AI Productivity

Most people use one AI tool for everything. Intermediate users orchestrate four or five — each for the part it does best. A practical guide to building multi-tool workflows that compound.

A structured 30-day approach to using AI as a personal tutor and curriculum designer. Four weeks, four phases, with the exact prompts and the habits that make learning stick.

AI is an unusually good sparring partner for decisions — if you stop using it as an answer machine. A workflow for using AI to make better choices, with the prompts that force it to disagree, not agree.

Deep Research — the autonomous research feature in ChatGPT, Gemini, Claude, and Perplexity — produces in fifteen minutes what used to take you a day. A practical guide to using it well.

Google's NotebookLM is the easiest way to chat with your own documents — books, papers, meeting notes, research files. A practical tour of what it does, where it shines, and the four use cases worth setting up this week.

A practical guide to using AI to handle spreadsheets — cleaning messy data, writing formulas, building pivot tables, and turning rows of numbers into clear answers. Works in Excel, Google Sheets, or via ChatGPT directly.

AI writing has a smell. Once you know what it is, you can stop producing it. Six concrete tells, six fixes, and a workflow that uses AI to make your writing better without making it sound like everyone else's.

A practical, repeatable system for triaging, drafting, and chasing email with AI — without needing a developer, an automation builder, or a productivity guru.

A realistic workflow for capturing meetings with AI — which tool to use, what it captures well, what it captures badly, and the prompt that turns a transcript into actual decisions and follow-ups.

A practical AI workflow for the job hunt — tailoring your CV to each role, drafting cover letters that sound like you, rehearsing interviews out loud, and the steps most candidates skip.

Drop in a 40-page document and get the parts that matter — the decisions, the risks, the things you would otherwise miss. A practical workflow for using AI to read what you do not have time to read.

The low-stakes, high-frequency wins that turn AI from a curiosity into a habit. Real prompts for trip planning, meal planning, household admin, and the small decisions that eat your week.

A four-prompt loop that turns any AI into a private tutor — explainer, examples, practice, and feedback. Works for any topic, any background.

Three small prompts that turn any AI into a careful, fast editor — without it rewriting you into someone you are not. One pass each for clarity, tone, and grammar.

Most AI emails sound like AI. A practical workflow for using ChatGPT to draft, sharpen, and finish the emails you would actually press send on — without the robot smell.

The blank input box is the hardest part. Seven copyable prompts that turn an empty conversation into a real win on your very first day.

Tina Huang. A fast catalogue of concrete use cases mapped to ChatGPT, Claude, Gemini and Perplexity. Good for seeding ideas for which step in your workflow actually belongs in which tool, rather than defaulting everything to one chatbot.

Matt Wolfe. Matt walks through the exact stack he uses daily — Claude for writing and Projects, Perplexity for research, plus the supporting cast for transcription, capture and images — and is honest about why each tool earns its slot. This is the closest analogue on YouTube to the article's "pick a default for each job" framing.

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. Tina puts Perplexity's Deep Research and ChatGPT/Claude analysis modes in the context of a working analyst's day — pulling together market signals, exploratory data analysis, and structured outputs. Useful as the "what else is this good for besides academic literature reviews" companion to the primary pick.

Andy Stapleton. Stapleton runs Deep Research against the exact use case it shines at — a literature review with a clear scope, time frame, and structure — and shows the clarifying-questions handshake, the 10-minute wait, the 40-source output, and where the citations and tone still need a human pass. Calls out the limits honestly (no BibTeX export, occasionally too generous in source choice).

Matt Wolfe. Once you understand the basics, this is the right second video. Matt runs through a long list of less obvious uses — turning a book into a study guide, briefing yourself on a competitor from their content, generating mind maps from a folder of PDFs — that help you see how flexible the "grounded notebook" frame actually is.

Paul J Lipsky. A clean three-step framing — curate sources, ask the right questions, produce final outputs — that maps almost one-to-one onto how the article tells you to think about NotebookLM. Recent enough (early 2026) to match the current UI, including Studio, audio overviews, and mind maps.

Leila Gharani. Older (early 2023) but still the cleanest worked example of pasting a real Excel question into ChatGPT, getting a SUMPRODUCT-or-conditional-formatting answer back, and then iterating with the model when the first formula doesn't quite fit. The model has improved a lot; the prompting workflow she shows is the same one you'll use today.

Leila Gharani. Live demo of the new `=COPILOT()` function on the kinds of messy real data the article describes — shift handover notes turned into a structured action table, free-text reviews tagged for sentiment, and inconsistent job titles normalized against a master list. Also calls out the limits clearly: don't use it for anything that has to be exact, freeze results as values when you're done.

David Perell. Perell teaches writing for a living and walks through how he actually drafts with LLMs without the output reading as AI — what he uses the model for (research, structuring, surfacing his own thinking), what he refuses to delegate (voice, taste, opinions), and the specific moves he uses to keep his prose his. Sits just under the 100K-view bar but earns its place because the topic is genuinely niche and this is the most thoughtful long-form take on it.

Scott Brant. Scott Brant's broader Copilot work is the closest thing to an honest demo of how an integrated AI assistant changes the daily Outlook/Teams loop — drafting replies, summarizing threads, pulling action items into tasks. Nominally about meetings, but the same reflexes transfer straight to Outlook for anyone in a Microsoft 365 shop. (No single AI-for-email video clears the 100k-view bar with a beginner inbox-zero focus, so this is the strongest adjacent walkthrough.)

Tina Huang. Tina lists where AI actually pays off across a workday and several entries are exactly the email moves the article describes — checking and summarizing inboxes, drafting replies, editing for tone, organizing brain-dumped tasks. Useful as a reality check on what's worth automating versus what just needs a better template.

Elegant Themes. Short overview of Otter as a standalone transcriber for people who don't live in Teams — Otter Pilot joining meetings on your behalf, calendar/Zoom integration, file uploads, and the free tier limits. Useful as a four-minute orientation before deciding which of the article's tools you want to actually try.

Scott Brant. A clean, end-to-end walkthrough of what the article describes: enable transcription, ask Copilot questions live, get an audio recap and a Word minutes document afterwards, and pull action items out into a follow-up workflow. Scott Brant is a Microsoft 365 educator who does this every week, and the demo is on a real meeting rather than a contrived one.

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.

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.

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.

Tina Huang. Tina Huang sorts a hundred-plus everyday AI use cases into productivity, daily life, learning, careers and relationships — and crucially walks through each one fast, with the actual prompt or screen on display. It's the most efficient way to see what "asking ChatGPT something" can actually look like beyond the seven examples in the article, without sitting through a 40-minute course.