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    <title>Code Safari</title>
    <description>A minimalist learning environment for developers. Structured paths, three reading modes, zero distractions.</description>
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    <lastBuildDate>Sun, 21 Jun 2026 08:36:36 GMT</lastBuildDate>
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    <item>
      <title><![CDATA[Features, Labels, and Why Data Quality Is Everything in ML]]></title>
      <description><![CDATA[Practitioners say models are only as good as their data — and they mean it literally. Here's what features and labels actually are, why feature choice often beats algorithm choice, and the data problems (bias, leakage, imbalance) that quietly wreck models.]]></description>
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      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[features labels data quality bias leakage machine learning]]></category>
      <category><![CDATA[machine-learning]]></category>
      <category><![CDATA[features]]></category>
      <category><![CDATA[labels]]></category>
      <category><![CDATA[data-quality]]></category>
      <category><![CDATA[data-bias]]></category>
      <category><![CDATA[no-math]]></category>
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    <item>
      <title><![CDATA[What 'Learning From Data' Actually Means in Machine Learning]]></title>
      <description><![CDATA[Machine learning isn't magic and it isn't ordinary programming. Instead of writing rules, you show a model examples and it discovers the rules itself. Here's what 'learning from data' really means — in plain English.]]></description>
      <link>https://www.codesafari.blog/blog/learning-from-data</link>
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      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[machine learning learning from data patterns models examples]]></category>
      <category><![CDATA[machine-learning]]></category>
      <category><![CDATA[ml-basics]]></category>
      <category><![CDATA[how-ml-works]]></category>
      <category><![CDATA[learning-from-data]]></category>
      <category><![CDATA[models]]></category>
      <category><![CDATA[no-math]]></category>
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    <item>
      <title><![CDATA[How a Machine Learning Model Goes From Idea to Deployment]]></title>
      <description><![CDATA[A working ML model is the result of a whole lifecycle — framing the problem, gathering data, training, evaluating, deploying, and monitoring for drift. Here's the end-to-end journey in plain English, and why the model itself is the smallest part.]]></description>
      <link>https://www.codesafari.blog/blog/model-idea-to-deployment</link>
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      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[machine learning lifecycle deployment monitoring drift mlops]]></category>
      <category><![CDATA[machine-learning]]></category>
      <category><![CDATA[ml-lifecycle]]></category>
      <category><![CDATA[deployment]]></category>
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      <category><![CDATA[mlops]]></category>
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    <item>
      <title><![CDATA[Training, Testing, and Why Models Overfit (Explained Simply)]]></title>
      <description><![CDATA[A model that scores perfectly on its own study material can still be useless. This is the most important idea in machine learning: training vs. testing, overfitting vs. underfitting, and why a model that memorises is a model that fails.]]></description>
      <link>https://www.codesafari.blog/blog/training-testing-overfitting</link>
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      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[training testing overfitting underfitting generalisation machine learning]]></category>
      <category><![CDATA[machine-learning]]></category>
      <category><![CDATA[overfitting]]></category>
      <category><![CDATA[training-testing]]></category>
      <category><![CDATA[generalisation]]></category>
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    <item>
      <title><![CDATA[The Three Flavors of Machine Learning: Supervised, Unsupervised, Reinforcement]]></title>
      <description><![CDATA[Almost all machine learning comes in three styles, defined by what kind of examples the model learns from. Here's supervised, unsupervised, and reinforcement learning explained in plain English — what each one is for, with everyday examples and no math.]]></description>
      <link>https://www.codesafari.blog/blog/types-of-machine-learning</link>
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      <pubDate>Wed, 17 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[supervised unsupervised reinforcement learning types of machine learning]]></category>
      <category><![CDATA[machine-learning]]></category>
      <category><![CDATA[supervised-learning]]></category>
      <category><![CDATA[unsupervised-learning]]></category>
      <category><![CDATA[reinforcement-learning]]></category>
      <category><![CDATA[ml-basics]]></category>
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    <item>
      <title><![CDATA[The Honest Limits: What Generative AI Is Still Bad At]]></title>
