How to Orchestrate 100+ Agents With Claude Code
, I’ll discuss how to orchestrate a lot of different agents using Claude Code or any other coding agents When you work with coding agents, you want to run as…
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Meet NeuroVFM: A New Neuroimaging Foundation Model Trained With Vol-JEPA on Uncurated Clinical MRI and CT Volumes
Frontier models learn mostly from public internet data. However, clinical neuroimaging rarely appears there, because MRI and CT scans contain identifiable facial features. Consequently, general models underperform on brain-imaging tasks….
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Why Specialization Is Inevitable
— Those who follow Dharma AI already know that we view specialization as one of the defining principles of effective AI systems, shaping everything from cost and performance to reliability…
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SAM 2: Meta’s Next-Gen Model for Video and Image Segmentation
The era has arrived where your phone or computer can understand the objects of an image, thanks to technologies like YOLO and SAM. Meta’s Segment Anything Model (SAM) can instantly…
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How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost
As organizations move from AI pilots to production AI factories, infrastructure decisions have shifted from peak chip specifications to cost per token: how many useful tokens they can deliver per…
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The Josh Bersin Company Unveils Galileo™ AI Assistant at Irresistible 2024 Conference
At the Irresistible 2024: The Global Conference for HR Leaders and Their Teams, The Josh Bersin Company, a leader in human capital advisory, proudly announces the general availability of Galileo™,…
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RAG vs Fine-Tuning Explained: What They Actually Do and When to Use Each
, I have written quite a lot about RAG, starting with the Hitchhiker’s Guide to RAG with ChatGPT API and LangChain, and then exploring various topics related to RAG and…
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A Coding Guide to NVIDIA’s Tile-Based GPU Programming: From cuTile and Triton Kernels to Flash Attention
if BACKEND == “triton”: @triton.jit def _vadd_kernel(a_ptr, b_ptr, c_ptr, n, BLOCK: tl.constexpr): pid = tl.program_id(0) offs = pid * BLOCK + tl.arange(0, BLOCK) mask = offs < n a =…
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🤗 Kernels: Major Updates
In our previous post (From Zero to GPU), we introduced the 🤗 Kernels project, which aims at standardizing how custom kernels are packaged, distributed, and consumed. We want the project…
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The Powerhouse GPU Revolutionizing Deep Learning
Introduction The rise of Large Language Models (LLMs) has marked a significant advancement in the era of Artificial Intelligence (AI). During this period, Cloud Graphic Processing Units (GPUs) offered by…
