Vibe Coding / AI Coding - Amjad MasadInterview with Replit Cofounder and CEO Amjad Masad aired on © Who Is Technology 2025.Tech CEO InterviewAI codingprompt ...

Transform interviews with InterviewAI. Practice smarter, hire faster, and streamline your process with advanced AI tools designed for candidates and recruiters.

(Taking above advice into account) this looks really hard… * * * # Advice for building **InterviewAI** : real-time AI interview feedback with webcam, speech, and careful feedback I would build **InterviewAI** as an **evidence-based interview practice coach** , not as a facial-emotion or “confidence” detector. The safest and strongest version is: webcam + microphone → visible/speech measurements → reliability checks → transcript/rubric analysis → evidence-grounded coaching feedback The risky version is: face/body/voice → emotion/confidence/honesty/personality/hireability score That second version can look impressive in a demo, but it is hard to validate and easy to overclaim. A better product reports **observable behavior** : * speaking pace * long pauses * filler words * answer length * whether the answer addressed the question * STAR structure: situation, task, action, result * face visible percentage * screen-facing / looking-away estimate * head movement stability * shoulder/posture visibility * hand-to-face episodes * camera/audio quality It should avoid unsupported psychological judgments such as: * “you lacked confidence” * “you looked nervous” * “you seemed dishonest” * “your personality is weak” * “you are not hireable” A good feedback sentence is: > “During answer 2, you had four pauses longer than 2.5 seconds and did not include a clear result. Try answering again with: situation, action, result, and one measurable outcome.” Not: > “You lacked confidence.” This matters technically and ethically. HireVue, a major video-interview vendor, discontinued facial analysis in screening assessments after concerns about AI use in employment decisions: SHRM report, EPIC note, Wired coverage. If InterviewAI stays a **user-owned practice tool** , risk is much lower than if it becomes an employer-facing candidate-ranking system. * * * ## 1. Recommended architecture Use a modular architecture. Do not build one giant “interview quality model” first. Client app ├── webcam capture │ ├── face detection / face landmarks │ ├── head pose or screen-facing estimate │ ├── pose landmarks │ ├── hand landmarks │ └── frame-quality checks │ ├── microphone capture │ ├── voice activity detection │ ├── speech-to-text │ ├── pause detection │ ├── speaking pace │ └── filler-word detection │ ├── interview engine │ ├── question generation │ ├── role-specific question bank │ ├── follow-up questions │ └── answer segmentation │ ├── feature aggregator │ ├── per-frame visual features │ ├── per-answer speech features │ ├── transcript features │ ├── rolling averages │ ├── confidence/reliability flags │ └── event timeline │ ├── feedback engine │ ├── deterministic metric summaries │ ├── rubric-based answer analysis │ ├── LLM-generated coaching │ ├── safety/claim checker │ └── practice recommendations │ └── report UI ├── per-answer feedback ├── timeline ├── evidence for each suggestion ├── reliability caveats └── progress over sessions The most important separation is: measurement layer ≠ interpretation layer The measurement layer should output facts: { "answer_id": "answer_002", "duration_seconds": 74, "wpm": 142, "long_pauses": 4, "filler_words": 11, "face_visible_ratio": 0.93, "screen_facing_ratio": 0.68, "hand_to_face_events": 5, "posture_feedback_valid": false, "posture_invalid_reason": "shoulders not visible enough" } The interpretation layer turns those facts into coaching: > “Your action was clear, but the result was missing. You also had four long pauses. Try answering again using situation, action, result, and one measurable outcome.” * * * ## 2. Build in stages ### Stage 1 — transcript-only MVP Start here before webcam analysis. question → user speaks answer → ASR transcript → WPM / pauses / fillers → rubric feedback → report Features: * generate interview questions * record audio * transcribe answer * compute speaking pace * detect long pauses * count filler words * check answer structure * generate feedback Example output: Answer length: 74 seconds Speaking pace: 142 WPM Long pauses: 4 Filler words: 11 STAR structure: