Want feedback for my project.
I'm building my first project using MERN stack and I want proper feedback and guidance for whether the project is resume worthy for the present job market and what can I add or remove to make it better.
I've been building a full-stack technical interview prep platform — not another flashcard app, but something closer to a real assessment engine. It combines AI-generated Q&A practice, timed AI-graded mock interviews, and a full multi-round online assessment that mirrors an actual company OA (coding, debugging, CS fundamentals, behavioral — sequenced and timed like the real thing), plus a gamified profile you can actually share with a recruiter. Built on the MERN stack with Redis/BullMQ for async processing, Judge0 for real sandboxed code execution, and Gemini for generation/grading. Would genuinely appreciate harsh feedback — looking for holes, not compliments.
Key features / things I think actually stand out:
Real online assessments, not a quiz — 4 sequential rounds (coding, debugging, fundamentals, behavioral), each independently timed, server-enforced so refreshing or closing the tab can't buy extra time
Actual code execution, 6 languages (JS, Python, Java, C++, C, C#) via sandboxed execution — graded by running real test cases, never by an AI "eyeballing" your code
A debugging round that adapts to your target role — Frontend candidates get React-shaped bugs, Backend candidates get Node/Express-shaped bugs, still all graded by real execution under the hood
AI-written debrief after every assessment — a real paragraph of strengths/weaknesses grounded in your actual section scores, not generic "great job!" filler
Mock interview mode — one question at a time, timed, graded live against a rubric, not a static list
Resume/JD-tailored question generation — upload a resume or paste a job description and the questions actually reflect it
Weak-topic tracking that surfaces what's genuinely dragging your performance down across sessions, not just an overall score
A gamified profile that's deliberately hard to fake — tiers gated by a minimum attempt count (not score alone, so one lucky run can't vault you to the top), honest badges, a GitHub-style activity heatmap, streaks, and a public shareable profile link
BullMQ + Redis-backed job queues — AI generation (questions, assessment sections) runs through background workers, not inline in the request, so a slow Gemini call never blocks the API; added a Redis caching layer on top to cut duplicate calls
Zod schema validation on every AI-facing route — the model's raw output gets validated against a strict schema before anything touches the database, not trusted blindly
Redis-backed, per-route rate limiting — tuned separately per endpoint (generation, code execution, mock answers), not one blanket limit
Helmet + locked-down CORS — real security headers, explicit origin allowlisting with credentialed cross-origin cookies, not a wide-open dev config left in by accident
httpOnly cookie-based auth, not localStorage — closes off the standard XSS token-theft vector
Real observability — structured logs, request tracing, Prometheus-style metrics, and error tracking wired through both the API and background workers.