PodcastsTechnologyCoding Chats

Coding Chats

John Crickett
Coding Chats
Latest episode

76 episodes

  • Coding Chats

    AI assisted software engineering need leaders not coders

    09/04/2026 | 49 mins.
    Coding Chats episode 73 - John Crickett interviews Benjamen Pyle across topics ranging from tech influencer trust to the software engineer vs. craftsman debate. Benjamen argues that what makes an influencer worth following isn't follower count but authenticity and genuine intellectual evolution over time.The conversation then turns to AI, where Benjamen— initially a skeptic converted by Claude Code — observes that the developers getting the most out of AI are those with strong leadership and problem-solving skills, drawing a parallel between directing an AI assistant and managing a team effectively.

    Chapters
    00:00 Evaluating Tech Influencers
    06:15 Craftsmanship vs. Engineering in Software
    12:06 Career Ownership and Development
    20:47 Finding and Utilizing Mentors
    30:28 The Value of Diverse Mentorship
    36:49 Navigating Careers Outside Big Tech
    42:43 AI and Leadership in Programming
    49:42 Exploring Related Content
    49:50 Outro Final Coding Chats.mp4

    Benjamen's Links:
    https://binaryheap.com
    https://pylecloudtech.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
    Follower counts and engagement metrics don't equal credibility — dig into someone's post history and body of work before trusting a tech influencer.
    Changing your opinion is a strength, not a weakness, as long as the change is driven by genuine learning rather than external incentives like sponsorships.
    Most developers aren't truly "data-driven" despite the industry's rhetoric — people tend to follow trends and stay in safe, popular lanes.
    The "software engineer" label is contested — real engineering disciplines are governed by hard facts and standards, whereas software dev still argues about tabs vs. spaces.
    Many developers just want to clear their sprint tickets and go home, and that's fine — but it's a different mindset from those who treat the craft as a passion.
    AI isn't just a code-writing shortcut — used well, it's more like coordinating a team of engineers, QA, and analysts all at once.
    Developers who struggle with AI tend to be those who just spam it with prompts; those who thrive treat it more like a leadership and delegation challenge.
    Strong soft skills — clear communication, problem decomposition, managing priorities — are turning out to be the key differentiator in who gets the most from AI tools.
    Benjamen was initially skeptical of AI but changed his mind after hands-on experience with Claude Code, which he sees as a good example of his "strong opinions, weakly held" philosophy in action.
  • Coding Chats

    Soft skills for software engineers - why coding isn't the hard part

    02/04/2026 | 54 mins.
    Coding Chats episode 72 - Charles Humble and John Crickett explore why professional skills — communication, critical thinking, and documentation — are arguably more important than writing code itself. Drawing on his O'Reilly shortcut article series and a career that began with an English Literature degree, Charles makes the case that these so-called "soft skills" are actually core to the job, and that they can be learned through practice by anyone, regardless of background or natural talent.

    The conversation also digs into the seismic impact of AI on the software industry. Charles shares his nuanced take: while generative AI tools are reshaping how code gets written, the durable skills — understanding systems, debugging, domain knowledge, and clear communication — matter more than ever. Rather than panic or uncritical adoption, Charles encourages engineers to focus on what remains irreplaceable, and to approach an uncertain future with curiosity and a willingness to take shots on goal.

    Chapters
    00:00 The Importance of Professional Skills for Software Engineers
    06:24 Navigating the Impact of AI on Software Engineering
    12:09 The Evolving Role of Software Engineers
    17:50 AI for the Rest of Us: Bridging the Knowledge Gap
    25:43 The Ethical Implications of AI and Communication
    27:12 Ethics in AI Development
    31:04 Improving Communication Skills for Engineers
    38:00 Overcoming the Fear of Writing
    42:15 The Importance of Public Speaking
    50:17 The Journey of Continuous Learning
    54:30 Exploring Related Content

    Charles's Links:
    https://www.linkedin.com/in/charleshumble/\
    https://bsky.app/profile/charleshumble.bsky.social

