#multi-agent AI coding
695 related articles
·3 min
Gstack Agents: Open-Source AI Expert Panel Joins Your Google Meet Video Conferences
Gstack Agents is an MIT open-source tool that adds 18+ AI personas (CEO, CSO, YC Partner, etc.) as voice bots to Google Meet, providing real-time multi-perspective structured feedback on your product demos.
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·3 min
HeyZoku: Command Multiple AI Coding Agents to Work Together Using Your Voice
HeyZoku is a Mac voice-first agentic dev environment that runs 10 coding agents simultaneously. Command Claude, Codex, and Cursor by name with on-device voice recognition and one-time pricing.
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·3 min
HeyZoku: Command Multiple AI Coding Agents to Work Together Using Your Voice
HeyZoku is a Mac voice-first agentic dev environment that runs 10 coding agents simultaneously. Command Claude, Codex, and Cursor by name with on-device voice recognition and one-time pricing.
Read more →
·1 min
Heard: An Open-Source Tool That Adds a Voice Layer to AI Coding Agents
Heard is a free, open-source macOS tool that turns AI coding agent outputs into intelligent voice summaries, helping developers monitor multiple agents hands-free.
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·3 min
OpenComputer: Deploy Managed AI Agents with a Single Sentence — A Minimalist Launch Solution for Developers
OpenComputer is an AI agent managed deployment platform that gives you a live agent URL from a single description. This article analyzes its workflow design, market positioning, and security/cost considerations.
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·3 min
Velane: A Deep Dive into the Cloud Platform Designed Specifically for AI Agent Tool Execution
Deep dive into how Velane provides dedicated cloud infrastructure for AI Agents through zero cold start sandboxes, version control, multi-environment management, and 800+ integrations.
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·3 min
From Linear Pipelines to State Machines: A Practical Guide to Advanced Agent Architecture
Learn how to advance from linear pipeline to state machine Agent architecture through a YouTube script-to-storyboard case study, covering fault tolerance, LLM evaluation frameworks, and LangGraph vs AutoGen selection.
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·3 min
Local Merge Queues: Solving Code Conflicts for Parallel AI Programming Agents
Deep dive into how local merge queues solve code conflict challenges when multiple AI programming agents work in parallel, covering merge queue principles and multi-agent development trends.
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·3 min
Local Merge Queues: Solving Code Conflicts from Parallel AI Coding Agents
Deep dive into how local merge queues solve code conflict challenges when multiple AI coding agents work in parallel, covering merge queue principles and multi-agent development trends.
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·3 min
Long Documents Don't Work: Why AI Agents Can't Follow Your Rules
Analysis of why AI Agents can't reliably follow long policy documents, covering context dilution, rule conflicts, and soft constraint limitations, with more reliable governance architectures.
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·2 min
agent-manager: An Open-Source Tool for Managing Multiple AI Coding Assistants with tmux
agent-manager is a lightweight tmux-based TUI tool that helps developers manage multiple AI coding assistants like Claude Code, Codex, and OpenCode from a unified interface for status monitoring, interaction, and code review.
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·3 min
Implementing Zanzibar in Lean4: A Formally Verified Permission Engine for AI Projects
Deep dive into a Datalog permission DSL built on Google Zanzibar using Lean4 theorem prover. How formal verification strengthens AI permission management.
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·4 min
Getting Started with Claude Code: Environment Setup, Common Commands, and Git Workflow in Practice
A detailed guide to Claude Code installation, domestic model switching, project analysis commands, and Git workflow practice to help developers quickly master this AI programming collaboration tool.
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·5 min
July 24 AI Daily Brief: Flux 3 Multimodal Release, Kimi K3 Lags in Testing, Qwen Tops TTS Rankings
July 24 AI news: Black Forest Labs launches Flux 3 multimodal model, Kimi K3 lags in US-UK gov tests, Alibaba Qwen tops TTS rankings, Etched raises $300M, AMD unveils MI430X.
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·5 min
Loop Engineering Explained: The New Programming Paradigm Where AI Prompts Itself
Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.
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·3 min
Learning AI Large Language Models: A Complete Roadmap from Beginner to Practitioner
A systematic breakdown of the AI LLM learning roadmap covering prompt engineering, AI Agent development, RAG knowledge bases, model fine-tuning, and hands-on projects for beginners.
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·5 min
Learn AI Agent Development from Scratch: A 3-Step Practical Path to Get Started Quickly
How to learn AI Agent development from scratch? This article outlines a clear 3-step path: Python crash course, LLM theory & practice, and LangChain framework project implementation.
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·3 min
How AI Agents Are Reshaping Enterprise Work: From Task Automation to Organizational Restructuring
Explore how AI agents are redefining enterprise work—from applied AI partnerships and multi-agent collaboration to structural workflow redesign and organizational transformation.
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·5 min
Running GPT-5.6 Inside Claude Code: Best Practices for Subagent Orchestration and System Prompts
Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.
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·3 min
A Beginner's Guide to AI Agents: Two Learning Paths for Developers and Practitioners
How to learn AI Agents from scratch? This guide covers two clear paths: developers go from Python to LLMs to open-source framework source code; practitioners use Claude Code or similar tools to get results fast.
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