This post outlines a proof-of-concept for a blockchain-based credit card system integrating multi-signature cryptography and smart contracts to manage spending. It emphasizes creating a secure, flexible architecture while addressing challenges like scalability and regulatory compliance. The proposed system aims to enhance transparency, security, and user control in financial transactions.
Author: ivan.turkovic
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.
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.