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Interview Assignment: Blog-to-Video Automation Pipeline

Overview

This is a screening assignment for our developer position. The task involves building an automated pipeline that converts blog posts from our knowledge hub into engaging video content using AI technologies and automation workflows.

Background

Content repurposing is crucial for modern digital marketing, but manually converting blog posts into videos is time-consuming and resource-intensive. This assignment focuses on building an automated system that can transform written content into professional video presentations while maintaining the educational value and brand consistency.

Task Description

Build an automated pipeline using Python that converts blog posts from Alphanome's knowledge hub into video content. The system should handle text processing, script generation, visual asset creation, and final video composition with appropriate transitions and branding.

Source Content

  • Access to Alphanome's knowledge hub blog posts at https://www.alphanome.ai/knowledgehub
  • Focus on AI technology or finance related content
  • Consider both technical and narrative elements of the content

Technical Requirements

Core Features

  • Extract and process blog post content programmatically
  • Generate optimized video scripts from blog content
  • Create dynamic visual assets (charts, graphs, text overlays)
  • Implement text-to-speech with natural-sounding voices
  • Automate video composition with transitions
  • Apply consistent branding elements
  • Generate appropriate video thumbnails

Technical Specifications

  • Use Python as the primary language
  • Implement content extraction and processing pipeline
  • Integrate with text-to-speech APIs
  • Use video composition libraries (e.g., MoviePy, OpenCV)
  • Create automated visual asset generation
  • Include progress monitoring and error handling
  • Build modular components for each stage of the pipeline

Quality Standards

  • Write maintainable, documented code
  • Include comprehensive type hints
  • Follow Python PEP 8 style guidelines
  • Implement proper logging and monitoring
  • Document each pipeline component
  • Include unit tests for critical components

Evaluation Criteria

Technical Implementation (35%)

  • Code quality and organization
  • Content processing accuracy
  • Pipeline automation effectiveness
  • Error handling and recovery
  • Resource management
  • Testing coverage

Video Quality (25%)

  • Visual appeal and professionalism
  • Audio clarity and synchronization
  • Transition smoothness
  • Branding consistency
  • Overall production value

Content Adaptation (25%)

  • Script generation quality
  • Visual asset relevance
  • Key message retention
  • Audience engagement potential
  • Content flow and pacing

Performance and Scalability (15%)

  • Processing time optimization
  • Resource usage efficiency
  • Parallel processing implementation
  • API usage optimization
  • Storage management

Submission Guidelines

Required Files

Your submission should be a ZIP file containing:

  • Complete source code of the pipeline
  • README.md with:
    • Setup instructions
    • API configuration steps
    • Pipeline execution guide
    • Component documentation
    • Performance optimization notes
  • Requirements.txt or similar dependency file
  • Sample output videos (2-3 examples)
  • Testing scripts and documentation

Submission Process

Send your submission as a compressed ZIP file to [email protected]

Important Notes

  • Implement appropriate rate limiting for all APIs
  • Include error handling for each pipeline stage
  • Document assumptions about blog post structure
  • Consider implementing caching mechanisms
  • Focus on maintainability and scalability

Time Expectation

  • The assignment should take approximately 5-10 days to complete
  • Focus on core functionality first, then add improvements if time permits

Questions?

If you have any questions about the assignment, please email [email protected]

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