The system right now is highly reliable, I have no fear of doing a live demo of it, but live demos come off as strange because my feed is a strange mix of arXiv abstracts, Guardian articles about association football, etc. so it comes off as idiosyncratic and personal. (Oddly when I started this project I loved the NFL and hated the Premier League, when I started doing feature engineering as to "Why does it perform so well for arXiv papers and so poorly for sports" I started studying football articles in detail and started thinking "How would I feel if my team got relegated?" and "Wow, that game went 1-0 and it was an own goal" and next thing I knew I was hanging on every goal in every game Arsenal and Man City play -- it changed me.)
It's not even that hard for me to swap algorithms in and out but it should be easier, for instance I like the scikit-learn system for model selection mostly but there are some cases like SVC-P where I want to bypass it and I am not so sure how to comfortably fit fine-tuned transformer models into the system.
Another problem with it is that it depends on AWS Lambda and Suprfeeder for ingestion, it costs me less than $5 a month to run and about 10 cents per feed but (1) that's not cost-effective if I want to add a few hundred blogs like
and (2) I know many people hate AWS and other cloud services.
If somebody were interested in contributing some elbow grease that would help the case for open source, alternately a hosted demo of some kind would also be possible but I'm not ready to put my time and money into it. Contact me if you're interested in finding out more.
