Brief

A developer recounts their experience using Claude Code for a discount system, which repeatedly generated flawed logic for stackable discounts. After struggling with prompt-based generation, they adopted a spec-driven approach using OpenAPI and Gherkin files. This method, taking only fifteen minutes to define, allowed Claude Code to generate a more accurate implementation, highlighting the value of upfront specification over iterative prompting for complex logic. AI

MLOps is gaining prominence as the critical discipline for deploying and maintaining machine learning models in production. While model training was once the primary focus, the operational aspects of MLOps are now considered more vital for real-world AI applications. This includes strategies for deployment, serving, and managing models, with specific attention to the unique challenges of Large Language Models (LLMs) compared to traditional ML models. Various tools and architectures, such as those utilizing Docker, Flask, AWS, and MLflow, are essential for building robust MLOps pipelines. AI