Most AI interview prep tools are polite. They tell you "great answer!" and move on — which means you walk into the real interview with a false sense of confidence.
I built the opposite.
InterviewIQ is an AI mock interview coach that scores you like a strict hiring manager. No fake praise. If your answer is vague, it says so. If you'd get a "No Hire" in real life, it tells you — and tells you exactly why.
Live demo: https://interviewiq-hazel.vercel.app
Repo: https://github.com/ravikumarve/interviewiq
What it does
You pick a role (Frontend, Backend, Data Science, PM, DevOps, AI/ML — or type any custom role), optionally paste a real job description, and answer 5 questions from a live AI interviewer with a 90-second timer.
Then you get a dossier-style report:
- Score out of 100 and a hiring verdict: Hire / Lean Hire / Lean No Hire / No Hire
- Strengths and weaknesses — specific, not generic
- A targeted practice plan for your weakest skill
- Peer benchmarking — "You beat 66% of Frontend candidates" with a live leaderboard
Why the "strict evaluator" matters
This is the differentiator. I tested it: answer "Yes, I know React" to a React question, and the evaluator scores you below 30 with a No Hire verdict. Answer with a detailed, example-backed story, and you get 85+ with a Hire.
The evaluation prompt is deliberately aggressive:
const EVALUATOR_SYSTEM = (role, jd) =>
`You are a strict hiring manager for "${role}". Evaluate the candidate's answers. ` +
`Penalize one-word/vague answers HARD: if most answers are under 20 words, score MUST be below 30. ` +
`Never pass (>50) without concrete technical knowledge. Do not invent strengths.`;
Most AI wrappers optimize for "user feels good." I optimized for "user is ready for the real thing."
Adaptive follow-ups
If you give a thin answer (under 14 words), the interviewer doesn't just move on. It fires a follow-up probe — "Can you give a concrete example?" — up to twice per question, without consuming your question count.
Verified in a real flow: a 10-word vague answer triggered "Can you give an example of a React component…", and the counter stayed at "Question 2 of 5."
JD targeting
Paste a real job posting and every question adapts to its exact stack, tools, and responsibilities. The evaluator scores you against those requirements and calls out missed core skills.
Verified: pasted a "Senior React Engineer — Redux/GraphQL/WCAG" JD → Q1 was React-specific, Q3 was accessibility-specific, and the final evaluation penalized a candidate who never touched GraphQL or WCAG.
Tech stack
- Next.js 16 (App Router, TypeScript, Tailwind CSS)
-
NVIDIA NIM API —
meta/llama-3.1-8b-instruct, ~450ms responses - Ollama fallback — local CPU inference if the API key is missing (perfect for demos)
- jsonblob — zero-signup shared store for the leaderboard (swap-in ready for Upstash Redis)
- Vercel — one-command deploy
What I learned building it in 8 hours
- Strict prompts beat big models. llama-3.1-8b with a hard "never pass without substance" prompt outperformed a larger model with a soft one. Evaluation quality is prompt engineering, not model size.
-
JSON extraction needs a fallback. Models wrap JSON in prose sometimes. I built an
extractJson()that does balanced-brace recovery — it saved the eval flow twice during testing. - A local fallback is a superpower. When the NVIDIA key rate-limits, Ollama on my laptop keeps the demo alive. Users see "evaluating…" instead of an error page.
- The viral loop is the differentiator. The peer percentile + share button ("I beat 66% of Frontend candidates") is what makes users share the report. Building that in early matters more than polish.
Try it
The live demo takes about 8 minutes for a full interview. Paste a real job description you're targeting and see how specific it gets.
Built solo in 8 hours for Hack Devengers 1.0. Questions, feedback, and ruthless criticism welcome — I built an app that doesn't fake praise, so I can take it. 😄
