Calmynt

Headline

Calmynt — A Low-Stimulus Task Manager That Adapts to Overload

Inspiration

Calmynt was inspired by observing friends — including some on the autistic spectrum — experience repeated burnout cycles triggered not by workload itself, but by visually stacked calendars and rigid productivity systems.

The issue wasn’t discipline. It was cognitive saturation.

Most productivity tools assume stable energy and constant executive capacity. Real life is variable.

What if a task manager reduced input when overwhelm begins instead of increasing urgency?

What it does

Calmynt is a low-stimulus task manager designed around fluctuating energy.

1. Energy-Aware Daily Capping
Tasks are prioritised by energy demand and estimated duration, with a strict visible limit to reduce decision fatigue.

2. Overload Mode
When triggered, the interface contracts into a simplified state that presents:

One stabilising action
One 5-minute micro-step
One explicit stop condition

Instead of escalating productivity pressure, Calmynt restores forward motion through controlled minimalism.

How I built it

Calmynt was built using an AI-assisted application framework.

Core architecture includes:

A structured Task data model with energy tagging
A capped Today view (maximum 5 tasks)
A single-task Focus state
A dynamic Overload Mode that reduces interface complexity

Example micro-step logic:

if task.microstep.nil?
  task.microstep = "Open #{task.title} and write the first 2 bullet points."
end

Challenges I ran into

The biggest challenge was constraint discipline.

Productivity tools naturally expand toward dashboards, analytics, and AI suggestions. I intentionally removed features to protect conceptual coherence.

Designing an overload state that felt meaningfully different — yet calm — required careful UI reduction.

Accomplishments that I'm proud of

Building a complete, coherent product solo within hackathon constraints
Designing a visible state transformation that demonstrates emotional UX awareness
Maintaining clarity while implementing deterministic behavioural logic
Prioritising sustainability over optimisation