Blog Posts on Wyrd Technology

Adoption curves for generative AI keep climbing.

The myth of Pandora is not a story about curiosity or punishment.

Ask an engineering leader who has been navigating AI adoption for the past year to describe their team’s operating model, and most can give you a reasonable answer.

A previous piece on this site made the case that traditional engineering org structures break down in the AI era, and introduced a set of conceptual role archetypes for what might replace them.

Yesterday, I was in one of those conversations that starts as a catch-up between engineers and ends as something more like a collective working-out of what the industry is becoming.

When a board hears that 90% of code will be written by AI within a year, what do they do about engineering headcount?

You ask Claude Code to refactor your authentication system from basic auth to OAuth2.

There’s a pattern emerging across organisations deploying AI: impressive capabilities built on ungoverned data.

A few weeks ago, a post crossed my LinkedIn feed that captured something I’ve been watching with growing concern.

I recently asked Claude to help design a system for tracking 200 product features across multiple pricing tiers, with dependency relationships between features.

You know you should measure testing effectiveness.

On October 20, 2025, AWS’s us-east-1 region experienced a cascading failure lasting more than 15 hours.

I recently tried the Model Context Protocol, Anthropic’s attempt to standardise how AI systems connect to data sources.

When you bring an engineer onto a complex project, you don’t just throw them code and say “figure it out”.

Ninety per cent of developers now use AI at work, up from 76% just a year ago.

You’re three hours into a complex refactoring.

This is the third article in a series on testing discipline in the AI era You’ve identified the problem.

This is the second article in a series on testing discipline in the AI era The warning signs are everywhere once you know what to look for.

I’ve been coding heavily with AI recently, developing patterns to ensure high-value testing practices are maintained throughout the development process.

Why Claude Code became my go-to example for the future of productivity If you’ve been following my usual deep dives into AI, engineering, and process optimisation, you’ll notice something different about today’s piece.

When AI Became My Best Development Partner “Refactor my data storage to use S3 instead of storing large items in NoSQL.”

“How are you actually structuring your work when AI is doing half the thinking?”

Sarah stared at the quarterly metrics on her screen, feeling the familiar knot of confusion that had been growing for months.

Following my post last week about mentoring developers when AI writes half their code, an old acquaintance and exceptional leader, Meri Williams, reached out to share that they had just given a talk on the same topic at LDX3: “Rethinking growing engineers…

How AI tools create a mentorship paradox that demands new approaches to developing engineering talent The developer beside you types a brief prompt into GitHub Copilot and watches as perfect React components materialise on screen.

You’ve been coding for three years, but you still panic when Stack Overflow is down.

In March 2024, Neil Hollis (@neilpaints on Instagram) stood on stage at Adepticon, holding the Golden Demon award for best single Warhammer 40,000 miniature.

The software engineering job market has entered an unprecedented state of paralysis.

A systematic framework for treating team decisions like code changes, using Git workflows to preserve institutional knowledge and accelerate AI adoption.

How engineering leaders can systematically capture, evaluate, and execute on the constant stream of ideas that flows through their minds Engineering leaders face a unique cognitive burden: brilliant at generating solutions and spotting opportunities, yet…

This article explores how structured written communication serves as the foundation for engineering process improvements and successful AI adoption.

A comprehensive guide to building organisational capabilities that prioritise reliability, sustainability, and long-term success.

How to stop repeating yourself and start getting consistently brilliant results from AI We’ve all been there.

Why Traditional Coding Interviews Are Failing and How to Build Teams for an AI-Native World Last week, I watched a junior developer use Claude to solve a distributed systems problem that would have sent me into an existential crisis just two years ago.

The Debate That Time Forgot At Wyrd Technology, we’ve lost count of how many times we’ve sat in client meetings listening to the same warnings: “Hybrid apps are slow.”

Introduction: A Model of Confusion I’ve been experimenting with Claude 4 this week.

Part 2 of a two-part “AI Will Write All Our Code” series In Part 1, we looked at the early stages of AI integration—add-on tools, search assistants, and rule-replacing prompt hacks.

Part 1 of a two-part “AI Will Write All Our Code” series So… I’ve Been Thinking About AI You’ve probably seen the posts.

Part 3 of the “Why Agile Metrics Can Be Misleading” Series In the first two parts of this series, we explored how Agile metrics like story points and velocity—originally meant to support transparency and delivery—often become counterproductive.

Part 2 of the “Why Agile Metrics Can Be Misleading” Series The Real Cost of Ritual Agile promised to make software delivery more adaptive, more collaborative, and more focused on outcomes.

Part 1 of the “Why Agile Metrics Can Be Misleading” Series Chasing Speed: The False Promise of Agile Metrics Agile was supposed to be a game-changer.

At Wyrd Technology, we’ve always aimed to blend innovative software consultancy with thoughtful, deliberate solutions to complex technical challenges.