For my team, it is Github + Slack + Google Drive. Most of the ongoing discussion/planning is on slack which we try to maximize by adding each activity we can there including screenshots of imp conversations, ideas and decisions made. We store all the work including creative output and more structured plans/data on company-wide shared Google Drive. Github stores all the code base and product documentation.

This is a text to image search using deep learning, vector similarity search. Ask me anything.

What's the point of the deep learning model? Why not just search the metadata of the images?

What in your system is doing the text-to-vector encoding, and how did you train it?

We're using Transformers with `sentence-transformers/paraphrase-distilroberta-base-v1` model.

The framework is Jina (https://github.com/jina-ai/jina/) so it's pretty high-level. You can see the indexing/search Flow on lines 37-52 of https://github.com/alexcg1/jina-meme-search-example/blob/mai...

We rely on pre-trained models at the moment, since Jina supports loads of them out of the box.

For image search we use Big Transfer Encoder (https://github.com/jina-ai/executors/tree/main/jinahub/encod...) but may switch to CLIPImage encoder at some point

Thank you for sharing. This is helpful.