Starting Brindle Innovations has been a crash course in entrepreneurship, sales, and marketing—none of which were in my engineering playbook. With a newborn at home and a business to grow, every day is a balancing act. It’s tough, it’s humbling, but it’s also the most exciting challenge I’ve ever taken on.

How a simple game and a laptop webcam helped us train AI to track gaze patterns—and uncover insights into visual attention in children with ASD.

Traditional RNA alignment tools struggle with massive datasets, slowing down critical research. This article explores a new AI-driven approach using vectorization and machine learning to significantly expedite the process.

I poured my heart into the ClearEdge Brain Health Toolkit, even securing FDA clearance. But it wasn't enough. I learned a painful but crucial lesson: technical feasibility doesn't equal market demand. Here’s how to avoid my mistake and validate your business idea before investing too much.

Overfitting and underfitting are two of the biggest challenges in machine learning. One leads to a model that memorizes data but fails in the real world, while the other results in a model too weak to capture meaningful patterns. In this guide, we’ll break down both problems, show you how to spot them, and—most importantly—how to fix them!

Supervised and unsupervised learning are two fundamental approaches in machine learning, but choosing the right one depends on your data and goals. Supervised learning relies on labeled data to make predictions, while unsupervised learning finds hidden patterns in large, unlabeled datasets. In this article, we’ll explore their key differences, dataset requirements, and real-world applications to help you decide which is best for your project!

This article explores how startups can successfully bridge the gap between business strategy and engineering. It emphasizes the importance of early collaboration, rapid prototyping, and material optimization to create scalable, cost-effective solutions. By aligning engineering efforts with core business goals, startups can avoid costly mistakes and increase their chances of long-term success. Practical steps like gathering feedback, building cross-disciplinary teams, and planning for compliance are key to turning innovative ideas into viable, sustainable products.