    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
    "Soft skills" is a misleading term — Communication, critical thinking, and documentation aren't soft skills; they're literally the job.
    Non-technical skills can be learned — You don't need natural talent. Like anything, they improve with deliberate practice.
    Career success often comes from non-coding skills — Charles found his own progression was driven more by presenting to executives and systems thinking than by programming ability.
    Communication becomes critical as you progress — From mid-level upwards, working with stakeholders, mentoring, and documentation determine who makes it to senior and beyond.
    Nobody knows what programming will look like in two years — Even Kent Beck acknowledges the deep uncertainty ahead.AI has shifted engineers from "extract" to "explore" — Programmers who felt settled in well-defined work have been thrown into a messier, less certain phase by generative AI.
    The durable skills are the same ones that always mattered — Debugging, domain knowledge, system design, and communication are as valuable now as ever — arguably more so.
    "Coding is dead" is nonsense — Software engineering has always been mostly about understanding what to build and why. Writing code was always a small part of it.
    Try things and see what happens — No grand plan needed. If you don't kick the ball, you're guaranteed not to score.
  • Coding Chats

    Build better tech teams with neurodiversity

    26/03/2026 | 46 mins.
    Coding Chats episode 71 - Anita Kalmane-Boot talks to John Crickett about neurodiversity, its spectrum, strengths, challenges, and how organizations can foster inclusive environments, especially in software teams. Discover practical strategies for recruitment, team building, and accommodating neurodivergent individuals to enhance innovation and productivity.

    Chapters
    00:00 Understanding Neurodiversity
    03:32 The Spectrum of Neurodivergence
    06:30 Strengths of Neurodivergent Individuals
    09:08 Creating Inclusive Teams
    12:10 Improving Recruitment Practices
    15:00 Work Environment for Neurodivergent Individuals
    17:43 The Connection Between Neurodiversity and Software Engineering
    23:38 Exploring Neurodiversity in Engineering
    24:39 The Impact of AI on Neurodivergent Workers
    27:08 Inclusive Recruitment Practices
    32:57 The Role of Managers in Hiring
    38:46 Disclosing Neurodivergence in Job Interviews
    44:11 The Future of Neurodiversity in the Workplace
    46:11 Exploring Related Content

    Anita's Links:https://www.linkedin.com/in/anitakalmane/

    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
    Neurodiversity covers a wide spectrum — including ADHD, autism, and dyslexia — not just a single condition.
    Neurodivergent individuals often have exceptional strengths like pattern recognition, deep focus, and creative problem-solving.
    These traits make neurodivergent thinkers particularly valuable in software engineering and tech roles.
    Traditional hiring processes can unintentionally screen out neurodivergent candidates.
    Small recruitment adjustments — like sharing questions in advance or allowing written responses — can open the door to better talent.
    Managers are key to creating environments where neurodivergent employees can thrive.
    Many neurodivergent people struggle with whether to disclose during interviews — psychological safety reduces that burden.
    AI has the potential to reduce friction for neurodivergent workers, but also brings new challenges.
    Embracing neurodiversity isn't just ethical — it leads to stronger, more innovative teams.
  • Coding Chats

    5 mistakes start-up CTOs should avoid when scaling the tech team

    19/03/2026 | 1h 1 mins.
    Coding Chats episode 70 - Aaron LeClair discusses the top five mistakes startup CTOs make, covering everything from misunderstanding development pipelines to failing to make the leadership identity transition. The conversation explores AI adoption parallels, team diversity, hiring pitfalls, the "move fast and break things" mantra, and why a CTO's first team should be the C-suite — not the engineering team.

