<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Machine Learning on App Coding</title>
    <link>https://appcoding.com/tags/machine-learning/</link>
    <description>Recent content in Machine Learning on App Coding</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Wed, 18 Feb 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://appcoding.com/tags/machine-learning/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>AI in Mobile Apps: What Is Working Beyond the Hype</title>
      <link>https://appcoding.com/2026/02/18/ai-in-mobile-apps-what-is-working-beyond-the-hype/</link>
      <pubDate>Wed, 18 Feb 2026 00:00:00 +0000</pubDate>
      <guid>https://appcoding.com/2026/02/18/ai-in-mobile-apps-what-is-working-beyond-the-hype/</guid>
      <description>&lt;p&gt;The integration of AI capabilities into mobile applications has followed the familiar hype cycle pattern: an initial period of breathless coverage about what AI would do for apps, followed by a quieter period of teams discovering which AI features users actually value and which are dismissed as gimmicks within the first week of use. The dust has not fully settled, but the outline of what works is becoming clear.&lt;/p&gt;&#xA;&lt;p&gt;The AI features that have demonstrated durable user value are mostly not the ones that received the most attention during the hype phase. Large language model chatbots embedded in apps — the most visible AI feature of the 2023-2024 period — have retention profiles that most teams find disappointing. Users try them, find them useful or impressive in isolated interactions, and then forget to use them because the chat interface requires more effort than the specific task typically warrants.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
