<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0"
     xmlns:atom="http://www.w3.org/2005/Atom"
     xmlns:content="http://purl.org/rss/1.0/modules/content/"
     xmlns:dc="http://purl.org/dc/elements/1.1/"
     xmlns:media="http://search.yahoo.com/mrss/">
  <channel>
    <title>Ömer Faruk Aydın — Full-Stack &amp; AI Engineering Insights</title>
    <link>https://omerfarukaydn.com/</link>
    <description>İstanbul Topkapı Üniversitesi Bilgisayar Programcılığı mezunu, full-stack developer ve AI entegratörü Ömer Faruk Aydın'ın kişisel blogu. Next.js, React, TypeScript, Python, AI entegrasyonu, SaaS mimarisi ve trading botları üzerine teknik yazılar.</description>
    <language>tr-tr</language>
    <copyright>© 2026 Ömer Faruk Aydın</copyright>
    <managingEditor>omerfarukaydin3455@gmail.com (Ömer Faruk Aydın)</managingEditor>
    <webMaster>omerfarukaydin3455@gmail.com (Ömer Faruk Aydın)</webMaster>
    <lastBuildDate>Thu, 20 Aug 2026 10:00:00 +0300</lastBuildDate>
    <pubDate>Thu, 20 Aug 2026 10:00:00 +0300</pubDate>
    <ttl>60</ttl>
    <atom:link href="https://omerfarukaydn.com/feed.xml" rel="self" type="application/rss+xml" />
    <image>
      <url>https://omerfarukaydn.com/favicon.svg</url>
      <title>Ömer Faruk Aydın</title>
      <link>https://omerfarukaydn.com/</link>
    </image>

    <item>
      <title>XGBoost + Bayesian Ensemble ile Kripto Sinyal Üretimi</title>
      <link>https://omerfarukaydn.com/blog/xgboost-trading-bot.html</link>
      <guid isPermaLink="true">https://omerfarukaydn.com/blog/xgboost-trading-bot.html</guid>
      <pubDate>Wed, 12 Aug 2026 10:00:00 +0300</pubDate>
      <dc:creator>Ömer Faruk Aydın</dc:creator>
      <category>Trading Systems</category>
      <category>XGBoost</category>
      <category>Bayesian Optimization</category>
      <category>Python</category>
      <category>FastAPI</category>
      <description>13 indikatörü tek bir modelde birleştirip ağırlıkları Bayesian optimizasyonla güncellemek, klasik sabit-ağırlıklı stratejilerden %37 daha yüksek Sharpe oranı verdi. Tüm pipeline'ı Python + FastAPI + CCXT ile nasıl kurduğumu anlatıyorum.</description>
      <content:encoded><![CDATA[<p>13 indikatörü tek bir modelde birleştirip ağırlıkları Bayesian optimizasyonla güncellemek, klasik sabit-ağırlıklı stratejilerden %37 daha yüksek Sharpe oranı verdi.</p>]]></content:encoded>
      <media:content url="https://omerfarukaydn.com/images/blog-xgboost-cover.png" medium="image" width="1200" height="630" type="image/png" />
    </item>

    <item>
      <title>Multi-Tenant SaaS Mimarisi: RLS, Subdomain ve Auth</title>
      <link>https://omerfarukaydn.com/blog/multi-tenant-saas.html</link>
      <guid isPermaLink="true">https://omerfarukaydn.com/blog/multi-tenant-saas.html</guid>
      <pubDate>Tue, 28 Jul 2026 10:00:00 +0300</pubDate>
      <dc:creator>Ömer Faruk Aydın</dc:creator>
      <category>SaaS Architecture</category>
      <category>Multi-Tenant</category>
      <category>Supabase</category>
      <category>PostgreSQL RLS</category>
      <category>Next.js</category>
      <description>PostgreSQL Row Level Security, Next.js middleware ve OAuth ile multi-tenant SaaS mimarisi. Sıfırdan tenant routing, isolated database access ve subdomain-based architecture.</description>
      <content:encoded><![CDATA[<p>PostgreSQL Row Level Security, Next.js middleware ve OAuth ile multi-tenant SaaS mimarisi.</p>]]></content:encoded>
      <media:content url="https://omerfarukaydn.com/images/blog-saas-cover.png" medium="image" width="1200" height="630" type="image/png" />
    </item>

    <item>
      <title>llama.cpp ile Yerel LLM: VRAM-Aware Model Routing</title>
      <link>https://omerfarukaydn.com/blog/llama-cpp-local-llm.html</link>
      <guid isPermaLink="true">https://omerfarukaydn.com/blog/llama-cpp-local-llm.html</guid>
      <pubDate>Mon, 15 Jun 2026 10:00:00 +0300</pubDate>
      <dc:creator>Ömer Faruk Aydın</dc:creator>
      <category>Local AI</category>
      <category>llama.cpp</category>
      <category>GGUF</category>
      <category>Ollama</category>
      <category>VRAM</category>
      <description>VRAM-aware routing ve OpenAI-uyumlu API ile yerel LLM'leri sıfır kod değişikliğiyle kullanmak. llama.cpp, GGUF formatı, Ollama ve VRAM-bilinçli yönlendirme stratejileri.</description>
      <content:encoded><![CDATA[<p>VRAM-aware routing ve OpenAI-uyumlu API ile yerel LLM'leri sıfır kod değişikliğiyle kullanmak.</p>]]></content:encoded>
      <media:content url="https://omerfarukaydn.com/images/blog-llama-cover.png" medium="image" width="1200" height="630" type="image/png" />
    </item>
  </channel>
</rss>
