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            <title><![CDATA[The LLM Year in Review: What Actually Mattered in 2025 (And What Was Noise)]]></title>
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            <pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The prediction was: bigger models win. The reality was: DeepSeek R1 rewrote the rules in January and nothing was the same after that. What 2025 taught us about reasoning, inference-time compute, and the economics of intelligence.]]></description>
            <content:encoded><![CDATA[The prediction was: bigger models win. The reality was: DeepSeek R1 rewrote the rules in January and nothing was the same after that. What 2025 taught us about reasoning, inference-time compute, and the economics of intelligence.]]></content:encoded>
            <author>clint@1putthealth.com (Clint Johnson)</author>
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            <title><![CDATA[From GPT-2 to DeepSeek: The Architectural Changes That Actually Mattered]]></title>
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            <pubDate>Tue, 04 Feb 2025 00:00:00 GMT</pubDate>
            <description><![CDATA[I've been reading ML papers for 10 years. Most don't matter. These architectural choices did. RoPE, GQA, SwiGLU: each one solved a real scaling problem. What to look for when a new model claims 'better architecture.']]></description>
            <content:encoded><![CDATA[I've been reading ML papers for 10 years. Most don't matter. These architectural choices did. RoPE, GQA, SwiGLU: each one solved a real scaling problem. What to look for when a new model claims 'better architecture.']]></content:encoded>
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