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Home - AI & Machine Learning - Google Introduces Jetpack Compose Glimmer: A New Spatial UI Framework Designed Particularly for the Subsequent Era of AI Glasses
AI & Machine Learning

Google Introduces Jetpack Compose Glimmer: A New Spatial UI Framework Designed Particularly for the Subsequent Era of AI Glasses

NextTechBy NextTechFebruary 18, 2026No Comments5 Mins Read
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Google Introduces Jetpack Compose Glimmer: A New Spatial UI Framework Designed Particularly for the Subsequent Era of AI Glasses
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Google is shifting past the oblong display screen. For over 10 years, Google designers have explored easy methods to construct interfaces for clear shows. The result’s Jetpack Compose Glimmer, a design system constructed particularly for show AI glasses. For devs and information scientists, this can be a shift from designing for pixels to designing with gentle.

The Additive Show Constraint

Most builders are used to LCD or OLED screens. Nonetheless, AI glasses use additive shows. These shows solely add gentle to the consumer’s sight view. They can’t create opaque black or make the actual world darker.

On an additive show, black is 100% clear. It’s not a shade; it’s a void. In case you use an ordinary Materials Design card (gentle floor with darkish textual content), it fails. The sunshine floor turns into a vivid block of sunshine that drains the battery and creates halation. Halation is an impact the place vivid gentle bleeds into darkish areas, making textual content unreadable.

To unravel this, devs should use darkish surfaces and vivid content material. Utilizing black as a basis offers a ‘clear plate’ for the UI. This enables the digital components to harmonize with the bodily world with out creating distracting glare.

From Pixels to Visible Angles

Software program Devs usually measure UI in pixels or factors. In a clear spatial surroundings, these items are irrelevant. The perceived dimension of an object modifications primarily based on its distance from the attention.

Google group now measure UI in visible angles or levels. The show in these glasses is projected at a perceived depth of 1 meter, which is about an arm’s size. This distance requires the consumer to actively shift their focus from the background to the UI.

To make sure legibility, Google established a minimal readable textual content dimension of 0.6 levels. Maintaining textual content above this threshold ensures that the interface stays ‘glanceable’ in several environments.

Engineering Typography for Gentle

Customary fonts usually fail on clear lenses. Google group modified Google Sans Flex utilizing its optical dimension axis to repair this. These technical changes make letters extra distinct:

  • Elevated Counters: The inner openings in letters like ‘a’ and ‘e’ are bigger to forestall them from blurring.
  • Modified Dots: The dots on ‘i’ and ‘j’ are moved farther from the principle letter physique.
  • Variable Letter-Spacing: The system optimizes spacing via code to maximise readability at a look.

The Additive Distinction Method

Google group use a particular formulation to calculate visibility. That is the additive distinction ratio.

The formulation is: (Setting Brightness + Show Brightness) / Show Brightness.

In the actual world, colours behave otherwise. Extremely saturated colours usually ‘disappear’ or look ghostly in opposition to a vivid sky. Glimmer makes use of a impartial, desaturated palette by default. By conserving colours nearer to white, the UI stays secure and visual whatever the lighting within the room.

Designing Movement for Human Consideration

On a heads-up show, movement is usually a main distraction. In customary cell improvement, a notification would possibly seem in 500 milliseconds. On AI glasses, that is too quick. It creates an abrupt ‘blink’ that may startle the consumer.

Glimmer makes use of a slower, extra deliberate transition for notifications. These animations happen over 2 seconds. This length permits the notification to enter the consumer’s peripheral imaginative and prescient gracefully. It invitations focus moderately than demanding it.

Nonetheless, user-triggered actions (like a voice command or gesture) nonetheless require low-latency suggestions. Glimmer makes use of ‘focus rings’ to supply immediate affirmation that the system has acquired an enter. This creates a stability between ambient notifications and responsive controls.

Key Takeaways

  • Black is Transparency, Not a Coloration: As a result of AI glasses use additive shows, they will solely add gentle; they can’t create true black or shadows. On this surroundings, black is 100% clear. To make sure legibility, devs should use darkish surfaces for containers and vivid colours for textual content and icons.
  • Visible Angles Substitute Pixels: Customary items like pixels (px) are changed by visible angles (levels). Because the UI is projected at a perceived depth of 1 meter, objects should be sized relative to the human eye’s perspective. The minimal threshold for readable textual content is about at 0.6 levels.
  • The Additive Distinction Method: Devs should account for environmental gentle utilizing the formulation: (Setting Brightness + Show Brightness) / Show Brightness. As a result of saturated colours usually ‘disappear’ in opposition to vivid real-world backgrounds, a impartial, desaturated palette is used to take care of visibility.
  • Optical Typography Optimization: Customary typefaces endure from halation (gentle bleeding). Google Sans Flex is modified by way of its optical dimension axis to extend inside letter openings (counters) and increase letter-spacing, stopping characters from blurring collectively on a clear lens.
  • Movement Timing is Context-Dependent: Customary 500ms animations are too abrupt for a heads-up show. To respect human peripheral imaginative and prescient, Glimmer makes use of 2-second transitions for notifications to ‘invite’ focus, whereas sustaining low-latency suggestions (like focus rings) for direct consumer inputs to make sure responsiveness.

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Michal Sutter is an information science skilled with a Grasp of Science in Information Science from the College of Padova. With a strong basis in statistical evaluation, machine studying, and information engineering, Michal excels at reworking complicated datasets into actionable insights.

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