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Perspectives on AI, offshore development, team structure, and building technology that actually delivers.
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The disclosure obligation for AI-written text falls away as soon as a human has reviewed it on its substance and someone carries editorial responsibility. No label required, provided there is a name behind it — and that exposes exactly where the legislator places responsibility.
Anyone who looks only at which AI tools employees use misses the more expensive problem: shadow dev, where end users build entire applications with a handful of prompts and neither IT nor security ever looks at them.
Oneminded runs from two locations: consultants in Eindhoven and developers in Jakarta. That model only works if both sides know each other well enough to collaborate without misunderstandings.
What happens if you don't try to speed up an AI workflow one more time, but rebuild it from scratch instead? Over the past few weeks at Oneminded, that's exactly what we did with our own AI setup.
Before you bolt a Model Context Protocol (MCP) onto an AI stack, there's really only one question that matters: has anyone actually looked closely at what goes in, what comes out, and who's authorized that? MCP makes it strikingly easy to connect AI...
You've just had a call with a customer. Recording wasn't allowed, so you're relying on memory, and right after the call that's usually fine.
Installing a skill feels harmless, but underneath that layer is code running with your permissions. NVIDIA recently released SkillSpector: is this skill secure enough to install?
The discussion about the limits of AI is almost always about models: how smart is it, what can't it do yet, when does the next version arrive.
Creating a quote takes twenty minutes, but sometimes it doesn't reach the client until three days later. That is exactly where AI can make the difference.
Building an agent takes an afternoon nowadays. Harnessing an agent is a different conversation: providing context, connecting tools, and making the return measurable.
$1,500 per month, per AI tool, per engineer. That's the cap Uber set last week for Claude Code and Cursor, after the entire AI budget for 2026 was exhausted in four months.
KPMG surveyed 1,200 companies: only 8 percent sees a clear return. The costs of AI agents are high and unpredictable — but so is the return. Whoever makes the distinction has control over both.
Over the past few weeks, we set up our Azure deployment framework internally — one shared foundation for consultants in the Netherlands and developers in Jakarta, built entirely through Infrastructure as Code.
National edge-first AI vision architecture for parking and mobility operations across heterogeneous sites.
Maximum three links between you and the developers isn't just a tagline — it's a delivery philosophy with measurable results.
Talent density, English proficiency, and a culture of ownership are making Indonesian developers the first choice for European scale-ups.
Every project should start with one question: what should this deliver for your business? Here's how to keep that question alive throughout delivery.
The shift from AI as a tool to AI as a multiplier is already happening. Here's what it means for how we build teams and price projects.
Miscommunication, cultural gaps, and unclear requirements kill more projects than bad code. Here's our framework for preventing it.
Both models have their place. The wrong choice can derail a project before a line of code is written.