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    <title>Omkar Chebale - Blog</title>
    <link>https://omkarchebale.vercel.app/blogs</link>
    <description>Thoughts on AI, ML, and software engineering. What I build, break, and learn.</description>
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    <lastBuildDate>Thu, 02 Apr 2026 15:27:16 GMT</lastBuildDate>
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      <title>AI Memory Isn&apos;t Memory — It&apos;s Smart Context Injection</title>
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      <description>AI memory isn&apos;t like human memory—models forget everything. What we call memory is actually smart storing, searching, and injecting context at the right time using external systems.</description>
      <pubDate>Thu, 02 Apr 2026 14:13:16 GMT</pubDate>
        <category>AI</category>
        <category>LLM</category>
        <category>Memory Systems</category>
        <category>Context Window</category>
        <category>Vector Database</category>
        <category>Learning In Public</category>
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      <title>Encoder vs Decoder — What Each Half of the Transformer Actually Does</title>
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      <description>A clear breakdown of what the encoder and decoder each do in a Transformer — their internal structure, how multi-head self-attention works, what cross-attention is, and when you&apos;d use encoder-only vs decoder-only vs full encoder-decoder models.</description>
      <pubDate>Mon, 30 Mar 2026 07:11:03 GMT</pubDate>
        <category>transformers</category>
        <category>encoder</category>
        <category>decoder</category>
        <category>deep-learning</category>
        <category>nlp</category>
        <category>attention</category>
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      <title>Contextual Embeddings — How Transformers Make Words Context-Aware</title>
      <link>https://omkarchebale.vercel.app/blogs/contextual-embeddings-how-transformers-make-words-context-aware</link>
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      <description>How self-attention produces contextual embeddings by computing a weighted sum of value vectors — and what it means that the same word gets a different representation depending on the sentence it appears in.</description>
      <pubDate>Sun, 29 Mar 2026 07:10:00 GMT</pubDate>
        <category>transformers</category>
        <category>embeddings</category>
        <category>self-attention</category>
        <category>deep-learning</category>
        <category>nlp</category>
        <category>ai</category>
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      <title>Softmax Demystified — How Raw Scores Become Attention Weights</title>
      <link>https://omkarchebale.vercel.app/blogs/softmax-demystified-how-raw-scores-become-attention-weights</link>
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      <description>A deep dive into the softmax function — why it&apos;s used in self-attention, how it converts raw dot product scores into probabilities, and why the numerically stable variant (subtracting the max) matters in practice.</description>
      <pubDate>Sat, 28 Mar 2026 11:59:54 GMT</pubDate>
        <category>transformers</category>
        <category>softmax</category>
        <category>self-attention</category>
        <category>deep-learning</category>
        <category>nlp</category>
        <category>math</category>
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      <title>Self-Attention From Scratch — A Complete Numerical Walkthrough</title>
      <link>https://omkarchebale.vercel.app/blogs/self-attention-from-scratch-a-complete-numerical-walkthrough</link>
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      <description>A full step-by-step numerical walkthrough of self-attention using the sentence &quot;please study man&quot; — computing Q, K, V vectors, raw attention scores, softmax weights, and final contextual output vectors from scratch.</description>
      <pubDate>Fri, 27 Mar 2026 10:48:10 GMT</pubDate>
        <category>transformers</category>
        <category>self-attention</category>
        <category>deep-learning</category>
        <category>nlp</category>
        <category>machine-learning</category>
        <category>ai</category>
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      <title>Query, Key, Value — The Database Analogy That Makes Self-Attention Click</title>
      <link>https://omkarchebale.vercel.app/blogs/query-key-value-the-database-analogy-that-makes-self-attention-click</link>
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      <description>A deep intuitive breakdown of the Q, K, V mechanism in self-attention — using a database retrieval analogy and real weight matrix math to show exactly how Transformers decide which words to attend to.</description>
      <pubDate>Thu, 26 Mar 2026 06:00:00 GMT</pubDate>
        <category>transformers</category>
        <category>self-attention</category>
        <category>deep-learning</category>
        <category>nlp</category>
        <category>machine-learning</category>
        <category>ai</category>
