Software engineering

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How to Build a Dynamic Zero-Trust Network Simulation with Graph-Based Micro-Segmentation, Adaptive Policy Engine, and Insider Threat Detection

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In this tutorial, we build a realistic Zero-Trust network simulation by modeling a micro-segmented environment as a directed graph and forcing every request to earn access through continuous verification. We implement a dynamic policy engine that blends ABAC-style permissions with device posture, MFA, path reachability, zone sensitivity, and live risk signals such as anomaly and […]

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Fastino Labs Open-Sources GLiGuard: A 300M Parameter Safety Moderation Model That Matches or Exceeds Accuracy of Models 23–90x Its Size

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As LLM-powered applications move into production — and as AI agents take on more consequential tasks like browsing the web, writing and executing code, and interacting with external services — safety moderation has quietly become one of the most operationally expensive parts of the stack. Most developers who’ve deployed a production LLM system know the

Fastino Labs Open-Sources GLiGuard: A 300M Parameter Safety Moderation Model That Matches or Exceeds Accuracy of Models 23–90x Its Size Read More »

Mira Murati’s Thinking Machines Lab Introduces Interaction Models: A Native Multimodal Architecture for Real-Time Human-AI Collaboration

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Most AI systems today work in turns. You type or speak, the model waits, processes your input, and then responds. That’s the entire interaction loop. Thinking Machines Lab, an AI research lab, is arguing that this model of interaction is a fundamental bottleneck. Thinking Machines Lab team introduced a research preview of a new class

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Google DeepMind Introduces an AI-Enabled Mouse Pointer Powered by Gemini That Captures Visual and Semantic Context Around the Cursor

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The mouse pointer has sat at the center of personal computing for more than half a century. It tracks cursor position. It registers clicks. Beyond that, it does almost nothing. Google DeepMind researchers outlined a set of experimental principles and demos for an AI-enabled pointer that goes considerably further: one that understands not just where

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Build a Hybrid-Memory Autonomous Agent with Modular Architecture and Tool Dispatch Using OpenAI

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Editors Pick, Software engineering, Staff, Tutorials

In this tutorial, we begin by exploring the architecture behind a hybrid-memory autonomous agent. This system combines semantic vector search, keyword-based retrieval, and a modular tool-dispatching loop to create an agent capable of reasoning, remembering, and acting autonomously. We walk through each layer of the design from the ground up, starting with abstract interfaces that

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Meet AntAngelMed: A 103B-Parameter Open-Source Medical Language Model Built on a 1/32 Activation-Ratio MoE Architecture

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A team researchers from China have released AntAngelMed, a large open-source medical language model that the team describes as the largest and most capable of its kind currently available. What Is AntAngelMed? AntAngelMed is a medical-domain language model with 103 billion total parameters, but it does not activate all of those parameters during inference. Instead,

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Understanding LLM Distillation Techniques 

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Modern large language models are no longer trained only on raw internet text. Increasingly, companies are using powerful “teacher” models to help train smaller or more efficient “student” models. This process, broadly known as LLM distillation or model-to-model training, has become a key technique for building high-performing models at lower computational cost. Meta used its

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Meta and Stanford Researchers Propose Fast Byte Latent Transformer That Reduces Inference Memory Bandwidth by Over 50% Without Tokenization

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A team of researchers from Meta, Stanford University, and the University of Washington have introduced three new methods that substantially accelerate generation in the Byte Latent Transformer (BLT) — a language model architecture that operates directly on raw bytes instead of tokens. Byte-Level Models Are Slow at Inference To understand what this new research solves,

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Sakana AI and NVIDIA Introduce TwELL with CUDA Kernels for 20.5% Inference and 21.9% Training Speedup in LLMs

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Scaling large language models (LLMs) is expensive. Every token processed during inference and every gradient computed during training flows through feedforward layers that account for over two-thirds of model parameters and more than 80% of total FLOPs in larger models. A team researchers from Sakana AI and NVIDIA have worked on a new research that

Sakana AI and NVIDIA Introduce TwELL with CUDA Kernels for 20.5% Inference and 21.9% Training Speedup in LLMs Read More »