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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Clear out junk files and repair common Windows errorsFree Scan →Ambient uses Kotlin Multiplatform to share its sound engine and playback behavior across eight implementation targets—Android, iOS, macOS, watchOS, visionOS, Windows, Linux, and the web—while each target uses its own interface and system audio and graphics integrations. The project is an example of selective code sharing, not a single codebase that makes every platform identical: its author describes native and platform-specific components alongside common Kotlin logic.
What “eight platforms” means in Ambient
Hayami Shuhei’s Ambient runs on Android, iOS, macOS, watchOS, visionOS, Windows, Linux, and the web. The count refers to those implementation targets. iPad uses the iOS app, and Android TV uses the Android APK; neither is counted as a separate implementation.
Kotlin Multiplatform (KMP) lets a project share selected code while compiling for different platforms. In Ambient, that shared code describes sound scenes, generates audio, manages playback behavior, and provides data used by visualizations. It does not replace platform-specific interfaces or the APIs needed to play audio, store data, respond to interruptions, and draw graphics.
That boundary is the key architectural choice: keep the sound engine and playback rules common, then connect them to each platform using the integration that fits that environment. Kotlin’s target and source-set concepts help explain the arrangement: targets are platforms common code can be compiled for, while source sets group code and dependencies associated with those targets. Sharing logic therefore does not mean producing one identical binary or eliminating platform-specific code.
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How the shared engine connects to each platform
The following map describes Ambient as its author presents it; it is not a list of what every KMP application can target with standard tooling. The desktop apps use a small C interface to exchange commands and visual data. On iOS, watchOS, and visionOS, Swift imports a Kotlin framework. In the browser, Kotlin/JS runs the engine in an AudioWorklet, apart from the page’s UI thread.
| Ambient target | Bridge to shared engine | Interface, audio, and graphics described by the author |
|---|---|---|
| iOS and iPadOS | Kotlin/Native framework | SwiftUI, AVAudioEngine, and Metal |
| Android and Android TV | Kotlin/JVM module | Android Views, AudioTrack, and Vulkan or OpenGL ES |
| watchOS | Kotlin/Native framework | SwiftUI, AVAudioEngine, and Canvas particles |
| visionOS | Custom Kotlin/Native target | SwiftUI, AVAudioEngine, RealityKit, and Metal particles |
| macOS | Kotlin/Native C bridge | SwiftUI, AVAudioEngine, and Metal |
| Windows | Kotlin/Native C bridge | Win32, WASAPI, and Vulkan |
| Linux | Kotlin/Native C bridge | GTK4, ALSA, and Vulkan |
| Web | Kotlin/JS in an AudioWorklet | HTML controls, Web Audio, and WebGPU |
The bridge is only part of the work. Ambient’s platform applications still handle system concerns such as audio interruptions, background playback, and controls. KMP lets the project reuse engine behavior; it does not make those operating-system responsibilities disappear.
How Ambient creates sound that changes over time
Ambient synthesizes its soundscapes in real time rather than downloading or looping fixed recordings. Its author reports that the engine produces stereo pulse-code modulation (PCM) audio at 48 kHz: 48,000 samples per second for each channel. This describes the implementation, not an independently measured performance result.
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A scene can combine continuous sounds, such as wind, with shorter events, such as bird calls. Noise generators and oscillators create the signal; filters shape it, and envelopes govern how sounds begin and fade. Parameters change gradually so a scene can evolve instead of repeating as a fixed recording.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThe author describes an audio loop that reuses buffers and active-sound state and is designed to run independently of graphics frame timing. Tests can set a known random seed to reproduce a sequence, while ordinary playback can start with different seeds. For a scene change, two renderers overlap in an equal-power crossfade; changing the audio source uses a separate short linear crossfade. These are reported design details, not benchmarks of latency, CPU use, or sound quality.
How the visuals stay connected to the sound
The engine publishes structured snapshots describing active sounds, their relative contribution to the mix, current energy, and transition progress. Native renderers read these snapshots. In the browser, snapshots reach the page less often than audio blocks are produced, keeping the audio-generation work separate from the page’s visual updates.
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Ambient uses different graphics integrations across targets: Metal on iOS and macOS; Metal with RealityKit on visionOS; Vulkan, with an OpenGL ES fallback, on Android; Vulkan on Windows and Linux; and WebGPU in the browser. The watch app uses a smaller particle scene drawn with SwiftUI Canvas. The result is a shared source of sound-state data paired with renderers suited to each platform, rather than a single graphics implementation imposed everywhere.
