Coding Chats

Coding Chats Episode 85 - John talk to Ian, a 51-year-old software engineer with 25 years of experience, who describes going through something like grief as AI takes over work he spent decades mastering. He no longer reads the code it writes, which leads him and host John to debate when that’s acceptable and when it isn’t. Ian calls the pace of change “exciting and terrifying at the same time.”

John argues the grief is unwarranted: cheaper software will mean more to build, and engineers still own requirements, testing and delivery. They disagree on a few points, such as on-the-fly user interfaces, but both lean optimistic about the profession, and agree to revisit their predictions in two years.

00:00 – Welcome and why Ian is "grieving" over AI in software development
03:11 – Do we still need to read the code? Trust, outcomes and safety-critical systems
06:15 – Code review in practice, and what makes someone an engineer
10:21 – Requirements, analysts and the full software lifecycle
13:41 – From punch cards to DevOps: the pace of change
18:22 – Why requirements work stays human (the drill bit story)
20:29 – Is English the programming language of the future?
23:04 – How much software will be written? The spreadsheet analogy
27:05 – Do we still need tests? UI stability versus custom UIs
29:23 – Demand for software and the future of junior developers
35:18 – UIs that evolve with the user
40:24 – Internal tools, AI-generated software and the "mainframes of the future"
43:01 – Training juniors and long-term demand for engineers
44:06 – Missing the craft: coding as a hobby
46:21 – Adapting to industry-wide disruption
48:00 – Wrap-up and a two-year follow-upIan's

Links:
https://www.linkedin.com/in/%E2%80%8Bian-miell-694496/

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
AI coding tools have gone from a helpful accelerator to something that can work across whole microservice codebases, which is why experienced engineers are feeling real grief.
Whether you need to read AI-generated code depends on the stakes: skip it for a throwaway tool, read every line for safety-critical systems.
Human code review has always been weak, with big pull requests tending to get a rubber-stamp “LGTM”.
Engineers have always been the people who care what’s under the hood, but where that “hood” sits keeps moving up the stack.
Requirements gathering, such as finding out what customers actually need rather than what they ask for, remains a core engineering skill that AI doesn’t replace.
Cheaper software could create more demand for engineers, much as spreadsheets led to more accountants rather than fewer.
Junior developers can still learn by starting at a higher level of abstraction and working down, as earlier generations did with BASIC, C and assembler.
Part of the grief is losing the craft of writing code by hand, though coding can survive as a hobby, like Sudoku or crosswords.
The pace of change is dizzying and no one can say what software will look like in two years, so the hosts agree to revisit their predictions then.

Coding Chats Episode 84 - John talks to Raj, who spent over ten years maintaining COBOL on mainframes before becoming a Principal Solutions Architect at AWS, with no computer science degree. He shares how he made the leap by building cloud projects on the side and connecting his legacy experience to modernization work, plus the communication skills that help engineers get promoted.

He also explains why he left big tech to start CloudColosseum, and why he thinks AI will make solutions architects more valuable. If you’re stuck on a legacy stack or wondering how to stay relevant in the age of AI, this one’s for you.

00:52 From Mainframes to Big Tech: A Journey
04:01 The Shift to Cloud: Embracing New Technologies
06:28 Navigating the Transition: Learning New Languages and Skills
09:43 Cultural Shift: Adapting to Fast-Paced Environments
12:44 The Role of a Solutions Architect: Bridging Technology and Business
15:50 Overcoming Communication Barriers: The Importance of Executive Communication
18:34 Teaching the Next Generation: Transitioning Others to Cloud Careers
31:56 Building Trust Through Domain Knowledge
34:35 The Importance of Trying and Iterating
38:33 Transitioning to Solutions Architect: Timeframes and Skills
40:37 The Drive Behind Founding Cloud Coliseum
43:49 AI's Impact on Solutions Architects
50:10 The Reality of AI Hype and Job Market Dynamics
56:34 Navigating the Future of AI and Job Security

Raj's Links:

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
Your existing experience isn’t throwaway, so connect it to cloud and AI instead of starting from scratch.
You don’t need to be a cloud expert to become a solutions architect, because knowing the legacy systems companies are migrating is a huge advantage.
Side projects and proofs of concept can open doors, and you can build them without quitting your job.
Executive communication is often what separates senior engineers from the next level, since business leaders control the budget.
Technical skills alone are hard to win on over a long career, but adding business communication makes you a complete package.
Cloud and AI reward trying things, because the cost of a mistake is tiny compared with the old days of expensive, exam-style learning.
You only need to master the core concepts, about 10% of the dictionary, to get hired as a solutions architect.AI is useful but overhyped, so learn the fundamentals (RAG, agents, MCP, evals, guardrails), tie them to your existing knowledge, and keep a close eye on AI-generated code.

