It happened during a quiet Friday sermon at the local masjid.
The room was dense with silence, the kind that feels heavy and intentional.
Suddenly, a jarring ringtone shattered the atmosphere—someone’s phone, vibrating against the hardwood floor.
It wasn't my phone, but the collective wince of the entire room was visceral.
A hundred people stopped mid-thought, turning their heads toward the source of the noise.
I sat there, my own phone tucked in my pocket, realizing that I had almost been that person just a week prior.
It was a moment of pure, avoidable human friction.
We live in an age where our devices are supposed to be smart, yet they consistently fail at the most basic context-awareness.
I found myself manually toggling my sound profile before every meeting, lecture, or appointment.
It is a recurring cognitive tax.
If I remembered, great.
If I forgot, I risked social embarrassment.
Even worse, once the meeting ended, I would inevitably leave my phone on silent for the rest of the day, missing important calls from family or clients.
Existing solutions often felt like overkill—they required account creation, constant background sync to a cloud server, or permissions that felt invasive for a task as simple as changing a volume setting.
I wanted something that lived entirely on the device, functioning as a silent, invisible utility that didn't need to 'phone home' to function.
When I started building Muffle, I decided early on that the entire architecture would be zero-cloud.
This wasn't just a philosophical choice; it was a technical constraint I imposed to ensure the app remained performant and trustworthy.
By forcing myself to avoid backend dependencies, I had to rely heavily on Android’s and patterns.
The biggest challenge was the 'Prayer Time' trigger.
Most developers would reach for a Firebase Cloud Function to calculate these times based on the user's location.
Instead, I integrated the library locally.
I had to handle complex time-zone offsets and geographic calculations directly on the device.
This meant the app had to be efficient with battery life; if my calculation logic was inefficient, the user would notice a drop in their daily battery percentage immediately.
To manage these routines, I used a Room database as the local source of truth.
Every time a user adds a rule, it is serialized locally.
The core logic runs inside a using a that listens for system state changes.
Here is a snippet of how I handle the sound profile state transition: kotlin val audioManager = context.getSystemService(Context.AUDIO_SERVICE) as AudioManager when (action) { "SILENT" -> audioManager.ringerMode = AudioManager.RINGER_MODE_SILENT "VIBRATE" -> audioManager.ringerMode = AudioManager.RINGER_MODE_VIBRATE "DND" -> audioManager.setInterruptionFilter(NotificationManager.INTERRUPTION_FILTER_PRIORITY) "NORMAL" -> audioManager.ringerMode = AudioManager.RINGER_MODE_NORMAL } This simple implementation is the heart of the app.
By keeping the logic local, the app survives reboots without needing to re-fetch rules from a server.
It creates a 'set and forget' experience.
If a user sets a routine for a location, the app uses to trigger transitions.
Because there is no server, the user's location history never leaves their device.
This privacy-first approach is the primary selling point for users who are increasingly skeptical of background data collection.
What surprised me most during development was the fragility of the when the device enters 'Doze' mode.
I initially assumed that setting an alarm would be enough to trigger my routine exactly on time.
I was wrong.
Android’s aggressive battery optimizations often delayed my triggers by minutes, which is unacceptable when you are trying to silence a phone before a meeting starts.
I spent two weeks refactoring the task scheduler to use .
This was a steep learning curve.
I had to manage wakelocks carefully to ensure the device would wake up long enough to process the sound change, but not long enough