updated

Author: programmerraja

index.xml

@@ -6,6 +6,24 @@
       <description>Last 10 notes on 🧠 Second Brain</description>
       <generator>Quartz -- quartz.jzhao.xyz</generator>
       <item>
+    <title>Sherlock Holmes The case of Invalid ELHO error</title>
+    <link>https://programmerraja.github.io/post/2025/Sherlock-Holmes-The-case-of-Invalid-ELHO-error</link>
+    <guid>https://programmerraja.github.io/post/2025/Sherlock-Holmes-The-case-of-Invalid-ELHO-error</guid>
+    <description></description>
+    <pubDate>Sun, 12 Jan 2025 12:47:29 GMT</pubDate>
+  </item><item>
+    <title>PR Nightmares: 5 Common Coding Mistakes I&#039;ve Seen in the Last 2 Years</title>
+    <link>https://programmerraja.github.io/post/2025/PR-Nightmares-8-Common-Coding-Mistakes-Ive-Seen-in-the-Last-2-Years</link>
+    <guid>https://programmerraja.github.io/post/2025/PR-Nightmares-8-Common-Coding-Mistakes-Ive-Seen-in-the-Last-2-Years</guid>
+    <description>Hey everyone! If you’re a regular follower, you might already know a bit about me. But if not, let me quickly introduce myself: I’m a full-stack developer, and I’ve been working at a startup for the past two years.</description>
+    <pubDate>Sun, 12 Jan 2025 12:09:14 GMT</pubDate>
+  </item><item>
+    <title>Sherlock Holmes: The Case Redis Overload During a DDoS Attack</title>
+    <link>https://programmerraja.github.io/post/2025/Sherlock-Holmes-and-The-Case-Redis-Overload-During-a-DDoS-Attack</link>
+    <guid>https://programmerraja.github.io/post/2025/Sherlock-Holmes-and-The-Case-Redis-Overload-During-a-DDoS-Attack</guid>
+    <description>It was a calm day until disaster struck. We received alerts about DDoS and brute-force attacks originating from random bot IPs. Our team quickly mobilized to mitigate the attacks.</description>
+    <pubDate>Sun, 05 Jan 2025 01:12:19 GMT</pubDate>
+  </item><item>
     <title>Untitled</title>
     <link>https://programmerraja.github.io/Untitled</link>
     <guid>https://programmerraja.github.io/Untitled</guid>
@@ -47,24 +65,6 @@
     <guid>https://programmerraja.github.io/post/2024/Sherlock-Holmes-The-Case-Of-Debugging-ECONNREFUSED</guid>
     <description>When we migrated our application to Kubernetes, we encountered an unexpected issue that severely impacted user experience: frequent ECONNREFUSED errors ...</description>
     <pubDate>Thu, 31 Oct 2024 15:22:25 GMT</pubDate>
-  </item><item>
-    <title>K.Boopathi</title>
-    <link>https://programmerraja.github.io/</link>
-    <guid>https://programmerraja.github.io/</guid>
-    <description>Welcome to My World! 🌍 Hi! I’m K. Boopathi, a passionate Full Stack Developer with a love for tackling complex problems and staying ahead of the curve in the world of Web Development, DevOps, Generative AI, and beyond.</description>
-    <pubDate>Sat, 26 Oct 2024 10:18:30 GMT</pubDate>
-  </item><item>
-    <title>The Curious Case of the $15,000 Spam My Unexpected Investigation</title>
-    <link>https://programmerraja.github.io/post/2024/The-Curious-Case-of-the-15000-Spam-My-Unexpected-Investigation</link>
-    <guid>https://programmerraja.github.io/post/2024/The-Curious-Case-of-the-15000-Spam-My-Unexpected-Investigation</guid>
-    <description>Last Friday started off like any typical day—I was busy with my work in my office when I stumbled upon a suspicious message. My instincts told me it was spam, but my curiosity got the best of me.</description>
-    <pubDate>Sat, 19 Oct 2024 13:31:59 GMT</pubDate>
-  </item><item>
-    <title>Vector DB</title>
-    <link>https://programmerraja.github.io/notes/2024/Vector-DB-and-embedding</link>
-    <guid>https://programmerraja.github.io/notes/2024/Vector-DB-and-embedding</guid>
-    <description>How it works A dataset of three sentences, each has 3 words (or tokens) In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b).</description>
-    <pubDate>Sat, 19 Oct 2024 10:53:07 GMT</pubDate>
   </item>
     </channel>
   </rss>

notes/2024/AI-Content-System.html

@@ -314,6 +314,7 @@ <h3 id="resources">Resources<a role="anchor" aria-hidden="true" tabindex="-1" da
 <li><strong><a href="https://github.com/NirDiamant/RAG_Techniques" class="external">RAG Techniques<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></strong>: A GitHub repository that compiles techniques, methods, and best practices for working with RAG systems.</li>
 <li><strong><a href="https://github.com/ruc-nlpir/flashrag" class="external">Beyond the Basics of RAG<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></strong>: Advanced topics and concepts for pushing the limits of RAG technology.</li>
 <li><a href="https://microsoft.github.io/autogen/" class="external">Microsoft AutoGen<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a>**: A toolkit provided by Microsoft to automate the process of generating language models and leveraging RAG workflows.</li>
+<li><a href="https://github.com/bRAGAI/bRAG-langchain" class="external">https://github.com/bRAGAI/bRAG-langchain<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a> Everything you need to know to build your own RAG application</li>
 </ol>
 <h2 id="dataset-for-finetuning">DataSet For finetuning<a role="anchor" aria-hidden="true" tabindex="-1" data-no-popover="true" href="#dataset-for-finetuning" class="internal"><svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></a></h2>
 <p>Data Creation Techniques</p>

