My friend asked for interview practice, so I built an AI interviewer that runs locally

A developer built Mock Interview Partner, a locally-run AI mock interviewer that takes a resume and target role and generates tailored questions, follow-ups, strict 1-to-5 per-answer scoring and a final report with weaknesses and a practice plan. The tool runs entirely on a laptop using the open-weight llama3.2:3b model via Ollama on a 6 GB RTX 4050, so no resume or interview recording leaves the machine. The developer said the model initially over-scored weak answers until explicit score anchors and strict-grader instructions were added, and a friend preparing for placements reported the improved sample answers and per-question review were the most useful parts.

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend https://dev.to/challenges/hacktoberfest-weekend-2026-10-01 FRIEND is in placement season and told me one thing scares them most: SPECIFIC FEAR, e.g. "I freeze when they ask about my own project." Paid mock interview tools cost money and want a full resume uploaded to someone else's server. So I built Mock Interview Partner : upload a resume, pick a role, and it interviews you. It asks questions about your actual projects, pushes back on vague answers with follow-ups, scores each answer strictly from 1 to 5, and writes a report with weaknesses and a practice plan. It runs completely on a laptop. Optional: a 30 to 60 second screen recording. A free, private mock interviewer for placement season. Upload a resume, choose a target role, and get tailored questions, follow-ups, strict per-answer feedback and a final report. It runs entirely on your own laptop with an open-weight model, so nothing is sent to a server. Built for the Hacktoberfest Weekend Challenge: Build for a Friend . A resume contains a phone number, address, grades and email, and a mock interview records someone's honest mistakes. Neither belongs on a server you don't control. Running a local open-weight model means nothing leaves the laptop, and no internet is needed once the model is downloaded. Open source also gives you: llama3.2:3b through Ollama, fully on a 6 GB RTX 4050 The flow is: resume text → structured profile → question plan → answer → score and feedback → optional follow-up → final report. Everything behavioural lives in plain text: the prompts, three interviewer personas and a rubric.yaml with score anchors. What went wrong: WRITE ONE REAL FAILURE. Ideas: the model gave 4 or 5 out of 5 to weak answers until I added explicit score anchors and told it to grade like a strict interviewer. Or the 7B model split between GPU and CPU on 6 GB of VRAM and was painfully slow, so I moved to the 3B model X seconds per answer instead of Y . I ran a mock interview with FRIEND NAME, who is preparing for placements, and asked for honest feedback afterwards. Their first reaction was a simple "wow." What they found most useful was the improved sample answers and the review after each question. When I asked what the tool showed them that they hadn't noticed, they said: "I missed too many things, and this app pointed them out. I improved my answer." I also asked whether any scoring felt unfair, since a strict grader is only useful if it's fair. They said no, the feedback looked fair to them. Their verdict on using it again before placements: "Yes, it will help me a lot." That last answer is what I built it for: a free place to practise as many times as they need, without a resume or answers going to anyone else's server.