Coding Chats

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

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

00:52 From Mainframes to Big Tech: A Journey

04:01 The Shift to Cloud: Embracing New Technologies

06:28 Navigating the Transition: Learning New Languages and Skills

09:43 Cultural Shift: Adapting to Fast-Paced Environments

12:44 The Role of a Solutions Architect: Bridging Technology and Business

15:50 Overcoming Communication Barriers: The Importance of Executive Communication

18:34 Teaching the Next Generation: Transitioning Others to Cloud Careers

31:56 Building Trust Through Domain Knowledge

34:35 The Importance of Trying and Iterating

38:33 Transitioning to Solutions Architect: Timeframes and Skills

40:37 The Drive Behind Founding Cloud Coliseum

43:49 AI's Impact on Solutions Architects

50:10 The Reality of AI Hype and Job Market Dynamics

56:34 Navigating the Future of AI and Job Security

Raj's Links:

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways

Your existing experience isn’t throwaway, so connect it to cloud and AI instead of starting from scratch.

You don’t need to be a cloud expert to become a solutions architect, because knowing the legacy systems companies are migrating is a huge advantage.

Side projects and proofs of concept can open doors, and you can build them without quitting your job.

Executive communication is often what separates senior engineers from the next level, since business leaders control the budget.

Technical skills alone are hard to win on over a long career, but adding business communication makes you a complete package.

Cloud and AI reward trying things, because the cost of a mistake is tiny compared with the old days of expensive, exam-style learning.

You only need to master the core concepts, about 10% of the dictionary, to get hired as a solutions architect.AI is useful but overhyped, so learn the fundamentals (RAG, agents, MCP, evals, guardrails), tie them to your existing knowledge, and keep a close eye on AI-generated code.

Coding Chats Episode 83 - In this episode, VB from OpenAI’s developer experience team explains why he thinks plan mode is obsolete and how coding agents have shifted from step-by-step instructions to outcome-driven, long-running work. We cover how to write a good agents.md, why tests still matter when agents write the code, spec-driven development, and why cloud agents may be the future of dev tooling.

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

00:00 Introduction to Developer Experience at OpenAI

03:29 Bringing AI Models to Developers

06:06 The Shift from Planning to Outcome-Focused Development

09:05 The Role of Testing in AI Development

11:51 Creating Effective Agents.md for AI Projects

17:17 The Evolution of Developer Tools at OpenAI

25:21 Cloud Development: The Future of Coding

26:48 Insights from OpenAI's Dev Day

28:00 Revolutionizing Development with GPT 6.1 Soul

32:35 Understanding Caching for Developers

39:02 Proactive Agents: Enhancing Productivity

42:39 Community Initiatives and Open Source Contributions

48:36 Exploring Related Content

VB's Links:

https://openai.com/

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways

Plan mode is obsolete because current models plan inherently, so you’re better off spending your time up front defining the outcome you want.

Developers are moving from prescribing steps to defining intended outcomes (like “get this CI green”) and letting the agent plan, execute, and adjust on its own.

Spec-driven development has its place, especially for API-shaped work, but it backfires if the spec isn’t kept in sync with every change.

A strong but lightweight agents.md should cover where code lives, how the agent knows it’s doing a good job, your style rules, and what a good PR looks like.

Tests still matter when agents write the code, because they catch regressions before merge, but the agent needs guidance on which tests are worth writing.

Treat agents.md as a living document that grows as you work with the agent, not something you get right in one shot.

To get the most from prompt caching, use one thread per feature and a couple of narrowly scoped subagents instead of running a dozen agents and letting the cache go cold.

Coding workloads have large inputs and small outputs, which is why caching makes them so much cheaper and faster than typical chat use.

Cloud agents are where dev tooling is heading, since they remove “works on my machine” problems and free long-running tasks from your laptop.

If you’re still on the fence about coding agents, give one real task to an agent, watch how it interprets your request, and refine your instructions from there.

