Text search is something we do everyday. Fast and reliable search is such a staple, it is easy to forget there can be elegance to those underlying mechanisms. It is time to pull back the curtain and dig into one of the foundational search algorithms, the Boyer-Moore fast string search. It is a good algorithm to demonstrate some of the methods used to achieve fast search results. As typical, I’ll reach for F# to show implementation details.

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Visualizations like charts and graphs can be powerful tools, but they are often static. An even more powerful story can be told over time with animations and videos. Using F#, along with a couple tools, I’ll do just that. Today’s focus is on the Palmer Drought Severity data for the U.S. over that last one-hundred years. This is a lighter post, so hopefully the video is mesmerizing enough to compensate for any lack of depth.

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Constraint solvers are a useful tool to have in the toolbox. You can pull one off the shelf, but there is value in understanding how they work. To that end, I will explore implementing the AC-3 constraint solver using F#.

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When developing F#, there is a nice selection of quality development environments to choose from. One option that is often overlooked is Vim. IDEs have some really nice functionality, but often I just need a good editor with the right plug-ins. When it comes down to it, I’ll use whatever tools are necessary to get the job done. But all things being equal, the more I can live in Vim, generally the happier I am. The Vim muscle-memory built up over time has a strong pull toward a level of efficiency. So for anyone who falls into that category, this is for you.

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Today I want to provide a quick overview of F#‘s scripting capabilities and how they can integrate into your daily workflow. Depending on your current perspective, you may not have considered using F# as a daily scripting tool. If not, then you should. Why leave all the untapped power just setting there?

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