Whitepapers, guides, and insights on AI-driven development and engineering leadership.
An overview of the AI development landscape in 2026 — exploring current capabilities, emerging tools, and practical approaches for integrating AI into software development workflows.
A framework for integrating AI tools into development teams — covering collaboration patterns, review processes, and organizational structures that maximize AI's effectiveness while maintaining quality and control.
A guide on the use of Claude Code, with agent & skills, to support clear controlled development workflows, and useful automous AI behaviours to support your software development needs.
An examination of security risks with look at the use of an LLM Gateway based solution - Lite-LLM - providing a gateway protecting the enterprise for traffic out and attacks coming in.
A practical guide for rolling out AI development tools across engineering teams — covering change management, training strategies, adoption patterns, and building organizational buy-in.
Understanding the security landscape when adopting AI in development workflows — identifying vulnerabilities, establishing guardrails, and implementing secure AI-assisted coding practices.
Navigating the journey from AI proof-of-concept to production-ready systems — covering scalability, reliability, monitoring, and the operational considerations for deploying AI solutions.