New to AI

Jeff Su. Part two of the same series, covering the parts of the job search the first video doesn't — landing coffee chats via LinkedIn, asking good questions in the interview, writing thank-you emails, and the role-play prompt for practising salary negotiation. Watch this after the article and you'll have ChatGPT-assisted scripts for every awkward part of the funnel, not just the application itself.

Jeff Su. Ten minutes of the most-watched ChatGPT-for-job-search video on YouTube, and it earns it. Jeff shows the multi-prompt approach for tailoring a cover-letter hook to a real job description, then the "rewrite this bullet as I accomplished X by doing Y resulting in Z" pattern for resumes, then a clean walk-through of preparing the "tell me about yourself" answer. The exact moves the article recommends, demonstrated on screen with the prompts visible.

OpenAI. The OpenAI product and engineering team — Thomas Dimson, Nick Turley, Michelle Pokrass, Olivier Godement — walking through how GPTs work, what the three pieces (instructions, actions, knowledge) actually do, and the design choices behind them. Useful when the article's "just try it" advice has stuck and you want to understand the seams before you stretch the tool further.

Matt Wolfe. Matt Wolfe builds a GPT live on screen and then reviews a handful of real, useful ones (SimpsonizeMe, ConvertAnything, Grimoire). The build-from-scratch section is the closest thing on YouTube to the article's "ten minutes from blank to working assistant" framing, and the second half shows what good GPTs actually look like once they exist.

Grace Leung. A more practical two-tool workflow: Perplexity for finding the documents and citations, NotebookLM for actually reading and synthesising them. Useful if your "long document" problem is really a "long stack of documents" problem — market research, regulatory filings, multi-source reports.

Tiago Forte. NotebookLM is the AI tool the article most enthusiastically recommends for long documents, and Tiago Forte — the Building a Second Brain guy — gives the cleanest tour of why. He demonstrates summarising meeting notes, querying long PDFs, and the source-grounding feature that stops the model from inventing facts the documents don't contain. After watching, you'll understand why "upload the PDF to NotebookLM" is the article's default suggestion.

Jeff Su. Jeff opens with the spectrum the article hinges on — ChatGPT and Gemini at the "creative" end, Perplexity and Google Search at the "accurate, real-time, sourced" end — and then shows you the third option in action. The Olympics-medals comparison in the middle is the cleanest "this is when you ask a chatbot, this is when you Google" demo we've seen. After watching, you'll have a third tool in your search routine and a working rule for picking between the three.

Theo Von. The section roughly twelve minutes in, where Altman admits there is no legal privilege for ChatGPT conversations and that OpenAI can be ordered to hand them over in a lawsuit, is the single most-quoted piece of footage on this topic — and worth hearing in his own voice rather than via a news clip. The rest of the conversation is wide-ranging, but that one exchange is the honest answer to the question the article asks: "what does the company actually do with what I type?"

Acerola. A developer-essayist works through the "AI is just slop / AI is magical / AI is theft" trio of myths with patience and code on the screen. If the article's myth #4 or #7 ("it's just plagiarism," "it's just plagiarism but for art") nagged at you, this is the video that earns the right to disagree with you.

Cleo Abram. Cleo Abram walks through both the "it will kill us all" and "more profound than fire" extremes, asks Eric Schmidt the awkward questions, and lands on a sober middle. The framing — "we're living inside a trolley problem" — is exactly the spirit of the article: neither dismiss the technology nor catastrophise it, just understand what it actually is.

OpenAI. Four minutes of someone holding up a handwritten linear equation to the camera and ChatGPT tutoring them through it without giving the answer. It is the clearest, shortest demo of "the model can actually see what I'm showing it" and frames the use cases the article recommends — handwritten notes, simple math, captured documents — better than any walkthrough we found.

OpenAI. Two instances of voice mode talking to each other, one of which has camera access to describe the room. Three minutes long and the most efficient way to internalise what makes voice mode different from old "press the microphone, wait, listen" interfaces — interruption, tone, music, real-time vision, all in one clip.

