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Google AI Introduces ‘Groundsource’: A New Methodology that Uses Gemini Model to Transform Unstructured Global News into Actionable, Historical Data

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Google AI Research team recently released Groundsource , a new methodology that uses Gemini model to extract structured historical data from unstructured public news reports. The project addresses the lack of historical data for rapid-onset natural disasters. Its first output is an open-source dataset containing 2.6 million historical urban flash flood events across more than 150 countries. The Hydro-Meteorological Data Gap Machine learning models for early warning systems (EWS) require extensive historical baselines for training and validation. However, hydro-meteorological hazards like flash floods lack standardized, global observation networks. The Impact of Flash Floods: According to the World Meteorological Organization (WMO), flash floods cause approximately 85% of flood-related fatalities , resulting in over 5,000 deaths annually. Limitations of Existing Data: Satellite-based databases, such as the Global Flood Database (GFD) and the Dartmouth Flood Observatory (DFO), are...
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How to Build an Autonomous Machine Learning Research Loop in Google Colab Using Andrej Karpathy’s AutoResearch Framework for Hyperparameter Discovery and Experiment Tracking

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In this tutorial, we implement a Colab-ready version of the AutoResearch framework originally proposed by Andrej Karpathy . We build an automated experimentation pipeline that clones the AutoResearch repository, prepares a lightweight training environment, and runs a baseline experiment to establish initial performance metrics. We then create an automated research loop that programmatically edits the hyperparameters in train.py, runs new training iterations, evaluates the resulting model using the validation bits-per-byte metric, and logs every experiment in a structured results table. By running this workflow in Google Colab, we demonstrate how we can reproduce the core idea of autonomous machine learning research: iteratively modifying training configurations, evaluating performance, and preserving the best configurations, without requiring specialized hardware or complex infrastructure. Copy Code Copied Use a different Browser import os, sys, subprocess, json, re, random,...
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Stanford Researchers Release OpenJarvis: A Local-First Framework for Building On-Device Personal AI Agents with Tools, Memory, and Learning

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Stanford researchers have introduced OpenJarvis , an open-source framework for building personal AI agents that run entirely on-device . The project comes from Stanford’s Scaling Intelligence Lab and is presented as both a research platform and deployment-ready infrastructure for local-first AI systems. Its focus is not only model execution, but also the broader software stack required to make on-device agents usable, measurable, and adaptable over time. Why OpenJarvis? According to the Stanford research team, most current personal AI projects still keep the local component relatively thin while routing core reasoning through external cloud APIs. That design introduces latency, recurring cost, and data exposure concerns, especially for assistants/agents that operate over personal files, messages, and persistent user context. OpenJarvis is designed to shift that balance by making local execution the default and cloud usage optional. The research team ties this release to its earlier ...
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How to Design a Streaming Decision Agent with Partial Reasoning, Online Replanning, and Reactive Mid-Execution Adaptation in Dynamic Environments

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In this tutorial, we build a Streaming Decision Agent that thinks and acts in an online, changing environment while continuously streaming safe, partial reasoning updates. We implement a dynamic grid world with moving obstacles and a shifting goal, then use an online A* planner in a receding-horizon loop to commit to only a few near-term moves and re-evaluate frequently. As we execute, we make intermediate decisions that can override the plan when a step becomes invalid or locally risky, allowing us to adapt mid-run rather than unthinkingly following a stale trajectory. Copy Code Copied Use a different Browser import random, math, time from dataclasses import dataclass, field from typing import List, Tuple, Dict, Optional, Generator, Any from collections import deque, defaultdict try: from pydantic import BaseModel, Field except Exception: raise RuntimeError("Please install pydantic: `!pip -q install pydantic` (then rerun).") class StreamEvent(BaseModel):...
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NVIDIA Releases Nemotron 3 Super: A 120B Parameter Open-Source Hybrid Mamba-Attention MoE Model Delivering 5x Higher Throughput for Agentic AI

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The gap between proprietary frontier models and highly transparent open-source models is closing faster than ever. NVIDIA has officially pulled the curtain back on Nemotron 3 Super , a staggering 120 billion parameter reasoning model engineered specifically for complex multi-agent applications. Released today, Nemotron 3 Super sits perfectly between the lightweight 30 billion parameter Nemotron 3 Nano and the highly anticipated 500 billion parameter Nemotron 3 Ultra coming later in 2026. Delivering up to 7x higher throughput and double the accuracy of its previous generation, this model is a massive leap forward for developers who refuse to compromise between intelligence and inference efficiency. The ‘Five Miracles’ of Nemotron 3 Super Nemotron 3 Super’s unprecedented performance is driven by five major technological breakthroughs: Hybrid MoE Architecture: The model intelligently combines memory-efficient Mamba layers with high-accuracy Transformer layers. By only activating a...
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Google AI Introduces Gemini Embedding 2: A Multimodal Embedding Model that Lets Your Bring Text, Images, Video, Audio, and Docs into the Embedding Space

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Google expanded its Gemini model family with the release of Gemini Embedding 2 . This second-generation model succeeds the text-only gemini-embedding-001 and is designed specifically to address the high-dimensional storage and cross-modal retrieval challenges faced by AI developers building production-grade Retrieval-Augmented Generation (RAG) systems. The Gemini Embedding 2 release marks a significant technical shift in how embedding models are architected, moving away from modality-specific pipelines toward a unified, natively multimodal latent space. Native Multimodality and Interleaved Inputs The primary architectural advancement in Gemini Embedding 2 is its ability to map five distinct media types— Text, Image, Video, Audio, and PDF —into a single, high-dimensional vector space. This eliminates the need for complex pipelines that previously required separate models for different data types, such as CLIP for images and BERT-based models for text. The model supports interleav...
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Fish Audio Releases Fish Audio S2: A New Generation of Expressive Text-to-Speech (TTS) with Absurdly Controllable Emotion

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The landscape of Text-to-Speech (TTS) is moving away from modular pipelines toward integrated Large Audio Models (LAMs). Fish Audio’s release of S2-Pro, the flagship model within the Fish Speech ecosystem, represents a shift toward open architectures capable of high-fidelity, multi-speaker synthesis with sub-150ms latency. The release provides a framework for zero-shot voice cloning and granular emotional control using a Dual-Auto-Regressive (AR) approach. Architecture: The Dual-AR Framework and RVQ The fundamental technical distinction in Fish Audio S2-Pro is its hierarchical Dual-AR architecture. Traditional TTS models often struggle with the trade-off between sequence length and acoustic detail. S2-Pro addresses this by bifurcating the generation process into two specialized stages: a ‘Slow AR’ model and a ‘Fast AR’ model. The Slow AR Model (4B Parameters): This component operates on the time-axis. It is responsible for processing linguistic input and generating semantic tokens...
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