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

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…

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Model Context Protocol (MCP) vs. AI Agent Skills: A Deep Dive into Structured Tools and Behavioral Guidance for LLMs

In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively. Two common approaches that have emerged are skills and MCPs. While they may appear…

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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

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…

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Stanford Researchers Release OpenJarvis: A Local-First Framework for Building On-Device Personal AI Agents with Tools, Memory, and Learning

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.…

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How to Design a Streaming Decision Agent with Partial Reasoning, Online Replanning, and Reactive Mid-Execution Adaptation in Dynamic Environments

@dataclass class AgentConfig: horizon: int = 6 replan_on_target_move: bool = True replan_on_obstacle_change: bool = True max_steps: int = 120 think_latency: float = 0.02 act_latency: float = 0.01 risk_gate: float = 0.85 alt_search_depth: int = 2 @dataclass class StreamingDecisionAgent: cfg: AgentConfig…

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3 Questions: On the future of AI and the mathematical and physical sciences | MIT News

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research…

Read More3 Questions: On the future of AI and the mathematical and physical sciences | MIT News