AI-Powered Local Video Search & Analysis
A project built to help you transcribe, analayze and index your personal video library to help you search for the "exact" part of the video you're looking for with the help of local ML models and a local vector database. Your videos will never leave your computer or server (Docker support).
Hey đź‘‹, I'm Ilias Haddad, a software developer and content creator (on the side). I love creating and capturing videos, but with that, I accumulated a lot of videos, which makes it hard for me to manage and search across all of them. A project that started as a personal solution that transcribes videos only using Whisper, envolles to include video frame analysis, multi-modal embedding, chat assistant, image and text search. What I need from you : Feedback on the UX/workflow Bug reports and edge c
congrats @iliashaddad3 this looks great. looks powerful. can't wait to try it.
The local video library seems to be limited, is there a way to open it to a search in youtube (like top 30 videos) etc
Local video search tends to buckle when libraries hit tens of thousands of clips and you need fast reindexing plus precise timecodes for “exact moment” retrieval. A solid pattern is segment-level indexing with VAD, word-level alignment and diarization via WhisperX on top of faster-whisper, then store vectors + metadata with strong persistence (often Postgres + pgvector) to keep one backup and one query plane. :contentReference[oaicite:0]{index=0} How are you modeling segment boundaries and synci
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.
Check the similar products section on this page, or browse the category pages linked in the tags above. Each category page shows all products for a given year, sorted by engagement.