Personal blog of Prasad Ajinkya
When the creator of Ruby on Rails declares 37signals 'pencils down' on handwritten code, something structural has shifted. A breakdown of DHH's Rails World 2026 keynote, the irony of Rust, Omarchy, and why p(bloom) > p(doom).
Why enterprise AI adoption stalls at the “clever toy” stage, and how building an MCP gateway over deterministic, Git-backed institutional knowledge provides a blueprint for scalable AI infusion.
Conversational AI chatbots are the least interesting application of AI in finance. The real transformation in Indian credit and WealthTech is happening in back-office engines processing unstructured GST, banking, and underwriting data.
Vector search promises infinite semantic memory for AI agents, but in production it introduces non-determinism, context fragmentation, and opaque debugging. Here is why we switched our agent memory to Git and Markdown.
I attended the Entrepreneur Future Summit in Pune, diving into practical applications of AI, scalable frameworks, and insights from global founders. Here's a recap of the best sessions and networking highlights.
The usual workarounds for AI memory are brittle. What I needed was a shared external memory that any agent could read and write. Here's how I built a two-layer memory architecture using GitHub and GitLab.
I got tired of walking out of a doctor's appointment wondering if it was the right call. So I built an AI-powered personal health memory system to cross-reference prescriptions against global evidence-based medicine.
If you're using multiple AI agents to get work done, you quickly run into a context fragmentation problem. The solution isn't a complex vector database. It's a simple GitHub repository.