If you have a specific target in mind for your brute force attack, you can greatly increase your odds of success with personal information.
Using machine learning and Python, we can find customers with predictable month to month patterns and target our sales team based on those patterns.
Your site can no longer afford to have “programmer design”. Here are some of the fundamentals I’ve learned over the years to keep my designs looking professional.
I had the opportunity two weeks ago to give a lightning talk at PyOhio. I had five minutes to give a talk on literally anything I wanted, so I talked about Flask.
One feature of D that is confusing to a lot of new comers is the behavior of strings in relation to range based features like the foreach statement and range algorithms. In this post, I will detail the practice of auto decoding and some of its pitfalls.
Over the past two weeks, I have been working to port the excellent date string capability of the date util library to D. I did this for a couple of reasons:
Today, the new addition to D's standard library, std.ndslice, was merged into master. std.ndslice is multidimensional array implementation, not unlike Numpy, with very low overhead, as it's based on D's concept of ranges which avoids a lot of copying and allows for lazy generation of data. In this article, I will show some of the advantages std.ndslice has over Numpy and why you should consider D for your next numerical project.