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.

Skill Leap AI. Shows the specific pattern the article uses — letting a Custom GPT trigger Zapier actions to send emails, update sheets, or create calendar events from a chat. The setup details have shifted slightly since the video was made, but the model of "your AI assistant calls a no-code automation that does the actual work" is exactly right.

Tom Nassr | XRAY. Walks through what a Zap actually is, how triggers and actions fit together, and how to build a working Google Sheets → Slack automation from scratch — exactly the mental model you need before adding AI to the mix. Also tours Zapier Tables, Interfaces, and AI features so you have a sense of what's in the box.

IBM Technology. Sits below our usual 100K bar but earns the slot because it's the single best short explanation of why an employee using a personal ChatGPT account on work problems is the actual risk most companies face. Crume's "don't say no, say how" framing is the same posture the article takes — you're not trying to ban AI, you're trying to make safe use the easy default.

IBM Technology. Jeff Crume's lightboard explainer of the three places generative AI introduces risk — the data, the model, and the usage — and what good controls look like for each. Useful for the article's argument that "be careful" isn't enough; you need to think about which category of risk you're actually exposed to as an employee.

Tina Huang. Walks through the structured self-study system Huang used to learn SQL in 11 days for a Meta interview — active recall, spaced repetition, and using ChatGPT to generate practice problems and explain mistakes. Useful as the "how do I actually execute my plan" follow-up to the primary pick.

Tina Huang. Hands you a study-plan prompt template ("Act as a coding tutor that creates study plans…") that takes the student's goal, time commitment, and resource preferences, then generates a structured plan. The framing is coding, but the prompt pattern is exactly what the article applies to any 30-day learning sprint.

Ali Abdaal. Lays out the same kinds of patterns the article uses — challenge prompts ("give me the steelman counterargument," "give me a scathing critique"), explanatory theories, and the Solomon method (have the AI play your 90-year-old self) — and is unusually clear about where the AI stops being useful (mental health, accountability). Pairs well with the article's insistence that the value is in better questions and tradeoff-mapping, not "tell me what to do."

Tina Huang. The prompting chapter (02:30–09:20) lays out two stackable mnemonics — "tiny crabs ride enormous iguanas" (task, context, references, evaluate, iterate) and "ramen saves tragic idiots" (revisit, separate, try analogous, introduce constraints) — that map cleanly onto the iterate-and-refine patterns in the article. Useful when a single-shot formula isn't getting you there.

Jeff Su. Six-part formula — task, context, exemplars, persona, format, tone — explained with the same office-work examples the article uses (workout plans, resumes, internal emails). Watch this first; almost every pattern in the article is a focused application of one of these six components.

Jeff Su. The Claude Projects chapter (starts around 04:46) is the clearest short explanation on YouTube of project-level vs chat-level context — illustrated with a product marketing example where one project document feeds many chats with different briefs. Pairs naturally with the article's argument that Projects and Custom GPTs solve the same problem in slightly different shapes.

Skill Leap AI. Walks through the GPT builder exactly the way the article describes — name and description, conversation starters, instructions, knowledge files, capabilities, and access settings. Uses a real example (an AI knowledge assistant fed with the creator's own scripts) so you see what good knowledge-file content looks like in practice.

Kevin Stratvert. Calm, end-to-end walkthrough of the current Sora workflow: getting access, setting up a Cameo, prompting, choosing orientation, and using the desktop version. Useful as a "pick one tool and actually try it this week" follow-up to the comparison.