Baseload

Inspiration

We track steps, heart rate, sleep cycles, but nobody's tracking the thing that actually determines how your day feels: your cognitive load. Everyone's had those days where you're not doing anything physically demanding, but your brain is completely fried. You can't focus, decisions feel impossible, and you don't even know why. We wanted to build something that makes that invisible weight visible.

What it does

Baseload tracks your cognitive load in real time and breaks it down into metrics that actually make sense. It distinguishes between active cognitive demand (how hard your brain is working right now) and cognitive bandwidth (how much total capacity you actually have available). These are two very different things. You can be doing nothing and still have zero bandwidth left. The app helps you see that gap, understand it, and act on it before you burn out.

How we built it

We designed Baseload as a speculative wellness tool, imagining a near future sensor capable of measuring neural load patterns passively as a wearable. The interface was built with a focus on clinical clarity; every card surfaces one insight with minimal friction. We followed a design language inspired by Function Health and Apple Health, keeping the data dense but the experience calm. The Trends tab was designed to give users longitudinal awareness: not just "how am I now," but "how am I changing over time."

Challenges we ran into

The hardest part was making an invisible, abstract concept feel tangible without oversimplifying it. Cognitive load isn't one number; it's a relationship between demand, capacity, recovery, and context. Finding the right metrics to surface took a lot of iteration. We also wrestled with tone: too clinical and it feels cold, too casual and it loses credibility.

Accomplishments that we're proud of

The distinction between cognitive load and cognitive bandwidth. Most wellness apps flatten complex states into a single score. We deliberately kept those as separate, layered metrics because understanding the gap between them is where the real insight lives. We're also proud of how clean the interface reads; every card earns its space.

What we learned

Designing for something unmeasurable forces you to think harder about what the data means to someone, not just what it shows. We learned that a single label like "Moderate" only tells half the story. Pairing it with "you've used 75% of today's bandwidth" turns awareness into action, and that layering is where the real design work lives.

What's next for Baseload

Right now, we're integrating the wearable sensor with the Screen Time API, linking real-time cognitive load data to whatever app a user has open. But that only captures one slice of the picture: screen time. Everything else, workouts, outdoor walks, social interactions, lives outside that data pipeline entirely and has to be logged by hand.

The next step is to move beyond that constraint with a custom, context-aware sensor that can passively detect and tag the activities, environments, and interactions consuming the most cognitive energy throughout the day. Critically, this entire pipeline of sensing, tagging, and AI inference will run locally on the device, with no data leaving the user's hands, directly addressing the privacy concerns that come with tracking something as intimate as everyday activities.

From there, we layer in prediction: forecasting a user's cognitive capacity for the day ahead based on sleep quality, recovery patterns, and historical trends. And ultimately, active interventions like guided cognitive resets, attention boundary nudges, and load balancing recommendations that help users redistribute their mental energy before they hit a wall.

Built With

• figma