#multi-Agent parallel execution
544 related articles
·4 min
The Cursor Swarm Experiment: What AI Rebuilding SQLite from Scratch in Rust Reveals About Cost
Cursor's swarm experiment had AI teams implement a SQLite engine in Rust from scratch using only 835 pages of docs. All-frontier models cost $10K+, hybrid role division just $1.3K—a 7.9x gap.
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·4 min
Claude Code from Scratch: A Complete Guide from Installation to Enterprise-Grade Development
A complete guide to Claude Code from beginner to enterprise practice: covering CLI installation, connecting domestic LLMs via CC Switch, basic commands, Git workflow integration, automated bug fixing, and engineering capabilities like MCP and SubAgents.
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·3 min
Context Engineering: Why Does More Context Make AI Dumber?
Anthropic introduces Context Engineering, revealing Context Rot: the more tokens in the window, the worse AI retrieval accuracy. Learn the three principles, just-in-time retrieval, and three moves against context overflow.
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·2 min
After Getting Burned by AI Coding for Three Months, I Built WishGraph
After three months of costly AI coding mistakes, a developer built WishGraph: separating discussion and execution into dual windows with parallel multi-agent collaboration to make complex projects manageable again.
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·3 min
Pinterest Medic: Engineering an AI Agent for Automated Spark Failure Diagnosis
How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.
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·3 min
The Rise of General AI: Breakthroughs in Mathematical Reasoning and Competitive Programming
Frontier AI is going general: costs are dropping, general models are beating specialized ones in math and competitive programming, and multi-agent workflows are maturing fast.
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·3 min
GPT-5.6 Released: How Multi-Agent Workflows Are Ending the Chat Box Era
OpenAI releases GPT-5.6 with Sol, Terra, and Luna models plus ChatGPT Work execution environment, shifting AI from chatbots to autonomous multi-agent workflows that directly operate local files and business systems.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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·3 min
Claude Code Getting Started: Installation, Switching to Chinese LLMs, and Git Workflow Guide
A complete guide to Claude Code: CLI installation, switching to DeepSeek and other Chinese LLMs via CC Switch, and conversational Git workflows for developers.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Agent Topics
A focused guide to core LLM application engineer interview topics, covering agent architecture, Multi-Agent, Langfuse evaluation, security, and RAG optimization.
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·7 min
The Complete Guide to LLM Application Engineer Interviews: Six High-Frequency Topics in the Agent Domain
A focused guide to the core interview topics for LLM application engineers, covering agent architecture, Multi-Agent, Langfuse evaluation & tracing, security, and RAG optimization.
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·6 min
The AI Builder's Handbook: A Complete Guide to LLM Principles, RAG Architecture, and Agent Design
From the autocomplete nature of LLMs, tokens, and context windows to RAG vector databases, the MCP protocol, and AI agent loop design — this article uses vivid analogies to unpack the reality of AI engineering.
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·7 min
From Chat to Agent Platform: A Practical Guide to Engineering AI Applications
A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
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·6 min
30-Minute Hands-On: Building a Working AI Agent from Scratch
Skip the dry theory and get hands-on! This article demonstrates step by step how to build a working AI Agent from scratch in 30 minutes using AI coding tools—covering the agent skeleton, tool system, memory mechanism, Flask web UI, and DeepSeek API integration.
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·3 min
LangChain v1.3 Hands-On Tutorial: Deep Dive into Agents, RAG, and Local Model Deployment
Deep dive into LangChain v1.3: compare LangChain, LangGraph, and DeepAgent paradigms, explore RAG pipelines, multi-agent systems, and local LLM deployment for enterprise AI apps.
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·4 min
5 Tips to Cut Claude Opus 5 Costs by Up to 80%
Opus 5 moving to API billing? 5 proven tips to cut token costs by up to 80%: lower Effort Level, architect-executor split, Ponytail compression, Deep Research, and Advisor Mode — while outperforming Opus 4.8.
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·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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·4 min
UK Sovereign AI: How Cosine AI Is Punching Above Its Weight Against American Compute Dominance
Cosine AI founder reveals how the UK's first sovereign LLM is being built — from government compute grants and RL credit attribution to multi-agent orchestration and synthetic data pipelines.
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·2 min
Vibe Coding on a Budget: The Brain-vs-Hands Multi-Agent Strategy for Saving Tokens
Always burning through your AI coding quota? This guide breaks down a brain-vs-hands multi-agent strategy: use strong models only for planning, and cheap models like DeepSeek for execution.
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·4 min
Codex Major Upgrade: GPT-5.6 Powers Full-Stack Development from Idea to Deployment
OpenAI Codex gets a major upgrade with GPT-5.6: extended reasoning, multi-agent parallelism, browser control, one-click Sites deployment, task orchestration, and mobile dev support.
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