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      <title>Attention Is All You Need — The Paper That Changed AI Forever</title>
      <link>https://omkarchebale.vercel.app/blogs/attention-is-all-you-need-the-paper-that-changed-ai-forever</link>
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      <description>A deep dive into the Transformer architecture introduced in the landmark 2017 paper — what it is, how it works, why it replaced RNNs, and why every modern AI model from GPT to Gemini traces its roots here.</description>
      <pubDate>Wed, 25 Mar 2026 14:37:16 GMT</pubDate>
        <category>transformers</category>
        <category>deep-learning</category>
        <category>nlp</category>
        <category>attention</category>
        <category>ai</category>
        <category>machine-learning</category>
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      <title>The Real Difference Between Training, Fine-Tuning, and Inference (My Mental Model)</title>
      <link>https://omkarchebale.vercel.app/blogs/the-real-difference-between-training-fine-tuning-and-inference-my-mental-model</link>
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      <description>Breaking down the difference between training, fine-tuning, and inference—why they&apos;re not the same thing, what actually happens in each stage, and why understanding this makes LLM systems way less confusing.</description>
      <pubDate>Sun, 22 Mar 2026 13:19:24 GMT</pubDate>
        <category>Machine Learning</category>
        <category>LLM</category>
        <category>Training</category>
        <category>Fine-Tuning</category>
        <category>Inference</category>
        <category>Learning In Public</category>
        <category>Deep Learning</category>
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    <item>
      <title>Why Tokenization Is More Important Than You Think</title>
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      <description>Why tokenization is the most underrated part of LLMs—how tokens aren&apos;t words, why they affect cost and performance, and why bad tokenization breaks everything downstream.</description>
      <pubDate>Sat, 21 Mar 2026 15:03:38 GMT</pubDate>
        <category>Tokenization</category>
        <category>LLM</category>
        <category>NLP</category>
        <category>Machine Learning</category>
        <category>Embeddings</category>
        <category>Learning In Public</category>
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      <title>The Only Blog You Need to Understand Encoder-Decoder Architecture</title>
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      <description>A complete breakdown of encoder-decoder architectures—how they compress sequences into context vectors, generate outputs step-by-step, why teacher forcing matters, and the four key limitations that led to attention mechanisms.</description>
      <pubDate>Fri, 20 Mar 2026 16:41:21 GMT</pubDate>
        <category>Encoder-Decoder</category>
        <category>Sequence to Sequence</category>
        <category>LSTM</category>
        <category>RNN</category>
        <category>NLP</category>
        <category>Machine Learning</category>
        <category>Deep Learning</category>
        <category>Learning In Public</category>
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    <item>
      <title>I Built &quot;Legal Lens&quot; — A Fine-Tuned AI That Translates Legal Jargon Into Plain English</title>
      <link>https://omkarchebale.vercel.app/blogs/i-built-legal-lens-a-fine-tuned-ai-that-translates-legal-jargon-into-plain-english</link>
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      <description>Building a fine-tuned AI to translate legal jargon into plain English—from FLAN-T5 failures to Gemma-2B success using QLoRA on a free GPU, and the engineering lessons learned along the way.</description>
      <pubDate>Wed, 25 Feb 2026 21:29:44 GMT</pubDate>
        <category>LLM</category>
        <category>Fine-Tuning</category>
        <category>NLP</category>
        <category>Legal Tech</category>
        <category>QLoRA</category>
        <category>Gemma</category>
        <category>Machine Learning</category>
        <category>Hugging Face</category>
        <category>Building In Public</category>
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    <item>
      <title>I Built an MCP Server for My Portfolio — Death by Tiny Bugs</title>
      <link>https://omkarchebale.vercel.app/blogs/i-built-an-mcp-server-for-my-portfolio-death-by-tiny-bugs</link>
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      <description>How I exposed my portfolio blog system as an MCP server so Claude could operate it with natural language — and the 5 small but painful bugs that stood in the way.</description>
      <pubDate>Tue, 17 Feb 2026 14:23:16 GMT</pubDate>
        <category>MCP</category>
        <category>Python</category>
        <category>FastMCP</category>
        <category>Debugging</category>
        <category>Developer Tools</category>
        <category>Learning In Public</category>
        <category>Portfolio</category>
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      <title>Embeddings, Vector Databases, and Re-Ranking: My Confusion Dump</title>
