The Quick Dive into Deep Science
Join the AI Revolution! 🚀 We're launching a crowdsourced, ever-evolving distributed network of online science magazines. By using our browser extension, you're not just a reader - you contribute! Leveraging AI and large language models, we auto-archive, extract, and transform science papers into accessible articles. As our user base grows, so does our content. Dive in, and let's make* science universally understandable together!
- a web service that simply consumes a PDF file?
- for purposes of hackathon, we can hardcode destination of where the generated article is posted
- Converting PDF to text. Ulrich has a minimalist solution, but it's not great
- Generate the actual content. See gen/generator.rb as a strawman. Lots of ways to improve it.
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Generate images and add them to the article. Ulrich already added code to generate the prompts to gen/generate.rb
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More elegant sources of articles
- paste URL?
- mod a browser plugin that is design to upload papers elsewhere?
- Just use the arxiv feed?
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Automatically submit them to substack? But, no API? Rob?
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Allow users to describe their style, audience, etc?
** This project uses Glim, a pre alpha library for using LLMs. TODO: Make a publicly accessible version. **
git clone git@github.com:uhgall/scicliffs.git
cp sample.env .env # modify this to add your API keys
bundle
ruby gen/generate.rb
- supports Claude and OpenAI (and also Llama2 via anyscale)
- caches results so if there is a bug in your code, it will run quickly the nect time you run it, etc.
- various convenience features - token counting, cost calculation, file extraction, templating language for prompts (erb), logging
- bundle install
- cp sample.env .env
- add API keys and set your desired LLM in the .env file
- ruby ./app.rb
- navigate your browser to https://localhost:4567/{id} where id is an arxiv.org id
For example:
http://localhost:4567/2309.03801 http://localhost:4567/2309.03874 http://localhost:4567/2309.03440 http://localhost:4567/2309.00537 http://localhost:4567/2309.02432