Guinness is one of those beers (specifically, a stout) that people take seriously and the Guinness brand has taken full advantage of that in their marketing. They even sell a glass designed specifically for enjoying their flagship creation, which has led to a trend that the company surely appreciates: “splitting the G.” But that’s difficult […] The post This Arduino device helps ‘split the G’ on a pint of Guinness appeared first on Arduino Blog.

And how do we derive its value for sine waves?

AI is everywhere, but most websites are still managed manually by humans using content management systems like WordPress and Drupal. These systems provide means for tagging and categorizing content. But over time, these structures degrade. Without vigilance and maintenance, taxonomies become less useful and relevant over time. Users struggle to find stuff. Ambiguity creeps in. Search results become incomplete and unreliable. And as terms proliferate, the team struggles to maintain the site, making things worse. The site stops working as well as it could. Sales, engagement, and trust suffer. And the problem only gets worse over time. Eventually, the team embarks on a redesign. But hitting the reset button only fixes things for a while. Entropy is the nature of things. Systems tend toward disorder unless we invest in keeping them organized. But it’s hard: small teams have other priorities. They’re under pressure to publish quickly. Turnover is high. Not ideal conditions for consistent tagging. Many content teams don’t have governance processes for taxonomies. Folks create new terms on the fly, often without checking whether similar ones exist. But even when teams have the structures and processes needed to do it right, content and taxonomies themselves change over time as the org’s needs and contexts evolve. The result is taxonomy drift, the gradual misalignment of the system’s structures and content. It’s a classic “boiled frog” situation: since it happens slowly, teams don’t usually recognize it until symptoms emerge. By then, the problem is harder and more expensive to fix. Avoiding taxonomy drift calls for constant attention and manual tweaking, which can be overwhelming for resource-strapped teams. But there’s good news on the horizon: this is exactly the kind of gradual, large-scale, boring challenge where AIs can shine. I’ve worked on IA redesigns for content-heavy websites and have seen the effects of taxonomy drift firsthand. Often, one person is responsible for keeping the website organized, and they’re overwhelmed. After a redesign, they face three challenges: Implementing the new taxonomy on the older corpus. Learning to use the new taxonomy in their workflows. Adapting and evolving the taxonomy so it remains useful and consistent over time. AI is well-suited to tackling these challenges. LLMs excel at pattern matching and categorizing existing text at scale. Unlike humans, AIs don’t get overwhelmed or bored when categorizing thousands of items over and over again. And with predefined taxonomies, they’re not as prone to hallucinations. I’ve been experimenting with using AI to solve taxonomy drift, and the results are promising. I’m building a product to tackle this issue, and looking implement the approach in real-world scenarios. If you or someone you know is struggling to keep a content-heavy website organized, please get in touch.

A simple question that takes some effort to answer in a satisfying way.

Losing the performance crown