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.Chapters00:00 Introduction to Start-up Advising02:09 The Day-to-Day of a Start-up Advisor05:39 Hiring Challenges in Start-ups07:39 Defining the Role of a CTO10:36 Common Mistakes in CTO Hiring12:59 Bridging the Gap: Technical and Business Communication16:40 Utilizing Client Feedback for Product Improvement20:06 Transitioning from Proof of Concept to Product24:01 Exploring Computer Vision in AI24:06 Balancing Technical Excellence and Business Focus24:09 Exploring Related ContentAlex'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/@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.TakeawaysA 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.Chapters00:00 — 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 Database13:00 — How Oracle Is Meeting AI Developers Where They Are15:00 — Why Memory Is the Most Universal Concept in AI21:00 — From Computer Vision to LLMs: How Richmond Found His Path24:00 — Catastrophic Forgetting: The Problem That Hasn't Gone Away26:00 — Is AGI Real? Why the Goalposts Keep Moving33:00 — Handling PII, Data Sovereignty, and Access Control in AI Apps42:00 — The Rise of Memory Engineering: AI's Most Underrated DisciplineRichmond'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/@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: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.Chapters0:00 — Every Problem is a Systems Problem3:00 — Labelling vs. Diagnosing: The Human Systems Approach6:15 — Poor Performance Is a System Failure, Not a People Failure9:10 — AI, Flat Orgs, and the Pressure on Engineering Managers11:30 — Diagnosing a Broken Team: A Real-World Turnaround24:05 — People Are Not Interchangeable Components26:00 — Culture: What Happens When Nobody's Watching33:00 — The Power Gradient and Cross-Team Collaboration39:00 — The C-Suite Distance Problem42:00 — Building Culture in Remote and Distributed Teams46:00 — Software Engineering Is a HumanityRobert's Links:https://www.linkedin.com/in/robert-n-harris/coded2lead.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.TakeawaysPeople 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.Chapters00:00 Understanding the Role of Forward Deployed Engineers03:21 The Integration Process: Challenges and Solutions06:25 Optimizing AI Solutions with Codex09:38 Leveraging Codex for Team Efficiency12:28 Best Practices for Using Codex in Engineering Workflows15:29 Setting Up for Success in Enterprise AI Projects18:26 Navigating Stakeholder Engagement and Requirements21:16 The Future of AI in Enterprise Solutions25:53 Building Proof of Concept Solutions28:33 Collaborative Development and Model Improvement30:45 The Rise of Codex and User Adoption33:36 Integrating AI into Software Development36:10 Standardization vs. Customization in AI Tools39:05 The Evolving Role of Forward-Deployed Engineers42:48 Understanding the FDE Role at OpenAI46:10 The Recruitment Process at OpenAI49:50 Exploring Related Content49:58 Outro Final Coding Chats.mp4Arnaud's Linkshttps://www.linkedin.com/in/arnaudfrn/https://openai.com/index/introducing-openai-frontier/https://community.openai.com/t/introducing-the-new-codex-for-almost-everything/1379125https://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/@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.

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.Chapters00:00 Introduction to Laura Dietz and Her Journey03:12 Exploring LLMs as Judges06:16 Challenges in Evaluating Search Systems08:49 The Evolution of User Queries and Expectations11:46 The Role of LLMs in Information Retrieval14:44 Defining Quality in Search Results17:27 The Complexity of User Intent19:54 Human-AI Collaboration in Code Review22:53 The Future of LLMs in Software Development25:23 Balancing Human and AI Roles28:20 Innovative Approaches to AI Evaluation34:10 The Art of Assembling Ideas36:39 Balancing Cost and Quality in LLMs39:09 Evaluating LLM Performance43:50 The Future of LLMs and Training Data49:19 Exploring New Architectures in AI55:16 Understanding In-Context Learning01:00:45 The Role of AI in Creative Expression01:06:59 Exploring Related ContentLaura'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/@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.TakeawaysUsing 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.Chapters00:00 Introduction to Beam and Erlang02:45 The Unique Features of Erlang and Beam05:17 Concurrency and Fault Tolerance in Beam07:34 Applications and Use Cases of Erlang10:00 Error Handling and Process Supervision12:49 Performance Considerations in Beam15:09 Learning and Adopting Erlang and Elixir17:28 The Future of Erlang, Elixir, and Gleam37:04 Exploring Related ContentErik's Links:https://happihacking.com/ https://happihacking.com/blog/https://github.com/happi/theBeamBookhttps://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/@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.TakeawaysBEAM 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.Chapters00:00 Introduction to AI in Software Development03:24 Embedding Quality Gates in Development06:03 The Importance of Consistency in Code09:09 Ownership and Critical Thinking in Engineering12:00 Balancing Tool Freedom and Intellectual Property14:56 Navigating AI Tools and Workflows17:47 Managing Burnout in AI Development20:47 The Evolution of Coding and Instant Gratification23:47 Documenting Ideas and Project Management26:54 Using AI for Ideation and Collaboration31:38 The Joy of Learning Through AI34:11 Codo: Enhancing Code Quality and Governance37:22 Comparing Code Review Tools40:10 The Future of AI in Software Development50:51 The Importance of Dogfooding Products56:12 Exploring Related ContentNnenna's Links:https://nnennahacks.com https://linkedin.com/in/nnenna-ndukwe/https://x.com/nnennahacksJohn'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.TakeawaysCombine 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.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.

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.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.

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.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.