What I Built Interview Coach — an AI-powered interview practice tool that uses Google Gemma 4 to conduct realistic mock interviews, evaluate your answers in real-time, and generate detailed performance reports. It runs entirely in the browser. No backend, no server, no accounts. Just Gemma 4's brain and your ambition. hajirufai.github.io Live Demo: hajirufai.github.io/gemma4-interview-coach (bring your own free API key from Google AI Studio) GitHub Repo: github.com/hajirufai/gemma4-interview-coach 91% of candidates who fail online assessments never practiced under timed conditions. Interview prep tools exist, but they're either: Expensive ($30-50/month for premium) Generic (same questions for everyone) Passive (read answers, not practice them) What if you had a personal interview coach that: Adapts to YOUR experience level Gives feedback on YOUR specific answers Is completely free, forever? That's what Gemma 4 makes possible. Mode What It Does 🗣️ Behavioral STAR-method questions on leadership, conflict, teamwork 💻 Technical Coding problems, algorithms, data structures 🏗️ System Design "Design Twitter" style architecture challenges 📝 Assessment Simulated OA with aptitude + coding + logic 🏆 Certification Exam-style questions (AWS, Azure, GCP, etc.) 📊 Case Study Business cases with structured frameworks Each mode has a unique system prompt that shapes how Gemma 4 behaves — asking follow-ups, evaluating with specific criteria, and calibrating difficulty to entry/mid/senior/lead levels. This wasn't "I needed an LLM and Gemma 4 was there." Every architectural decision traces back to specific Gemma 4 capabilities: Interview practice isn't a one-shot Q&A. It's a 15-20 turn conversation where the coach needs to: Remember your answer to Q1 when evaluating Q8 Notice patterns ("You keep avoiding specifics — let me push harder") Generate a final report that references the entire session Gemma 4's 128K context means the full conversation — system prompt + 15 questions + 15 answers + 15 feedback blocks — fits comfortably in a single context window. No chunking, no summarization, no lost context. System prompt: ~800 tokens Per Q&A round: ~500 tokens (question + answer + feedback) 15 rounds: ~7,500 tokens Final report: ~2,000 tokens Total: ~10,300 tokens ← well within 128K Gemma 4 has built-in "thinking" tokens (the API returns them with thought: true). When the model evaluates your answer, it first reasons internally: { "text": "The user's answer mentions leading a team...\n- Did they follow STAR? Partially...\n- Specificity? Low...\n- Selected response: provide feedback on adding metrics", "thought": true }, { "text": "Good start! You mentioned leading the migration, but I'd love more specifics..." } This produces dramatically better feedback than models that generate evaluations in a single pass. The thinking tokens ensure the model actually considers what was good AND what was missing before responding. Interview coaching is conversational. Every second of latency breaks the "interview feel." The 26B MoE variant activates only ~4B parameters per token, delivering: 1-3 second response times on Google AI Studio free tier Near-31B quality for reasoning tasks Lower compute costs if self-hosted For comparison, the 31B Dense model takes 5-10 seconds per response — fine for deep analysis but disruptive for rapid-fire interview Q&A. This was non-negotiable. Interview prep should not be gated behind a paywall. Gemma 4 runs on: Google AI Studio free tier (no credit card) Locally via Ollama on a decent laptop Hugging Face for research Even the 2B/4B variants run on phones and Raspberry Pi Browser ──(HTTPS)──> Google AI Studio API │ Gemma 4 26B MoE or 31B Dense The entire app is one HTML file (~560 lines). No React build, no Node.js server, no database. Why? Privacy: Your API key and interview responses never touch a third-party server. Everything stays in the browser. Cost: $0 hosting. Put it on any CDN, GitHub Pages, or just open the file locally. Speed: No proxy server round-trip. Browser → Gemma 4 → Browser. Simplicity: git clone && open index.html — that's the full setup. The tradeoff: Users need their own API key. I chose this intentionally — it keeps the tool free forever and teaches users about AI APIs in the process. At any point during a session, you can hit "Score Me" for a mid-session evaluation: 📊 Session Scorecard 1. Communication Clarity: 7/10 2. Technical Depth: 6/10 3. Problem-Solving Approach: 8/10 4. Self-Awareness: 7/10 5. Overall Readiness: 7/10 Top Strengths: • Strong structured thinking • Good use of STAR method • Honest about knowledge gaps Areas to Improve: • Add specific metrics and numbers • Reduce filler words • Practice time management Overall Score: 35/50 Verdict: Almost Ready — one more session should do it The end-of-session report includes a personalized study plan with 3 specific actions for the coming week. Get a free API key from aistudio.google.com/apikey Open the app Choose your mode, role, and experience level Practice! It works on desktop and mobile. No installation needed. Frontend: Vanilla HTML + Tailwind CSS (CDN) AI: Google Gemma 4 26B MoE / 31B Dense via Generative Language API Markdown: Custom lightweight renderer State: In-memory (browser) Deploy: Static file (anywhere) Gemma 4's thinking tokens are game-changing for evaluation tasks. The model genuinely considers multiple aspects before responding, producing feedback that feels like a real interviewer's assessment. 128K context is overkill for most apps — but perfect for coaching. The ability to reference earlier answers creates a coherent coaching experience that shorter-context models can't match. The MoE architecture is underappreciated for interactive apps. The speed difference between 26B MoE and 31B Dense is night-and-day for conversational UX. Choose MoE for chat, Dense for analysis. Zero-backend AI apps are viable and powerful. Browser → API → Browser eliminates 90% of infrastructure complexity. The main cost is that users bring their own key — but for free tools, that's a feature, not a bug. What's Next Image input: Upload screenshots of coding challenges for visual analysis (Gemma 4 supports multimodal) Voice mode: Speak your answers for a more realistic interview feel Session history: LocalStorage persistence so you can track improvement over time Community question banks: Curated questions per role/company Live: hajirufai.github.io/gemma4-interview-coach Code: github.com/hajirufai/gemma4-interview-coach License: MIT — fork it, improve it, ship it. Built by Haji Rufai — creator of Interview Buddy, an AI-powered interview preparation platform.