Videos

Google for Developers. The video introduces multilingual text embeddings that can run locally and support semantic search and RAG without sending every document to a hosted API. For Estonian companies, that is a useful technical complement to the article's internal-knowledge-search pattern: multilingual retrieval is valuable only when it also respects data locality, permissions and source authority.

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.

AWS Events. This lightning talk names the business moments where human control is needed: high-stakes decisions, irreversible actions, regulatory requirements, trust-building phases, ambiguous edge cases and graceful degradation. It also shows concrete implementation mechanisms such as MCP elicitations, Step Functions callback waits and approval nodes.

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.

Lenny's Podcast. Hamel Husain and Shreya Shankar walk through the entire eval workflow on a real property-management AI assistant — looking at traces, open and axial coding of errors, deciding when to stop, building an LLM-as-judge, and validating it against human judgment. This is the rare long-form conversation that is genuinely aimed at PMs and team leads rather than ML engineers, and it covers the same "30 minutes a week after setup" rhythm the article recommends.

Liam Ottley. A live build of an AI voice agent that calls inbound leads, qualifies them, and tries to book a discovery call — Make.com plus a voice provider, with the qualification script and handoff logic shown. Good complement to the email side: same enrichment-then-personalization pattern, different channel, different failure modes.

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. A wider tour of the current AI marketing stack — workflow automation, model routing, AI video and voice tools, ad creation from competitor analysis. Good way to see which tools are doing what across the category before you decide where to put the first three Zaps or n8n flows for your own team.

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.

Big Think. A tight 8-minute version of Mollick's "four scenarios" model — static, linear, exponential, AGI — and why teams should plan against scenario two or three rather than betting everything on either extreme. Useful when you're trying to get a leadership team to agree on what they're actually preparing for before you write the playbook.

Sana. An hour with Mollick on what AI inside organizations actually looks like — why "cut costs" is the wrong framing, why traditional org charts are bending, and what "AI-native" teams do differently. Sits below the usual 100k bar but it is the cleanest practitioner-level conversation about adoption strategy from the researcher most consistently cited on this topic, and the playbook concerns in the article map almost 1:1 onto his framing.

Matthew Berman. Walks through the LMSYS RouteLLM paper and code: a small classifier sits in front of a strong/weak model pair and decides which one to call, hitting roughly 95% of the strong model's quality at a fraction of the cost. The view count is under the usual 100k bar, but for the specific "show me real model routing, not just model comparisons" niche this is the cleanest explanation on YouTube and lines up directly with the article's quality/cost tradeoff section.

Tina Huang. A clean tour of the current model landscape grouped by tier — flagships, lite models, mid-tier, specialized — with concrete picks for what each tier is actually good for. This is the "know your options before you route" half of the article, and Huang frames cost-vs-capability the same way the article does without leaning on benchmark hype.

Anthropic. A two-minute Anthropic demo of Claude planning a small multi-app task — search the web, check Maps, drop a calendar invite — by driving the desktop directly. Useful contrast to Operator's cloud-browser-only model and a good gut check on the article's point that computer-use agents work best on short, well-bounded chores rather than open-ended work.

OpenAI. The actual launch demo of Operator, where the team books restaurants, orders groceries, buys event tickets, and lets Operator stall on a redirect or hand control back when it hits a login. It is the clearest picture you will find of how a browser agent feels in practice — the screenshot-and-click loop, the confirmations before "stateful" actions, the prompt-injection guard rails — which is exactly the texture the article is trying to set expectations for.

Nate Herk | AI Automation. Walks through a sequential multi-agent newsletter pipeline in n8n — planner, researchers, editor, headline writer — that takes a topic and audience as input and ships a sourced newsletter out the other end. The view count sits below the usual 100k bar, but on this niche (no-code multi-agent newsletter builds) it is the cleanest, most complete tutorial currently on YouTube and maps directly onto the briefing pattern in the article.

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.

Fireship. Fireship's three rules — pick a boring popular stack, get good at Git, treat yourself as the product manager — are the same guardrails the article is trying to install. Five minutes well spent before you let an AI write to your repo unsupervised.

Tech With Tim. Tim is one of the steadier educators in this space, and this is the most complete "open Cursor, build something, ship it" walkthrough for a non-developer audience — setup, prompting, debugging, basic Git and even a touch of MCPs. Watch it once and you'll know what the article means by "build a small internal tool."

Anthropic. The protocol's designers — Theo Chu, David Soria Parra and Alex Albert — walking through why MCP exists, the components (server, client, transport), the reception since the November 2024 release, and which servers they actually use day-to-day. Useful as the canonical source after the Nate Herk overview.

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.

Greg Kamradt. The article spends a lot of words on chunking; this is the longest, most patient explanation of what each chunking strategy is actually doing — from character-recursive through document-aware to semantic and agentic splitting. Pair it with Greg's free ChunkViz tool to build intuition before you start tuning.