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      <title>Debugging as a teachable habit.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>Debugging is not a remedial afterthought. This piece treats it as core computing thinking: reproduce, narrow, hypothesise, test, and learn from failure.</description>
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      <title>Fluent answers are not understanding.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>A fluent AI answer can look like competence. This piece separates linguistic fluency from understanding, and what that distinction means for teaching and assessment.</description>
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      <title>LLM fluency is not reasoning.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>Large language models produce fluent text with remarkable consistency. This piece separates linguistic performance from reasoning, understanding, and reliable truth.</description>
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      <title>Measure before you optimise.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>Dashboards and metrics are easy to collect and hard to interpret. This piece argues for measuring the right thing before improving what is merely visible.</description>
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      <title>Productive struggle in learning.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>Difficulty is not always waste. This piece explains productive struggle, when to preserve it, and how instant help from AI can remove the work that makes learning stick.</description>
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      <title>Recursion without mystery.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>Recursion becomes clearer when you separate the base case, the recursive step, and the trust that smaller subproblems are already solved. A short guide with Python examples.</description>
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      <title>Teaching computing beyond syntax.</title>
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      <pubDate>Tue, 01 Sep 2026 12:00:00 GMT</pubDate>
      <description>Pupils can write working code and still miss the idea. This piece argues for teaching computing as explanation, structure, and judgement — not only correct output.</description>
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      <title>Bellman–Ford, explained clearly.</title>
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      <pubDate>Wed, 26 Aug 2026 12:00:00 GMT</pubDate>
      <description>A clear guide to the Bellman–Ford algorithm, including relaxation, negative edges, negative cycles, complexity, and a Python implementation.</description>
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