Software rarely fails because it lacks a feature; it fails because it cannot scale, cannot recover, or cannot be securely maintained. Yet, Non-Functional Requirements (NFRs) are routinely treated as an afterthought, reduced to vague, untestable wishes like "the system must be reliable." This upfront ambiguity is the leading cause of crippling technical debt, endless refactoring, and late-stage architectural collapse. Enter the NFR Refiner (submitted as part of Kaggle's AI Agents: Intensive Vibe Coding hackathon project): an intelligent assistant that transforms static engineering methodologies into a live, interactive experience. The NFR Refiner agent doesn't just passively record requirements. It actively interviews stakeholders, enforces strict quality standards, fetch real-world industry benchmarks, and translates fuzzy human intent into strictly verifiable Planguage based specifications. Brief Historical Context Software process engineering has long struggled wi...
The Turing test was created to distinguish intelligent machines from human thinkers, and the question it raises is nearly as old as computer science itself. Over time, countless variations of the test have focused on one familiar idea: spotting errors, awkwardness, or other signs of imperfection that might reveal a machine behind the answer. That approach is becoming less useful as modern systems grow better at sounding polished and convincing. In this blog, I explore a simple extension to the classic test: instead of asking whether an answer is flawless, I ask whether it is stereotypical. My argument is that this shift reveals a more subtle weakness—one that many of today’s leading AI systems still struggle to hide. The New Test Harness The core idea behind this new test harness is straightforward: instead of judging an answer in isolation, the evaluator would run it through multiple AI systems and measure how strongly it reflects the same familiar patterns; see the image below. The ...