#knowledge persistence
273 related articles
·4 min
Dify Getting Started: A Complete Guide to Building AI Workflows Without Code
A complete guide to Dify's core features and 1.8.0 deployment. Covers 5 app types, Docker setup, Workflow vs Chatflow differences, and RAG knowledge bases for beginners.
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·4 min
Tencent Open-Sources Agent Memory: A Fully Local AI Long-Term Memory Solution with Zero External Dependencies
Tencent Cloud open-sources TencentDB Agent Memory — a fully local AI Agent memory system with a 4-tier progressive pipeline, zero external API dependencies, and 8,100+ GitHub Stars. Ideal for finance, healthcare, and privacy-sensitive use cases.
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·4 min
OpenAI Sites: Turn Ideas into Published Websites Instantly — No Coding Required
OpenAI's new Sites tool lets anyone describe an idea and instantly generate a live, shareable website or lightweight app — no coding skills needed.
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·6 min
OpenAI Releases GPT-5.6: Cybersecurity Capabilities Take Center Stage
OpenAI launches the GPT-5.6 model family with cybersecurity as its biggest highlight. A deep analysis of GPT-5.6's differentiation, double-edged-sword effect, and enterprise strategy.
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·4 min
Kun: An Open-Source AI Coding Agent Deeply Optimized for DeepSeek
Kun is an open-source AI coding agent optimized for DeepSeek and domestic users, with nearly 5,000 GitHub stars. Features include requirements drafting, inline diffs, cost visualization, and mobile monitoring. Real-world cache hit rates reached 97%, keeping costs extremely low.
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·6 min
AI's Second Half: The Decisive Battle Shifts from Models to Infrastructure
Model capabilities are converging, making inference cost and scalability the new focus of AI competition. A deep analysis of AI infrastructure's core layers.
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·4 min
Why General-Purpose Agents Don't Work: How a Job Interview Reshaped One Developer's Thinking
An Agent developer's three-round interview reveals why general-purpose Agents are a dead end for startups. The path forward: vertical Agents, domain context, and iteration speed as a moat.
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·4 min
Spring AI Alibaba Graph in Practice: HR Recruitment Workflow Agent Design and Core Technical Analysis
Build an HR recruitment workflow Agent with Spring AI Alibaba Graph, covering resume parsing, job matching, tiered question generation, HITL checkpointing, and time travel state rollback across 20 core technical points.
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·3 min
Files over Tools: Rethinking AI Agent Architecture with a Virtual Filesystem
Why give AI Agents a virtual filesystem and bash instead of more tools? A deep dive into the "Files over Tools" design philosophy, tool bloat, Unix principles, security sandboxing, and hybrid architectures.
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·2 min
Zero-Cost Multi-Agent AI System: A Complete Breakdown of the Free-Tier Build
A CS student built a multi-agent AI system with memory, 8 sub-agents, and real-time web research using only free infrastructure like Cloudflare Workers and GitHub Actions. Full breakdown inside.
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·6 min
Databricks Agent Framework in Practice: A Guide to ChatAgent Wrapping and Production Deployment
A deep dive into Databricks Agent Framework (Mosaic AI): unify LangGraph/OpenAI agents via ChatAgent, log & evaluate with MLflow, version with Unity Catalog, and deploy Model Serving Endpoints for production AI agents.
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·7 min
ManagedAgents.sh: A Deep Dive into the Model-Agnostic Open-Source Managed Agent Platform
ManagedAgents.sh is a model-agnostic managed agent platform from OpenComputer, supporting Claude, Pi, and Codex runtimes with Slack and GitHub integration.
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·6 min
A Three-Month AI Agent Learning Roadmap: From Zero to Hands-On Agent Development
A systematic breakdown of the complete AI Agent learning roadmap, covering prompt engineering, the ReAct paradigm, memory mechanisms, and multi-agent collaboration, with hands-on project advice.
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·6 min
Omnigent Open-Sourced: A Meta-Harness for AI Coding Agents Built by Databricks
Databricks open-sources Omnigent, a Meta-Harness for orchestrating Claude Code, Codex, and more AI coding assistants together—with built-in guardrails, cross-model workflows, and real-time collaboration. Get started in 10 minutes.
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·7 min
Getting Started with LangChain 1.3: Core Concepts of LLMs and Agents
An in-depth breakdown of LangChain 1.3's core concepts, covering the three major limitations of LLMs, Agent architecture, memory management, and a complete learning path. Master LangChain and LangGraph to quickly build AI development skills.
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·7 min
The Prompt is the Platform: How AI Agents Deeply Participate in System Design and Specification
Resonate's founder proposes "The Prompt is the Platform": as AI agents generate production-grade implementations from abstract specs, engineers' value shifts to specification. A deep dive into deterministic simulation and forbidden-fruit debugging.
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·7 min
The Agent Engineer Transition Roadmap: A Deep Dive into Three Core Skill Systems
Want to become an Agent engineer? This article systematically covers three core skill tracks—LLM fundamentals, LangChain architecture development, and enterprise deployment—to help you avoid detours.
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·7 min
Getting Started with Dify: A Complete Guide from Local Deployment to Building Enterprise-Grade AI Applications
A detailed guide to Dify, the open-source LLM app development platform, covering its core features and full local deployment via VMware + Ubuntu + aaPanel + Docker. Supports 100+ models like DeepSeek and ChatGPT to build enterprise AI apps fast.
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·7 min
LangChain Hands-On Guide: From LLM Calls to Agent Development
Master LangChain from scratch: the three limitations of LLMs, init_chat_model unified interface config, the Message type system, and the path from LLM calls to Agent development.
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·7 min
Getting Started with LangChain: The Core Framework Connecting LLMs and AI Applications
A deep dive into LangChain's positioning and value—why do LLMs need a middle layer? How does LangChain serve as the 'glue' unifying multi-model interfaces and supporting Agent development? Learn its core modules and learning path.
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