Coding Chats

Hosted by John Crickett

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May 2026

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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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May 28, 202624 min

Startup Advisor Secrets: Hiring, CTOs & Going From POC to Product

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. Chapters 00:00 Introduction to Start-up Advising 02:09 The Day-to-Day of a Start-up Advisor 05:39 Hiring Challenges in Start-ups 07:39 Defining the Role of a CTO 10:36 Common Mistakes in CTO Hiring 12:59 Bridging the Gap: Technical and Business Communication 16:40 Utilizing Client Feedback for Product Improvement 20:06 Transitioning from Proof of Concept to Product 24:01 Exploring Computer Vision in AI 24:06 Balancing Technical Excellence and Business Focus 24:09 Exploring Related Content Alex's Links: Alex's LinkedIn: https://www.linkedin.com/in/alexander-berkovich-startup-advisor/ 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 A CTO's value is in leadership and strategy, not just how much they can code.AI has fundamentally changed the hiring process and what makes a good candidate. Document every corner you cut in a POC — it will catch up with you later. Engineers should seek direct client feedback to understand the real impact of their work. Communication is the most underrated skill in any startup team. A POC and a product are very different things — don't let one accidentally become the other. Startups offer breadth of experience that large enterprises simply can't match. Hiring for the right mindset matters more than hiring for pure technical skill. Small technical decisions can ripple out and affect cost, timelines, and the whole product. The best teams stay connected to the end goal, not just the task in front of them.

May 21, 202646 min

AI Agents Have a Memory Problem (And You're Probably Making It Worse)

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. The pair also explore why memory is the most universally relatable concept in AI, the history of how neuroscience shaped LLMs, and the problem of Catastrophic Forgetting that still haunts models today. A sharp AGI debate lands on a sobering point: an LLM is just a function — tokens in, tokens out — and most AI engineers are unknowingly rediscovering solutions that database engineers spent decades building. Chapters 00:00 — What Is Agent Memory and How Does It Work? 05:00 — File System vs Database: Which Should You Use for Agent Memory? 09:00 — Why Building on Files Means You'll Reinvent the Database 13:00 — How Oracle Is Meeting AI Developers Where They Are 15:00 — Why Memory Is the Most Universal Concept in AI 21:00 — From Computer Vision to LLMs: How Richmond Found His Path 24:00 — Catastrophic Forgetting: The Problem That Hasn't Gone Away 26:00 — Is AGI Real? Why the Goalposts Keep Moving 33:00 — Handling PII, Data Sovereignty, and Access Control in AI Apps 42:00 — The Rise of Memory Engineering: AI's Most Underrated Discipline Richmond's Links: LinkedIn: https://www.linkedin.com/in/richmondalake/ X: https://x.com/richmondalake 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: File systems are fine for prototyping, but the moment you hit production scale you're just slowly reinventing the database. File systems are fine for prototyping, but the moment you hit production scale you're just slowly reinventing the database. Agent memory isn't a new concept — it's data management, and database engineers have been solving it for decades. Memory is the single most relatable entry point for explaining AI to anyone, technical or not. Catastrophic Forgetting isn't a solved problem — it plagued RNNs and still quietly haunts LLMs today. An LLM is ultimately just a function: tokens in, tokens out — which should temper any claims about sentience or AGI. The definition of AGI keeps shifting to match whatever AI can't do yet, making the whole debate almost meaningless. Most AI engineers have less than ten years of experience and are unknowingly rediscovering solutions that search and database engineers spent decades building. "Vector search is all you need" is one of the most dangerous oversimplifications in AI engineering right now. Memory engineering — the crossover between data engineering, search optimisation, and agent design — is an emerging discipline that doesn't have a name yet but absolutely should. The real moat in AI products isn't the LLM itself, it's everything built around it — the harness, the memory, the retrieval pipeline.

May 14, 202649 min

I got into computers to avoid people then they put me in charge of them!

