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Articles

(May 26) #graphql #rest

GraphQL and REST are two of the most popular ways of accessing backend functionality with the latter rising in popularity in recent years. They have different philosophies when it comes to how you access the backend, and many articles exist that list those differences. In this article however, Sean Smith looks into more specific differences that go a little bit deeper, like caching, error handling, HTTP semantics, etc.

(Jul 02) #ruby

Ruby on Rails has a few options when it comes to templating. Aside from the built-in ERB, there's Mustache, Haml, Erubi, and more. But what if you're not a fan of any of them, or just want a simple solution with no external dependencies? Luckily in this article Benedikt Deicke has written an introduction to creating your own template engine for Ruby.

(Jul 02) #operating-systems

Code generation techniques like Just In Time (JIT) compilation have been around for some time now and many programming languages use them one way or another. However, back when operating systems where being developed, very few if any of these techniques were utilized. Inspired by a PhD thesis, John Regehr looks into how we would go about implementing code generation in modern operating systems.

(Jul 02) #machine-learning

GitHub have an interesting challenge: as part of a repo's metadata, they need to figure out the programming language used. Sometimes it's as easy as using the file extension of a file, but in many cases a number of heuristics are required to kinda sorta guess the language. The engineering team weren't very satisfied with their current solution and decided to develop a new one and in this article Kavita Ganesan explains how it works. Of course it uses Machine Learning.

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Programming language of the day: Pipelines. "Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires implementation of components to be defined seperately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline, while providing access to thousands of active libraries for machine learning, data analysis and processing. Skip to Getting Started to install the Pipeline compiler."

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Pek