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    <title><![CDATA[Claude Code Anywhere: Your Whole Dev Machine, In Your Pocket (Part 1 of 1)]]></title>
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    <pubDate>Thu, 25 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[Claude Code Anywhere]]></category>
    <description><![CDATA[Self-host a free, secure pipeline from your phone to your real Mac with Tailscale, tmux, and one isolated Claude Code session per project. Your actual files and full toolchain — not a cloud sandbox.]]></description>
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    <title><![CDATA[Fine-Tuning & Serving: Serve a Model with vLLM (Part 3 of 3)]]></title>
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    <pubDate>Wed, 24 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[Fine-Tuning & Serving]]></category>
    <description><![CDATA[Turn a fine-tuned model into a fast, OpenAI-compatible API. Merge the LoRA adapter, start a vLLM server with one command, and call it from the OpenAI client — a drop-in for your existing code.]]></description>
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    <title><![CDATA[Fine-Tuning & Serving: LoRA & QLoRA on One GPU (Part 2 of 3)]]></title>
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    <pubDate>Wed, 24 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
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    <description><![CDATA[How parameter-efficient fine-tuning works: LoRA trains tiny adapter matrices instead of the whole model, and QLoRA adds 4-bit quantization so an 8B model fits a free Colab GPU. Concept + an official runnable notebook.]]></description>
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    <title><![CDATA[Fine-Tuning & Serving: Fine-Tuning vs RAG (Part 1 of 3)]]></title>
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    <pubDate>Wed, 24 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[Fine-Tuning & Serving]]></category>
    <description><![CDATA[The most common AI-engineering fork: retrieve knowledge (RAG) or change the model itself (fine-tuning)? A decision framework — when each wins, when to combine them, and what fine-tuning cannot do.]]></description>
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    <title><![CDATA[RAG from Scratch: Evaluating Retrieval (Part 6 of 6)]]></title>
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    <pubDate>Tue, 23 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[RAG from Scratch]]></category>
    <description><![CDATA[Stop guessing whether a change helped. Build a golden query set, score retrieval with Hit@k and MRR, then A/B two retrievers over the same set — the capstone of the RAG track.]]></description>
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    <title><![CDATA[RAG from Scratch: Reranking (Part 5 of 6)]]></title>
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    <pubDate>Tue, 23 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[RAG from Scratch]]></category>
    <description><![CDATA[A bi-encoder retrieves fast but is fooled by negation; a cross-encoder reads query and passage together and fixes it. Retrieve wide, rerank narrow — with the recall ceiling made concrete.]]></description>
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    <title><![CDATA[RAG from Scratch: Hybrid Search (Part 4 of 6)]]></title>
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    <pubDate>Tue, 23 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[RAG from Scratch]]></category>
    <description><![CDATA[Vector search understands meaning; BM25 nails exact tokens. Each has a blind spot. Fuse them with Reciprocal Rank Fusion so retrieval stays correct when either one is wrong.]]></description>
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    <title><![CDATA[RAG from Scratch: Chunking Strategies (Part 3 of 6)]]></title>
    <link>https://cloudcodetree.com/tutorials/chunking-strategies-for-rag/</link>
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    <pubDate>Tue, 23 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[RAG from Scratch]]></category>
    <description><![CDATA[Chunking is the biggest lever on RAG quality. Compare whole-document, fixed-size, and paragraph-aware+overlap chunking on the same query — with real numbers.]]></description>
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    <title><![CDATA[RAG from Scratch: Give Your RAG a Vector Database (Part 2 of 6)]]></title>
    <link>https://cloudcodetree.com/tutorials/vector-database-for-rag/</link>
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    <pubDate>Tue, 23 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[RAG from Scratch]]></category>
    <description><![CDATA[Move from in-memory numpy to Chroma: embed once, persist to disk, query fast, and filter by metadata. The next step after the RAG intro.]]></description>
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    <title><![CDATA[RAG from Scratch: Build a RAG Over Your Blog (Part 1 of 6)]]></title>
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    <pubDate>Tue, 23 Jun 2026 12:00:00 GMT</pubDate>
    <dc:creator><![CDATA[Chris Harper]]></dc:creator>
    <category><![CDATA[RAG from Scratch]]></category>
    <description><![CDATA[Your first hands-on AI-engineering project: retrieve answers from your own posts with embeddings + cosine search — no vector DB, no API key. ~60 lines of Python.]]></description>
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