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Scaling AI Agents Securely with AgentCore Gateway Interceptors As enterprises rapidly adopt AI agents to automate workflows and boost productivity, they encounter a critical scaling challenge: managing secure access to thousands of tools organization-wide. Modern deployments have moved beyond a few agents calling a handful of APIs — now hundreds of
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# Spring Data AOT Repositories in IntelliJ IDEA 2025.3 Spring’s AOT (Ahead-of-Time) engine has been [around](https://spring.io/blog/2021/12/09/new-aot-engine-brings-spring-native-to-the-next-level) since the Spring Native era, but **Spring Data** had not significantly benefited from it — until now. Historically, repository infrastructure relied heavily on **proxies**, **reflection**, and
AI Playbooks
# Flutter Theming Handbook — Building Scalable, Brand-Driven UI Systems Design in product engineering isn't just about how something **looks** — it shapes how an experience **feels**, how users **interact** with it, and how **brand consistency** comes alive across every screen. Flutter offers powerful tools for theming, but true mastery goes
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# Meituan Database Team Launches Database Capacity Evaluation System The Meituan database team has developed a **Database Capacity Evaluation System** to address **capacity assessment challenges** and **prevent change-related risks**. This system: - Uses **online traffic replay** in a sandbox environment to verify the safety of database changes. - Applies **accelerated replay*
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📑 Table of Contents * Problem Essence: When the Scheduler Becomes the Performance Bottleneck * Core Contributions of This Work * Solution: Giving the Scheduler Semantic Awareness * Design Principles: Considerations for Production * Performance Evaluation: Let the Data Speak * Implementation Details: The Devil Is in the Details * From Technology to Philosophy: Redefining the Cognitive Boundaries
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!image ROOT Optimizer — A Balance of Speed and Stability in LLM Training The core of ROOT lies in two major innovations: AdaNewton and soft-threshold denoising. --- Background: Adam vs. Muon in LLM Optimization Two prominent names in LLM optimization are Adam (including AdamW) and Muon: * Adam/AdamW: * A seasoned "
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Effective Harnesses for Long-Running Agents Original Article › As AI agents gain capabilities, they are tackling more complex, long-duration tasks — sometimes taking hours or days to complete. A persistent challenge is keeping agents coherent across multiple memory cycles, since each session starts with limited context. --- The Core Problem: Context Window
AI Content Creation
AiLiMei — Intelligent Ethnic Category Recognition & Matching This document outlines an AI-driven solution leveraging large language models (LLMs) and an ethnic culture knowledge base to address supply-demand mismatches on cross-border e-commerce platforms serving niche cultural groups — such as Muslim and Indian communities. --- 1. Introduction For global e-commerce to effectively
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# Transcript: Platform Engineering in the Enterprise **Speaker:** Rachael Wonnacott — Associate Director, Container Platforms at Fidelity International --- ## Introduction I’m **Rachael Wonnacott**, Technical Product Owner for the Kubernetes platform at Fidelity International. My role spans **developer experience**, **application lifecycle management**, and **supply chain security**. Although the term *platform engineering* became
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Trillion‑Parameter AI Models: Challenges, Bottlenecks & Next‑Gen Paradigms As frontier models like GPT‑4o and Llama 4 push performance boundaries, the AI industry faces severe constraints: * Transformer inefficiency * Extreme compute demand * Weak linkage to the physical world A ZTE paper, Insights into Next-Generation AI Large Model Computing Paradigms,
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Browsers Are Getting Heavier — And Our Attention Is Fragmented When writing a proposal, Word needs to stay open. When searching for information, you switch back to the browser. When replying to messages, you jump to WeChat. The result: fragmented screens and fragmented attention. Even calling AI for help becomes a
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!image --- A Paradigm Shift in AI: From the Scaling Era to the Research Era For years, the AI community has been obsessed with scaling — using more data, bigger models, and greater compute — as the road to AGI. But an in‑depth interview with Ilya Sutskever suggests a fundamental shift: