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.
The Hidden Economics of “Free” AI Tools: Why the SaaS Premium Still Matters
This post discusses the hidden costs of DIY solutions in SaaS, emphasizing the benefits of established SaaS tools over “free” AI-driven alternatives. It highlights issues like time tax, knowledge debt, reliability, support challenges, security risks, and scaling problems. Ultimately, it advocates for a balanced approach that leverages AI to enhance, rather than replace, reliable SaaS infrastructure.
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 AI-Native Rails App: What a 2025 Architecture Looks Like
Introduction For the first time in decades of building products, I’m seeing a shift that feels bigger than mobile or cloud.AI-native architecture isn’t “AI added into the app” it’s the app shaped around AI from day one. In this new world: And honestly? Rails has never felt more relevant than in 2025. In this post,…
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.
PgVector for AI Memory in Production Applications
PgVector is a PostgreSQL extension designed to enhance memory in AI applications by storing and querying vector embeddings. This enables large language models (LLMs) to retrieve accurate information, personalize responses, and reduce hallucinations. PgVector’s efficient indexing and simple integration provide a reliable foundation for AI memory, making it essential for developers building AI products.
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 SaaS Model Isn’t Dead, it’s Evolving Beyond the Hype of “Vibe Coding”
The article critiques the rise of “vibe coding,” emphasizing the distinction between quick prototypes and genuine MVPs. It argues that while AI can accelerate product development, true success relies on accountability, stability, and structure. Ultimately, SaaS is evolving, prioritizing reliable infrastructure and reinforcement over mere speed and creativity.