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| 🔗 Stories, Tutorials & Articles |
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| Why are AI companies so bad at naming their models? |
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| GPT-4o, Llama-4, Claude 3.7 Sonnet. Why can’t AI companies come up with compelling model names? |
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| Microsoft AI CEO: ‘It’s Smarter to Be 6 Months Behind’ — Here’s Why |
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| Microsoft plays it cool with an "off-frontier" AI strategy, sidestepping heavyweights like OpenAI. It's a cost-cutting, reliability-boosting move. Even with deep pockets sunk into OpenAI, they're building pint-sized brainiacs with their Phi project. The grand plan? Stand-alone strength by 2030. |
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| Inside the CodeBot: A Gentle Introduction to How LLMs Understand Nullability |
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| LLMs get nullability. The more you train them, the sharper they become. Pythia, with her heftier brain, deciphers nullability faster, thanks to top-notch inference tricks. |
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| Optimize Gemma 3 Inference: vLLM on GKE |
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| GKE Autopilot's GPU means business—AI inference tasks don’t stand a chance. Just two arguments and, bam, you’ve unleashed NVIDIA's beastly Gemma 3 27B model, which chugs a massive 46.4GB VRAM. ⚡️ Meanwhile, vLLM squeezes the models with bf16 precision, though optimization requires wrestling with algorithms that could make anyone’s head spin. NVIDIA's double-barrel A100s floor it at 411 Tokens/s, burning through $2.84 million tokens like a hot knife through butter. CPUs? They dawdle—like a sloth trying to sprint. 💸 |
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| Cold-Starting LLMs on Kubernetes in Under 30 Seconds |
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| Redesigning LLM cold start strategy sliced launch times from 10 minutes to under 30 seconds by exploiting FUSE and object storage for on-demand GPU loading—a revelation for Kubernetes scaling. |
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| How to use any Python AI agent framework with free GitHub Models |
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| GitHub Models dishes out no-cost access to models that mirror OpenAI's magic, but with a twist—easy integration with Python. Just snag a Personal Access Token and dive in. Swap models faster than you change socks. 📈 |
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| Understanding RAG: Retrieval Augmented Generation Essentials for AI Projects 🔰 |
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| Retrieval-Augmented Generation (RAG) turns Large Language Models into knowledge-sniffing bloodhounds. It fetches real-time intel to crush those pesky hallucinations and refresh its smarts on demand. Why stick with static models when RAG gives your AI brains a live data feed? Real-time accuracy without lugging through intensive retraining. |
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| What the heck is MCP and why is everyone talking about it? |
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| Picking the right AI model for GitHub Copilot is like matchmaking. It's about the project's quirks, and balancing razor-sharp accuracy with processing muscle. |
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| Start building with Gemini 2.5 Flash |
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| Gemini 2.5 Flash is your quick-thinking friend with an on/off brainstorm switch, juggling the holy trinity: quality, cost, and speed. It tackles Hard Prompts like a pro, only overshadowed by 2.5 Pro. |
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| An Intro to DeepSeek's Distributed File System |
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| 3FS from DeepSeek dazzles with slick tricks, including CRAQ for ironclad consistency and a clever ChunkEngine built in Rust. It sprints through scalable reads, but gets tripped up by write latency. In Zipfian workloads, that bottleneck might just drive you bananas. |
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