Discrete spatial diffusion models data while obeying scientific principles - Tech Xplore
<a href="https://news.google.com/rss/articles/CBMijwFBVV95cUxQTHBab19CSWJDeHkwWnQ1Sk9nTkJtNEtsRF9jR2loOUdjUTgtX2dWcDlYOUxDR2hzU0V5enlIQ0JLUVhzWGV5MVVrVWs3UlVhYlV5NnNWSnFJT0VWWFhZeGtpRmdKejZFeUxFYV80c3Fuc1B5YTRLczhaajVXdzVuQk1kTlktbDRyWnVMeFU5OA?oc=5" target="_blank">Discrete spatial diffusion models data while obeying scientific principles</a> <font color="#6f6f6f">Tech Xplore</font>
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A Very Fine Untuning
How fine-tuning made my chatbot worse (and broke my RAG pipeline) I spent weeks trying to improve my personal chatbot, Virtual Alexandra , with fine-tuning. Instead I got increased hallucination rate and broken retrieval in my RAG system. Yes, this is a story about a failed attempt, not a successful one. My husband and I called fine tuning results “Drunk Alexandra” — incoherent answers that were initially funny, but quickly became annoying. After weeks of experiments, I reached a simple conclusion: for this particular project, a small chatbot that answers questions based on my writing and instructions, fine tuning was not a good option. It was not just unnecessary, it actively degraded the experience and didn’t justify the extra time, cost, or complexity compared to the prompt + RAG system
Google's TurboQuant saves memory, but won't save us from DRAM-pricing hell
<h4>Chocolate Factory’s compression tech clears the way to cheaper AI inference, not more affordable memory</h4> <p>When Google unveiled <a target="_blank" rel="nofollow" href="https://research.google/blog/turboquant-redefining-ai-efficiency-with-extreme-compression/">TurboQuant</a>, an AI data compression technology that promises to slash the amount of memory required to serve models, many hoped it would help with a memory shortage that has seen prices triple since last year. Not so much.…</p>
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[Side A] Completely Defending Python from OOM Kills: The BytesIO Trap and D-MemFS 'Hard Quota' Design Philosophy
<blockquote> <p><strong>From the Author:</strong><br> Recently, I introduced <strong>D-MemFS</strong> on Reddit. The response was overwhelming, confirming that memory management and file I/O performance are truly universal challenges for developers everywhere. This series is my response to that global interest.</p> </blockquote> <h3> 🧭 About this Series: The Two Sides of Development </h3> <p>To provide a complete picture of this project, I’ve split each update into two perspectives:</p> <ul> <li> <strong>Side A (Practical / from Qiita):</strong> Implementation details, benchmarks, and technical solutions.</li> <li> <strong>Side B (Philosophy / from Zenn):</strong> The development war stories, AI-collaboration, and design decisions.</li> </ul> <h2> Introduction </h2> <p>If you write in-mem
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