Coding Chats Episode 78 - John Crickett talks to Robert Harris, an experienced engineering leader. Robert shares hard-won lessons from years of leading software teams, drawing on a distinctive "human systems" lens to explain why so many engineering organisations struggle — not because of bad people, but because of broken systems, misaligned leadership, and invisible cultural forces. The conversation weaves together philosophy, practical management advice, and candid personal anecdotes, making it equally relevant for first-time engineering managers and seasoned CTOs. The central thread throughout is that software is fundamentally a human endeavour, and leaders who treat it like a purely technical one will keep running into the same problems. Chapters 0:00 — Every Problem is a Systems Problem 3:00 — Labelling vs. Diagnosing: The Human Systems Approach 6:15 — Poor Performance Is a System Failure, Not a People Failure 9:10 — AI, Flat Orgs, and the Pressure on Engineering Managers 11:30 — Diagnosing a Broken Team: A Real-World Turnaround 24:05 — People Are Not Interchangeable Components 26:00 — Culture: What Happens When Nobody's Watching 33:00 — The Power Gradient and Cross-Team Collaboration 39:00 — The C-Suite Distance Problem 42:00 — Building Culture in Remote and Distributed Teams 46:00 — Software Engineering Is a Humanity Robert's Links: https://www.linkedin.com/in/robert-n-harris/coded2lead.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 People run on emotion and safety, not logic — lead them accordingly. When someone underperforms, look at the system before you look at the person. Labelling people as "difficult" or "lazy" is a way of avoiding the real problem. AI is accelerating code generation, but the human bottleneck downstream is getting worse, not better. The institutional memory inside a team is worth far more than anything in your wiki. Culture is what happens when nobody's watching — not what's written on the wall. If you send Slack messages at 10pm, your team will think there's no such thing as work-life balance. Only authorised people should authorise work — casual remarks from leaders land as commands. Co-location without connection isn't culture, it's a terrarium. Computers are a science, but software is a humanity.

May 7, 202650 min

The Death of Writing Code: OpenAI's Engineer on the Rise of Harness Engineering

Coding Chats Episode 77 — Arnaud Fournier, Forward Deployed Engineer at OpenAI, talks to John Crickett about how AI is fundamentally reshaping software engineering. He explores how OpenAI's own engineers have largely moved away from writing code line-by-line, shifting instead to what he calls "harness engineering" — orchestrating agents, preparing context, and steering AI to do the heavy lifting. The conversation covers practical ground for engineers at every level: how to successfully adopt agentic coding in your workflow, best practices for integrating tools like Codex into enterprise environments, and what it's really like to work at the frontier of AI deployment across industries like semiconductors, life sciences, and finance. Chapters 00:00 Understanding the Role of Forward Deployed Engineers 03:21 The Integration Process: Challenges and Solutions 06:25 Optimizing AI Solutions with Codex 09:38 Leveraging Codex for Team Efficiency 12:28 Best Practices for Using Codex in Engineering Workflows 15:29 Setting Up for Success in Enterprise AI Projects 18:26 Navigating Stakeholder Engagement and Requirements 21:16 The Future of AI in Enterprise Solutions 25:53 Building Proof of Concept Solutions 28:33 Collaborative Development and Model Improvement 30:45 The Rise of Codex and User Adoption 33:36 Integrating AI into Software Development 36:10 Standardization vs. Customization in AI Tools 39:05 The Evolving Role of Forward-Deployed Engineers 42:48 Understanding the FDE Role at OpenAI 46:10 The Recruitment Process at OpenAI 49:50 Exploring Related Content 49:58 Outro Final Coding Chats.mp4 Arnaud's Links https://www.linkedin.com/in/arnaudfrn/ https://openai.com/index/introducing-openai-frontier/ https://community.openai.com/t/introducing-the-new-codex-for-almost-everything/1379125 https://openai.com/index/scaling-codex-to-enterprises-worldwide/ 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.

