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Ruby is a delightful language to work with. And yet, for years, we've been hearing about how MRI is too slow and inefficient for "serious" use. What does that mean? What would a "serious" Ruby runtime look like?

To understand these questions, we need to dig into the design of MRI. How does it work, and what are the underlying principles? What does common Ruby code look like as its executed by MRI? Once we've discovered what makes MRI tick, we can compare it to modern runtimes like Hotspot and V8 to understand why MRI is slower. From there we can take a look at JRuby and Rubinius and see how they seek to close the gap between Ruby runtimes and the competition.

In this talk, we'll dive into the internals of MRI, seeking to understand how it executes Ruby code. We'll look at ways to make our code faster, reduce garbage collection overhead, and find more concurrency in our programs. At the end, you will have a better understanding of how MRI works and how to write better code for it.

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In this talk titled "The Mechanics of Ruby" presented by Adam Keys at LoneStarRuby Conf 2013, the speaker explores the inner workings of Ruby, specifically focusing on Matz's Ruby Interpreter (MRI). The discussion addresses the performance criticisms of Ruby and evaluates the design trade-offs that characterize the language. The key points covered in the presentation are as follows:

- **Historical Context**: Adam shares the story of Ruby's creator, Matz, who transitioned from writing in C, Smalltalk, and Perl to creating Ruby. This history underscores the language's evolution and the pragmatism surrounding its design decisions.

- **Criticism of Ruby**: The speaker contrasts common criticisms of Ruby, such as its garbage collection efficiency, concurrency limitations, and the complexity of frameworks like Rails. He encourages a scientific approach to understanding these elements rather than merely complaining about them.

- **Ruby as a C-like Language**: Adam posits that Ruby can serve as an effective way to write C code, providing examples that show how simple Ruby programs can sometimes execute faster or as efficiently as their C counterparts.

- **C Extensions**: The presentation delves into creating C extensions for Ruby, detailing the processes involved, such as including the Ruby C API and managing Ruby objects within C. This exploration emphasizes the language's ease of use while navigating complex memory management scenarios typical in C.

- **Performance Analysis**: The discussion incorporates practical tools like DTrace, explaining how they can illuminate Ruby's performance metrics regarding memory allocation and garbage collection. This data can then inform optimizations for Ruby applications.

- **Comparison with Other Runtimes**: The speaker draws comparisons between MRI and other modern runtimes like JVM and V8, illustrating MRI's design choices that prioritize developer happiness over sheer performance.

- **Community and Ecosystem**: Adam advocates for supporting alternative Ruby implementations, such as JRuby and Rubinius, which aim to improve the performance of Ruby applications beyond what MRI can achieve.

- **Key Takeaways**: The talk emphasizes the importance of understanding Ruby's internals and employing insights gained to enhance development practices. Keys determine that while Ruby may not be the fastest language, its unique features promote developer engagement and satisfaction.

In conclusion, the discussion of MRI’s design inspires Ruby developers to dig into the intricacies of the language, fostering more proficient coding practices through a better grasp of Ruby’s mechanisms and performance attributes.

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