A 30-minute video is too long for one conversation. This guide walks the 8-step Skill that locks the narration and one look, then gives each 3-minute segment to a fresh sub-agent, five at a time, and polishes the cut in an empty-context session.
The folder structure behind my AI agent team, the Herdr setup where Grok Build, Claude Code and Kimi Code work side by side, and the two live tasks from the video: an article published by voice and a three-model front-end test.
Eight folders of plain Markdown, a CLAUDE.md index in each, and seven prompts that make your agent interview you. Every prompt, template and command from the video, in order.
Knowledge bases, memory, RAG and research feeds your agents can actually read
Every assistant you use needs the same orientation, and most people give it five times. One plain text file per folder, read by all of them, fixes that.
Models, tools, and frameworks are rented. The only AI asset that compounds is the knowledge base you wrote down on disk — and built your agents to read.
A practitioner's guide to domain-specific RAG knowledge bases across finance, tech, and consulting: the five-dimension framework, chunking that lifted retrieval accuracy, embedding selection, metadata design, and the maintenance cycle that keeps answers true after launch.
Fluent answers that cannot be traced back to a provision are unusable. This build grounds generation in a fixed corpus: 8.5M words, a Markdown-plus-JSON dual layer, an 8-step retrieval workflow, and a real case run end to end.
One canonical knowledge store any MCP-aware AI tool can query: three independently replaceable tiers, eight industry templates, and per-person access you revoke with a single command. Built so the knowledge stays with the business when the person leaves.
162 Markdown documents, one cloud knowledge base, searchable by meaning from Claude Code, Cursor, or n8n — and by your teammates. One command deploys it. The payoff is that a written answer gets made once and reused, instead of re-litigated three times a quarter.
Your knowledge sits in dozens of folders and hundreds of chat threads, so the answers come back generic. Build a knowledge base on your file system instead: the four-layer architecture, a step-by-step build sequence, and a checklist to tick off as you go.
Vector databases are overkill. A file-system knowledge base with multi-level CLAUDE.md routing finds any file in a 1,000-file system in three seconds. Here is the complete architecture — and why structure beats search when documents have to be findable on demand.
RSS hands over titles, timestamps and full text in a standard format - no scraping, no API keys, nothing to maintain. The operator's manual: five copy-paste prompts, a four-tier build map, and the workflow automation software that turns 150 sources into a five-minute read.
A curated list stays only as fresh as one maintainer's leftover hours, which is how most of them end up as 2023 archives in a 2026 world. This one keeps no maintainer in the loop, so what it says about ai automation tools reflects this week.
The client asked about a policy change you had not seen. It had been public a week, in a publication nobody subscribed to. 8,936 validated feeds, 21 categories, one OPML file — the input layer under any workflow automation software.
When I figure out how to get something real done with AI, you get the write-up — every step, including the parts that didn't work.