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In this tutorial, we build a document-intelligence workflow with iii. We begin by installing the iii engine and Python SDK, then start the engine as a background process and connect a Python worker to it. After the setup, we register separate functions for text normalization, tokenization, sentiment analysis, keyword extraction, reporting, and heartbeat tracking. We […]
NVIDIA AI team have released Cosmos 3. It is a family of omnimodal world models for physical AI. The models combine physical reasoning, world generation, and action generation. All three capabilities live inside one open model. NVIDIA open sourced the checkpoints, training scripts, deployment tools, and datasets. The Cosmos 3 release targets robotics, autonomous vehicles,
AMD is aggressively reshaping local AI development with massive memory capabilities in its new Ryzen AI Halo platform and Max PRO processors, leaving competitors scrambling to match this raw power. Here in my home office in the high desert of […] The post AMD Unleashes The Ryzen AI Halo Platform And Max PRO Processors To
MiniMax officially released MiniMax M3 on June 1, 2026. The model introduces MSA (MiniMax Sparse Attention), a new sparse attention architecture that gives M3 a 1M-token context window. M3 also supports image and video input and desktop computer operation natively. The API is live now. MiniMax M3 is available today via MiniMax Code, the MiniMax
Hermes Agent already remembers across sessions. The open-source agent from Nous Research ships with curated memory files and full-text session search. But a new community project argues that built-in memory is too shallow for serious work. A new library named ‘Memory OS‘ has been released under an MIT license by a developer (ClaudioDrews). It stacks
Meet Memory OS: A 6-Layer Open-Source Memory Stack Built on Top of Hermes Agent Read More »
The Transformer’s attention mechanism has barely changed since 2017. Most efficiency work has tried to replace softmax attention outright. A new paper takes a different route. It keeps softmax attention and bolts on a correction branch. A team of researchers from Northwestern University, Tilde Research, and University of Washington introduce a parameterized Local Linear Attention
In this tutorial, we implement a practical use case with Loguru, a powerful, flexible, and production-ready logging library for Python. We start by building a clean, idempotent logging setup that can be safely rerun without duplicating handlers or producing messy output. From there, we move step by step through structured logging, contextual logging, custom log
Genesis AI released Genesis World 1.0. The platform consists of four components: the Genesis World physics engine, Nyx (a real-time path-traced renderer), Quadrants (a Python-to-GPU compiler), and a simulation interface. It is designed to accelerate robotics foundation model development through simulation-based evaluation. Robotics model development has two bottlenecks: data and iteration speed. The field has
GPU communication overhead is a measurable bottleneck in production AI workloads. According to data cited by the mKernel project, communication can consume 43.6% of the forward pass and 32% of end-to-end training time. Across popular Mixture-of-Experts (MoE) models, inter-device communication can account for up to 47% of total execution time. Researchers from UC Berkeley’s UCCL
Meet mKernel: A Multi-GPU, Multi-Node Fused Kernel Library for GPU-Driven Communication Read More »
In this tutorial, we build a complete Ansible lab that runs end-to-end in Google Colab or any Linux environment. We start by installing ansible-core, setting up a local workspace, creating an Ansible configuration file, and defining both static and dynamic inventories. We then explore key Ansible concepts, including group variables, host variables, variable precedence, ad
