#AI operations tools
4071 related articles
·3 min
34 Model Iterations in Review: Most Problems Were Actually in the Evaluation Pipeline
After 34 model iterations, an AIOps engineer found most gains came from evaluation bugs. This article details three critical evaluation pitfalls and solutions for MLOps practitioners.
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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
Java + AI in Practice: Building an Enterprise-Grade Airline Intelligent Customer Service System
Learn how Java engineers can enter AI application development using Spring AI to build an enterprise-grade airline intelligent customer service system with RAG, Function Calling, and more.
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·3 min
FreqMark Text Watermarking Technology: How Frequency-Domain Watermarks Detect AI-Generated Content
Deep dive into FreqMark frequency-domain text watermarking: how Fourier transforms embed covert signals in AI-generated text for content tracing and detection.
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·3 min
SpiderFoot: A Deep Dive into the Open-Source OSINT Automation and Attack Surface Management Tool
Deep dive into SpiderFoot, an open-source OSINT automation tool with 200+ intelligence modules, attack surface management, and threat intelligence collection for penetration testing and enterprise asset monitoring.
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·2 min
LocalSend: An Open-Source, Free Cross-Platform Alternative to AirDrop
LocalSend is an open-source, free cross-platform file transfer tool supporting direct LAN transfers between Windows, macOS, Linux, Android, and iOS — no internet needed, HTTPS encrypted, 87K+ GitHub Stars.
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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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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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·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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·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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·3 min
What Are AI Agents? A Deep Dive into the Three Core Components: Perception, Decision, and Action
A deep dive into AI Agents: their definition and three core components—Perception, Decision, and Action. Learn what distinguishes real AI agents from chatbots and automation scripts.
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A Universal Orchestration Layer for Deep Learning: Why MLOps Still Lacks a Standard Framework
Deep learning training code is just the tip of the iceberg. This article explores why MLOps still lacks a standard framework-agnostic orchestration layer and offers practical tool combination advice.
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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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·3 min
EgoLite Browser Deep Dive: Why It's Better Than Playwright MCP for Personal AI Automation
Deep dive into EgoLite Agent browser, comparing it with Playwright MCP and Browser Use. Analyzing Space isolation, script-based operations, and Skill features.
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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
CVE-2026-53361 Vulnerability Analysis: Linux Kernel AF_UNIX Container Escape Mechanism and Defenses
In-depth analysis of CVE-2026-53361 Linux kernel vulnerability: AF_UNIX socket GC and MSG_PEEK race condition causing UAF, container escape exploitation chain, and defense strategies.
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·3 min
Claude Code Cross-Session Messaging Explained: No More Manual Relay Between Windows
Learn how Claude Code's cross-session messaging works—enabling direct communication between multiple windows without manual copy-pasting, boosting AI coding collaboration.
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