguish interviewer from candidate | Not needed — visual collaboration |
| Eye movement | Phone/second screen creates suspicion | Overlay on same screen — natural gaze |
| CoderPad detection | Specifically flagged | Never detected (kernel-level) |
| Price | $299–$899 | Free trial, $60/month Pro |
| Accuracy | ~30% test case pass rate reported | As accurate as your collaborator |
The stakes of a failed coding interview aren't just "try again next quarter." Companies share candidate data. Getting caught using a detectable tool can result in permanent blacklisting — not just from one company, but from entire hiring networks.
AI interview tools create a false sense of security. They work well enough in mock scenarios to convince you they'll work in the real thing. But when the pressure is on, when the interviewer asks a question the AI didn't anticipate, when the solution takes 20 seconds to load, when CoderPad flags your session — that's when the tool fails and your career pays the price.
Codex Interview eliminates this risk by eliminating AI from the equation entirely. Human intelligence doesn't hallucinate. Human collaborators understand context. And kernel-level stealth doesn't get flagged by proctoring systems.
Codex Interview offers a free tier — 2 codepads with full features, real-time collaboration, AI code assistance, multi-language syntax highlighting, and code execution. No credit card required.
Over 226 developers have used Codex Interview to land roles at Google, Meta, Amazon, TikTok, and Nvidia. The tool is trusted, tested, and free to try.
AI interview tools rely on large language models (LLMs) that are statistically trained to predict the next token — not to reason about code correctness. OpenAI's own research confirms that models hallucinate because training procedures reward guessing over saying "I don't know." For coding interviews, this means AI-generated solutions frequently fail test cases, miss edge cases, and produce code the candidate can't explain or defend.
No. Interview Coder and similar AI tools generate responses to individual prompts — they don't maintain conversational context across a multi-turn interview. When an interviewer asks follow-up questions about edge cases, time complexity, or design tradeoffs, the AI lacks the contextual understanding to provide relevant answers. Codex Interview solves this because your human collaborator follows the entire conversation in real time.
No. Codex Interview is a human-powered collaboration platform. It provides a real-time, undetectable coding pad where a trusted collaborator provides answers and guidance. While the platform includes AI code assistance as a feature, the core value proposition is human collaboration — eliminating the hallucination, lag, and accuracy problems that plague AI-only tools.
Codex operates at the kernel level of your operating system, making it invisible to all browser-based meeting and interview platforms. It doesn't appear in your dock, menu bar, or task manager. It doesn't trigger focus-change events or tab-switching alerts. A built-in test page lets you verify undetectability on your exact setup before any real interview.
General-purpose AI tools weren't designed for the specific constraints of live interviews. They require visible browser tabs, create detectable focus changes, and can't maintain the conversational context needed for follow-up questions. More importantly, they hallucinate — and in a coding interview, a wrong answer you can't explain is worse than no answer at all.
Codex Interview offers a free tier (2 codepads, full features) and a Pro plan at $60/month. Interview Coder charges $299/month or $899 for lifetime access — making Codex 80% cheaper with significantly more reliable results due to its human-powered approach.
Codex supports JavaScript, Python, Java, C++, and Go with syntax highlighting and code execution. Because answers come from a human collaborator, any language or framework your teammate knows is effectively supported — including system design, behavioral questions, and domain-specific topics that AI tools can't handle.
No. Codex Interview has never been flagged on CoderPad or any other interview platform. By contrast, enterprise CoderPad now includes Interview Coder-specific detection, and multiple hiring managers have confirmed catching and permanently blacklisting candidates using that tool.