Author name: Mark

Could European Businesses Really Ditch US Workplace Technology?

In December 2024, Russia’s communications authority, Roskomnadzor, blocked access to major foreign technology platforms — including Google, YouTube, Telegram, and WhatsApp — in regions such as Dagestan, Chechnya, and Ingushetia. These tests were part of Russia’s broader effort to develop a “sovereign internet,” ensuring domestic control over digital infrastructure in the event of global disconnection. At the same time, […]

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How I wrote a working web app with ChatGPT and Claude, and what you can learn from it

Prelude I’m a professional techie, but no longer a professional developer — my badge and git permissions were handed in over a decade ago. This post reflects my experience using Generative AI tools to build a working web application from the perspective of someone who is extremely rusty on the day-to-day tasks of being a software engineer.

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Forming. Storming. Norming. And the other one… the lesser known history of Tuckman’s team model

Forming. Storming. Norming. Performing. You may well have heard these stages of group maturity before, but do you know where the theory came from? This article is inspired by the Teamcraft podcast episode, available wherever you get your pods. In 1965 a young psychologist, freshly minted with a PhD from Princeton, joined the Naval Medical

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Part 6: The Best of Both Worlds: Human Developers and AI Collaborators

How Generative AI will impact product engineering teams — Part 6 | Postscript This is the final part of a six part series investigating how generative AI productivity tools aimed at developers, like Github Copilot, ChatGPT and Amazon CodeWhisperer, might impact the structure of entire product engineering teams. In this final part we consider many of the

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Part 5: Who wins and who loses? How different types of business could be impacted by AI tools.

How Generative AI will impact product engineering teams — Part 5 This is the fifth part of a six part series investigating how generative AI productivity tools aimed at developers, like Github Copilot, ChatGPT and Amazon CodeWhisperer might impact the structure of entire product engineering teams. In Part 4, we explored: Cui Bono — Who benefits? There are always winners

Part 5: Who wins and who loses? How different types of business could be impacted by AI tools. Read More »

Part 4: If AI coding tools reduce the number of engineers we need, where do we spend our budgets?

The Impact of AI on Product Engineering teams — Part 4 This is the fourth part of a six part series investigating how generative AI productivity tools aimed at developers, like Github Copilot, ChatGPT and Amazon CodeWhisperer might impact the structure of entire product engineering teams. In Part 3, we explored: What will our new organisations look like? LLMs

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