situation: present task: unclear action: present result: missing Feedback: Your action was clear, but the result was missing. Add one sentence explaining what changed because of your action. This is already useful and much easier to evaluate than facial emotion recognition. ### Stage 2 — basic webcam observables Add: * face visible percentage * face centeredness * screen-facing estimate * looking-away episodes * head movement variance Use these as **observations** , not psychological claims. ### Stage 3 — pose and hands Add: * shoulder visibility * upper-body stability * posture validity flag * hand-to-face episodes * large movement spikes Good feedback: > “Your hand moved near your face five times during this answer.” Bad feedback: > “You were anxious.” ### Stage 4 — job-aware coaching Add: * resume parsing * job-description matching * role-specific rubrics * retrieval of coaching examples * answer-to-rubric comparison Use embeddings/rerankers for this. ### Stage 5 — evaluation and safety Add: * manually labeled benchmark sessions * detector precision/recall/F1 * feedback faithfulness checks * unsupported-claim detector * accessibility modes * privacy controls * * * ## 3. Computer vision direction For the first version, use **landmark tracking** , not a custom visual model. Recommended first tools: * MediaPipe Face Landmarker for Web * MediaPipe Pose Landmarker for Web * MediaPipe Hand Landmarker for Web * OpenCV for image/video utilities * OpenFace for offline/research facial behavior analysis * MMPose / RTMPose later if MediaPipe is not enough MediaPipe is a good MVP choice because it is real-time, browser/mobile friendly, and gives the kind of coordinates you need: face, body, and hand landmarks. Use OpenFace mainly for research/offline validation. It supports facial landmarks, head pose, facial action units, and eye gaze. Use it to compare signals, not to claim internal emotion. * * * ## 4. Hugging Face models/libraries that can help Use Hugging Face mainly for **ASR, LLMs, datasets, evaluation, embeddings, demos, and optional experiments**. For real-time camera landmarks, MediaPipe is usually the better first tool. ### Speech-to-text Good candidates to test: * openai/whisper-large-v3-turbo * CohereLabs/cohere-transcribe-03-2026 * nvidia/canary-qwen-2.5b * nvidia/parakeet-tdt-0.6b-v3 ASR matters because transcript quality affects everything else: filler words, answer structure, relevance, and feedback quality. Evaluate ASR on your own mock-interview audio. Generic WER is not enough. Measure: * word error rate * filler-word recall * timestamp quality * pause boundary accuracy * speed/latency * accent robustness * microphone robustness Useful benchmark: * Open ASR Leaderboard * hf-audio/asr-leaderboard-longform ### Face/body/hand models on Hugging Face Useful HF-hosted MediaPipe-style models: * qualcomm/MediaPipe-Face-Detection * qualcomm/Facial-Landmark-Detection * qualcomm/MediaPipe-Pose-Estimation * qualcomm/MediaPipe-Hand-Detection * qualcomm/RTMPose-Body2d Use these for: * face visibility * face framing * head movement * shoulder visibility * upper-body stability * hand-to-face events Do not use them to infer confidence or nervousness. ### Facial-expression models Treat these as optional experiments only: * trpakov/vit-face-expression * mo-thecreator/vit-Facial-Expression-Recognition * Tanneru/Facial-Emotion-Detection-FER-RAFDB-AffectNet-BEIT-Large These classify expression labels like angry, happy, sad, surprise, neutral, etc. That is not the same as detecting interview confidence, nervousness, honesty, or hireability. Safe label: > “experimental facial-expression classifier output” Unsafe label: > “candidate confidence score” ### Feedback LLMs The LLM should receive structured evidence and write coaching feedback. It should not invent visual claims. Candidates to test: * Qwen/Qwen3.6-35B-A3B * Qwen3 collection * Qwen3.5 collection * meta-llama models Example LLM input: { "question": "Tell me about a time you handled conflict.", "transcript": "I had a disagreement with a teammate...", "speech_metrics": { "wpm": 138, "long_pauses": 3, "filler_words": 9 }, "vision_metrics": { "face_visible_ratio": 0.94, "screen_facing_ratio": 0.71, "hand_to_face_events": 4, "posture_feedback_valid": false }, "rubric": { "situation": true, "task": true, "action": true, "result": false }, "instruction": "Use only the evidence above. Do