Climate change is one of the greatest problems society has ever faced, with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. Since climate change is a complex issue, action takes many forms, from designing smart electric grids to tracking greenhouse gas emissions through satellite imagery. While no silver bullet, machine learning can be an invaluable tool in fighting climate change via a wide array of applications and techniques. These applications require algorithmic innovations in machine learning and close collaboration with diverse fields and practitioners.

Developing the site locally

1. Ensure git is installed
2. Ensure ruby is installed (ideally v2.6.6 or 2.7.x -- with the help of a ruby version manager!)
3. Ensure bundler is installed for downloading Ruby dependencies (run gem install bundler if not)
4. Clone this repository and cd into it (e.g. git clone https://github.com/climatechange-ai/climatechange_ai.git && cd climatechange_ai)
5. Run git submodule update --init --recursive to get additional files stored in a Git submodule
6. Run bundle install to install Ruby library dependencies
7. Run bundle exec jekyll serve to build and locally serve the site, wait ~20 seconds
8. Visit localhost:4000 in your web browser to test out the site locally!
9. Make any changes to files you want to test, then wait for the site to rebuild (should take ~10 seconds for each change)
10. If you're satisfied with your changes, commit them to a feature branch (e.g. git checkout -b my-branch, then git add -A, then git commit -m "meaningful commit message"), and push them to Github (git push origin my-branch)
11. Make a Github pull request for your branch, and solicit feedback in the #fdbk-website channel
12. Once the pull request is approved, you can merge it into master, which will trigger an automatic deploy to <climatechange.ai>.