This interview was recorded for GOTO Unscripted.
https://gotopia.tech

Check out more here:
https://gotopia.tech/articles/419

Roman Zhukov - Principal Architect - Security Communities Lead at Red Hat
Damian Brady - Staff Developer Advocate at GitHub

RESOURCES
Roman
https://github.com/rozhukov
https://www.linkedin.com/in/rozhukov

Damian
https://bsky.app/profile/damovisa.me
https://hachyderm.io/@damovisa
https://x.com/damovisa
https://github.com/Damovisa
https://www.linkedin.com/in/damianbrady
https://damianbrady.com.au

Links
https://www.redhat.com/en/blog/ai-assisted-development-and-open-source-navigating-legal-issues

DESCRIPTION
Roman Zhukov (Red Hat) and Damian Brady (GitHub) explore the evolving landscape of AI-assisted software development. They discuss how AI tools are transforming developer workflows, making developers about 20% faster on simple tasks while being 19% slower on complex ones.

The conversation covers critical topics including code quality and trust, security concerns with AI-generated code, the importance of education and best practices, and how developer roles are shifting from syntax experts to system architects. Both experts emphasize that AI tools serve as amplifiers rather than replacements, with humans remaining essential in the loop for quality, security, and licensing compliance.

RECOMMENDED BOOKS
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Alex Castrounis • AI for People and Business • https://amzn.to/3NYKKTo
Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • https://amzn.to/3JVngcx
Kelleher & Tierney • Data Science (The MIT Press Essential Knowledge series) • https://amzn.to/3AQmIRg
Lakshmanan, Robinson & Munn • Machine Learning Design Patterns • https://amzn.to/2ZD7t0x
Lakshmanan, Görner & Gillard • Practical Machine Learning for Computer Vision • https://amzn.to/3m9HNjP


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GOTO's resource offers insights, tutorials, and resources for software developers, architects, and technology leaders. Readers can learn about emerging technologies, industry trends, and best practices for software development. With articles, talks, and workshops, GOTO provides  guidance and expertise for staying ahead in the ever-evolving world of technology.

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AI-assisted coding tools like GitHub Copilot are transforming developer workflows, making developers feel 20% faster but potentially 19% slower on complex tasks requiring complete releases. The key challenges include code quality verification, security vulnerabilities, supply chain risks, and legal/ethical questions around code ownership. Best practices emphasize that developers remain responsible for AI-generated code, requiring proper attribution, security-focused prompting, code reviews, and provenance tracking. Organizations should educate developers on secure AI usage, implement custom instructions for AI tools, and maintain existing quality processes rather than replacing human oversight.

Handling AI-Generated Code: Challenges & Best Practices • Roman Zhukov & Damian Brady