Big Idea

The argument in one line.

The real bottleneck to memorizing what you learn is not the information itself but the friction of encoding it — and Claude Code, used as an agent that quizzes you, identifies gaps, and writes cards directly into Anki, can eliminate that friction without outsourcing the actual thinking.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…

You already use Anki or tried it and quit because making cards felt like too much work.
You study papers, technical videos, or long-form content and want a faster path from reading to retention.
You use Claude Code or a similar AI agent tool and want concrete workflows rather than generic advice.
You take notes in Obsidian and want those notes to link back to original source material with timestamp references.

SKIP IF…

You have never tried Anki and are not interested in spaced-repetition systems.
You want a polished product — this is a personal workflow that requires comfort with Python and the command line.
You are looking for passive AI-generated summaries rather than an active learning loop.

TL;DR

The full version, fast.

Anki's spaced-repetition algorithm is proven for long-term retention, but manually creating quality flashcards from dense material creates enough friction that most people abandon the habit. The creator's solution is a Claude Code workflow that treats each source type — PDF, web article, YouTube video — as its own pipeline: Claude Code generates a quiz from the material, the learner takes it to identify real gaps, and then Claude Code writes cards directly into the Anki SQLite database based only on those gaps. For YouTube, a Python script extracts the transcript with embedded timestamp links so every Anki card connects back to the exact moment in the video. All outputs are saved to Obsidian, creating a searchable knowledge base where every note traces to a primary source.

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Chapters

Where the time goes.

00:00 – 01:00

01 · Introduction: The Formula for Learning & Memorization

Opens with the thesis. Creator's background: research lab, AI engineer, AI instructor. Frames augmentation tech as often cringe-worthy but positions this system as genuine amplification.

01:00 – 02:00

02 · What is Anki & Spaced Repetition?

Explains Anki for newcomers. Free, open-source. Spaced-repetition algorithm automates review timing. Credits Michael Nielsen's 'Augmenting Long-Term Memory' as the founding inspiration.

02:00 – 03:00

03 · The Anki-Finding Process & Its Challenges

Introduces 'Ankifying' — the act of converting noticed information into a card on the spot. Names the core friction: context-switching between source and card tool.

03:00 – 04:00

04 · Building an Anki Workflow Over the Years

Personal testimony: seven years, multiple programming languages, spoken languages, martial arts. Acknowledges that even experienced users still hit friction. Quotes Nielsen: 'Anki is a virtuoso skill.'

04:00 – 05:00

05 · Introducing Claude Code as an AI Agent Tool

Describes Claude Code as a terminal-based agentic tool from Anthropic. Clarifies GUI alternatives for non-technical users. Notes it can amplify work across any computer-based domain.

05:00 – 06:00

06 · Using AI Prompts to Bulk Generate Anki Cards

Demonstrates the naive first approach: paste article + prompt into any chatbot, bulk-import resulting text file into Anki. Identifies the critical flaw: cards reflect AI judgment, not the learner's attention.

06:00 – 07:00

07 · Experimenting With Claude Code + Anki for Different Sources

Reframes the problem by source type. Each source — PDF, web article, YouTube — has distinct friction points requiring its own flow. Introduces the PDF quiz approach.

07:00 – 08:00

08 · Creating Quizzes from PDFs Using Claude Code Skills

PDF pipeline: feed paper link to Claude Code, render a quiz as a vibe-coded HTML app, take the quiz in browser, identify gaps, send gaps back to Claude Code for targeted card creation. Explains Claude Code skills as reusable packaged expertise.

08:00 – 09:00

09 · Automating Card Creation Directly Into Anki's Database

Key upgrade: a Claude Code skill writes cards directly to the Anki SQLite database, eliminating the text-file import step entirely. Reaffirms that the learner still drives the quiz loop — AI removes logistics, not judgment.

09:00 – 10:00

10 · Applying This Approach to YouTube Videos

Expands the framework to YouTube, noting that most recent learning happens there. Identifies the new friction point: navigating long videos (e.g., 3-hour Karpathy lectures) to find specific moments.

10:00 – 11:00

11 · Using YouTube Timestamps to Enhance Anki Cards

Historical technique: right-click video → 'Copy link to current timestamp' → paste into Anki Source field. Has worked for years. Now wants to automate the card creation around it.

11:00 – 12:00

12 · Extracting Video Transcripts for Smarter Learning

Uses youtube-transcript-api Python package to pull the full transcript programmatically. Paste into Claude Code with a custom prompt that extracts only the sections of interest, with clickable timestamp links preserved in the output.

12:00 – 13:00

13 · Custom Prompts & Timestamp Extraction Workflow

Demonstrates a dedicated Claude Code slash command that summarizes a YouTube video and returns bullet points with embedded YouTube timestamp URLs. Compares Gemini and NotebookLM — both offer clickable timestamps in the tool, but links don't survive copy-paste.

