#DAG task scheduling
99 related articles
·6 min
GPT-5.6 Hands-On Deep Dive: Three-Tier Models + Ultra Multi-Agent, Codex Fully Evolved
OpenAI launches GPT-5.6 with three tiered models—Sol, Terra, and Luna—Ultra multi-agent parallel collaboration, Codex integrated into ChatGPT desktop, and an upgraded Computer Use.
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·2 min
Spring AI Alibaba Admin: A Deep Dive into the Java Visual AI Workflow Platform
Spring AI Alibaba Admin is a visual AI workflow platform for Java, comparable to Dify. It supports Dify-to-Graph migration, multi-model integration, and code export. This article covers core features and local deployment tips.
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·2 min
AI Infrastructure or App Feature? The Architecture Debate Sparked by CogniCore
CogniCore asks: should persistent memory, context engines, and state management be standalone AI infrastructure or in-app features? A deep dive into 5 key directions and the missing middleware of the agent era.
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Multi-Agent Collaboration: A GPT Team …
·2 min
Multi-Agent Collaboration: A GPT Team Engineering Paradigm and Practical Guide
Explore multi-agent collaboration architecture: role division, communication protocols, coordination mechanisms, and how Workbench templates help developers build efficient AI agent teams.
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·3 min
AI Agent Production Guide: Four Orchestration Patterns That Actually Work
Most AI agents never make it past the demo stage. This guide covers four production-grade agent patterns—workflow orchestration, policy-constrained execution, anomaly handling, and load routing—to help teams build reliable agent systems.
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·3 min
Flyte Deep Dive: A Practical Guide to Choosing Your ML Workflow Orchestration Tool
Deep dive into Flyte's core capabilities: cloud-native GPU scheduling, intelligent caching, checkpoint recovery, and conditional deployment — plus a full comparison with Argo and KubeFlow Pipelines.
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·3 min
AI Second Brain Tech Stack: Building a Proactive Intelligent Assistant for ADHD Users
How to build a true AI second brain for ADHD users: LangGraph, n8n, RAG, vector databases, and layered architecture for a proactive personal assistant.
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·2 min
Karma: An AI Coding Agent Orchestration Tool for Building Multi-Agent Collaborative Systems
Karma is an orchestration layer for AI coding agent frameworks, solving multi-agent collaboration, task decomposition, state management, and observability challenges. Compatible with Aider, OpenHands, and more.
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·2 min
Spring AI Alibaba Graph in Action: Build an HR Recruitment Workflow Agent from Scratch
Build a full HR recruitment Workflow Agent with Spring AI Alibaba Graph: résumé scoring, interview generation, Human-in-the-Loop, and state rollback across 20 technical concepts.
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CUDA Kernel Fusion: A Practical Guide …
·2 min
CUDA Kernel Fusion: A Practical Guide to Reducing Memory Bandwidth and Launch Overhead
Learn how CUDA Kernel Fusion merges multiple GPU kernels to reduce global memory traffic and launch overhead, with real-world examples from AI inference and deep learning.
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·3 min
LangChain 1.3 in Practice: A Complete Learning Path from LLMs to Agent Development
A comprehensive guide to LangChain 1.3 — covering the full learning path from Models to Agent development, including Harness architecture, LangGraph, memory management, HITL, and Guardrails.
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·4 min
Coze in Practice: A Complete Guide to Building Multi-Agent Collaboration Teams
A systematic guide to Coze's positioning and capabilities, covering Agent-building platform categories, Skill modules, workflow orchestration, and multi-Agent team building.
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·4 min
From New Grad SWE to Platform Engineer: Career Paths and Tech Stack Choices in the AI Era
How can new graduates transition from software engineer to platform engineer? This article breaks down the path of joining as a Grad SWE first, then transferring internally, analyzes C# vs Python trade-offs, and offers a 14-month prep plan for AI/ML infrastructure.
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·5 min
GPT-5.6 Released: Hands-On Analysis of Programmatic Tool Calling and Autonomous Agents
GPT-5.6 is officially released with core upgrades including programmatic tool calling, autonomous subagent delegation, and higher token information density. A hands-on card game build reveals its Agentic power.
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·6 min
AI Agent Development in Go: A Hands-On Guide to ByteDance's Eino Framework and Its Seven Core Capabilities
Build production-grade AI Agents with a pure Go stack using ByteDance's Eino framework. A deep dive into seven core capabilities: multi-Agent orchestration, long-task execution, command approval, RAG, MCP, Skills, and database reporting.
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·2 min
Behavior Cloning Training Failing? A Systematic Guide to Data Quality and Tuning
Behavior cloning model barely moves after training? This guide systematically covers data alignment, class imbalance, learning rate, and epoch count — with a step-by-step debugging checklist and advanced alternatives like DAgger.
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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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·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
Dify Local Deployment in Practice: A Complete Guide to Building an AI Application Platform with Docker
A complete guide to Dify local deployment: from Docker environment setup, source code pulling, and container startup to first access. Build a private AI app development platform across Linux, Windows, and Mac for fast enterprise AI deployment.
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