Developer Builds On-Device Android Engine to Auto-Silence Phone Based on Location

·3 views

A developer created a background Android service called Muffle that automatically manages a phone's audio profile using geofencing, calendar events, and time-based triggers. The project was motivated by repeated personal incidents of forgetting to silence a phone in quiet settings, such as during prayer sessions and office meetings. Rather than using continuous GPS polling, the system relies on Google Play Services' GeofencingClient API, which delegates location monitoring to the OS and uses Wi-Fi, cell towers, and GPS in combination to conserve battery. A ForegroundService was implemented to keep the app responsive even under Android's aggressive Doze mode, ensuring geofence transition events still fire reliably. The architecture prioritizes on-device processing and user privacy, deliberately avoiding cloud-based triggers that can fail without network connectivity.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

AI Harness Decoded: Buzzword or Legitimate Middleware Concept?

The term 'AI Harness' has become one of the most debated phrases in AI engineering, often used loosely to describe anything from a simple API wrapper to complex middleware systems. In practice, a genuine AI harness functions as a reverse proxy and transactional middleware layer that isolates, audits, and budget-controls LLM inference before it interacts with production infrastructure. The author argues that a well-built harness ensures the LLM never holds control logic, handling only text processing while the surrounding code manages auth, routing, budget enforcement, and schema translation. To demonstrate this, three open-source npm packages are presented — sayay-guard for budget control, styrr-llm for physical routing, and tinkuy-agent for format translation — each with zero hard dependencies. The piece is the second installment in a series called TokenOps on AWS, which aims to reduce AI infrastructure concepts to concrete engineering primitives.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Engineers Replace Bloated AI Dependencies with 200-Line Python RAG System

A production outage caused by memory exhaustion prompted engineers to rethink the infrastructure behind agentic AI systems. A single agent handling 10,000 retrieval-augmented generation queries crashed an 8GB cloud instance due to dependency bloat and unquantized float32 embeddings. Common libraries such as faiss-cpu, grpcio, and Docker containers were identified as key culprits, consuming excessive memory, leaking file descriptors, and blocking event loops. The team replaced these heavy dependencies with a lightweight 200-line Python solution using only sqlite3, array, and heapq, applying uint8 vector quantization to cut memory usage by 32 times. The approach demonstrates that surgical, audited code can outperform popular but resource-heavy libraries in memory-constrained environments.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds WhatsApp Web Extension to Cut Daily Translation and Reply Friction

A developer created MSG.AI, a browser extension for WhatsApp Web, to eliminate the repetitive workflow of copying messages into translation tools and manually managing customer replies. Rather than building a standalone app with a separate inbox, the developer chose an extension model so users could stay within their existing WhatsApp Web sessions. The tool supports in-chat translation, reusable replies, and batch messaging with configurable controls. An AI-assisted draft feature was included, but designed to require human review before sending, given the real business consequences of customer commitments. The project evolved from a bulk-messaging utility into a daily translation aid after the developer observed that translation was the more frequent pain point.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How Chrome Extensions Like MSG.AI Integrate Features Into WhatsApp Web

Browser extensions that add features to WhatsApp Web rely on a split architecture across multiple execution environments rather than a single script. The system is divided into four components: a user-facing interface, a background service worker, a content script, and a page bridge, each with distinct responsibilities and access levels. Chrome isolates content scripts from the page's own JavaScript for security, so a lightweight bridge is used when deeper integration with WhatsApp Web's page context is needed. Combining all functions into one script is discouraged because failures become hard to diagnose and the code grows brittle over time. MSG.AI, one such extension, illustrates this by running a side panel alongside WhatsApp Web, requiring careful state synchronization between the panel, background worker, and injected page controls.

0 comments Read more at DEV Community