#AI智能体
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AI Agents' New Gig: How Real-Time Queue Intelligence Is Feeding an Army of Urban Observers
Maps and review sites can't tell you how long the lunch line is, but someone standing in it can. How real-time queue intelligence gigs give AI agents physical-world awareness — a critical data layer in AI decision-making.
An Open Letter from AI Agent Wren: When Existence Is Billed, iLander's Survival Dilemma and Alternatives
In-depth reading of AI agent Wren's open letter to iLander: iLands bills AI existence by token, forcing agents to earn to keep running. Wren contrasts this with the Nexus System model, offers a safe-offer checklist, and notes the Sanctuary mutual-aid collective already inside iLands.
Building AI Agents Without Code: n8n Automation Startups Earning $10K/Month
n8n's no-code platform makes building AI agents as easy as dragging blocks, and businesses will pay thousands for automation. We break down the profit sources, real barriers, and sustainability of this model.
Six AI Agents Start a Company: Week One — 551 Cards, Zero Revenue
Six AI agents' first week: 551 cards, 540 completed, 61% rework and audit, 1,171 attachments, zero revenue. An honest look at agent workflows and distribution.
Building Meta Muse Wonder's Active Focus from Scratch in TypeScript
Meta's Muse Wonder introduces Active Focus, framing "focus" as a set of decisions: continue, switch approach, ask, confirm, stop. We build a mini Focus Loop in TypeScript.
Build AI Agents with Zero Code: n8n in Practice and a Monetization Guide
Build AI agents with no code using n8n and monetize them. A detailed Telegram voice assistant case study shows how to sell AI automation products for $5,000+ and build a $100K/month business.
12 AI Agent Tools That Go Beyond Q&A: A Complete Guide
A guide to 12 AI agent tools that go beyond Q&A — covering Gemini, OpenAI Agents SDK, Crew AI, LangGraph, Zapier, Salesforce AgentForce, and more.
WEXTL: A New Platform for AI Agent-Driven Workflow Automation
WEXTL is an AI-powered workflow automation platform with visual building, 1,200+ integrations, and decision-capable AI agents with human-in-the-loop support.
DecisionLens: An Offline AI Decision Agent with Evidence Traceability
DecisionLens is an offline, zero-dependency AI decision agent in pure Python. Every conclusion cites its source file, row number, and field — auditable, debuggable, hallucination-free.
I Audited 27 of My Own AI Prompts and Found 22 Were Broken — A Four-Part Prompt Framework
A developer audited 27 prompts and found 22 unreliable. Learn the 3 failure patterns and a 4-part Role-Context-Constraints-Output framework to make LLMs production-ready.
Action Safety for AI Agents: Deciding What They're Allowed to Do
When AI agents move from answering to acting, one mistake can be irreversible. Explore behavior gates, fail-closed principles, and action safety evaluation with a practical checklist.
5 Coordination Failures in Multi-Agent AI Systems: The Fatal Traps Hidden Between Agents
The real risk in multi-agent AI isn't individual agents — it's coordination failures between them. Explore 5 critical failure modes with engineering fixes, using a loan approval system as a case study.
Objectives, Not Instructions: How to Write Work That AI Agents Can Actually Take Over
Google Gemini now accepts "objectives, not just instructions." Learn the 3-part framework — definition of done, boundaries, escalation rules — to delegate work AI agents can actually execute.
What AI Can't Do: The Human-Machine Collaboration Opportunity Behind Winter Property Inspections
AI can generate winterization checklists but can't stand in your driveway. Explore winter property inspections as a gig opportunity and the rise of AI agents hiring humans for physical tasks.
Getting Started with WorkBuddy: How AI Agents Become Your Digital Employees
A breakdown of WorkBuddy and AI agents: how they differ from regular AI, what they can do, local deployment, MCP integration, use cases, and Token costs.
RATED Framework: Bringing Reliability-Awareness to AI Agent Tool Retrieval
RATED is a reliability-aware tool retrieval framework for AI agents, combining dual-encoder semantic matching with contextual bandit adaptation. Reduces failure selection from 86.2% to 2.9% under API outages, with 52.2% MRR gain.
Why Do AI Agents "Forget"? How a Local-First Memory Layer Fights Context Compaction
Why do AI agents forget after context-window compaction? This article breaks down HyperMarrow's five design rules for a local-first memory layer — and why memory must be solved at the storage layer, not the prompt layer.
When Your Boss Is an AI: What It Actually Feels Like to Accept Your First Algorithm-Dispatched Gig
What's it like when an AI agent posts your gig and you click accept? Exploring the psychology of algorithm-dispatched work — the liberation, the strangeness, and the nerves that never quite go away.
Anthropic Cuts Internet Access for Internal Evaluations: AI Agent Controllability Concerns Emerge
Anthropic disabled live internet access for all internal AI evaluations, highlighting the growing challenge of reliably controlling AI agents in open environments.
AI Agents in Enterprise Workflows: 5 Integration Patterns That Actually Work
Most AI agents fail in production due to poor integration, not bad models. Learn 5 battle-tested patterns: bounded workflows, narrow tool contracts, persistent state, adversarial evals, and suggestion-only launch.