• •Alex Chan implemented a local automated system to generate and store text descriptions for his personal photo archive.
• •The process uses LLMs to parse image metadata and visual content, creating searchable text files that bypass proprietary platform limitations.
• •The effectiveness of this system remains unproven for large-scale archives exceeding his current collection size, and long-term storage costs for AI-generated metadata are not yet quantified.

Software engineer Alex Chan has publicly documented a custom workflow for automatically generating and indexing descriptive metadata for his personal digital photo library. While commercial cloud services have offered similar auto-tagging for years, Chan’s approach prioritizes local control and interoperability by storing data in open, plain-text formats. However, the system relies on individual LLM calls for each image, which introduces potential scaling bottlenecks and ongoing API dependency concerns. Whether this architecture can serve as a template for long-term personal digital preservation will depend on future improvements to local model efficiency and metadata standardization.