Videos

Crowdin. Shashi Bhushan starts with workflow mapping rather than tool selection, then covers source-text quality, human review, AI proofreading, glossary checks, product-team involvement, pilots and privacy constraints. That is almost exactly the operating model the article recommends for Estonian teams working across Estonian, English, Russian, Finnish and customer-specific terminology.

AI Engineer. Dex Horthy explains why reliable agent systems are mostly disciplined software around a few LLM calls: own the prompt, own the context window, keep control flow deterministic and use tool calls to contact humans when the workflow needs judgment. That maps directly to the article's approval, exception and escalation patterns.

Anthropic. A three-minute Anthropic walkthrough of running a real eval inside the Workbench — auto-generating realistic test cases, grading outputs, tweaking the prompt, and re-running the same suite side-by-side. The view count sits below the usual bar, but for "how do I actually do this without writing code" this is the cleanest official demo and slots neatly under the more strategic Husain/Shankar conversation.

Nick Saraev. Saraev builds the exact pipeline the article describes — Apollo for leads, Apify for scraping, n8n to enrich and run a multi-line icebreaker generator off each lead's site, then Instantly for sending — and is candid about per-lead costs and reply rates. It's the cleanest demonstration of "real personalization at scale," not just "mail merge with a first name."

Greg Isenberg. Isenberg builds a real content pipeline in n8n with The Boring Marketer — scraping top-performing posts on YouTube and X, drafting new pieces with Claude, researching with Perplexity, generating images, and publishing to LinkedIn with a human-approval step. It is exactly the "agent in the middle, tools on either side" shape the article describes, and the human-review stage is shown rather than just mentioned.

Kevin Stratvert. Same workflow as Ollama but in a GUI: download LM Studio, pull a Llama or Gemma model, chat, drop a PDF in and ask questions about it. Good for readers who'd rather not live in the terminal — also useful for getting a feel for how a 1B–3B model actually performs against a heavier one.

Tech With Tim. A tight, no-nonsense Ollama walkthrough — install, pull a model, chat, then poke at the local HTTP API from Python and create a custom model with a Modelfile. Covers exactly the workflow the article describes for daily use on a Mac, including how to think about model size vs. your machine's RAM.

Nate Herk | AI Automation. A 16-minute, no-jargon explanation of what an MCP server is, how clients like Claude and n8n use them, and what you actually do differently once you have one. Lines up almost exactly with the article's "connect Claude or Cursor to your tools" framing.

TheAIGRID. A faster, feature-first tour: uploading mixed sources (PDFs, YouTube transcripts, blog posts), generating a briefing doc, focusing the chat on a single source, and the audio-overview podcast. Good if you want a quick map of the surface area before committing time to a longer walkthrough.

Tiago Forte. Tiago is the *Building a Second Brain* author and treats NotebookLM as exactly what the article describes — a personal RAG over your own notes, PDFs and clippings. He shows the citation-grounded chat, the limits of the tool, and how it fits next to a Readwise/Obsidian workflow, which is the natural endpoint for most readers of the article.

Futurepedia. Builds a working n8n agent from scratch in one sitting and stops to explain what an agent actually is, how it differs from a linear workflow, and where the guardrails go. After this you'll recognize every node in the article's diagram and have a feel for what reasonable defaults look like.

Jack Roberts. A working automation builder names the three considerations that actually drive the choice — native AI agents, self-hosting, and the long-term roadmap of each tool — including notes from a conversation with Make's head of Applied AI on where Make is heading. Closely mirrors the article's "don't switch just because of shiny-object syndrome" framing.