Can I vibecode Careerflow?

Build a comprehensive, production-ready personal replacement for Careerflow in an empty repository. You will create an AI-powered career platform designed to help job seekers streamline their job search, optimize application materials, and stay organized.
Use Next.js 15, TypeScript, SQLite, Drizzle ORM, Tailwind CSS, and the OpenAI API. 
The core features to implement are:

1. Job Search Management (CRM):
- Build a centralized Kanban board dashboard to track job applications across various stages (e.g., Saved, Applied, Interview, Offer, Rejected).
- Create a detailed view for each job to store the job description, company details, salary, location, and application deadline.
- Provide a simple bookmarklet or web scraper utility (to replace a browser extension) that can extract job details from any URL and save them directly into the CRM.

2. Resume and Application Optimization:
- Implement an AI Resume Builder that allows users to create ATS-friendly resumes by inputting their work history, education, and skills.
- Integrate an AI-powered bullet point writer that uses the OpenAI API to enhance descriptions and tailor them to specific job requirements.
- Build an ATS Score Checker that compares the user's resume against a saved job description, calculates a match score (0-100), and highlights missing keywords or formatting improvements.
- Create an AI Cover Letter Generator that automatically drafts personalized cover letters based on the user's resume data and the target job description.

3. LinkedIn Profile Optimization:
- Develop a LinkedIn Profile Analyzer tool where users can input their profile details (headline, summary, experience).
- Provide an instant score and a personalized checklist of improvements.
- Include AI-generated rewrites for headlines and summaries to increase visibility to recruiters, along with expert tips.

4. Networking and Preparation:
- Build a Networking Tracker to organize and manage professional contacts, store notes, log interactions, and set reminders for follow-ups.
- Implement an Interview Preparation module that provides AI-driven mock interview questions tailored to the saved job descriptions, allowing users to practice and receive self-review rubrics or AI feedback on their answers.

Technical Requirements:
- Make the first run work locally with one documented command.
- Store all user data locally by default in SQLite to ensure privacy and make data export to JSON or CSV straightforward.
- Put API keys and secrets in .env, ship .env.example, and never commit credentials.
- Provide clear empty states, loading skeletons, success notifications, and recoverable error handling for all AI operations and data mutations.
- Ensure the UI is highly polished, responsive, and features modern micro-interactions.
- Write a thorough README with setup instructions, architecture overview, and data backup steps.
- Deliberately leave out user accounts, billing, telemetry, analytics, and mass-application spamming features.