automq
imgbeddings
automq | imgbeddings | |
---|---|---|
8 | 8 | |
1,421 | 122 | |
50.4% | - | |
9.9 | 0.0 | |
3 days ago | about 2 years ago | |
Java | Python | |
GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
automq
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Tiered storage won't fix Kafka
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.
- Streaming Platform Comparision:Kafka/Confluent/Pulsar/AutoMQ/Redpanda/Warpstream
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Show HN: AutoMQ – A Cost-Effective Kafka distro that can autoscale in seconds
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.
- FLaNK Stack Weekly for 20 Nov 2023
imgbeddings
- FLaNK Stack Weekly for 20 Nov 2023
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Content-Based Image Retrieval
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.
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How to build a working AI only using synthetic data in just 5 minutes
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
- GitHub - minimaxir/imgbeddings: Python package to generate image embeddings with CLIP without PyTorch/TensorFlow
- Show HN: Python package to create image embeddings without PyTorch/TensorFlow
- I've released a Python package which lets you generate vector representations of images clustering/similarity search/classifier building with a twist: neither PyTorch nor TensorFlow is used!
- [P] I've released a Python package which lets you generate vector representations of images with a twist: neither PyTorch nor TensorFlow is used!
- Show HN: Python package to create image embeddings with o PyTorch/TensorFlow
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