![]() The fact that it’s #1 on HN today (congrats!) makes me think I’m not the only one. I’ve been watching this project for a long time and personally am very excited. If there's anything we can do better, please let us know, we would like to. We are hoping to use this post as an opportunity to collect feedback from fellow hackers. We did a soft-launch on r/degoogle sometime ago, and have since then ironed out issues and polished the product.īut we are far from where we want to be in terms of features (object and face detection, location clustering, image filters. We have documented our architecture and open-sourced our clients. Under the hood it uses XChaCha20 and XSalsa20 for encryption and Argon2 for key derivation. We're relying on libsodium for performing all cryptographic operations. We will be providing additional replicas as an addon in the future. We've built a fault-tolerant data replication layer that replicates your data to two different storage providers in the EU. You can also use our electron app to maintain a local copy of your backed up files. You can access these across your devices, and share them with other ente users, end-to-end encrypted. We've so far built Android, iOS, web apps that encrypt your files and back them up in the background. Over the last year we've been building ente, a privacy-friendly, easy-to-use alternative to Google Photos. Hope this helps some others out there who are just getting familiar with Github, Colab and Jupyter.Show HN: We built an end-to-end encrypted alternative to Google Photosġ180 points by vishnumohandas on | hide | past | favorite | 405 comments There are definitely more elegant ways of doing this, but this is just a quick and easy way that I’ve found to be useful while coming across obstacles like not being able to run Keras in Jupyter notebooks on my local machine and Colab has proven to be a nice workaround without having to drop money on some new laptop during these difficult economic times. It’ll open in a new Colab tab, and you’ll see all of the new changes you just made previously on your local machine. Want to open it back up in Colab? Go to the notebook on the Github repo and click the Colab button at the top of the notebook. If changes to the notebook are made from your local machine, you can push those like normal back to Github. Just leave a comment, otherwise I can make a separate story about it. I can help you with that if you need to know how to clone it. Once the notebook is saved on Github, if you’d like to also back it up on your local machine or work on it locally, use Terminal or Github Desktop and clone it to your computer. No matter whether you are opening the file from Google Drive or from Github, you should still save it to Github same as shown before in step three in order to push your changes to the Github repository. If you prefer to just have the Colab notebook autosave, then you can just work on that Colab notebook independently by opening it up from Google Drive instead of opening it from Github with the shortcut button. Click the green “New” button under the repository section. If you need to make a whole new repository to start with, go to Github and create a new one. ![]() If you already have a repository you want to work with, then move to step two of this process to create or open a Colab notebook. It’s not the most elegant but I have found it to be useful and it gets the job done for now, while I continue to learn more about using Colab and all of it’s connections to Github. Here is a little workflow that I’ve found useful in managing my notebooks and Github repositories. ![]() Trying to learn the small differences between Jupyter notebooks and Colab in order to keep everything flowing smoothly and all my changes synced has been challenging. Colab is a cloud-based notebook and has the needed updates to run Keras and has come in handy. To get around this, I have been trying to integrate the use of Google Colab with my workflow. The older Mac only updates to El Capitan, so it’s unable to run the latest versions of Keras in Jupyter notebooks. I have an older ’09 Macbook Pro and had trouble while trying to run the latest Keras to use for my latest Flatiron school neural network projects. A quick workflow for Google Colab, Github and Jupyter notebooks on Mac.
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