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: development
From Intentions to Impact: Your 2025 Strategy Guide (Part 2)
The Resolution Graveyard It’s December 22nd. In nine days, millions of people will make promises to themselves that they won’t keep. They’ll join gyms they’ll stop visiting by February. They’ll buy courses they’ll never finish. They’ll write goals in fresh notebooks that will gather dust by March. Why? Because they skipped Part 1. If you…
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.
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.
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.
Artisanal Coding (職人コーディング): A Manifesto for the Next Era of Software Craftsmanship
Artesanal coding emphasizes the importance of craftsmanship in software development amidst the rise of AI and “vibe coding.” It advocates for intentional, quality-driven coding practices that foster deep understanding and connection to the code. By balancing AI assistance with craftsmanship, developers can preserve their skills and create sustainable, high-quality software.
Brainrot and the Slow Death of Code
The rise of AI tools in software development is leading to a decline in genuine coding skills, as developers increasingly rely on automation. This reliance dampens critical thinking and creativity, replacing depth with superficial efficiency. Ultimately, the industry risks producing inferior code devoid of understanding, undermining the essence of craftsmanship in programming.
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.
The AI Detox Movement: Why Engineers Are Taking Back Their Code
In 2025, AI tools transformed coding but led developers to struggle with debugging and understanding their code. This sparked the concept of “AI detox,” a period where developers intentionally stop using AI to regain coding intuition and problem-solving skills. A structured detox can improve comprehension, debugging, and creativity, fostering a healthier relationship with AI.