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| 🔗 Stories, Tutorials & Articles |
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| Inside NVIDIA GPUs: Anatomy of high performance matmul kernels |
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NVIDIA Hopper packs serious architectural tricks. At the core: Tensor Memory Accelerator (TMA), tensor cores, and swizzling—the trio behind async, cache-friendly matmul kernels that flirt with peak throughput.
But folks aren't stopping at cuBLAS. They're stacking new tactics: warp-group MMA, SMEM pipelining, Hilbert curve scheduling, and cluster-wide data reuse. All in plain CUDA C++ with a dusting of inline PTX. No magic libraries, just smart scheduling and brutal efficiency. |
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| The productivity paradox of AI coding assistants |
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A July 2025 METR trial dropped a twist: seasoned devs using Cursor with Claude 3.5/3.7 moved 19% slower - while thinking they were 20% faster. Chalk it up to AI-induced confidence inflation.
Faros AI tracked over 10,000 developers. More AI didn’t mean more done. It meant more juggling, more PRs, same velocity.
Apiiro's 2024 report? Not subtle. Privilege escalation paths up 322%. Critical CVEs in AI-generated code? 2.5x higher. |
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| Building a Natural Language Interface for Apache Pinot with LLM Agents |
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MiQ plugged Google’s Agent Development Kit into their stack to spin up LLM agents that turn plain English into clean, validated SQL. These agents speak directly to Apache Pinot, firing off real-time queries without the usual parsing pain.
Behind the scenes, it’s a slick handoff: NL2SQL tools map intent, validators check for query gaffes, and conversation memory keeps the chat flowing. No need to repeat yourself like it’s 2019. |
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| Becoming a Research Engineer at a Big LLM Lab - 18 Months of Strategic Career Development |
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| To land a big career role like Mistral, mix efficient tactical moves (like LeetCode practice) with strategic ups, like building a powerful portfolio and a solid network. Balance is key; aim to impress and prepare well without overlooking the power of strategy in shaping a successful career. |
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| Jupyter Agents: training LLMs to reason with notebooks |
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| Hugging Face dropped an open pipeline and dataset for training small models—think Qwen3-4B—into sharp Jupyter-native data science agents. They pulled curated Kaggle notebooks, whipped up synthetic QA pairs, added lightweight scaffolding, and went full fine-tune. Net result? A 36% jump on DABStep’s easy benchmark. |
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| Implementing Vector Search from Scratch: A Step-by-Step Tutorial |
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| There’s no doubt that search is one of the most fundamental problems in computing. Vector search matches meanings by converting text into numerical vectors and finding the closest matches in a high-dimensional space. By following this step-by-step guide, you can build a vector search system from scratch in Python using cosine similarity and visualize how it works. |
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| 5 Free AI Courses from Hugging Face |
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Hugging Face just rolled out a sharp set of free AI courses. Real topics, real tools—think AI agents, LLMs, diffusion models, deep RL, and more. It’s hands-on from the jump, packed with frameworks like LangGraph, Diffusers, and Stable Baselines3.
You don’t just read about models—you build ‘em in Hugging Face Spaces, show them off on the Hub, and battle it out on public leaderboards. Challenge-based learning done right. |
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