AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
Snaps are containerised software packages that are simple to create and install. They auto-update and are safe to run. And because they bundle their dependencies, they work on all major Linux systems
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The official website's repository for the Snap store
Taken from the repository README
Loading the full README.
Read the full README ↗Snaps are applications packaged with all their dependencies to run on all popular Linux distributions from a single build. They update automatically and roll back gracefully. This repo is the application for the snapcraft.io website.
If you are interested in Snaps, Snapping and Snapcraft, there is an active discourse forum that we encourage developers to join.
The site is largely maintained by the Web and Design team at Canonical. It is a stateless website project based on Flask and hosted on a Charmed Kubernetes cluster.
If you have found a bug on the site or have an idea for a new feature, feel free to create a new issue, or suggest a fix by creating a pull request. You can also find a link to create issues in the footer of every page of the site itself.
If you have found a bug elsewhere in the snap world:
snap info and use the contact information to find where you can get help.The simplest way to run the site locally is using dotrun:
dotrun
Once the server has started, you can visit http://127.0.0.1:8004 in your browser. You stop the server using <ctrl>+c.
If you're running dotrun on macOS or Windows, you will have to use a different command to launch dotrun with an additional argument in order to get JavaScript code working:
dotrun -p 5004:5004
For more detailed local development instructions, see HACKING.md.
Another development tool you can use to run this project is Workshop. Launch the Workshop by running
workshop launch # you only need to do this the first time
and start the server with
workshop run start
Workshop depends on LXD, which is only available on Linux. If you're running macOS or Windows, you can use Workshop inside a
MultipassVM. Beware: this setup is even more experimental and you might encounter strange issues. Configure Multipass to use the VirtualBox virtualization driver and allocate at least 4GB RAM and 10GB disk for the VM; after setting up the workshop in the VM, enable port forwarding for ports 8004 and 5004.
The content of this project is licensed under the Creative Commons Attribution-ShareAlike 4.0 International license, and the underlying code used to format and display that content is licensed under the LGPLv3 by Canonical Ltd.
With ♥ from Canonical