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    <title>DEV Community: Ashish Sharda</title>
    <description>The latest articles on DEV Community by Ashish Sharda (@ashishjsharda26).</description>
    <link>https://dev.to/ashishjsharda26</link>
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      <title>DEV Community: Ashish Sharda</title>
      <link>https://dev.to/ashishjsharda26</link>
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    <item>
      <title>Rust-Powered APIs with Axum – A Complete 2025 Guide</title>
      <dc:creator>Ashish Sharda</dc:creator>
      <pubDate>Mon, 02 Jun 2025 12:30:51 +0000</pubDate>
      <link>https://dev.to/ashishjsharda26/rust-powered-apis-with-axum-a-complete-2025-guide-5574</link>
      <guid>https://dev.to/ashishjsharda26/rust-powered-apis-with-axum-a-complete-2025-guide-5574</guid>
      <description>&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%2F0ik1v8lhsuz32fmbg395.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%2F0ik1v8lhsuz32fmbg395.png" alt="Image description" width="800" height="800"&gt;&lt;/a&gt;&lt;br&gt;
If you’re building backend systems in 2025 and not using Rust + Axum, you’re missing out on a powerhouse combo.&lt;/p&gt;

&lt;p&gt;I just published a complete guide walking through:&lt;br&gt;
✅ REST API structure with Axum&lt;br&gt;&lt;br&gt;
✅ Type-safe extractors&lt;br&gt;&lt;br&gt;
✅ Async database queries with sqlx&lt;br&gt;&lt;br&gt;
✅ Observability (tracing + Prometheus)&lt;br&gt;&lt;br&gt;
✅ WebAssembly for client-side validation&lt;br&gt;&lt;br&gt;
✅ Graceful shutdowns, Docker, and more&lt;/p&gt;

&lt;p&gt;Check it out on Medium 👇&lt;br&gt;&lt;br&gt;
🔗 &lt;a href="https://medium.com/@ashishjsharda/rust-powered-apis-with-axum-a-complete-2025-guide-213a28bb44ac" rel="noopener noreferrer"&gt;https://medium.com/@ashishjsharda/rust-powered-apis-with-axum-a-complete-2025-guide-213a28bb44ac&lt;/a&gt;&lt;/p&gt;

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      <category>rust</category>
      <category>softwareengineering</category>
      <category>api</category>
      <category>tutorial</category>
    </item>
    <item>
      <title>Our AI/ML Model Hit 99.8% Accuracy—Then Failed in Production. Here's Why.</title>
      <dc:creator>Ashish Sharda</dc:creator>
      <pubDate>Mon, 26 May 2025 14:10:00 +0000</pubDate>
      <link>https://dev.to/ashishjsharda26/our-aiml-model-hit-998-accuracy-then-failed-in-production-heres-why-580a</link>
      <guid>https://dev.to/ashishjsharda26/our-aiml-model-hit-998-accuracy-then-failed-in-production-heres-why-580a</guid>
      <description>&lt;p&gt;We built a fraud detection model that was “perfect” in testing—99.8% precision, extensive validation, bias audits, the works.&lt;/p&gt;

&lt;p&gt;Then we deployed it.&lt;/p&gt;

&lt;p&gt;Within 3 weeks, it was letting real fraud through and flagging legitimate users at 10x the expected rate.&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%2Flfza868ppfhg1p90dlmu.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%2Flfza868ppfhg1p90dlmu.png" alt="Image description" width="800" height="533"&gt;&lt;/a&gt;&lt;br&gt;
In this postmortem, I break down:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Why test metrics lie in production&lt;/li&gt;
&lt;li&gt;How feedback loops and real-time behavior broke our model&lt;/li&gt;
&lt;li&gt;The hidden cost of AI maintenance&lt;/li&gt;
&lt;li&gt;The ethical implications we missed&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;📘 Full article: &lt;a href="https://tinyurl.com/3jpzrx6r" rel="noopener noreferrer"&gt;https://tinyurl.com/3jpzrx6r&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Curious to hear your production AI failure stories or lessons learned. What would you do differently?&lt;/p&gt;

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      <category>ai</category>
      <category>machinelearning</category>
      <category>mlops</category>
      <category>datascience</category>
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