Calculating the shortest path between two nodes in large graphs can be a time-consuming process. One approach to solving this is to calculate an estimated path. There are many situations where the exact path isn’t necessary; an estimated path is good enough. This is the case especially if the performance benefits of estimating are high enough. I’ll look at implementing the algorithm from A Sublinear Algorithm for Approximate Shortest Paths in Large Networks in F#. It offers a hefty performance boost at the cost of some preprocessing; well worth the time.

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Performing estimated counting of distinct elements in large datasets is a common task. While there are straightforward approaches, they can be memory-intensive and slow for massive datasets. Today I’m going to take a look at the F0 Estimator introduced in the paper Distinct Elements in Streams: An Algorithm for the (Text) Book∗. As often, this will be an implementation in F#.

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Since LLMs are currently all the rage, I wanted to spend some time today to dig into how to leverage the Anthropic API using F#. Anthropic provides a language model that can be utilized for various natural language processing tasks, and it can be a handy tool to add to the application toolkit.

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Today’s post is really light, and nothing too involved. It is composed mostly of notes and examples using the fluent http client Flurl with F#.

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Vector search is an essential tool for many modern applications, particularly for information retrieval. It allows for efficient searching and retrieval of information based on similarity between vectors. Today I will explore how to implement a naive vector search with F#. Whether you are a seasoned F# developer or just starting out, this will provide you some useful information if you want to better understand the basics of vector search in your own projects.

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