It's essentially a small search engine for videos that runs locally on your laptop. Previously, the system just extracted information about whole video files and maintained a searchable list of those files.
I'm putting the finishing touches on a major architectural change to solve a request from someone who creates highlight reels for professional soccer matches. They needed to tag and search for specific moments within videos - like all goals scored by a particular player across dozens of camera angles of dozens of matches.
This required re-engineering the entire data model to support metadata entries that point to specific segments of a file entity rather than just the file itself.
Instead of treating each file as an atomic unit, I now separate the concept of "content" from the actual files that contain it. This distinction allows me to:
1. Create "virtual clips" - time segments within a video that have their own metadata, tags, and titles - without generating new files
2. Connect multiple files that represent the same underlying content (like a match highlight with different ad insertions for YouTube vs. Twitch)
3. Associate various resolution encodings with the same logical content
For example, a content creator might have multiple versions of the same video with different ad placements for different channels. In the old system, these were just separate, unrelated files. Now, they're explicitly modeled as variants of the same logical content, making organization and search much more powerful.
I also completely reworked the job system, moving from code running in the Electron main process to spawning Temporal in a child process. I know it sounds like overkill for a desktop app, but it's been surprisingly perfect for a desktop app that's processing hundreds of terabytes of video.
When you're running complex ML pipelines on random people's home computers, things go wrong - custom FFmpeg versions that don't support certain codecs, permission issues with mounted volumes, their local model server crashes. With Temporal, when a job fails, I just show users a link to the Temporal Web UI where they can see exactly what failed and why. They can fix their local config and retry just that specific activity instead of starting over. It's cut my support burden dramatically.
My developer experience is so much better too. The ML pipeline for face recognition has multiple stages (small model looks for faces, bigger model does pose detection, embedding generation with even larger model) that takes minutes to run. With Temporal, I can iterate on just the activity that's failing without rerunning the entire pipeline. Makes developing these data-intensive workflows so much more manageable.