Mindful AI Coding

Understanding foundational cloud concepts (SaaS, IaaS, and PaaS) to demystify infrastructure and stop being intimidated by cloud jargon

Leveraging Platform as a Service (PaaS) to effectively outsource complex infrastructure and security management to hosters

Adopting microservices over monoliths to give AI a smaller, more manageable code surface area that prevents drift and hallucinations

Moving past unstructured "vibe coding" in favor of rigorous upfront planning

Managing the AI like a smart but inexperienced junior intern who requires explicit rules and supervision

Writing comprehensive Product Requirements Documents (PRDs) and architectural plans before generating a single line of code

Recognizing that a SaaS application is a living thing that requires a pipeline to evolve safely while users are actively on the site

Implementing software pipeline via tools like GitHub Actions to quickly and safely inject new features

Using version control (Git) and Infrastructure as Code (Terraform) to maintain a stable environment and recover from AI-generated mistakes

Complementing non-deterministic LLMs by relying on deterministic Python scripts for repetitive tasks like deployments, security s

Treating cloud hosting budgets as a fundamental design constraint that must be projected, estimated, and tracked from the start

Understanding that software regressions are the bane of all software, and AI often exacerbates them due to its limited context window

Creating a "safety net" by establishing mandatory end-to-end testing frameworks that run on every single code commit

Drill down to root cause and produce features customers actually need

Avoid false signals by implementing telemetry to log and verify what features customers are actually using

Optimizing for the only two metrics that truly matter for a product's long-term success: high velocity and high quality