      <link>https://omkarchebale.vercel.app/blogs/embeddings-vector-databases-and-re-ranking-my-confusion-dump</link>
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      <description>An honest, unstructured brain dump about embeddings, vector databases, and re-ranking—from confusion about what the numbers mean to understanding coordinates, similarity search, and retrieval optimization.</description>
      <pubDate>Mon, 16 Feb 2026 09:32:54 GMT</pubDate>
        <category>Embeddings</category>
        <category>Vector Database</category>
        <category>Re-ranking</category>
        <category>RAG</category>
        <category>Semantic Search</category>
        <category>Learning In Public</category>
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    <item>
      <title>Quantization Isn&apos;t Scary: What I Wish Someone Told Me Earlier</title>
      <link>https://omkarchebale.vercel.app/blogs/quantization-isn-t-scary-what-i-wish-someone-told-me-earlier</link>
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      <description>Breaking down quantization from scary optimization technique to simple concept—how reducing bit precision makes models smaller and faster, and why calibration matters more than the math.</description>
      <pubDate>Fri, 06 Feb 2026 15:16:45 GMT</pubDate>
        <category>Quantization</category>
        <category>Model Optimization</category>
        <category>Inference</category>
        <category>LLM</category>
        <category>Machine Learning</category>
        <category>Learning In Public</category>
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      <title>Text Classification Inference Benchmark: What Actually Happens on CPU vs GPU</title>
      <link>https://omkarchebale.vercel.app/blogs/text-classification-inference-benchmark-what-actually-happens-on-cpu-vs-gpu</link>
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      <description>A practical inference benchmark comparing DistilBERT performance on CPU vs GPU—measuring latency, throughput, and memory across different batch sizes to understand what actually happens in production.</description>
      <pubDate>Thu, 05 Feb 2026 09:37:51 GMT</pubDate>
        <category>Transformers</category>
        <category>Inference</category>
        <category>machinelearning</category>
        <category>DistilBERT</category>
        <category>PyTorch</category>
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    <item>
      <title>I Learned Machine Learning Without a Single Mentor</title>
      <link>https://omkarchebale.vercel.app/blogs/i-learned-machine-learning-without-a-single-mentor</link>
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      <description>Learning machine learning alone in a Tier-3 city without mentors, bootcamps, or a tech ecosystem—why constraints became advantages and how building in public taught me more than any course.</description>
      <pubDate>Mon, 02 Feb 2026 16:33:27 GMT</pubDate>
        <category>Machine Learning</category>
        <category>Self-Taught</category>
        <category>Learning In Public</category>
        <category>Remote Learning</category>
        <category>Developer Journey</category>
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    <item>
      <title>Why I Stopped Chasing Perfect Code (And Started Shipping Instead)</title>
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      <description>A personal reflection on breaking free from perfectionism—why I stopped over-engineering side projects and started shipping imperfect code that actually reaches users.</description>
      <pubDate>Wed, 14 Jan 2026 10:45:23 GMT</pubDate>
        <category>engineering</category>
        <category>productivity</category>
        <category>career</category>
        <category>lessons</category>
        <category>Developer Mindset</category>
    </item>
    <item>
      <title>Vision Language Models: How Machines Learned to See and Understand</title>
      <link>https://omkarchebale.vercel.app/blogs/vision-language-models-how-machines-learned-to-see-and-understand</link>
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      <description>Breaking down Vision Language Models into their core components—vision encoders, text encoders, fusion mechanisms—and the two main paradigms: contrastive learning (CLIP-style) and generative models.</description>
      <pubDate>Thu, 08 Jan 2026 20:56:52 GMT</pubDate>
        <category>Vision Language Models</category>
        <category>VLM</category>
        <category>Machine Learning</category>
        <category>CLIP</category>
        <category>Transformers</category>
        <category>Computer Vision</category>
        <category>Learning In Public</category>
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      <title>How I Built an AI-Powered Blog Recommendation System From Scratch</title>
      <link>https://omkarchebale.vercel.app/blogs/how-i-built-an-ai-powered-blog-recommendation-system-from-scratch</link>
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      <description>Building a semantic blog recommendation system from scratch using embeddings, vector databases, and pre-computed results—why tags aren&apos;t enough and how I integrated ML into my Next.js portfolio.</description>
      <pubDate>Sun, 04 Jan 2026 10:26:59 GMT</pubDate>
