Blog Posts

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.” “The user experience is terrible.” “You’ll never match native performance.” These concerns aren’t dishonest. They were entirely valid assessments of hybrid development circa 2015, when PhoneGap dominated and native bridges felt like digital duct tape. But here’s what we’ve witnessed over the past decade: whilst critics continue rehearsing these decade-old talking points, the hybrid development landscape has undergone a complete transformation. We’ve built hybrid applications that power demanding business workflows, financial platforms processing real-time data, and consumer applications serving millions of users. Instagram leverages React Native to share 85-99% of their codebase between iOS and Android. Bloomberg delivers financial data through hybrid architectures. These aren’t compromised solutions, they’re strategic advantages.

I’ve been experimenting with Claude 4 this week. It’s impressive: sharp, nuanced, and quite good at pulling meaning from dense technical writing. Naturally, I wanted to explore further, especially Claude Code. That is when I hit the wall. Despite paying for a Pro account, I found that access to Claude Code is not included. Instead, it sits behind a separate pay-as-you-go API billing model, with no practical way to trial it without committing to a credit card and a potentially unpredictable spend. That friction, minor as it seems, is enough to stall exploration.

In Part 1, we looked at the early stages of AI integration—add-on tools, search assistants, and rule-replacing prompt hacks. Now, we step into the deeper end of the pool. This is where AI stops sitting quietly on the side and starts calling your APIs. Where it moves from actions to them. Where agents take meetings, models adapt in real-time, and your architecture diagram starts to look like it was drawn by a sentient whiteboard marker.

You’ve probably seen the posts. “By the end of the year, 90% of all code will be written by AI.” “Software engineers will become prompt whisperers.” “We trained a GPT model on our Slack history and now it’s running product strategy.” It’s an exciting time to be in tech—if only because watching the hype train barrel down the tracks without brakes is strangely exhilarating. If you believe the internet, we’re just months away from replacing delivery teams with a well-tuned prompt and a generous OpenAI bill.

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. When used as performance measures or planning anchors, they distort behaviour, encourage estimation games, and shift the team’s focus from solving real problems to managing appearances. But once you’ve recognised that traditional metrics are part of the problem, the obvious next question is: what should we measure instead?