Actually, blindly submitting ALL stories from those sources is not the smartest thing to do. Number one, you flood the system. Number two, there is no guarantee that new articles from those sources will necessarily do well. The more refined way to do this is to train a recommendation engine from an article aggregation system on some of the top articles that have been most liked on HN. Then, as the engine will collect and rank articles for you automatically and you can just use your script to take the article from the recommendation site and post them to HN. This way you have more assurance that your submissions will be liked on HN, and also the engine will likely find new article sources that you have never heard of before. I friend of mine is using this method to become a power user on Digg and Reddit. I think the uses several sites and has coded his own ranking system but the bulk of his recommendations come from www.euraeka.com, which is a news aggregator and recommendation engine. Basically he signs up for an account, finds out what the top articles on Digg are for lets say last year, then picks the top 25, searches them in Euraeka and then the engine starts finding articles that are like those. All he does afterwards is submit the recommendations on Digg. I think he does the same for Reddit.
Anyways, Euraeka is just one of the engines out there. I am sure you can find others as well. The only question is which engine is spitting out the "best" recommendations. By "best" I mean which engine manages to most closely estimate the taste of the crowds on sites like HN, Digg, Reddit, etc.