    Chapters
    00:00 Scaling the Pipeline: Common Mistakes of CTOs
    03:13 Understanding the Development Environment
    05:59 The Importance of Team Diversity
    09:03 Building Effective Teams
    11:53 Hiring for Fit: The Cost of Misalignment
    14:36 The Role of Leadership in Team Dynamics
    33:52 Building Effective Teams as a Leader
    37:35 Transitioning from Engineer to Leader
    43:31 Hiring the Right Technical Leaders
    46:01 Understanding the Role of CTO in Start-ups
    54:40 The Balance of Speed and Quality in Development
    01:01:24 Exploring Related Content

    Aaron's Links:
    https://www.linkedin.com/in/aaronleclair/

    John's Links:J
    ohn'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
    Scaling your dev team without first fixing QA, product management, and stakeholder flow will create more problems than it solves.AI adoption falls into the same trap — faster code generation doesn't help if requirements, testing, and deployment are still bottlenecks.
    Invest in tooling, DevOps, and documented processes early, as poor systems frustrate great engineers just as much as poor management.
    Always ask why a process exists — the original reason may no longer apply, and changing it is often easier than expected.
    Build teams like an Ocean's 11 cast: diverse in skills, backgrounds, and working styles, not a clone army of specialists in the same stack.
    Hire generalists with depth in different areas who can flex as start-up needs shift, and reserve deep specialists for your true business differentiators.
    A failed hire is most often a leadership failure — you had more information than the candidate, so treat every miss as a learning opportunity.
    The most important things a CTO does are hiring and developing people — if a leader is still submitting PRs to a team of more than three, that's a red flag.
    A CTO's primary team is the C-suite, not the engineering team — treating engineers as "your team" creates an us-vs-them culture that damages the whole business.
    Match technical leadership seniority to your company stage — pre-product-market-fit you need a generalist head of engineering, not a full CTO."Move fast and break things" is valid pre-product-market-fit for validating hypotheses, but once you have real customers it becomes an excuse for poor process.
  • Coding Chats

    Why most companies are getting AI wrong and how to build a culture that actually adapts

    12/03/2026 | 54 mins.
    Coding Chats episode 69 - John Crickett and Sairam Sundaresan discuss the evolving landscape of artificial intelligence (AI) and its implications for learning, software development, and organizational culture. Sairam emphasizes the importance of bridging the gap between technical and business perspectives on AI, advocating for a hands-on approach to learning. They explore the hype surrounding AI, particularly large language models (LLMs), and the need for a cultural transformation within organizations to effectively adopt AI technologies. The discussion also touches on the future of software engineering in an AI-driven world, highlighting the blurred lines between roles and the necessity for continuous learning and adaptation.

    Chapters
    00:00 Bridging the Gap: Understanding AI for Everyone
    03:44 Learning AI: A Practical Approach
    06:29 The Evolution of AI: From Hype to Reality
    09:33 Generative AI: The Current Landscape and Future Directions
    12:35 Transformative Use Cases: Beyond Basic Applications
    15:23 The Art of Questioning: Engaging with AI Effectively
    18:36 Navigating Large Codebases: AI as a Tool for Engineers
    21:24 Writing and Coding: Learning from the Masters
    27:42 Harnessing Subagents for Efficiency
    29:48 Bridging the Gap Between Business and Tech
    31:35 Cultural Transformation in AI Adoption
    34:22 Understanding AI Fundamentals for Better Collaboration
    36:11 The People Problem in AI Implementation
    39:26 Evolving Roles in Software Engineering
    42:26 The Resurgence of Software Engineering
    44:37 Leading an AI-First Organization
    49:16 Learning by Doing in AI
    52:03 Navigating the Landscape of AI Research and Publications
    54:05 Exploring Related Content

    Sairam's Links:
    Book- AI for the Rest of Us:https://www.amazon.com/dp/B0F29THNLT
    Substack Gradient Ascent: https://newsletter.artofsaience.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
    AI is essential for modern products and services.
    Bridging the gap between business and engineering is crucial.
    Learning AI requires a hands-on approach, not just theory.
    Cultural transformation is necessary for successful AI adoption.
    Understanding the basics of AI is vital for all roles.
    The hype around AI often overshadows other important areas.
    Software engineering is evolving with AI technologies.AI tools can enhance productivity but require thoughtful use.
    Continuous learning is key in the fast-paced AI landscape.
    The roles within organizations are becoming more integrated.

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About Coding Chats

On Coding Chats, John Crickett interviews software engineers of all levels from junior to CTO. He encourages the guests to share the stories of the challenges they have faced in their role and the strategies and tactics they have used to overcome those challenges providing actionable insights other software engineers can use to accelerate their careers.
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