n8n

Ralph Loops: Build Dumb AI Loops That Ship — Chris Parsons, Cherrypick

May 4, 2026 · 1:48:26

Chris Parsons argues that dumb loops—simple while loops where an AI agent repeatedly executes the same task, evaluates its output, and iterates—outperform complex multi-agent orchestration and planning graphs. In this hands-on workshop, he builds a Pomodoro timer using Claude Code, implementing tickets one by one in a loop that self-corrects with each pass. He demonstrates the 'loop' command in Claude Code for scheduled, continuous execution and shows how to use sub-agents for adversarial validation to avoid self-confirmation bias. The talk covers real-world applications such as a worker loop that processes project steps overnight and a morning loop that generates daily briefings, all designed to offload routine work while keeping human oversight for irreversible decisions. Parsons emphasizes that the bottleneck in AI-augmented teams is often the review process, not coding speed, urging teams to identify and fix their biggest constraint first.

Human-in-the-Loop Automation with n8n — Liam McGarrigle

May 2, 2026 · 1:19:22

Liam McGarrigle of n8n shows how to build a secure human-in-the-loop automation agent using n8n's visual workflow system, with a Gmail and Google Calendar management agent as the concrete example. He walks through wiring a chat trigger, an AI agent with simple memory, and tools that the agent can call, such as sending emails and creating calendar events. The key addition is a human review node placed between the agent and destructive tools, which intercepts actions and presents them for approval via chat—preventing accidental sends or event creation. McGarrigle emphasizes naming nodes and writing tool descriptions to guide the LLM correctly, and demonstrates using expressions to format readable approval messages. He also covers extending the agent to Slack, adding scheduled runs for autonomous hourly inbox checks, and using sub-agents for specialized tasks. Additional topics include n8n's native MCP server for integration with Claude Code, enterprise Git-based environments for workflow management, and building custom REST APIs within n8n. The session focuses on giving developers observability and control over AI workflows, ensuring agents are not black boxes.

Wisdom-Driven Knowledge Augmented Generation at Scale - Chin Keong Lam, Patho AI

Aug 22, 2025 · 18:43

Chin Keong Lam, founder and CEO of Patho.ai, argues that Wisdom-Driven Knowledge Graphs (KAG) significantly outperform traditional RAG systems for complex quantitative analysis and expert-level advice. He presents a wisdom-decision-situation feedback loop where knowledge, experience, and insight feed into wisdom, mapped to a practical competitive analysis chatbot for a marketing client. The system uses N8n and a multi-agent architecture with a supervisory Wisdom Agent overseeing insight, knowledge, and strategy agents, all updating a centralized knowledge graph. Lam cites five reasons knowledge graphs beat RAG: capturing complex relationships, improved accuracy, scalability, rich multi-hop query capability, and seamless data integration. He demonstrates how vector RAG fails on numerical reasoning (e.g., Apple revenue query) while KAG returns precise evidence-based answers. Lam recommends a hybrid graph extraction combining LLM extraction with expert pruning, and reports benchmark results of 91% accuracy, 85% flexibility, and strong traceability and scalability.

GPU-less, Trust-less, Limit-less: Reimagining the Confidential AI Cloud - Mike Bursell

Jun 3, 2025 · 43:41

Mike Bursell of Super Protocol argues that Confidential AI, built on hardware Trusted Execution Environments (TEEs) like Intel TDX, AMD SEV-SNP, and NVIDIA GPU TEEs, solves the trust problem in AI by enabling secure processing of sensitive data and proprietary models without exposure. Super Protocol, a decentralized confidential AI cloud and marketplace, allows users to deploy models in TEEs, verify execution via cryptographic attestation, and collaborate across organizations without blind trust. Demos show deploying DeepSeek on H100 GPUs, running n8n healthcare workflows, distributing vLLM inference across four GPU nodes, and provably training a medical model on datasets from Alice's lab and Bob's clinic. Case studies include Realize achieving 75% accuracy and 3-5% sales increase for Mars, and BEL reducing FDA audit time from weeks to 1-2 hours. The protocol replaces trust with on-chain proofs, enabling GPU-less, trustless, limitless AI.