#optimizing

A good maker story is not just about a clever build. It is about what the project teaches, what problem it solves and what curious readers can borrow for their own experiments. Maker projects matter because they show technology at work with the cover removed: parts, constraints, mistakes, trade-offs and the small decisions that make an idea real. Continue reading Optimizing Pancakes from…

The Gini index, commonly used in decision tree algorithms, quantifies the impurity or disorder of a set of data points. It ranges from … source

That is Optimizer, a weekly e-newsletter despatched each Friday from Verge senior reviewer Victoria Tune that dissects and discusses the most recent telephones, smartwatches, apps, and different gizmos that swear they’re going to vary your life. Optimizer arrives in our subscribers’ inboxes at 10AM ET. Decide in for Optimizer right here. In the previous few years, I’ve seen an rising variety of…

B2B Tech Marketing Just Got Harder and Here’s How to Stay Ahead

AI-generated content is everywhere. Search algorithms are smarter. And every B2B tech company is fighting for the same keywords, eyeballs, and clicks.

So what separates the brands guessing what works from those knowing what works? 👉 A/B testing but done the 2025 way.

We’re talking about testing content depth, author authority, schema markup, pricing pages, and even posting times because every variable matters when AI shapes visibility.

The new playbook isn’t about being louder. It’s about learning faster.

📈 Companies that test consistently see nearly 50% higher conversions and in enterprise deals, that can mean millions in pipeline value.

In my latest guide, I break down: 🔹 The smartest A/B tests for AI-driven marketing 🔹 What to measure beyond CTRs 🔹 The AI tools that make experimentation easier

✨ Read the full article here →

Learn best practices around how to achieve high availability with Tableau Server. We’ll cover advanced tips and tricks that ensure … source

Many real-world planning tasks involve both harder “quantitative” constraints (e.g., budgets or scheduling requirements) and softer “qualitative” objectives (e.g., user preferences expressed in natural language). Consider someone planning a week-long vacation. Typically, this planning would be subject to various clearly quantifiable constraints, such as budget, travel logistics, and visiting…