Looking back at the year, my blog became a running commentary on how AI is fundamentally reshaping software development, and not always in the ways people expect. I’ve been splitting my attention between technical deep-dives and broader observations about where this whole industry is heading. Here’s what caught my attention month by month. March 2025:…
Category: ruby
A Christmas Eve Technology Outlook: Ruby on Rails and Web Development in 2026
As we gather with loved ones this Christmas Eve, wrapping presents and reflecting on the year behind us, it’s the perfect moment to gaze into the technology crystal ball and envision what 2026 holds for web development and particularly for Ruby on Rails, the framework that’s been delighting developers for over two decades. While children…
Ruby 5.0: What If Ruby Had First-Class Types?
The article envisions a reimagined Ruby with optional, inline type annotations called TypedRuby, addressing limitations of current solutions like Sorbet and RBS. It proposes a syntax that integrates seamlessly with Ruby’s philosophy, emphasizing readability and gradual typing while considering generics and union types. TypedRuby represents a potential evolution in Ruby’s design.
TypedScript: Imagining CoffeeScript with Types
The content envisions a hypothetical programming language called “TypedScript,” merging the elegance of CoffeeScript with TypeScript’s type safety. It advocates for optional types, clean syntax, aggressive type inference, and elegance in generics, while maintaining CoffeeScript’s aesthetic. The idea remains theoretical, noting practical challenges with adoption in the current ecosystem.
A Love Letter to CoffeeScript and HAML: When Rails Frontend Development Was Pure Joy
The author reflects on the nostalgia of older coding practices, specifically with Ruby on Rails, CoffeeScript, and HAML. They appreciate the simplicity, conciseness, and readability of these technologies compared to modern alternatives like TypeScript. While acknowledging TypeScript’s superiority in type safety, they express a longing for the elegant developer experience of the past.
Rails Templating Showdown: Slim vs ERB vs Haml vs Phlex – Which One Should You Use?
This guide compares Ruby on Rails templating engines: ERB, Slim, Haml, and Phlex. It highlights each engine’s pros and cons, focusing on aspects like performance, readability, and learning curve. Recommendations are made based on project type, emphasizing the importance of choosing the right engine for optimal efficiency and maintainability.
Why AI Startups Should Choose Rails Over Python
AI startups often fail due to challenges in supporting layers and product development rather than model quality. Rails offers a fast and structured path for founders to build scalable applications, integrating seamlessly with AI services. While Python excels in research, Rails is favored for production, facilitating swift feature implementation and reliable infrastructure.
The Two Hardest Problems in Software Development: Naming Things & Cache Invalidation
The post discusses the common struggles developers face with naming conventions and cache invalidation, humorously portraying them as universal challenges irrespective of experience or technology. It emphasizes that while AI and Ruby tools assist in these areas, the inherent complexities require human reasoning. Ultimately, these issues highlight the uniquely human aspects of software development.
Saving Money With Embeddings in AI Memory Systems: Why Ruby on Rails is Perfect for LangChain
In the exploration of AI memory systems and embeddings, the author highlights the hidden costs in AI development, emphasizing token management. Leveraging Ruby on Rails streamlines the integration of LangChain for efficient memory handling. Adopting strategies like summarization and selective retrieval significantly reduces expenses, while maintaining readability and scalability in system design.
The Art of Reusability and Why AI Still Doesn’t Understand It
AI can generate code but lacks understanding of design intent, making it struggle with reusability. True reusability involves encoding shared ideas and understanding context, which AI cannot grasp. This leads to overgeneralized or underabstracted code. Effective engineering requires human judgment and foresight that AI is currently incapable of providing.