not infer confidence, nervousness, honesty, personality, or hireability." } ### Embeddings and rerankers Useful if you want job/resume-aware coaching: * Qwen/Qwen3-Embedding-0.6B * Qwen/Qwen3-Embedding-8B * Qwen/Qwen3-Reranker-8B * MTEB leaderboard Use embeddings for: resume → retrieve relevant past projects job description → retrieve required competencies question → retrieve rubric answer → retrieve coaching examples Do not use embedding similarity alone as an interview-quality score. ### Safety / prompt-injection guard If users upload resumes, job descriptions, or company pages, protect the feedback LLM from prompt injection. Options: * meta-llama/Llama-Prompt-Guard-2-86M * protectai/deberta-v3-base-prompt-injection-v2 * meta-llama/Llama-Guard-4-12B Also add a custom validator that blocks unsupported feedback such as: you looked nervous you lacked confidence you seemed dishonest you are not hireable your personality is weak * * * ## 5. Should you train your own model? Do not train your own model first. Start with: 1. pretrained ASR 2. pretrained landmarks 3. rule-based metrics 4. LLM feedback from structured evidence 5. manual evaluation Fine-tune only after you have: * a narrow observable target * labeled data * a baseline * a clear failure case * evaluation metrics Good fine-tuning targets: Target | Good label ---|--- ASR adaptation | transcript text filler detection | token-level filler labels long pauses | timestamped pause segments looking away | timestamped looking-away segments hand-to-face | timestamped hand-near-face events answer quality | rubric scores by human reviewers feedback style | human coach feedback examples Bad fine-tuning targets: Target | Problem ---|--- confidence | vague internal state nervousness | not directly observable honesty | not valid from video/audio personality | ethically and scientifically risky hireability | high-stakes and bias-prone * * * ## 6. Real-time webcam inference best practices ### Run models slower than camera FPS Suggested rates: Component | Rate ---|--- webcam preview | 30 FPS face landmarks | 10–15 FPS pose landmarks | 5–10 FPS hand landmarks | 5–10 FPS expression classifier, if any | 1–3 FPS full LLM feedback | after each answer Do not generate feedback every frame. ### Smooth signals Landmarks jitter. Use: * moving average * exponential moving average * median filter * hysteresis thresholds * minimum event duration Bad: frame 1834 = looking away Better: looking-away event: start: 00:41.2 end: 00:46.8 duration: 5.6 seconds confidence: medium ### Use calibration At session start: Please sit naturally and look at the screen for 5 seconds. Capture baseline: * head yaw * head pitch * face center * shoulder position * distance from camera * lighting quality Then compare future movement to that user’s baseline. ### Use reliability gates Do not report metrics when evidence is weak. Condition | Action ---|--- face visible < 60% | do not report screen-facing estimate shoulders not visible | do not report posture hand landmarks unstable | do not report hand-to-face events audio quality poor | warn transcript metrics may be unreliable multiple faces visible | pause analysis or warn low lighting | ask user to improve lighting A good caveat: > “I could not reliably evaluate posture because your shoulders were not visible enough.” ### Store features, not raw video Prefer: { "timestamp_ms": 12800, "face_visible": true, "head_yaw": -0.12, "head_pitch": 0.04, "screen_facing_estimate": true, "left_hand_near_face": false, "right_hand_near_face": true } Do not store raw video unless the user explicitly opts in. Browser webcam access uses `getUserMedia()`; see MDN getUserMedia. * * * ## 7. Evaluation plan Do not evaluate by asking: > “Does the generated feedback sound good?” A polished LLM can produce convincing but false feedback. Evaluate by asking: > “Did the system correctly detect the observable things it claims to detect, and did the feedback stay faithful to those measurements?” ### Build a labeled benchmark Create: 30–100 mock interview sessions 5–10 minutes each 10+ users different webcams different microphones different lighting different accents camera-on and camera-off cases Manually label: Label | Type ---|--- long pauses | timestamped start/end filler words | transcript tokens looking away | timestamped segments face visible | per frame or per second hand near face | timestamped segments shoulders visible | segment-level posture feedback valid | yes/no STAR structure | present/missing answer relevance | rubric score unsupported feedback claim | yes/no ### Metrics Component | Metric ---|--- ASR | WER, filler recall, timestamp error pause detector | precision, recall, F1, boundary error looking-away detector | event F1, false positives/min hand-to-face detector | event precision/recall posture validity | accuracy of “can judge / cannot judge” answer rubric | agreement with human reviewer feedback | faithfulness, helpfulness, unsupported-claim rate overall | user usefulness rating + objective detector scores Example report: InterviewAI v0.2 evaluation Dataset: 48 mock interview sessions 14 participants 7 webcam/microphone setups 326 answer segments ASR: WER: 8.9% filler-word recall: 0.81 average timestamp error: 420 ms Long pauses: precision: 0.91 recall: 0.85 F1: 0.88 Looking-away estimate: precision: 0.76 recall: 0.69 F1: 0.72 false positives: 0.7/min Hand-to-face: precision: 0.82 recall: 0.64 F1: 0.72 Feedback: evidence faithfulness: 96% unsupported psychological claims: 0% human coach agreement: 0.74 * * * ## 8. Datasets and benchmarks ### Interview-question datasets Useful for question generation and technical QA examples: * AI-Mock-Interviewer/Train_data * K-areem/AI-Interview-Questions * ali-alkhars/interviews Use them for: * question generation * role-specific question bank * technical interview examples * simple instruction-tuning experiments Do not treat them as reliable answer-quality labels. ### ASR datasets Useful for transcription evaluation: * hf-audio/asr-leaderboard-longform * edinburghcstr/ami * facebook/omnilingual-asr-corpus * nilc-nlp/CORAA-MUPE-ASR ### Face/head/gaze/pose datasets Useful for component experiments: * ETHZurich/biwi_kinect_head_pose * vigil1917/GazeGene * fhswf/wholebody-pose-estimation-fingerspelling ### FER/SER datasets Use only for optional research: * deanngkl/ferplus-7cls * abhilash88/fer2013-enhanced * laion/emonet-face-hq * ak0255/Synthesis_SER * GDGiangi/SEIRDB Do not use FER/SER datasets as proof that you can detect interview confidence or nervousness. ### Prefer no-script datasets Prefer Hugging Face datasets stored as Parquet, JSON, CSV, image folders, or audio files. Avoid datasets that require custom Python dataset builder scripts or `trust_remote_code=True` when possible. Relevant docs: * Hugging Face dataset loading scripts * Dataset scripts no longer supported discussion * * * ## 9. Similar projects to study Use these for architecture and UX ideas, not as proof of validity. * egekaraca/ai-interview-coach * KentTDang/AI-Interview-Coach * yaotingchun/VoxLab * SergioSediq/interview-coach * Aditi-T27/InterviewAnalyser * Mohamed-samy2/Video-Interview-Analysis * SatyamPote/Ai-Video-Interviewer * AI mock interview Space Projects that say “confidence analyzer” or “candidate scoring” are useful cautionary examples. Their architecture may be interesting, but the framing is risky. Better names: Interview practice coach Interview delivery analyzer Observable behavior feedback system Mock interview feedback assistant Riskier names: confidence detector nervousness analyzer honesty detector hireability evaluator personality detector * * * ## 10. Legal/safety/product-positioning warnings If InterviewAI is a **candidate-owned practice tool** , the risk is much lower. If it becomes an **employer-facing automated screening tool** , the risk increases sharply. Relevant references: * NYC Local Law 144 AEDT page * Illinois Artificial Intelligence Video Interview Act * EEOC AI and algorithmic fairness initiative * EEOC AI and ADA resources * NIST AI Risk Management Framework Recommended disclaimer: InterviewAI analyzes observable practice signals such as transcript quality, speaking pace, pauses, filler words, face visibility, screen-facing estimate, and hand/pose landmarks. InterviewAI does not infer honesty, personality, mental health, true confidence, emotional state, or hireability. * * * ## 11. Common mistakes to avoid ### Mistake 1 — leading with emotion recognition Facial expression recognition sounds impressive, but it is not the strongest core feature. It is hard to validate and easy to overclaim. Use it as: optional experimental expression classifier Not as: confidence detector ### Mistake 2 — using one overall score too early Avoid: