Minimum Viable Expectations for Developers and AI (wjgilmore.com)

Developer skepticism around AI is fading as real productivity wins become clear, and this piece proposes a practical set of Minimum Viable Expectations (MVEs) for teams adopting AI. Key recommendations: adopt an AI-first IDE for intelligent code completion to save hours on boilerplate; use tools like GitHub Copilot to automate code reviews on pull requests and catch subtle edge cases; and integrate Model Context Protocol (MCP) servers (Anthropic’s open standard from Dec 2024) so IDEs can query internal and third‑party knowledge sources — MCP directories already list ~16,000 public servers and vendors like GitHub and Stripe run MCP endpoints. These integrations reduce context switching and let developers execute tasks (create/review/implement tickets) without leaving the editor. Technically, the author highlights AI’s strength in test generation — citing real products with far greater coverage thanks to models such as Claude 4 Sonnet and Opus 4.1 — and recommends baking tests into generated code. Teams should continuously refine AI coding guidelines (style, shell commands, test requirements, dependency rules) because context materially improves model outputs. The implication for AI/ML and engineering orgs: expect measurable time savings, improved code quality, and a shift of developer effort from CRUD work to higher‑level problem solving — but manage adoption with clear tooling, review, and governance practices.