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Feature Stores from Scratch: A Minimal Working Implementation

Data Analytics1 day ago2Views 0Likes 0Comments

# Introduction Most teams discover they need a feature store the hard way. A fraud model works in the notebook and quietly breaks in production. A support agent gives a generic answer because it has no idea who the user is. A recommender pipeline duplicates the same "30-day spend" calculation across three jobs,…

Qwen Team Open-Sources Qwen3.6-35B-A3B: A Sparse MoE Vision-Language Model with 3B Active Parameters and Agentic Coding Capabilities

AI News1 day ago1View 0Likes 0Comments

The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the Qwen3.6 generation, and it is making a compelling argument that parameter efficiency matters far more than raw model size. With 35 billion total parameters but only 3 billion activated…

Introducing DiffusionGemma

OpenAI1 day ago3Views 0Likes 0Comments

Why diffusion for text? While the AI research community has explored diffusion-based text generation for years, applying it to large models has remained a challenge. DiffusionGemma changes this by shifting how models use hardware. The trade-off with traditional models Most language models act like a typewriter, generating one token at a time from left to…

How to Build Advanced Cybersecurity AI Agents with CAI Using Tools, Guardrails, Handoffs, and Multi-Agent Workflows

Robotics1 day ago2Views 0Likes 0Comments

In this tutorial, we build and explore the CAI Cybersecurity AI Framework step by step in Colab using an OpenAI-compatible model. We begin by setting up the environment, securely loading the API key, and creating a base agent. We gradually move into more advanced capabilities such as custom function tools, multi-agent handoffs, agent orchestration, input…

A Deep Dive into Calibration of Language Models: Platt Scaling, Isotonic Regression, Temperature Scaling

Data AnalyticsJune 6, 20265Views 0Likes 0Comments

# Introduction A model that says it is 90% confident should be right 90% of the time. When that relationship breaks down, you get a miscalibration problem. The model's scores stop telling you anything useful about reliability. For large language models (LLMs), miscalibration is widespread. A 2024 NAACL survey found that confidence scores…

A Hands-On Coding Tutorial on Qualcomm AI Hub Models for Classification, Object Detection, and Hardware-Aware Deployment

AI NewsJune 6, 20263Views 0Likes 0Comments

In this tutorial, we work through an end-to-end workflow for Qualcomm AI Hub Models. We start by setting up the required package, discovering the available model collection, and loading MobileNet-V2 for local PyTorch inference. We also handle an important input-shape issue by converting NHWC image tensors into the NCHW format expected by the model. From…

New AI Tools for the Future of Science

OpenAIJune 6, 20265Views 0Likes 0Comments

For centuries, the scientific method has been the greatest engine of human progress. At Google, our mission is deeply rooted in building tools to accelerate it. We believe that a new era of discovery won’t come from narrow, specialized models, but general agents that empower researchers across every scientific field. That’s why we are introducing…

NVIDIA Releases Cosmos 3: A Two-Tower Mixture-of-Transformers Foundation Model Unifying Physical Reasoning, World Generation, and Action Generation

RoboticsJune 6, 20267Views 0Likes 0Comments

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,…

Mocking a Year of IoT Sensor Time Series Data with Mimesis

Data AnalyticsJune 1, 20266Views 0Likes 0Comments

# Introduction Mocking Internet of Things (IoT) sensor data that would be otherwise difficult to gather at scale can constitute a valuable approach to facilitate experimental analyses, projects, and studies. However, it requires much more than random value generation: it necessitates a chronological timeline, device metadata, and a need to reflect natural environmental…

Google DeepMind Introduces Vision Banana: An Instruction-Tuned Image Generator That Beats SAM 3 on Segmentation and Depth Anything V3 on Metric Depth Estimation

AI NewsJune 1, 20266Views 0Likes 0Comments

For years, the computer vision community has operated on two separate tracks: generative models (which produce images) and discriminative models (which understand them). The assumption was straightforward — models good at making pictures aren’t necessarily good at reading them. A new paper from Google, titled “Image Generators are Generalist Vision Learners” (arXiv:2604.20329), published April 22,…