Interview score: 74/100 Confidence: 62/100 Professionalism: 80/100 Prefer: Speech: 146 WPM 3 long pauses 9 filler words Answer structure: situation: present action: present result: missing Camera: face visible: 94% screen-facing estimate: 71% hand-to-face events: 5 ### Mistake 3 — no reliability gates If the evidence is weak, say so. Do not fake posture or gaze feedback. ### Mistake 4 — letting the LLM invent observations Do not prompt: Analyze this candidate's confidence. Prompt: Use only the transcript and measured metrics. Do not infer mental state, honesty, personality, or hireability. ### Mistake 5 — no labeled evaluation set Most demos fail here. Build a small labeled mock-interview benchmark and report objective metrics. ### Mistake 6 — ignoring accessibility Eye contact, speech rhythm, posture, facial movement, and gesture patterns vary across people. Include: * camera-off mode * transcript-only mode * no penalty for gaze differences * manual self-review * user-controlled goals * clear caveats * * * ## 12. Recommended stack ### Web MVP Layer | Recommendation ---|--- frontend | Next.js / React webcam/mic | `getUserMedia`, MediaRecorder real-time CV | MediaPipe Tasks Web backend | FastAPI or Node ASR | Whisper / Cohere Transcribe / Canary / Parakeet feedback LLM | Qwen / Llama / API model embeddings | Qwen3 Embedding reranking | Qwen3 Reranker storage | PostgreSQL + object storage evaluation | Python, scikit-learn, Hugging Face Evaluate demo | Hugging Face Spaces or custom web deployment Useful docs: * Hugging Face Datasets * Hugging Face Evaluate * Hugging Face Spaces * Hugging Face model cards * ONNX Runtime Web ### Local/privacy-oriented version Layer | Recommendation ---|--- CV | MediaPipe/OpenCV locally ASR | local Whisper/faster-whisper-style runtime LLM | local Qwen/Llama if hardware supports storage | local SQLite reports | local HTML/PDF export * * * ## 13. Strong README positioning Use this: InterviewAI is an AI interview-practice coach that combines transcript analysis, speech timing, and webcam landmark tracking to give users evidence-based feedback. It measures observable practice signals such as speaking pace, long pauses, filler words, answer structure, face visibility, screen-facing estimate, head movement, upper-body stability, and hand-to-face movement. It does not infer honesty, personality, mental health, true confidence, nervousness, or hireability. Avoid this: InterviewAI uses emotion recognition to detect whether candidates are confident, nervous, honest, and hireable. * * * ## 14. Direct answers to the seven questions ### 1. Recommended architecture? Use: webcam + microphone → landmarks + transcript → observable metrics → per-answer aggregation → rubric scoring → LLM feedback from evidence → report with caveats Keep measurement separate from interpretation. ### 2. Which HF models/libraries help? Use Hugging Face for: * ASR: Whisper, Cohere Transcribe, Canary, Parakeet * LLM feedback: Qwen/Llama-style instruction models * embeddings/rerankers: Qwen3 Embedding/Reranker * datasets: ASR, interview questions, pose/gaze experiments * evaluation: Hugging Face Evaluate * demos: Spaces * safety: Prompt Guard / Llama Guard-style models Use MediaPipe/OpenFace/MMPose for camera landmarks. ### 3. Train, fine-tune, or pretrained? Use pretrained models first. Fine-tune only after you have labeled data and a clear failing baseline. Do not train a model to predict “confidence” or “nervousness” first. ### 4. Best practices for real-time webcam inference? * lower FPS inference * smoothing * calibration * async processing * feature storage instead of raw video * reliability gates * uncertainty reporting * feedback after each answer, not every frame ### 5. How to evaluate reliability? Create a labeled mock-interview benchmark and measure: * long-pause F1 * filler recall * looking-away event F1 * hand-to-face precision/recall * ASR WER * rubric agreement * feedback faithfulness * unsupported-claim rate ### 6. Datasets/models/pipelines? Use: * `AI-Mock-Interviewer/Train_data` * `K-areem/AI-Interview-Questions` * `hf-audio/asr-leaderboard-longform` * MediaPipe / OpenFace / MMPose * Whisper / Cohere / Canary / Parakeet * Qwen / Llama for feedback * Qwen3 Embedding/Reranker for retrieval * your own `interviewai-eval-v1` for final validation ### 7. Common mistakes? Avoid: * emotion overclaiming * confidence/honesty/hireability