Can I vibecode Apify?

Build a deliberately narrow personal substitute for Apify, not a full clone.
Use exactly this stack: Next.js 15 + TypeScript + PostgreSQL + BullMQ and Docker Compose to wire it all together; do not offer alternative stacks or languages.
The core loop is: Build one explicit web scraping platform workflow with a trigger, validated steps, retries, logs, and a manual replay button. Do not aim to be a general platform; focus on deep, reliable implementation of this specific core scraping loop.
Ensure the first run works locally with a single documented command (e.g., `docker compose up --build`).
Store all user data locally by default, avoiding any external SaaS dependencies for core functionality, and make data export straightforward via a dedicated API endpoint or script in standard formats like CSV or JSON.
Put all secrets, API keys, and database credentials in a `.env` file, ship a `.env.example`, and implement pre-commit hooks or CI checks to ensure credentials are never accidentally committed to version control.
Implement the smallest polished interface that completes the core loop end to end, using realistic sample data that is clearly labelled and easy to delete.
Store every scraping job run, scraped dataset, and execution log in the local PostgreSQL database for auditability and durability.
Design all side effects to be idempotent and resilient. Ensure partial scrapes can be resumed or retried without duplicating existing valid data.
Build in rate limiting, exponential backoff for API errors or blocking, dead-letter queues for unprocessable tasks, manual replay UI, execution cancellation for stuck scrapes, and a mechanism for secret rotation.
Ship a complete Docker Compose setup with health checks for all services, sample seed data, comprehensive test suites, database backup scripts, and clear documentation.
Design a minimal but polished web UI with clear states for empty, loading, validation, success, and recoverable errors, ensuring accessible keyboard navigation, focus states, and sensible contrast.
Implement strict input validation using modern libraries (like Zod), enforce safe data handling, and gracefully handle external web page changes or outages without crashing the application. Never log secrets or private file contents.
Write focused unit tests for the core data model and scraping logic, and at least one robust end-to-end happy path test using an automated testing framework.
Create an exhaustive README detailing the setup process, system architecture, permission models, data storage locations, backup/restore steps, and limitations.
Strictly avoid adding multi-tenant user accounts, billing systems, telemetry, tracking analytics, ads, or third-party accounts.
Deliberately exclude these paid-product advantages: OAuth app verification; durable execution at massive scale; automatic schema drift handling; and enterprise controls.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims. Where an external API is optional, keep the app useful without it and explain the degraded mode.
Finish by running the test suite, building the application, and outputting the exact commands used to verify the system. Fix errors rather than merely describing them.