Lohnt sich dein eigenes Final Round AI?

Build a closest honest personal substitute for Final Round AI in an empty repository.
Use Next.js 15, TypeScript, SQLite, Drizzle ORM, and an optional OpenAI API; do not offer alternative stacks.
The core loop is: run ethical offline interview practice with post-session review, tailor user-authored materials against a supplied role, and keep every claim traceable to the user's own evidence, without live deceptive assistance.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Create a structured evidence bank for roles, projects, skills, dates, metrics, and source notes.
Import a job description and highlight requirements without inventing missing experience.
Generate a tailored resume variant only from approved evidence and show the source for each bullet.
Add application stages, contacts, tasks, dates, notes, documents, and a follow-up view.
Provide interview-question practice with answer notes and a self-review rubric, not deceptive live assistance.
Export resume data as JSON and PDF plus the application tracker as CSV.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out automatic mass application.
Deliberately leave out fabricated qualifications or deceptive interview assistance.
Deliberately leave out proprietary recruiter databases and guaranteed job outcomes.
Finish by running the tests and listing the exact commands used.