When the model is the machine - Fast Company
<a href="https://news.google.com/rss/articles/CBMidEFVX3lxTE1OdDBwMUNZZDhzOWRZV2NNNzlJQ0ZNSERfWHRiQzhRTnJtMFloaF9BUUpkTnFReUJRRG50Y0oxTGh4SVZEOWU3a3FJZkJ2UWp2TUFEQUlsWlItN2tWUUs5VkZxMDhMMFpaSmNva2FZZURqUjFq?oc=5" target="_blank">When the model is the machine</a> <font color="#6f6f6f">Fast Company</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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More in Models
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>
Introducing The Screwtape Ladders
The time has come for me to find a new home for my writings. Like many an author before me, I've enjoyed improving my craft and getting feedback on my essays here. LessWrong is a good incubator for honing one's skills in that arena. There's a chance to get your point out in front of a really broad audience of really smart people. There's been some cool moments. My oldest visible post, Write A Thousand Roads to Rome , got cited in a discussion with Eliezer Yudkowsky once. I keep seeing people bring up Loudly Give Up, Don't Quietly Fade as a motivator for speaking out. Sometimes it's really cool people working on awesome projects, and I feel a flash of sadness at 'aww, it's not going to happen' and also a bit of cool 'whoa, they remember that post?' You've all also let me get away with a lot
Anthropic Executive Sees Cowork Agent as Bigger Than Claude Code - Bloomberg.com
<a href="https://news.google.com/rss/articles/CBMitgFBVV95cUxOM0VfSzdRYUNpT21XMlVuNXhsVEY4TUFxM3UzWUJDOEhFcUtJQnhTbjY2VjBXOUw1d1ZOUDRKeHVKMzkta3pFVWRWSGNoQkp3aWVndlRBQlpVUGxVN0ZnQW80OUZnYWN6RlhJWHRjT0V4RVhPcGhxMmE3b3oyVDlUV2RLY0g2NEx4M1dfMXhvTlhPTW50eFR1cEhxcHB3SXpURnRtbDZtZHp6bGQ2Z09IMjZBODBjdw?oc=5" target="_blank">Anthropic Executive Sees Cowork Agent as Bigger Than Claude Code</a> <font color="#6f6f6f">Bloomberg.com</font>
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