<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Mobile on The Coders Blog</title><link>https://thecodersblog.com/tag/mobile/</link><description>Recent content in Mobile on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 06 May 2026 22:22:13 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/mobile/index.xml" rel="self" type="application/rss+xml"/><item><title>Building Real-World On-Device AI with LiteRT and NPU</title><link>https://thecodersblog.com/on-device-ai-with-litert-and-npu-2026/</link><pubDate>Wed, 06 May 2026 22:22:13 +0000</pubDate><guid>https://thecodersblog.com/on-device-ai-with-litert-and-npu-2026/</guid><description>&lt;p&gt;The chatbot stutters, the image recognition is sluggish, and sensitive data has to leave the device. Sound familiar? If you&amp;rsquo;re building AI-powered applications for mobile or embedded systems, you&amp;rsquo;re likely wrestling with latency, privacy concerns, and inefficient resource usage. It&amp;rsquo;s time to bring the intelligence closer to the user, directly onto their device, and leverage the specialized hardware designed for it.&lt;/p&gt;
&lt;h2 id="the-problem-cloud-reliance-bottlenecks-ai"&gt;The Problem: Cloud Reliance Bottlenecks AI&lt;/h2&gt;
&lt;p&gt;Sending every inference request to the cloud introduces significant bottlenecks. Latency is unavoidable, impacting real-time applications like live translation or augmented reality. Privacy becomes a major hurdle, as sensitive user data must traverse public networks. Furthermore, constant cloud connectivity drains battery life and incurs ongoing operational costs. The solution? On-device AI, powered by dedicated hardware like Neural Processing Units (NPUs).&lt;/p&gt;</description></item></channel></rss>