13:00 – 14:00

14 · Saving Outputs to Obsidian for a Connected Knowledge System

All Claude Code outputs — summaries with timestamp links, extracted sections — are saved to an Obsidian vault. Creates a searchable, source-linked knowledge base. Acknowledges this is overengineered but works well.

14:00 – 15:00

15 · You Don't Need All the Tools — Simplifying the Approach

Honest caveat: Claude Code + Obsidian + Anki is the full stack but overkill for most. A well-crafted chatbot prompt workflow delivers most of the benefit without the setup.

15:00 – 15:23

16 · Course Announcement & Wrap-Up

CTA for a paid course that covers all approaches with source materials, prompts, and folder structures. Standard like-and-subscribe close.

Atomic Insights

Lines worth screenshotting.

The bottleneck to memorizing what you learn is not information access — it is the friction of encoding what you decide matters.
Generating bulk flashcards from AI is a weak strategy because meaningful cards require you to choose what to remember, not the model.
A quiz-first workflow surfaces real knowledge gaps before any card is created, so every card you make earns its place.
Writing Anki cards directly into the SQLite database via Claude Code eliminates the import-file step and removes one more friction point.
YouTube's right-click 'copy link to timestamp' feature has quietly been one of the most powerful Anki source-linking tools for years.
Claude Code skills are packaged expertise files that let a single slash command execute multi-step domain workflows without re-prompting.
A Python transcript extraction script gives you a searchable, timestamp-linked record of any YouTube video that you own and can reuse.
Gemini and NotebookLM offer similar timestamp summaries for free, but the links don't survive copy-paste — you don't own the output.
Obsidian as the output layer makes every AI-generated note searchable and traceable back to the source video moment that generated it.
You do not need the full stack: a well-crafted chatbot prompt and copy-paste workflow delivers roughly 80 percent of the same benefit.
Spaced repetition schedules reviews at the moment your brain is about to forget — compressing years of recall into a manageable daily queue.
Treating different source types as separate pipelines is the key insight: PDFs, articles, and YouTube each have different friction points that require different automation.

Takeaway

Quiz yourself before you make a single card.

WHAT TO LEARN

The gap between reading something and remembering it is almost entirely a friction problem, and the quiz-first, card-second workflow is the most direct way to close it.

01Introduction: The Formula for Learning & Memorization

The creator frames this as augmentation that amplifies rather than replaces — the distinction matters for how you think about using AI in any learning workflow.

02What is Anki & Spaced Repetition?

Spaced repetition automates the review schedule so you focus attention on cards you are about to forget, not cards you already know — this is the compounding mechanism that makes the system work over years.

03The Anki-Finding Process & Its Challenges

Context-switching between a source and a card-creation tool is the primary reason most Anki habits collapse — any automation that removes that switch directly improves retention rates.

04Building an Anki Workflow Over the Years

Seven years of consistent Anki use still produced friction — the takeaway is that no amount of practice fully eliminates the card-creation bottleneck, which is exactly the problem automation should target.

05Introducing Claude Code as an AI Agent Tool

Claude Code's value in a learning system is not summarization — it is the ability to execute multi-step workflows (render a quiz, write to a database) from a single command, acting as an automation layer around your existing tools.

06Using AI Prompts to Bulk Generate Anki Cards

Bulk-generating cards from AI is a weak strategy because meaningful cards require you to choose what to remember, not the model.
The naive AI-to-Anki pipeline solves the wrong problem — it reduces card creation effort but increases card irrelevance.

07Experimenting With Claude Code + Anki for Different Sources

Treating each source type as a separate problem worth optimizing differently is the key structural insight — PDF friction is not the same as YouTube friction.

08Creating Quizzes from PDFs Using Claude Code Skills

A quiz-first workflow surfaces real knowledge gaps before any card is created, so every card you make addresses something you actually missed.
Claude Code skills are reusable expertise files that let one slash command drive a complex multi-step workflow without re-explaining the context each time.

09Automating Card Creation Directly Into Anki's Database

Anki cards built from identified gaps rather than AI summaries are more durable because they encode the specific moment of failure, not a model's best guess at what matters.
Writing directly to the Anki SQLite database removes one more step from the loop — reducing friction at the end of a workflow is just as important as reducing it at the beginning.

10Applying This Approach to YouTube Videos

Long educational videos like multi-hour lecture series create a specific friction type: navigating back to a specific moment after watching — the timestamp workflow is the fix.

11Using YouTube Timestamps to Enhance Anki Cards

Embedding a YouTube timestamp link in an Anki card's source field turns a flashcard into a portal back to the original context — when you review and fail to recall, you can jump directly to the moment that generated the card.

12Extracting Video Transcripts for Smarter Learning

A programmatically extracted transcript with timestamps is a reusable artifact you own — unlike a chatbot session, you can re-process it, search it, and embed it elsewhere.

