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    <title>DEV Community: Michael Jentsch</title>
    <description>The latest articles on DEV Community by Michael Jentsch (@michael_jentsch_f405b8dc3).</description>
    <link>https://dev.to/michael_jentsch_f405b8dc3</link>
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      <title>DEV Community: Michael Jentsch</title>
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      <title>LyricLens - AI-powered song lyrics analysis tool</title>
      <dc:creator>Michael Jentsch</dc:creator>
      <pubDate>Tue, 19 May 2026 17:34:55 +0000</pubDate>
      <link>https://dev.to/michael_jentsch_f405b8dc3/lyriclens-ai-powered-song-lyrics-analysis-tool-4i5n</link>
      <guid>https://dev.to/michael_jentsch_f405b8dc3/lyriclens-ai-powered-song-lyrics-analysis-tool-4i5n</guid>
      <description>&lt;p&gt;&lt;em&gt;This is a submission for the &lt;a href="https://dev.to/challenges/google-gemma-2026-05-06"&gt;Gemma 4 Challenge: Build with Gemma 4&lt;/a&gt;&lt;/em&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  What I Built
&lt;/h2&gt;

&lt;p&gt;LyricLens is an AI-powered lyrical forensics tool that helps listeners uncover the poetry, metaphors, and storytelling often missed in fast-paced music consumption.&lt;/p&gt;

&lt;p&gt;Music listeners often connect emotionally with songs without fully understanding their deeper meaning. LyricLens automates lyric analysis, delivering instant insights into metaphors, themes, and storytelling.&lt;/p&gt;

&lt;p&gt;LyricLens creates an immersive, atmospheric experience where users can get a detailed analysis covering core themes, emotional arcs, and literary devices.&lt;/p&gt;

&lt;h2&gt;
  
  
  Demo
&lt;/h2&gt;

&lt;p&gt;&lt;a href="https://lyriclens.jentsch.io/" rel="noopener noreferrer"&gt;https://lyriclens.jentsch.io/&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  Code
&lt;/h2&gt;

&lt;p&gt;&lt;a href="https://github.com/msoftware/lyriclens" rel="noopener noreferrer"&gt;https://github.com/msoftware/lyriclens&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  How I Used Gemma 4
&lt;/h2&gt;

&lt;p&gt;The core "intelligence" of LyricLens is powered by the Gemma 4 26B (A4B-IT) model. This specific variant offers a balance between reasoning capabilities and inference speed. The Gemma 4 26B architecture allows it to catch subtle metaphors and "read between the lines" providing a high-quality human-like analysis.&lt;/p&gt;

&lt;p&gt;For the radar chart to work, the model needs to output valid JSON consistently alongside its textual analysis, is reliable at following complex system instructions and generating structured data.&lt;/p&gt;

&lt;p&gt;I needed a model that could not only translate but reason in the target language to ensure the tone and nuance of the analysis remained consistent.&lt;/p&gt;

&lt;p&gt;Using the A4B model ensures that even a model of this size stays responsive and efficient for real-time analysis, significantly reducing latency and compute costs compared to dense alternatives.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fm0dzhiqlhyl9md2uovd9.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fm0dzhiqlhyl9md2uovd9.png" alt=" " width="800" height="593"&gt;&lt;/a&gt;&lt;/p&gt;

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      <category>devchallenge</category>
      <category>gemmachallenge</category>
      <category>gemma</category>
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