Workflow Engineering Podcast Summaries
Workflow Engineering on Yedapo: 5 summarized podcast and YouTube episodes. Each includes key takeaways, core concepts and notable quotes with timestamps.
Claude Fable 5 Made This Entire Video By Itself.
Nate Herk | AI Automation
Jun 12, 2026
The creator demonstrates a fully autonomous AI workflow where a single prompt in Claude Fable 5 researched, scripted, generated audio/video, and edited an entire YouTube video. This workflow highlights the leap in 'Mythos-class' model capabilities for long-horizon task execution and self-verification.
Key insight: Claude Fable 5 completed an entire video production process—including research, scriptwriting, voice cloning, avatar rendering, and multi-layered video editing—with zero human intervention after an initial prompt.
This Unlocks So Many Insane Hermes Use Cases
AI LABS
Jun 6, 2026
By pairing Claude Code with the Hermes agent, developers can create highly autonomous, self-evolving workflows that surpass standard LLM limitations. This setup leverages persistent memory and a self-improving skill system to automate complex business processes and software maintenance.
Key insight: Hermes can turn reusable chat workflows into persistent 'skills' that evolve over time, effectively allowing an AI to update its own instruction set based on project requirements.
How to verify AI-generated Office files before they ship
AI News & Strategy Daily with Nate B. Jones
May 27, 2026
Don't just prompt AI for a finished document. To ensure accuracy in Excel and PowerPoint, you must build a multi-stage, agentic workflow that treats knowledge work like code—preparing sources, defining structural blueprints, and enforcing adversarial verification before final output.
Key insight: The best way to prevent 'hallucinated' spreadsheets is to use a 'hostile reviewer' prompt: ask the AI to enumerate every potential error without attempting to fix them, forcing the model to switch from generation mode to inspection mode.
Agentic AI Systems, Clearly Explained
Simon Scrapes
May 9, 2026
Agentic AI is moving beyond simple chatbots into autonomous systems that execute complex goals. This evolution relies on harness engineering—wrapping LLMs with file-based context, memory, and tool access—to transition from reactive prompts to proactive, production-grade operations.
Key insight: The core of advanced agentic systems is surprisingly simple: it is just files in folders. 'Skills,' 'memory,' and 'brand voice' are essentially markdown files that the AI reads and updates, making these systems accessible to non-developers.
Prompting is dead. Here is how you create loops
Alex Finn
Stop manually prompting AI agents for every task. By implementing a 'spec-build-review' loop, you can transition from active coding to managing an autonomous system that handles development, testing, and deployment while you are away.
Key insight: You can use a simple slash command like '/loop 5 min /him build' to force an AI agent to autonomously iterate on tasks every five minutes without human intervention.