scoring * one overall score too early * no reliability gates * no labeled evaluation * raw-video storage by default * LLM-invented observations * ignoring accessibility * building an employer screening tool before addressing legal/fairness requirements * * * ## Final recommendation Build the boring measurable system first: observable signals + transcript analysis + reliability gates + evidence-grounded LLM feedback + manually labeled evaluation Do not start with: facial emotion recognition + confidence score + personality score + hireability score If the basic signals are noisy, a bigger model will mostly give you a more expensive noisy system. If the basic signals are reliable, the feedback layer can become genuinely useful.

I would be careful with the emotion recognition part. For a project like this, I would not try to build something that says the candidate is nervous, confident, honest, or anything like that from their face. That is the kind of feature that sounds impressive in a demo, but is very easy to overclaim and very hard to validate. A more useful version is to measure simple visible signals and report them back as practice feedback. Speaking pace, long pauses, looking away from the screen, too much head movement, slouching, repeated hand-to-face gestures, filler words, very short answers, and whether the answer actually addresses the question. Those are still imperfect, but at least they are observable. For the vision part, I would start with MediaPipe or OpenCV landmark tracking. I would not train a custom model at the beginning. Face landmarks and pose landmarks are enough to get rough head direction, shoulder posture, movement stability, and hand proximity to the face. If those basic signals are noisy, a bigger model will mostly give you a more expensive noisy system. You can run the webcam at a lower frame rate, extract landmarks every few frames, smooth the values over time, and combine those features with a speech-to-text transcript. Then the feedback layer can use the measured signals plus the transcript instead of pretending the camera alone understands interview quality. The part people usually underestimate is evaluation. Do not judge the system by whether the generated feedback sounds nice. Record some mock interviews, manually label simple things, and compare against that. Did it detect long pauses? Did it detect looking away? Did it confuse normal movement with bad posture? Keep the labels boring and concrete. I would also avoid psychological judgments in the output. Saying “you looked away from the screen during much of the answer” is fine. Saying “you lacked confidence” is mostly a guess dressed up as AI.

Hi everyone, I’m working on a project called **InterviewAI**. The idea is to build an AI-powered interview practice system that analyzes a user during a mock interview and gives feedback about their performance. The goal is not only to process what the user says, but also to analyze visual and behavioral signals from the camera. In general, the system should be able to: * Use the webcam in real time * Detect and track the user’s face/body * Analyze facial expressions or emotions * Detect signs such as posture, stability, eye contact, and self-touch gestures * Generate useful feedback for improving interview performance * Eventually combine computer vision results with an AI feedback system I’m still trying to understand the best technical direction for this project, especially for the computer vision and machine learning parts. My main questions are: 1. What would be the recommended architecture for this kind of real-time AI interview analysis system? 2. Which Hugging Face models or libraries could help with facial emotion recognition, body/pose analysis, or behavioral analysis? 3. Should I train my own model, fine-tune an existing model, or use pretrained models directly? 4. What are the best practices for real-time webcam inference? 5. How should I evaluate if the system is reliable and not giving misleading feedback? 6. Are there datasets, models, or pipelines you recommend for this type of project? 7. What common mistakes should I avoid when building a project like this? I’m looking for guidance on the correct workflow, possible model choices, and how to structure the project properly. Any advice, examples, or recommended resources would be really appreciated. Thank you!