notes/2024/Generative-AI.html

@@ -21,6 +21,7 @@ <h2 id="prompts">Prompts<a role="anchor" aria-hidden="true" tabindex="-1" data-n
 <h2 id="jailbreak_llms">jailbreak_llms<a role="anchor" aria-hidden="true" tabindex="-1" data-no-popover="true" href="#jailbreak_llms" class="internal"><svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></a></h2>
 <p>A dataset consists of 15,140 ChatGPT prompts from Reddit, Discord, websites, and open-source datasets (including 1,405 jailbreak prompts).</p>
 <p>check <a href="https://github.com/verazuo/jailbreak_llms" class="external">here<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></p>
+<p><a href="https://github.com/jplhughes/bon-jailbreaking" class="external">https://github.com/jplhughes/bon-jailbreaking<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></p>
 <h2 id="jailbreaking-large-language-models-with-symbolic-mathematics">Jailbreaking Large Language Models with Symbolic Mathematics<a role="anchor" aria-hidden="true" tabindex="-1" data-no-popover="true" href="#jailbreaking-large-language-models-with-symbolic-mathematics" class="internal"><svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></a></h2>
 <p>This step converts natural language prompts into mathematical representations while preserving its meaning, structure and relationships.</p>
 <p>Three mathematical branches are specifically used for this purpose:</p>

notes/2024/LLM-Hacking.html

@@ -28,6 +28,7 @@ <h3 id="advanced-techniques-outlines-and-xgrammar"><strong>Advanced Techniques:
 <li><a href="https://www.youtube.com/watch?v=gMeTK6zzaO4" class="external">LLM Function Calling - AI Tools Deep Dive<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://www.promptingguide.ai/applications/function_calling" class="external">Function Calling with LLMs<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://ankur-singh.github.io/blog/structured-output" class="external">Generating Structured Output with LLMs <svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
+<li><a href="https://www.youtube.com/watch?v=kE4BkATIl9c" class="external">OpenAI DevDay 2024 | Structured outputs for reliable applications<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://www.instill.tech/blog/llm-structured-outputs" class="external">The Best Way to Generate Structured Output from LLMs<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://blog.gopenai.com/choosing-the-best-structured-output-parser-approach-3-ways-to-generate-structured-output-d9686482729c" class="external">Choosing the Best Structured Output Parser Approach | 3 Ways To Generate Structured Output<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 </ul>

notes/2024/LLM-strucuted-output-and-Parser.html

@@ -13,6 +13,8 @@ <h3 id="unpacking-the-magic-of-transformers">Unpacking the Magic of Transformers
 <li><a href="https://www.youtube.com/watch?v=SMZQrJ_L1vo&amp;pp=ygUbdHJhbnNmb3JtZXJzIGxsbSBrcmlzaCBuYWlr" class="external">Transformers Indepth Architecture Understanding- Attention Is All You Need<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://www.youtube.com/watch?v=UPtG_38Oq8o&amp;t=1045s" class="external">The math behind Attention: Keys, Queries, and Values matrices<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://www.youtube.com/watch?v=SmZmBKc7Lrs&amp;t=188s" class="external">The Most Important Algorithm in Machine Learning<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
+<li><a href="https://www.kaggle.com/learn-guide/5-day-genai#GenAI" class="external">5-Day Gen AI Intensive Course with Google Learn Guide<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
+<li></li>
 </ul>
 <p><strong>Blogs:</strong></p>
 <ul>
@@ -53,6 +55,7 @@ <h3 id="prompt-engineering">Prompt Engineering<a role="anchor" aria-hidden="true
 <li><a href="https://github.com/brexhq/prompt-engineering" class="external">Brex’s prompt engineering guide<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://llama.meta.com/docs/how-to-guides/prompting/" class="external">Meta’s prompt engineering guide<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf" class="external">Google’s Gemini prompt engineering guide<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
+<li><a href="https://arize.com/course/prompt-optimization" class="external">Prompt Optimization<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a></li>
 <li><a href="https://fchollet.substack.com/p/how-i-think-about-llm-prompt-engineering" class="external">How I think about LLM prompt engineering<svg aria-hidden="true" class="external-icon" style="max-width:0.8em;max-height:0.8em;" viewBox="0 0 512 512"><path d="M320 0H288V64h32 82.7L201.4 265.4 178.7 288 224 333.3l22.6-22.6L448 109.3V192v32h64V192 32 0H480 320zM32 32H0V64 480v32H32 456h32V480 352 320H424v32 96H64V96h96 32V32H160 32z"></path></svg></a> (Francois Chollet, 2023)</li>
 </ul>
 <p>I gone through some research paper about prompt engineering that might helpfull for you</p>