      <description><![CDATA[Generative AI is genuinely powerful — and genuinely limited. An honest, no-hype look at what large language models are still bad at, why those weaknesses exist, and how to use AI well by working with the grain instead of against it.]]></description>
      <link>https://www.codesafari.blog/blog/limits-of-generative-ai</link>
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      <pubDate>Mon, 15 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[generative ai limits weaknesses what ai cant do reliability]]></category>
      <category><![CDATA[ai-limits]]></category>
      <category><![CDATA[generative-ai]]></category>
      <category><![CDATA[llm-weaknesses]]></category>
      <category><![CDATA[ai-reliability]]></category>
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      <title><![CDATA[Tokens, Context Windows, and Why AI Sometimes Forgets]]></title>
      <description><![CDATA[Why does AI lose track of long conversations, miscount letters, or 'forget' what you said earlier? It comes down to tokens and the context window. A clear, no-math explanation of how AI reads — and why it has a memory limit.]]></description>
      <link>https://www.codesafari.blog/blog/tokens-and-context-windows</link>
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      <pubDate>Sun, 14 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[tokens context window ai memory tokenization llm forgetting]]></category>
      <category><![CDATA[tokens]]></category>
      <category><![CDATA[context-window]]></category>
      <category><![CDATA[tokenization]]></category>
      <category><![CDATA[ai-memory]]></category>
      <category><![CDATA[how-ai-works]]></category>
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    <item>
      <title><![CDATA[Why AI Hallucinates — and What It Reveals About How It Thinks]]></title>
      <description><![CDATA[AI hallucinations aren't random glitches — they're a direct, predictable consequence of how language models work. Here's why models confidently make things up, when it's most likely, and how to reduce it.]]></description>
      <link>https://www.codesafari.blog/blog/why-ai-hallucinates</link>
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      <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai hallucination llm confidently wrong made up facts]]></category>
      <category><![CDATA[ai-hallucination]]></category>
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    <item>
      <title><![CDATA[What's Inside an AI Model: Training, Parameters, and Why Size Matters]]></title>
      <description><![CDATA[What is a model, really? A no-math look inside a large language model — what parameters are, how training actually works, what 'billions of parameters' means, and why size matters (but isn't everything).]]></description>
      <link>https://www.codesafari.blog/blog/inside-an-ai-model</link>
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      <pubDate>Fri, 12 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[ai model training parameters weights neural network size]]></category>
      <category><![CDATA[ai-model]]></category>
      <category><![CDATA[parameters]]></category>
      <category><![CDATA[training]]></category>
      <category><![CDATA[neural-network]]></category>
      <category><![CDATA[model-size]]></category>
      <category><![CDATA[how-ai-works]]></category>
    </item>

    <item>
      <title><![CDATA[From GPT to ChatGPT: A Short History of the LLM Era]]></title>
      <description><![CDATA[How did we get from autocomplete to AI that writes essays and code? A clear, non-technical history of large language models — the Transformer, the GPT series, the scaling race, and the ChatGPT moment that changed everything.]]></description>
      <link>https://www.codesafari.blog/blog/history-of-llms</link>
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      <pubDate>Thu, 11 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[history of large language models gpt transformer chatgpt]]></category>
      <category><![CDATA[llm-history]]></category>
      <category><![CDATA[gpt]]></category>
      <category><![CDATA[transformer]]></category>
      <category><![CDATA[chatgpt]]></category>
      <category><![CDATA[generative-ai]]></category>
      <category><![CDATA[ai-timeline]]></category>
    </item>

    <item>
      <title><![CDATA[What 'Generative' Actually Means: Next-Token Prediction, Explained Simply]]></title>
      <description><![CDATA[Generative AI sounds mysterious, but underneath it is one repeated trick: predicting the next token. Here's what that means, why it produces such convincing text, and what it tells you about how these models really work.]]></description>
      <link>https://www.codesafari.blog/blog/what-generative-means</link>
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      <pubDate>Wed, 10 Jun 2026 00:00:00 GMT</pubDate>
      <category><![CDATA[generative ai large language models next token prediction]]></category>
      <category><![CDATA[generative-ai]]></category>
      <category><![CDATA[llm]]></category>
      <category><![CDATA[next-token-prediction]]></category>
      <category><![CDATA[how-ai-works]]></category>
      <category><![CDATA[ai-basics]]></category>
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