Why visionOS required custom Kotlin/Native work
Ambient’s visionOS implementation depended on a custom fork of Kotlin/Native. The author describes extending the toolchain for device and simulator targets, runtime platform checks, linker settings, framework metadata, Gradle support for shared Apple source sets and packaging, API compatibility tooling, and generated bindings for Apple SDK frameworks used by audio playback. The reported target triples are arm64-apple-xros and arm64-apple-xros-simulator; the author also says a later rebuild used Xcode 27.
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This case study should not be read as evidence that visionOS is a turnkey target in the standard Kotlin distribution or that every KMP project can use the same setup. The author describes building on Kotlin/Native’s existing iOS and watchOS support and extending it for the headset and simulator with a custom toolchain.
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What the browser implementation does differently
In Ambient’s web version, the Kotlin/JS synthesizer and playback controller run inside an AudioWorklet. The page sends commands and receives state and visual data; the worklet generates audio directly. A small C++ module compiled to WebAssembly schedules GPU work for an ink simulation, while the author says audio generation itself remains in Kotlin/JS.
This division is a concrete example of sharing the audio engine without forcing every supporting task into the same language or execution context. The worklet handles audio separately from the page UI, while the WebAssembly module serves a graphics-scheduling role.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How Ambient links Premium access across devices
Ambient’s author describes a device-linking flow that lets an eligible purchase in the iOS or Google Play Android app unlock Premium on Windows, Linux, and the web. A receiving device displays a QR code; the mobile app scans it, and the user approves the link. The pairing code is valid for five minutes and does not itself contain the access token.
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In the described implementation, the server verifies purchase proof against an active RevenueCat entitlement, and device registrations are stored in D1. One eligible purchase can link up to three devices or browser profiles. Shared Kotlin code manages pairing state, approval, expiry, and access refresh; platform adapters handle QR scanning, HTTP, credential storage, and purchase proof.
The author reports approval checks every three seconds, linked-access refresh every minute, and offline access for up to 24 hours after prior verification. These figures describe Ambient’s implementation, not general requirements or recommendations for other entitlement systems.
What the extracted KMP Procedural Audio library provides
The project extracted its PCM playback layer as KMP Procedural Audio, which its author describes as a lightweight, MIT-licensed Kotlin Multiplatform library. It exposes an AudioPlayer and a PcmSource interface. A source fills a reusable buffer with 48 kHz stereo floating-point samples; the player sends those samples to platform audio and applies a short crossfade when the source changes.
The author lists Android, iOS, macOS, watchOS, Windows, Linux, and web as library targets. Ambient separately compiles for visionOS using its custom toolchain. Adopting the library does not require an application to use Ambient’s soundscape model or visual renderer.
How to choose a similar KMP architecture
Ambient demonstrates one way to structure an app where audio behavior is shared but platform presentation and media integration remain distinct. It does not show that this trade-off is right for every product. A team evaluating a similar design can use these questions to make the boundary explicit:
- What should be shared? Decide whether common code should cover only business or engine logic, or also the interface. KMP supports selective logic sharing; Compose Multiplatform is an option when sharing UI is also a goal.
- Which platform APIs remain necessary? Low-latency audio, graphics, background behavior, system controls, and distribution can require platform-specific integration even when the core logic is common.
- How mature is the particular target and UI route? JetBrains documents Compose Multiplatform as Stable for Android, iOS, and desktop (Windows, macOS, and Linux), while its WebAssembly web support is Beta. Those status labels describe Compose Multiplatform, not every KMP library or Ambient’s custom visionOS target.
- What does the build environment require? KMP builds use Gradle and Java. Kotlin’s documentation says iOS app development requires a Mac with Xcode and an available simulator; Android can run on an Android Virtual Device, desktop on the system JVM, and web in a browser.
- Can the team maintain the bridges? Shared code can reduce duplicated engine behavior, but native frameworks, C interfaces, platform adapters, and target-specific build work remain part of the project where the chosen integrations require them.
Ambient’s approach is especially instructive as an architectural example: it shares computation and playback rules, but lets each platform use its own UI, audio output, and graphics systems. The project’s visionOS work also shows why a target list should be checked against the actual toolchain and project setup rather than assumed to imply uniform official support.
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