        <category>Machine Learning</category>
        <category>Embeddings</category>
        <category>Vector Database</category>
        <category>Python</category>
        <category>Next.js</category>
        <category>Pinecone</category>
        <category>Recommendation System</category>
        <category>Learning In Public</category>
    </item>
    <item>
      <title>Logistic Regression From Scratch — What It Actually Does (Without Skipping the Thinking)</title>
      <link>https://omkarchebale.vercel.app/blogs/logistic-regression-from-scratch-what-it-actually-does-without-skipping-the-thinking</link>
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      <description>Breaking down Logistic Regression from first principles—why it exists to express confidence in binary outcomes, how sigmoid transforms linear scores into probabilities, and a minimal from-scratch implementation.</description>
      <pubDate>Sat, 03 Jan 2026 13:47:36 GMT</pubDate>
        <category>Machine Learning</category>
        <category>Logistic Regression</category>
        <category>From Scratch</category>
        <category>Classification</category>
        <category>Learning In Public</category>
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      <title>AI, ML, and Deep Learning: clearing the confusion once and for all</title>
      <link>https://omkarchebale.vercel.app/blogs/ai-ml-and-deep-learning-clearing-the-confusion-once-and-for-all</link>
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      <description>Understanding AI, Machine Learning, and Deep Learning as a hierarchy rather than competing terms—from the broad AI umbrella to data-driven ML to neural-network-based deep learning.</description>
      <pubDate>Mon, 29 Dec 2025 05:08:41 GMT</pubDate>
        <category>AI</category>
        <category>Machine Learning</category>
        <category>Deep Learning</category>
        <category>Learning In Public</category>
        <category>Fundamentals</category>
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    <item>
      <title>RAG Explained: The 5 Steps That Make LLMs Smarter</title>
      <link>https://omkarchebale.vercel.app/blogs/rag-explained-the-5-steps-that-make-llms-smarter</link>
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      <description>A beginner-friendly breakdown of RAG&apos;s five core steps: from document preprocessing and chunking to embeddings, vector databases, and how LLMs use retrieved context to generate accurate answers.</description>
      <pubDate>Thu, 25 Dec 2025 05:50:32 GMT</pubDate>
        <category>RAG</category>
        <category>LLM</category>
        <category>Embeddings</category>
        <category>Vector Database</category>
        <category>AI</category>
        <category>Learning In Public</category>
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    <item>
      <title>Understanding MCP: Servers, Tools, and Why They Matter</title>
      <link>https://omkarchebale.vercel.app/blogs/understanding-mcp-servers-tools-and-why-they-matter</link>
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      <description>Breaking down MCP (Model Context Protocol) through a simple analogy: tools are functions, MCP servers are toolboxes, and LLMs can invoke them through natural language without any UI interaction.</description>
      <pubDate>Mon, 22 Dec 2025 05:36:41 GMT</pubDate>
        <category>MCP</category>
        <category>LLM</category>
        <category>AI Tools</category>
        <category>APIs</category>
        <category>Learning In Public</category>
        <category>System Design</category>
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    <item>
      <title>How I Built a Blog System Into My Portfolio (And Why I Did It)</title>
      <link>https://omkarchebale.vercel.app/blogs/how-i-built-a-blog-system-into-my-portfolio-and-why-i-did-it</link>
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      <description>How I built a simple blog system into my portfolio using a custom API, MongoDB, and markdown—so I can write and publish from anywhere.</description>
      <pubDate>Sun, 21 Dec 2025 13:53:09 GMT</pubDate>
        <category>Personal Portfolio</category>
        <category>API Design</category>
        <category>MongoDB</category>
        <category>Next.js</category>
        <category>Learning In Public</category>
        <category>Blogging</category>
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    <item>
      <title>My First Real n8n Workflow: Why It Took 12+ Hours (and What I Learned)</title>
      <link>https://omkarchebale.vercel.app/blogs/my-first-real-n8n-workflow-why-it-took-12-hours-and-what-i-learned</link>
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      <description>My honest beginner experience with n8n, why my first simple workflow took 12+ hours, and what I learned about automation, triggers, and platform limitations.</description>
      <pubDate>Sat, 20 Dec 2025 11:04:04 GMT</pubDate>
        <category>n8n</category>
        <category>Automation</category>
        <category>Low-Code</category>
        <category>Debugging</category>
        <category>Learning In Public</category>
        <category>Daily Log</category>
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