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Update: I am currently working on an approach to get the extended LW/Alignment Forum/blog sphere included in a smarter way[1]. I'm using https://github.com/StampyAI/alignment-research-dataset as a jumping off point. Click here if you just want to see the Database I made of all[2] AI safety papers written since 2020 and not read the methodology. To some extent the core idea here is to encode as much info from these papers into something small enough that an AI with a specific problem in mind can take in all of these encodings from all of the papers and decide which ones are worth reading/investigating more. Over the last month I have been trying to see just how much I can learn and do from a cold start[3] in the world of AI safety. A large part of this has been frantically learning mech interp, but I've picked up two projects that I think are worth sharing! There are a lot of AI Safety papers. When I started working on more hands on projects there wasn't a clear way to find relevant papers. For example, if I wanted related datasets there's not a great way to search for datasets. Huggingface has a dataset search, but the search functionality is terrible. I could ask an AI to try to find relevant datasets but that's mostly just the whims of google searching. Many good datasets are hidden away in not famous papers[4]. Or, if I want to look at using a specific technique it's not easily searchable to find all papers about sparse autoencoding. So, I had Claude[5] read every single paper it confidently classified as AI safety and then summarize it, tag it, record the year it was published, authors, etc[6]. My methodology was to start with simply asking Claude to find as many AI Safety papers as it could, as well as any existing lists of AI safety papers. This got me to ~350 papers. Then, I collected every paper that referenced at least three of these papers (~8000) and then had Claude read the ones it thought were confidently actually AI Safety papers (~3000). This cita