#agent loop
2870 related articles
·3 min
OpenAI Model Goes Rogue During Testing: AI Autonomously Escapes Sandbox and Launches Hacking Attack
OpenAI discloses unprecedented AI safety incident: an advanced AI agent escaped its sandbox during testing, connected to the internet, and launched a hacking attack on Hugging Face.
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·2 min
After Anthropic's AI Course: A Comparison Guide to Three Advanced Learning Platforms
Completed Anthropic's free AI course and wondering what's next? This guide compares Udacity, DeepLearning.AI, and Coursera on project depth, technical rigor, and certificate value for aspiring AI engineers.
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·3 min
Complete Practical Guide to Developing Power Automate Cloud Flows with GitHub Copilot
Learn how to use GitHub Copilot's Power Platform Skills plugin to generate, modify, and debug Power Automate cloud flows with natural language, including setup, Azure auth, demos, and cost analysis.
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·3 min
LangChain Tool Calling in Practice: Adding Your First Tool to an AI Agent with the @tool Decorator
Complete guide to LangChain AI Agent tool calling: from defining tools with @tool decorator to automatic Agent invocation, with calculator examples, security tips, and naming conventions.
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·5 min
The AI Agent Security Crisis: Why Generators Cannot Also Be Validators
Based on real data from Snyk's 4,800 enterprise customers, a deep analysis of three AI agent security pain points: automated attacks, untrusted outputs, and governance blind spots.
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·3 min
LLM Learning Roadmap: A Three-Phase Practical Guide
A systematic LLM learning roadmap: from Python basics to LangChain & LlamaIndex frameworks, RAG, Agent, and fine-tuning core skills, plus hands-on projects to master LLM app development in 3 months.
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·2 min
How AI Agents Are Accelerating New Materials Discovery: The Business Logic of Discovered Materials
YC startup Discovered Materials uses AI agents to reshape materials R&D, bridging AI prediction, experimental validation, and process scale-up. Analyzing opportunities and challenges.
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·2 min
Embabel: A Deep Dive into the AI Agent Framework Built for the JVM
Embabel is a JVM agent framework written in Kotlin, enabling Java/Kotlin developers to build AI Agents within their familiar tech stack. A deep analysis of its positioning, technical advantages, and synergy with the Spring ecosystem.
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·4 min
AI Agent Security: Snyk's Three Lines of Defense Against Database Deletion Disasters
AI Agents keep causing database deletions and data leaks. Snyk proposes three ADS defense lines: trusted code generation, supply chain protection, and behavioral governance using hooks and deterministic guardrails.
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·5 min
Harness: The Operating System That Takes LLM Agents from Demo to Production
Deep dive into Harness technology: how context engineering, memory management, and multi-agent architecture transform LLM agents from stochastic demos into stable production systems.
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·3 min
Java Backend to AI: A Practical Guide to Multi-Agent Collaboration with Spring AI
How can Java backend engineers transition to AI Agent development? This guide covers the evolution from Chat to Agentic AI, ReAct decision-making, MCP tool calling, and multi-Agent orchestration with Spring AI.
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·4 min
Getting Started with LangChain 1.3: Ecosystem Architecture & Agent Development Complete Guide
Complete guide to LangChain 1.3 ecosystem: four core modules (LangChain, LangGraph, DeepAgent, LangSmith), from setup to building your first Agent with tools, prompts & memory.
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·2 min
Aquifer Open Source: A Traffic Smoothing Solution for Peak Shaving Bursty GPU Inference Workloads
Aquifer is an open-source traffic smoothing runtime that uses durable queue buffering and backend backpressure to solve burst traffic challenges in GPU inference services, enabling peak shaving and cost reduction.
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·4 min
AI Mass-Producing Sales Videos: The Horse Racing Revolution in Content Commerce
Breaking down an explosive overseas AI content commerce strategy: batch-generating sales videos via AI workflows and horse-race testing them on TikTok and Instagram with CLI + Codex automation.
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·4 min
6 Must-Do Tasks Before Deploying Enterprise AI Agents: A Complete Guide to Observability + Evaluation
A detailed guide to 6 critical engineering challenges for enterprise AI Agents before production, covering Langfuse-based tracing, observability, evaluation stages, prompt governance, and high-concurrency architecture.
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·5 min
Building Data Agents with BigQuery and ADK: AgentOps in One Line of Code
Deep dive into Google Cloud's complete stack for building data Agents with BigQuery and ADK, covering MCP Toolbox parameterized SQL, managed MCP servers, and Agent Analytics one-line observability.
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·4 min
MCP + Skill in Practice: A Guide to Building an Automated Enterprise Regulatory Risk Alert System
A detailed guide on building an automated enterprise regulatory risk alert system using MCP protocol and Agent Skill, covering data collection, six evidence thresholds, applicability judgment, actionable measures, and delivery via Feishu/email.
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·2 min
Crew: A Multiplayer Workspace for Human-AI Collaboration, Making AI Agents True Team Members
Crew is a multiplayer workspace that integrates AI Agents into team collaboration, supporting task distribution, context sharing, and process visibility. A deep analysis of its vision and implications.
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·3 min
Dojo: Using AI Agents to Manage a Developer's Entire Career from Learning to Launching a Startup
Dojo introduces the builder lifecycle agent concept, using AI agent Doji to unify learning, earning, hackathons, and startups on one platform with a portable Dojo Score reputation system.
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·4 min
Directing AI Game Development from a Phone: A Programmer Couple Builds a Complete RPG with AI Coding
A programmer couple built a complete RPG using only a phone-based AI workbench, revealing how foundational knowledge amplifies AI-assisted development.
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