Love the idea, I build something similar for focused for my country, Puerto Rico. https://traquealo.com

I remember a GitHub repository where someone documented problems and resolutions related to their house in the issues section.

I'm also trying to gather a personal backlog of things at home patiently waiting to be resolved, but can't even get myself through the collection phase...

Traquéalo: https://traquealo.com

A citizen service initiative that aims to serve as a platform for monitoring areas of need in Puerto Rico.

Ente is a tremendous proposal. I don't know why I hadn't heard of it before, but I don't think it meets what I'm looking for. But the fact that the software is completely open is impressive.

I currently use Ente.io as a secondary photo syncing service in addition to Google Photos.

While I really like it — snappy and encrypted — I was surprised by how much the missing Ultra HDR implementation affects me. Photos are currently uploaded with brightness information but not displayed with it. Therefore, my photos look great in Google Photos but far less vivid in Ente.

For what it's worth, I found a discussion about Ultra HDR. It doesn't seem to be a priority right now, though: https://github.com/ente-io/ente/discussions/779

From all the comments I've been reading, this combination seems solid. I'll definitely be checking it out thoroughly.

Thanks nico for sharing your experience! That's really helpful. The idea of using OpenCV to create a processing pipeline for face detection before passing it to Gemma is brilliant I hadn't thought of that. I'll definitely look into using gemma with ollama.

And for sure, if I get this to a point where it's open-source, I'll post the link here!

I've heard a lot about this topic recently but I can't find stories that capture experience using these metrics. Will it be possible to know some of these stories?

Is this an ongoing course or anyone can join and read the material at own pace?

Impressed with your response, thanks for the clarity you have presented through your examples. Once again, thanks a lot.

Technically you can do it in any language, but in software engineering we tend to stand on the shoulders of giants in order to get the job done on time.

A lot of original excellent data processing, statistical analysis, and ML libraries were built into Python and R, so all the deep learning stuff was built on top of those. R is somewhat harder to integrate into a production pipeline due to its typical reliance on something like RStudio, so Python ended up being the de facto standard as it is also well supported in cloud computing environments.

With TensorFlow API's being written for Swift, we might start to see Swift competing with Python.

Wow, I would never think to use Powershell outside of some Windows-specific tinkering. I guess every language has its diehard fans.

Libraries

Actually this is a great idea. Seems I'll try this approach for 2020 Q1.