#context management
4241 related articles
·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
Beef and Dairy Account for 41% of Global Farmland Biodiversity Damage
Research finds beef and dairy production accounts for 41% of global farmland biodiversity damage. Explore how livestock land use drives habitat loss and viable solutions including dietary shifts and alternative proteins.
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·3 min
Macro Open Source Project: Building an All-in-One Team Collaboration Platform with Shared AI Memory
Macro is an open-source team collaboration workspace built in Rust that integrates email, chat, docs, tasks, CRM and more through @-linking and shared AI memory to eliminate information silos.
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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
Cognitive Debt: When Developers Outsource Their Own Thinking
A real case of a tech lead who outsourced all thinking to AI and fell into cognitive hollowing. Explore the definition, dangers, and strategies for cognitive debt in the AI era.
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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
MCP Protocol Explained: The Unified Standard for AI Tool Calling and a Practical Guide
Deep dive into MCP (Model Context Protocol): how it unifies LLM tool calling standards, enables cross-model tool reuse, and decouples Agents from tools for efficient AI development.
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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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·3 min
Tutorial: Connect Claude, Codex, and Other LLMs to Copilot in VSCode
Learn how to connect Claude, Codex, and other LLMs to VSCode's Copilot Chat via a third-party API proxy plugin. Four steps: get a Key, install plugin, manage models, and switch freely.
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·2 min
ChatGPT Desktop Arrives on Linux: Developers Get a Native AI Coding Experience
OpenAI launches ChatGPT Linux desktop preview supporting ChatGPT, ChatGPT Work, and Codex. Linux developers gain native AI-assisted coding, code completion, and project integration capabilities.
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·3 min
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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·3 min
Troubleshooting Guide: Comet Browser Can't Access LAN Devices After Update
Comet browser v151 can't access Plex, Proxmox & LAN services? Deep analysis of Chromium's Private Network Access policy changes with practical solutions including flags settings and HTTPS configuration.
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