Coding Chats Episode 82 - John talks with Dave Westgarth, head of delivery at a Databricks consultancy. Dave shares his path from a psychology degree and McDonald's into tech, and they discuss why the Scrum Master role is so misunderstood, how it differs from a delivery manager, and whether either needs to be technical.They also look at how AI is changing delivery work. Faster code just moves the bottleneck upstream, John argues there's no AI-specific skill you can't learn in a day, and engineering fundamentals are what make AI useful.

Chapters

00:00 Journey to Technology: A Personal Story

10:33 The Role of a Scrum Master: Misunderstandings and Realities16:52 The Impact of AI on Scrum Master Roles

24:41 Hiring and Empowering the Right People

26:01 Scrum Master vs. Delivery Manager: Understanding the Roles

26:06 Technical Skills: Should Scrum Masters Be Technical?

32:19 The Value of Certifications in Professional Development

38:48 AI's Role in Delivery Management

47:13 Connecting with Dave Westgarth

Dave's Links:

https://www.technicalscrum.orghttps://

www.linkedin.com/in/dave-westgarth/

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways

Scrum Masters who just move Jira tickets and run ceremonies are the ones AI can replace.

Be loyal to agile's values, not the mechanics of the framework.

If AI makes code ten times faster, stakeholders and approvals need to keep up, or the bottleneck just moves.

In retrospectives, pick one small change instead of trying to fix everything.

Regular delivery cuts down the demand for status reports.

Technical skills help managers only if they don't take over the work and stop developing their people.

There's no AI-specific skill you can't learn in a day, but using AI well takes software engineering fundamentals.

Writing code is a small part of software engineering.

Tutorials show the perfect path, but the real learning happens when you get stuck.

Certifications work best as forced learning, not just CV decoration.

Coding Chats Episode 81 - Ben Howdle talks to John Crickett about being a first time CTO in a start-up. Ben, author of The First CTO, shares what changes when you move from building technology to leading a technical team: the pressure, the trade-offs, and the realization that writing code is only part of the job.Drawing on startup experience and lessons learned the hard way, Ben discusses how to balance quality against runway, manage people, and communicate technical decisions in terms that founders and other non-technical stakeholders can understand. He also explores why resourcefulness, resilience, and comfort with ambiguity matter so much—and why CTOs often spend more time selling ideas than they expect.Whether you’re stepping into your first CTO role or leading a technical team at an early-stage company, this conversation offers candid insights into the realities of technical leadership.

Chapters

00:45 The Loneliness of the CTO

16:57 Essential Skills for a First-Time CTO

22:53 Understanding the Hierarchy: Who is the Boss?

30:14 The Importance of Advisory Network

38:06 Transitioning to People Management

44:27 Understanding the 40% Rule

52:54 Exploring Related Content

Ben's Links:

https://www.linkedin.com/in/ben-howdle/

John's Links:

John's LinkedIn: https://www.linkedin.com/in/johncrickett/

John’s YouTube: https://www.youtube.com/@johncrickett

John's Twitter: https://x.com/johncrickett

John's Bluesky: https://bsky.app/profile/johncrickett.bsky.social

Check out John's software engineering related newsletters: Coding Challenges: https://codingchallenges.substack.com/ which shares real-world project ideas that you can use to level up your coding skills.

Developing Skills: https://read.developingskills.fyi/ covering everything from system design to soft skills, helping them progress their career from junior to staff+ or for those that want onto a management track.

Takeaways

Taking on the CTO title without experience can be the thing that forces the growth you wouldn't have sought otherwise.

At a startup, runway isn't just a finance metric — it should be the lens every technical decision gets filtered through.

The tension between building things well and building things fast enough to survive is one of the defining challenges of being a first-time CTO.

The most chaotic, uncertain period of your career might also be the one where you're most alive.

A first-time CTO needs resourcefulness, comfort with ownership, and resilience more than any specific technical skill.

When you have no one to bounce ideas off internally, build an informal council from your network — it can double as a recruiting pipeline.

The quicker you can identify your own blind spots and weaknesses, the quicker you can plug the gaps.

Early-career engineers often drown non-technical stakeholders in jargon to mask imposter syndrome — the real skill is meeting people where they are and speaking to business outcomes.

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.

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.

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.

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.

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.

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.