Show Notes
Coding Chats episode 74 - John Crickett talks to Nnenna Ndukwe, a developer advocate at Qodo, discussing how teams can maintain code quality in the age of AI coding tools. She argues that AI agents should be combined with traditional tools like linters and static analysis — not replace them — and that teams need to define and codify what "good code" looks like so that consistency can be enforced across the whole development lifecycle.
A recurring theme is developer ownership: as AI writes more code, engineers must stay in the driver's seat, genuinely reviewing what gets shipped rather than blindly accepting it. The episode also touches on dogfooding, with both agreeing that using your own tools internally is a strong signal of a product worth trusting.
Chapters 00:00 Introduction to AI in Software Development 03:24 Embedding Quality Gates in Development 06:03 The Importance of Consistency in Code 09:09 Ownership and Critical Thinking in Engineering 12:00 Balancing Tool Freedom and Intellectual Property 14:56 Navigating AI Tools and Workflows 17:47 Managing Burnout in AI Development 20:47 The Evolution of Coding and Instant Gratification 23:47 Documenting Ideas and Project Management 26:54 Using AI for Ideation and Collaboration 31:38 The Joy of Learning Through AI 34:11 Codo: Enhancing Code Quality and Governance 37:22 Comparing Code Review Tools 40:10 The Future of AI in Software Development 50:51 The Importance of Dogfooding Products 56:12 Exploring Related Content
Nnenna's Links: https://nnennahacks.com https://linkedin.com/in/nnenna-ndukwe/ https://x.com/nnennahacks
John's Links: John's LinkedIn: https://www.linkedin.com/in/johncrickett/ John’s YouTube: https://www.youtube.com/@johncrickett John's Twitter: https://x.com/johncrickett John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social
Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.
Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.
Takeaways Combine AI coding tools with deterministic tools (linters, static analysis) — don't ditch one for the other. Define what "good code" looks like for your team before expecting AI agents to enforce it. Embed quality checks early and consistently across every stage of the dev lifecycle. Developers must stay in the driver's seat — ownership and understanding of AI-generated code is a key differentiator. Code consistency (naming conventions, style, structure) becomes even more valuable when LLMs are in the mix. Coding rules need to live in a centralised, accessible place so all agents can rely on them. Dogfooding your own tools internally is a non-negotiable sign of a trustworthy product.
More from Coding Chats
Coding Chats Episode 83 - In this episode, VB from OpenAI’s developer experience team explains why he thinks plan mode is obsolete and how coding agents have shifted from step-by-step instructions to outcome-driven, long-running work. We cover how to write a good agents.md, why tests still matter when agents write the code, spec-driven development, and why cloud agents may be the future of dev tooling.
Coding Chats Episode 82 - John talks with Dave Westgarth, head of delivery at a Databricks consultancy. Dave shares his path from a psychology degree and McDonald's into tech, and they discuss why the Scrum Master role is so misunderstood, how it differs from a delivery manager, and whether either needs to be technical.They also look at how AI is changing delivery work. Faster code just moves the bottleneck upstream, John argues there's no AI-specific skill you can't learn in a day, and engineering fundamentals are what make AI useful.
Coding Chats Episode 81 - Ben Howdle talks to John Crickett about being a first time CTO in a start-up. Ben, author of The First CTO, shares what changes when you move from building technology to leading a technical team: the pressure, the trade-offs, and the realization that writing code is only part of the job.Drawing on startup experience and lessons learned the hard way, Ben discusses how to balance quality against runway, manage people, and communicate technical decisions in terms that founders and other non-technical stakeholders can understand. He also explores why resourcefulness, resilience, and comfort with ambiguity matter so much—and why CTOs often spend more time selling ideas than they expect.Whether you’re stepping into your first CTO role or leading a technical team at an early-stage company, this conversation offers candid insights into the realities of technical leadership.
Coding Chats Episode 80 - start-up advisor Alexander Berkovich shares his expertise on building successful start-ups, hiring strategies, CTO roles, and the importance of communication between technical and business teams. Discover practical tips for navigating the challenges of early-stage companies and how to align technical excellence with business goals.
Coding Chats Episode 79 - Richmond Alake, Director of AI Developer Experience at Oracle, joins John to discuss agent memory — how AI agents store, retrieve, and adapt to information. He argues that developers building memory on flat files are naively reinventing the database, and that once you factor in concurrency, security, and scalability, a proper database is inevitable. The conversation covers the full memory stack and how Oracle's AI database keeps embeddings and data together without shipping sensitive information to external providers.