OpenAI. This is the live keynote where ChatGPT's real-time voice mode was first demoed. Mark Chen does the breathing-exercise demo, Barrett Zoph does the math-tutor demo, and then they switch to real-time Italian–English translation. Twenty-six minutes of "ah, that's what they mean by talking to AI like a friend." The model and capabilities shown have only improved since.

AI Edge. The title is louder than the content. Underneath it is a calm tour of using a long-running ChatGPT thread for meal planning, calendar/booking work, errand triage, and trip prep — exactly the unglamorous "life admin" jobs the article covers. The "external memory" idea at the end (a small Google Doc the assistant reads back into each session) is the practical fix for the most common beginner complaint: "it keeps forgetting what I told it last week."

Dan Koe. A different angle on the same problem: pick a real project, then let the AI surface only what you need to know to finish it. Useful counterweight to the Mike and Matty video if you learn better by building than by studying, and it pushes back gently on the "consume more courses" reflex.

Mike and Matty. A 10-minute framework — Construct, Connect, Challenge — applied with specific ChatGPT prompts for each phase: ask for a syllabus, ask for analogies, ask for a practice test, ask for feedback on your explanation. These prompts work on essentially any subject from physiology to programming, and the tone is exactly the article's: AI as a tutor, not a substitute for thinking.

David Perell. David Perell is a working writer who teaches writing online, and this is his actual workflow — where he asks AI to argue with his draft, where he refuses to let it generate prose for him, and what prompts he keeps coming back to. The sections "How do I actually write with LLMs" and "How I think with LLMs" map directly onto the article's "use AI as an editor, not a ghostwriter" stance. The view count is just under our usual 100k bar; we made the exception because no larger-channel video on this exact topic is anywhere near this thoughtful.

Jeff Su. The "red team technique" section is the missing piece in most ChatGPT-for-email tutorials: write the draft, then ask the model to roleplay the recipient who gets fifty cold emails a day and tell you which sentences make them hit delete. That feedback loop is exactly what the article means by "emails you actually want to send," and Jeff demonstrates it on a real cold outreach email in under three minutes.

Andrej Karpathy. A follow-up where Karpathy actually sits in front of ChatGPT, Claude, Gemini and Perplexity and uses them like a power user. Watch this after the article and his "Intro" — you'll see the mental model put into practice in real time, including the small choices (which model, which mode, when to paste vs upload) that separate frustrated beginners from people who get useful answers on the first try.

freeCodeCamp.org. A longer, more patient course that earns its length. The chapters on best practices, zero-shot vs few-shot, and what hallucinations look like in prompted output are exactly the deepening you want once the article's five examples have clicked.

Tina Huang. Tina condenses Google's official Prompting Essentials course into the five-part TCREI structure (Task, Context, References, Evaluate, Iterate) with worked examples on each. After the article shows you five rewrites, this hands you the recipe Google itself teaches employees, so you can do the rewrite on your own prompts without copying ours.

Computerphile. A long, calm sit-down with AI-safety researcher Rob Miles on why a model that's trained to predict plausible text will always, structurally, sometimes invent facts. Recorded in early 2023, but the underlying mechanism it explains hasn't changed and the framing is still the cleanest way to internalise "it's a fluency engine, not a truth engine."

IBM Technology. Martin Keen sorts hallucinations into four named buckets — sentence contradictions, prompt contradictions, factual errors, nonsense — and walks through each on a lightboard. After the article gives you the why, this video gives you a vocabulary for spotting the type of mistake in the wild so you can decide how much to trust a given answer.

Tina Huang. A calm 19-minute map of the major model families — OpenAI's GPT line, Anthropic's Claude, Google's Gemini, plus the open-source players — and which tier inside each family is worth your time. After the article tells you "pick one and stick with it for a month," this video tells you what the dropdown menu inside that one is actually offering. Honestly opinionated without being a hot take.