Read recent articles on AI in our blog. Discover the latest AI-based tools for optimized engineering and other ways to adopt AI technology in the SDLC.
Agents already write code faster than teams can review it. The way through is to move the critical decisions upstream, into specs that humans review before any code is written and acceptance criteria that agents can verify after.
Learn how AI coding standards prevent codebase fragmentation by turning individual knowledge into shared, reusable guidelines for both human and AI contributors.
AI coding agents work in isolation. Multiplayer AI coding uses shared specs so teams and their agents stay aligned across sessions and roles.
Ankit Jain (CEO and co-founder of Aviator) and Chris Westerhold (Global Practice Director at Thoughtworks) break down why migrations stall, why fully automated AI code migrations are not realistic, and what a human-in-the-loop system looks like in practice.
Signals from the Hangar DX community on how AI is changing software engineering.
Comparing two ways of completing the same upgrade: migrating a Node.js 14 project to Node.js 18 by chatting with an AI assistant and performing the same migration through a spec-driven development approach, with a strict, pre-written plan.
Stop losing your best AI workflows to random markdown files.
Runbooks doesn’t replace Claude Code; it transforms AI from an individual tool into a multiplayer development platform through spec-driven development.
Spec-driven development replaces the chaos of ad hoc, prompt-driven vibe coding with a structured, durable way for engineering teams to work on AI coding projects.
The future of AI coding in enterprise lies in spec-driven development, shared context, and collaboration, not vibe coding.
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