Coding Chats

AI assisted software engineering need leaders not coders

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.Chapters00:00 Evaluating Tech Influencers06:15 Craftsmanship vs. Engineering in Software12:06 Career Ownership and Development20:47 Finding and Utilizing Mentors30:28 The Value of Diverse Mentorship36:49 Navigating Careers Outside Big Tech42:43 AI and Leadership in Programming49:42 Exploring Related Content49:50 Outro Final Coding Chats.mp4Benjamen's Links:https://binaryheap.com https://pylecloudtech.comJohn's Links:John's LinkedIn: https://www.linkedin.com/in/johncrickett/John’s YouTube: https://www.youtube.com/@johncrickettJohn's Twitter: https://x.com/johncrickettJohn's Bluesky: https://bsky.app/profile/johncrickett.bsky.socialCheck 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.TakeawaysFollower 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.

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

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.Chapters00:00 The Importance of Professional Skills for Software Engineers06:24 Navigating the Impact of AI on Software Engineering12:09 The Evolving Role of Software Engineers17:50 AI for the Rest of Us: Bridging the Knowledge Gap25:43 The Ethical Implications of AI and Communication27:12 Ethics in AI Development31:04 Improving Communication Skills for Engineers38:00 Overcoming the Fear of Writing42:15 The Importance of Public Speaking50:17 The Journey of Continuous Learning54:30 Exploring Related ContentCharles's Links:https://www.linkedin.com/in/charleshumble/\https://bsky.app/profile/charleshumble.bsky.socialJohn's Links:John's LinkedIn: https://www.linkedin.com/in/johncrickett/John’s YouTube: https://www.youtube.com/@johncrickettJohn's Twitter: https://x.com/johncrickettJohn's Bluesky: https://bsky.app/profile/johncrickett.bsky.socialCheck 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.

Build better tech teams with neurodiversity

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.Chapters00:00 Understanding Neurodiversity03:32 The Spectrum of Neurodivergence06:30 Strengths of Neurodivergent Individuals09:08 Creating Inclusive Teams12:10 Improving Recruitment Practices15:00 Work Environment for Neurodivergent Individuals17:43 The Connection Between Neurodiversity and Software Engineering23:38 Exploring Neurodiversity in Engineering24:39 The Impact of AI on Neurodivergent Workers27:08 Inclusive Recruitment Practices32:57 The Role of Managers in Hiring38:46 Disclosing Neurodivergence in Job Interviews44:11 The Future of Neurodiversity in the Workplace46:11 Exploring Related ContentAnita'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/@johncrickettJohn's Twitter: https://x.com/johncrickettJohn's Bluesky: https://bsky.app/profile/johncrickett.bsky.socialCheck 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.TakeawaysNeurodiversity 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.

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

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.Chapters00:00 Scaling the Pipeline: Common Mistakes of CTOs03:13 Understanding the Development Environment05:59 The Importance of Team Diversity09:03 Building Effective Teams11:53 Hiring for Fit: The Cost of Misalignment14:36 The Role of Leadership in Team Dynamics33:52 Building Effective Teams as a Leader37:35 Transitioning from Engineer to Leader43:31 Hiring the Right Technical Leaders46:01 Understanding the Role of CTO in Start-ups54:40 The Balance of Speed and Quality in Development01:01:24 Exploring Related ContentAaron's Links:https://www.linkedin.com/in/aaronleclair/John's Links:John's LinkedIn: https://www.linkedin.com/in/johncrickett/John’s YouTube: https://www.youtube.com/@johncrickettJohn's Twitter: https://x.com/johncrickettJohn's Bluesky: https://bsky.app/profile/johncrickett.bsky.socialCheck 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.TakeawaysScaling 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.

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

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.Chapters00:00 Bridging the Gap: Understanding AI for Everyone03:44 Learning AI: A Practical Approach06:29 The Evolution of AI: From Hype to Reality09:33 Generative AI: The Current Landscape and Future Directions12:35 Transformative Use Cases: Beyond Basic Applications15:23 The Art of Questioning: Engaging with AI Effectively18:36 Navigating Large Codebases: AI as a Tool for Engineers21:24 Writing and Coding: Learning from the Masters27:42 Harnessing Subagents for Efficiency29:48 Bridging the Gap Between Business and Tech31:35 Cultural Transformation in AI Adoption34:22 Understanding AI Fundamentals for Better Collaboration36:11 The People Problem in AI Implementation39:26 Evolving Roles in Software Engineering42:26 The Resurgence of Software Engineering44:37 Leading an AI-First Organization49:16 Learning by Doing in AI52:03 Navigating the Landscape of AI Research and Publications54:05 Exploring Related ContentSairam's Links:Book- AI for the Rest of Us:https://www.amazon.com/dp/B0F29THNLTSubstack Gradient Ascent: https://newsletter.artofsaience.comJohn's Links:John's LinkedIn: https://www.linkedin.com/in/johncrickett/John’s YouTube: https://www.youtube.com/@johncrickettJohn's Twitter: https://x.com/johncrickettJohn's Bluesky: https://bsky.app/profile/johncrickett.bsky.socialCheck 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.TakeawaysAI 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.