How AI Redefined the "Best Editor"
Think of a set; let’s call it the grunt work set. Tasks that should never consume our cognitive bandwidth, deterministic and rule-bound chores: linters, formatters, dead code checkers, type checkers… With generative AI, this set expanded and swallowed the code itself. The following tasks are now firmly inside that mechanical set: Boilerplate structures: Predictable skeletons that repeat across projects: CRUD layers, controller/service/repository scaffolds, DTOs, configuration classes, and API client boilerplate. Data transformations: Mapping one data structure to another: converting database models into API responses, parsing JSON and mapping fields, or filtering and reshaping arrays and dictionaries, chores that require zero creativity. Routine implementations: Code that involves no novel business logic or algorithmic design, merely chaining library API calls: making a standard HTTP request and checking the status code, or reading a file line by line. Test and verification code: Mechanical templates written to assert functions with already-known inputs and expected outputs: setting up mocks and fixtures, chaining assertion statements, or drafting one-off smoke test scripts and health-check calls. Infrastructure and deployment manifests: Declarative configurations bound by rigid syntax and specifications: writing Dockerfiles, defining Terraform blocks and Helm charts, composing GitHub Actions or GitLab CI workflow YAMLs, or writing Makefiles and deployment scripts, often copied from an older repository with only the variable names tweaked. ...
