<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM on Jeroen Nyckees</title><link>https://jenyckee.github.io/tags/llm/</link><description>Recent content in LLM on Jeroen Nyckees</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Fri, 24 Apr 2026 15:00:00 +0200</lastBuildDate><atom:link href="https://jenyckee.github.io/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>Building an AI-Powered Salary Search Engine with Local LLMs and Vector Search</title><link>https://jenyckee.github.io/posts/besalary-ai-salary-search/</link><pubDate>Fri, 24 Apr 2026 15:00:00 +0200</pubDate><guid>https://jenyckee.github.io/posts/besalary-ai-salary-search/</guid><description>&lt;p>What if you could search for salaries not by exact keywords, but by describing a job in natural language? &amp;ldquo;Senior developer in Brussels with a company car&amp;rdquo; or &amp;ldquo;nurse working in Antwerp&amp;rdquo; — and get relevant results based on semantic similarity rather than string matching.&lt;/p>
&lt;p>This is exactly what I built with &lt;strong>BeSalary&lt;/strong>: an AI-powered salary search engine that extracts structured data from Reddit posts using local LLMs and enables semantic search through vector embeddings. You can try it live at &lt;a href="https://besalary-wine.vercel.app/">besalary-wine.vercel.app&lt;/a>.&lt;/p></description></item></channel></rss>