I recently came across a story that perfectly encapsulates something I’ve been thinking about for months. It’s about a CPO who was handed an unlimited token budget and told to vibe code three MVPs for Q1 2026. They did something unexpected instead. Rather than firing up Lovable or Bolt to start generating code, they opened…
Category: AI
What I Wrote About in 2025
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:…
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…
The Future of Language Frameworks in an AI-Driven Development Era
As artificial intelligence increasingly writes the code that powers our digital world, we’re standing at a fascinating crossroads in software development history. The fundamental question looming over our industry is deceptively simple yet profoundly complex: if AI is writing our code, do we still need the elaborate conventions, configurations, and architectural patterns that have defined…
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.
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.
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.