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.

We also get into the practical details: how prompt caching works and why running one thread per task can cut your cost and latency, plus the biggest announcements from OpenAI’s Dev Day, including GPT 6.1 Sol, ultra fast inference, plugins, and proactive async agents. VB closes with the Codex Ambassadors program and Codex for open source. If you use Codex, AI coding agents, or any agentic coding workflow, there’s something here for you.

00:00 Introduction to Developer Experience at OpenAI
03:29 Bringing AI Models to Developers
06:06 The Shift from Planning to Outcome-Focused Development
09:05 The Role of Testing in AI Development
11:51 Creating Effective Agents.md for AI Projects
17:17 The Evolution of Developer Tools at OpenAI
25:21 Cloud Development: The Future of Coding
26:48 Insights from OpenAI's Dev Day
28:00 Revolutionizing Development with GPT 6.1 Soul
32:35 Understanding Caching for Developers
39:02 Proactive Agents: Enhancing Productivity
42:39 Community Initiatives and Open Source Contributions
48:36 Exploring Related Content

VB's Links:
https://openai.com/

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
Plan mode is obsolete because current models plan inherently, so you’re better off spending your time up front defining the outcome you want.
Developers are moving from prescribing steps to defining intended outcomes (like “get this CI green”) and letting the agent plan, execute, and adjust on its own.
Spec-driven development has its place, especially for API-shaped work, but it backfires if the spec isn’t kept in sync with every change.
A strong but lightweight agents.md should cover where code lives, how the agent knows it’s doing a good job, your style rules, and what a good PR looks like.
Tests still matter when agents write the code, because they catch regressions before merge, but the agent needs guidance on which tests are worth writing.
Treat agents.md as a living document that grows as you work with the agent, not something you get right in one shot.
To get the most from prompt caching, use one thread per feature and a couple of narrowly scoped subagents instead of running a dozen agents and letting the cache go cold.
Coding workloads have large inputs and small outputs, which is why caching makes them so much cheaper and faster than typical chat use.
Cloud agents are where dev tooling is heading, since they remove “works on my machine” problems and free long-running tasks from your laptop.
If you’re still on the fence about coding agents, give one real task to an agent, watch how it interprets your request, and refine your instructions from there.

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.

Chapters
00:00 Journey to Technology: A Personal Story
10:33 The Role of a Scrum Master: Misunderstandings and Realities16:52 The Impact of AI on Scrum Master Roles
24:41 Hiring and Empowering the Right People
26:01 Scrum Master vs. Delivery Manager: Understanding the Roles
26:06 Technical Skills: Should Scrum Masters Be Technical?
32:19 The Value of Certifications in Professional Development
38:48 AI's Role in Delivery Management
47:13 Connecting with Dave Westgarth

Dave's Links:
https://www.technicalscrum.orghttps://
www.linkedin.com/in/dave-westgarth/

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
Scrum Masters who just move Jira tickets and run ceremonies are the ones AI can replace.
Be loyal to agile's values, not the mechanics of the framework.
If AI makes code ten times faster, stakeholders and approvals need to keep up, or the bottleneck just moves.
In retrospectives, pick one small change instead of trying to fix everything.
Regular delivery cuts down the demand for status reports.
Technical skills help managers only if they don't take over the work and stop developing their people.
There's no AI-specific skill you can't learn in a day, but using AI well takes software engineering fundamentals.
Writing code is a small part of software engineering.
Tutorials show the perfect path, but the real learning happens when you get stuck.
Certifications work best as forced learning, not just CV decoration.

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.

Chapters
00:45 The Loneliness of the CTO
16:57 Essential Skills for a First-Time CTO
22:53 Understanding the Hierarchy: Who is the Boss?
30:14 The Importance of Advisory Network
38:06 Transitioning to People Management
44:27 Understanding the 40% Rule
52:54 Exploring Related Content

Ben's Links:
https://www.linkedin.com/in/ben-howdle/

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
Taking on the CTO title without experience can be the thing that forces the growth you wouldn't have sought otherwise.
At a startup, runway isn't just a finance metric — it should be the lens every technical decision gets filtered through.
The tension between building things well and building things fast enough to survive is one of the defining challenges of being a first-time CTO.
The most chaotic, uncertain period of your career might also be the one where you're most alive.
A first-time CTO needs resourcefulness, comfort with ownership, and resilience more than any specific technical skill.
When you have no one to bounce ideas off internally, build an informal council from your network — it can double as a recruiting pipeline.
The quicker you can identify your own blind spots and weaknesses, the quicker you can plug the gaps.
Early-career engineers often drown non-technical stakeholders in jargon to mask imposter syndrome — the real skill is meeting people where they are and speaking to business outcomes.