imgbeddings VS automq

Seconding the recommendation of CLIP embeddings, especially compared to image histograms + requiring OpenCV.

I wrote a naive, minimal dependency Python package to calculate image embeddings (https://github.com/minimaxir/imgbeddings) with some lookup demo notebooks and it works well in a pinch, although it's due for an upgrade.

Normally, this is Hacker News reductiveness, but yes, image classification via CLIP is that easy, especially with Hugging Face's API for it: https://huggingface.co/docs/transformers/model_doc/clip

I created a Python package to generate image embeddings from CLIP's vision model (without requiring a ML framework), and a simple linear classifier on those embeddings does the trick: https://github.com/minimaxir/imgbeddings

I agree with your viewpoint. The crux of the matter is not whether to use tiered storage or not, but what trade-offs have been made in the specific storage architecture and what benefits have been gained. Here(https://github.com/AutoMQ/automq?tab=readme-ov-file#-automq-...) is a qualitative comparison chart of streaming systems including kafka/confluent/redpanda/warpstream/automq. This comparison chart does not have specific numerical comparisons, but purely based on their trade-offs at the storage level, I think this will be of some use to you.

Yes, thank you for the clarification. AutoMQ has replaced the topic-partition storage with cloud-native S3Stream (https://github.com/AutoMQ/automq/tree/main/s3stream) library, thereby harnessing the benefits of cloud EBS and S3.