April 30, 20261 hr 7 min

LLM as a Judge: Why Your AI Might Be Marking Its Own Homework

Coding Chats episode 76 - John talks to Laura Dietz - a computer science professor whose work focuses on whether AI evaluation metrics actually tell the truth. She's known for her critical take on "LLM as a judge" — not because she thinks it's useless, but because she wants numbers that mean something rather than numbers that just make a system look good. The conversation tackles some uncomfortable realities for software engineers: using an LLM to write code and another to review it is a circular trap, prompt engineering shouldn't be a computer scientist's day job, and every time you reject your code AI's output, you're quietly generating the training data that shapes its successor. Chapters 00:00 Introduction to Laura Dietz and Her Journey 03:12 Exploring LLMs as Judges 06:16 Challenges in Evaluating Search Systems 08:49 The Evolution of User Queries and Expectations 11:46 The Role of LLMs in Information Retrieval 14:44 Defining Quality in Search Results 17:27 The Complexity of User Intent 19:54 Human-AI Collaboration in Code Review 22:53 The Future of LLMs in Software Development 25:23 Balancing Human and AI Roles 28:20 Innovative Approaches to AI Evaluation 34:10 The Art of Assembling Ideas 36:39 Balancing Cost and Quality in LLMs 39:09 Evaluating LLM Performance 43:50 The Future of LLMs and Training Data 49:19 Exploring New Architectures in AI 55:16 Understanding In-Context Learning 01:00:45 The Role of AI in Creative Expression 01:06:59 Exploring Related Content Laura's Links: https://www.cs.unh.edu/~dietz/https:// www.linkedin.com/in/laura-dietz-47036516/ 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 Using an LLM to both generate and evaluate outputs is circular — like a student grading their own homework. If your evaluation metric can go up without your system actually improving, it's not a real metric. A better human-in-the-loop isn't one that rubber-stamps AI suggestions — it's one that's guided to look in the right place. LLMs don't get bored, which makes them genuinely useful for code review — but that's not the same as making them accurate. "Faith-based engineering" — trusting AI output without validation — is a real and growing problem in software teams. Prompt engineering is a workaround, not a discipline; real engineers should be building systems, not crafting incantations. Every rejection you give your code AI is training signal — your frustration today is someone else's better tool tomorrow. The transformer attention mechanism is a weighted sum, and a sum isn't always the right operation — some problems need an AND, not an OR. AI tools are lowering the barrier to coding for people who were previously too intimidated to try, and that's worth celebrating. The same network effect that makes a platform valuable also makes monopoly in AI training data genuinely dangerous.

April 23, 202637 min

Let it crash! How Erlang and BEAM build bullet proof software

Coding Chats episode 74 - Erik Stenman talks to John Crickett about the BEAM virtual machine — the runtime behind Erlang, Elixir, and Gleam. Built by Ericsson in the 1980s for telephone switches, it was designed for fault tolerance and concurrency from day one, yet never achieved mainstream popularity despite being technically superior to many alternatives. The discussion covers what makes BEAM unique: lightweight isolated processes, a "let it crash" fault philosophy, and powerful built-in introspection. Erik also shares practical lessons from production use and explains why newer languages like Elixir and Gleam are finally bringing BEAM the attention it deserves. Chapters 00:00 Introduction to Beam and Erlang 02:45 The Unique Features of Erlang and Beam 05:17 Concurrency and Fault Tolerance in Beam 07:34 Applications and Use Cases of Erlang 10:00 Error Handling and Process Supervision 12:49 Performance Considerations in Beam 15:09 Learning and Adopting Erlang and Elixir 17:28 The Future of Erlang, Elixir, and Gleam 37:04 Exploring Related Content Erik's Links: https://happihacking.com/ https://happihacking.com/blog/ https://github.com/happi/theBeamBook https://www.amazon.com/dp/9153142535https://www.elixirconf.eu/trainings/the-beam-for-developers/ 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 BEAM was built for telephone switches in the 1980s — its reliability features translate surprisingly well to modern web and distributed systems. Erlang lost the popularity race to Java largely due to marketing, not technical merit. BEAM processes are extremely lightweight — hundreds of bytes, not kilobytes — allowing millions to run concurrently. "Let it crash" is a design philosophy, not laziness — isolating failures prevents one bad process from taking down the whole system. No shared memory between processes eliminates an entire class of concurrency bugs. Per-process garbage collection means no "stop the world" pauses like you get in Java. Hot code loading lets you upgrade a running system without downtime — but it requires careful thought about data structure changes. BEAM's built-in introspection lets you inspect a live system in real time, making debugging far faster. Elixir and Gleam are modernising the syntax and bringing new developers onto the BEAM platform. BEAM doesn't solve everything — good architecture still matters, but it gets you there faster than most alternatives.

April 16, 202656 min

AI writes it. You own it. Don't ship AI slop

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.

April 9, 202649 min

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. 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.

April 2, 202654 min

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. 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.

March 26, 202646 min

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. 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.