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    <title>DEV Community: Prajwal Kamde</title>
    <description>The latest articles on DEV Community by Prajwal Kamde (@prajwalkamde).</description>
    <link>https://dev.to/prajwalkamde</link>
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      <title>DEV Community: Prajwal Kamde</title>
      <link>https://dev.to/prajwalkamde</link>
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      <title>FriendPrep AI: An AI Interview Partner for a Friend</title>
      <dc:creator>Prajwal Kamde</dc:creator>
      <pubDate>Sat, 03 Oct 2026 07:45:13 +0000</pubDate>
      <link>https://dev.to/prajwalkamde/friendprep-ai-an-ai-interview-partner-for-a-friend-1e7g</link>
      <guid>https://dev.to/prajwalkamde/friendprep-ai-an-ai-interview-partner-for-a-friend-1e7g</guid>
      <description>&lt;p&gt;&lt;em&gt;This is a submission for the &lt;a href="https://dev.to/challenges/hacktoberfest-weekend-2026-10-01"&gt;Hacktoberfest Weekend Challenge: Build for a Friend&lt;/a&gt;&lt;/em&gt;&lt;/p&gt;

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

&lt;p&gt;I built &lt;strong&gt;FriendPrep AI&lt;/strong&gt;, an interview practice app for a friend preparing for job interviews. Instead of practicing with the same generic question list, they can enter their target role, skills, experience, resume, and a job description to get a more tailored practice session.&lt;/p&gt;

&lt;p&gt;The app generates interview questions, evaluates answers with scores and feedback, can ask follow-up questions, and creates a final report with areas to work on. I also worked on making the feedback honest: answers that don’t address the question shouldn’t be praised as strong.&lt;/p&gt;

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

&lt;p&gt;&lt;strong&gt;Live demo:&lt;/strong&gt; &lt;a href="https://friendprep-ai.onrender.com/" rel="noopener noreferrer"&gt;https://friendprep-ai.onrender.com/&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fqs36ttqbih0jnno3mtxu.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fqs36ttqbih0jnno3mtxu.png" alt="Creating Interview" width="800" height="536"&gt;&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fgm0hcq4zax5v5u8jeluq.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fgm0hcq4zax5v5u8jeluq.png" alt="Question 01" width="799" height="481"&gt;&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F5mp9d97zsof7qhw5ua3m.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F5mp9d97zsof7qhw5ua3m.png" alt="Question 02" width="800" height="475"&gt;&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fn9qdx9l4p4i5p0d9lin1.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fn9qdx9l4p4i5p0d9lin1.png" alt="Question 03" width="800" height="453"&gt;&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fn2l0db3nq1rg8b8ipjx6.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fn2l0db3nq1rg8b8ipjx6.png" alt="Question 04" width="799" height="460"&gt;&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F7ytfw64n1tfzkohq8d9v.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F7ytfw64n1tfzkohq8d9v.png" alt="Question 05" width="800" height="476"&gt;&lt;/a&gt;&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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fnbxgnwls46jxnphhxmv2.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.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fnbxgnwls46jxnphhxmv2.png" alt="Final Report" width="800" height="482"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The app is deployed on Render. &lt;/p&gt;

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

&lt;p&gt;&lt;strong&gt;GitHub repository:&lt;/strong&gt; &lt;a href="https://github.com/Prajwalkamde/friendprep-ai" rel="noopener noreferrer"&gt;FriendPrep AI&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  How I Built It
&lt;/h2&gt;

&lt;p&gt;FriendPrep AI is built with Django and PostgreSQL. Its AI layer uses the open-weight &lt;strong&gt;Qwen3-4B-Instruct-2507&lt;/strong&gt; model through Hugging Face Inference Providers.&lt;/p&gt;

&lt;p&gt;The app keeps provider calls behind an AI service layer, so question generation, answer evaluation, follow-ups, and the final report are handled separately from the Django views. That also makes it easier to change the model or inference provider later.&lt;/p&gt;

&lt;h2&gt;
  
  
  Why Does Open Innovation Matter?
&lt;/h2&gt;

&lt;p&gt;Interview answers and resumes can be personal, so it matters that the model isn’t a black box tied permanently to one provider. Using an open-weight model gives the project room to change models, inspect available model options, or eventually self-host inference.&lt;/p&gt;

&lt;p&gt;For this version, inference is hosted through Hugging Face, so candidate information is still sent to that provider. The open model gives the project a path toward more control later; I’m not claiming the current hosted setup keeps that data entirely local.&lt;/p&gt;

&lt;h2&gt;
  
  
  Prize Categories
&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Best Use of Render&lt;/strong&gt; — for hosting the app and its interview-practice experience on Render.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Best Use of GitHub Copilot&lt;/strong&gt; — for the coding-agent assistance used throughout the development process.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

</description>
      <category>devchallenge</category>
      <category>weekendchallenge</category>
      <category>hf26challenge</category>
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