Digests » 201
The most detailed introduction on DSLs: what they are, 19 examples of DSLs, the 5 top benefits of adopting them, a comparison of all the different approaches you can use to build them, resources and more.
On February 17th 2017, I was working on a corpus distillation project, when I encountered some data that didn't match what I had been expecting. It's not unusual to find garbage, corrupt data, mislabeled data or just crazy non-conforming data...but the format of the data this time was confusing enough that I spent some time trying to debug what had gone wrong, wondering if it was a bug in my code. In fact, the data was bizarre enough that some colleagues around the Project Zero office even got intrigued.
Making a computer mimic the human cognitive function of understanding text is a really hot topic nowadays. Applications range from sentiment analysis to text summary and language translation among others. We call this field of computer science and artificial intelligence Natural Language Processing, or NLP (gosh, please don’t confuse with Neuro-linguistic Programming).
I thought I'd write an update on git and SHA1, since the SHA1 collision attack was so prominently in the news.
A Recommender System predicts the likelihood that a user would prefer an item. Based on previous user interaction with the data source that the system takes the information from (besides the data from other users, or historical trends), the system is capable of recommending an item to a user. Think about the fact that Amazon recommends you books that they think you could like; Amazon might be making effective use of a Recommender System behind the curtains.