How to Pick an AI Coding Tool in 2026: A Framework, Not a Ranking
Five decision dimensions for choosing AI coding tools, an honest take on the four I've used, and a deliberately short list of the ones I'm watching but haven't tried.
Solutions Architect · AI Engineering · Notes
Hi, I'm Firman Hanafi. I work as a Solutions Architect at DOKU — translating product needs into systems that engineers can actually build. This is where I write about architecture patterns, AI for software engineering, and lessons from real projects.
Five decision dimensions for choosing AI coding tools, an honest take on the four I've used, and a deliberately short list of the ones I'm watching but haven't tried.
The architecture decisions that come up most often in Java backend work. Event-driven vs request-response, CQRS, hexagonal, when to split a microservice, REST vs gRPC vs GraphQL, ADRs.
A specific list of things I check when an AI generates a class skeleton, before letting it produce the implementation. The 5-minute step that saves the most time downstream.
The five-step loop I'd default to when generating code with AI: read, spec, skeleton, review, layered generation. The step engineers skip most often is the one with the highest leverage.