To overcome AI's tendency for generic descriptions of archival images, Tim McLear's scripts first extract embedded metadata (location, date). This data is then included in the prompt, acting as a "source of truth" that guides the AI to produce specific, verifiable outputs instead of just guessing based on visual content.
When an AI coding assistant gets off track, Tim McLear asks it to generate a summary prompt for another AI to take over. This "resume work" prompt forces the AI to consolidate the context and goal. This summary often reveals where the AI misunderstood the request, allowing him to correct the course and restart with a cleaner prompt.
To move beyond keyword search in their media archive, Tim McLear's system generates two vector embeddings for each asset: one from the image thumbnail and another from its AI-generated text description. Fusing these enables a powerful semantic search that understands visual similarity and conceptual relationships, not just exact text matches.
Instead of storing AI-generated descriptions in a separate database, Tim McLear's "Flip Flop" app embeds metadata directly into each image file's EXIF data. This makes each file a self-contained record where rich context travels with the image, accessible by any system or person, regardless of access to the original database.
To analyze video cost-effectively, Tim McLear uses a cheap, fast model to generate captions for individual frames sampled every five seconds. He then packages all these low-level descriptions and the audio transcript and sends them to a powerful reasoning model. This model's job is to synthesize all the data into a high-level summary of the video.
Tim McLear used AI coding assistants to build custom apps for niche workflows, like partial document transcription and field research photo logging. He emphasizes that "no one was going to make me this app." The ability for non-specialists to quickly create such hyper-specific internal tools is a key, empowering benefit of AI-assisted development.
Tim McLear's journey to automate metadata logging began with a single Python script. As its value became clear, he evolved it into a robust REST API service running on a dedicated machine. This service now handles various metadata tasks for his entire film production team, demonstrating a clear path from solo experiment to shared infrastructure.
While generative video gets the hype, producer Tim McLear finds AI's most practical use is automating tedious post-production tasks like data management and metadata logging. This frees up researchers and editors to focus on higher-value creative work, like finding more archival material, rather than being bogged down by manual data entry.