13Custom Prompts & Timestamp Extraction Workflow

The difference between Gemini/NotebookLM and a Claude Code slash command is data portability: the former gives you interactive clickable links; the latter gives you links in plain text you can reuse anywhere.
A dedicated slash command for a recurring workflow encodes your preferences once and removes the need to reconstruct the prompt each time.

14Saving Outputs to Obsidian for a Connected Knowledge System

Saving AI outputs with embedded source links to Obsidian creates a personal knowledge base where you can navigate from a note directly to the original video moment — the difference between a pile of summaries and an actual reference system.

15You Don't Need All the Tools — Simplifying the Approach

The full Claude Code + Obsidian + Anki stack delivers the best output but requires the most setup; a well-crafted chatbot prompt and copy-paste workflow delivers most of the same benefit and is a better starting point.

16Course Announcement & Wrap-Up

The creator's willingness to acknowledge the system is overengineered for most people is the CTA's most credible moment — it signals that the course is aimed at people who want the full workflow, not everyone.

Glossary

Terms worth knowing.

Anki: Free, open-source flashcard software that schedules card reviews using a spaced-repetition algorithm so you study each card at the optimal moment before forgetting it.
Spaced repetition: A learning technique that increases review intervals each time you successfully recall information, dramatically improving long-term retention per hour of study time.
Ankifying: The informal term for the process of deciding that a specific piece of information is worth remembering and converting it into an Anki flashcard.
Claude Code: Anthropic's terminal-based agentic AI tool that can control one or more agents inside a computer to perform tasks such as creating files, writing to databases, and rendering apps.
Claude Code skills: Reusable expertise files stored in a folder structure that give Claude Code agent a set of pre-defined, complex capabilities invokable through a simple slash command.
youtube-transcript-api: An open-source Python package that fetches the full transcript of any YouTube video, including timestamp data, without needing to manually copy it from the UI.
Obsidian: A local-first note-taking application that stores notes as plain Markdown files and supports internal links between notes, creating a personal knowledge graph on your own machine.
Vibe-coded HTML app: A quick, AI-generated browser-runnable web application assembled from a prompt rather than written from scratch — functional but not production-hardened.

Resources

Things they pointed at.

01:22toolAnki ↗

01:42linkAugmenting Long-Term Memory — Michael Nielsen ↗

03:58toolClaude Code ↗

12:11toolyoutube-transcript-api ↗

13:40toolObsidian ↗

13:13toolGemini ↗

13:20toolNotebookLM ↗

Quotables

Lines you could clip.

00:00

“I think I've learned the perfect formula for how to learn and memorize information combining Anki flashcards and Claude Code.”

Bold opening claim, specific tools named, immediately curiosity-triggering→ TikTok hook↗ Tweet quote

03:44

“Anki is a virtuoso skill. You have to develop it with years of practice and experience.”

Quotable admission that lowers unrealistic expectations — rare in AI-tool content→ IG reel cold open↗ Tweet quote

08:58

“I don't think this bypasses us as the main active agents of our knowledge.”

Addresses the core anxiety about AI outsourcing thinking — reframes the tool's role→ newsletter pull-quote↗ Tweet quote

14:49

“Feels like a Brain OS type of situation. Reduces friction to a point where you can really just work with ideas directly.”

Tight, vivid metaphor for the outcome — memorable and reusable→ TikTok hook↗ Tweet quote

The Script

Word for word.

Read-along

Don't just watch it. Burn it in.

See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.

metaphoranalogy

I think I've learned the perfect formula for how to learn and memorize information combining Anki flashcards and ClockCode.

So I've been building, working with, and thinking about AI for a while. First, as a research assistant at a major research lab, then as an AI engineer, and more recently as an AI instructor.

And I've always been fascinated with this idea of being able to augment yourself with technology. But this idea of augmentation can feel kind of cringe, like picture guys filming other people on the street with the rain band, metaglasses. It feels more like a wall between you rather than something that amplifies your ability to access the world and understand the world.

So I've been thinking about that for a while, and one of the things I really enjoy doing over the past seven years when I'm studying or when I'm researching something or writing or whatever it is is I like using Anki flashcards. Now if you don't know what an Anki flashcard is, it's just a simple software that's open source, free to download and use, and it allows you to create a bunch of cards that memorize information about the world.

And then what you can do is you can review them, and the schedule for review is automated for you by something called a spaced repetition algorithm, which is just a simple algorithm that essentially automates when you should review a card to maximize the chance that you're gonna remember the information inside.

The first time I heard about this was from a guy called Michael Nielsen. I read an article he wrote called Augmenting Long Term memory. He said that you could make memory a choice by making a decision about what are the things that you definitely want to memorize for the rest of your life.

Because imagine if everything that you're interested in, everything that you wanna learn deeply, you could just make that a reality. Now the the actual practice of it is not exactly like that, and it's a little bit harder, but I think I got pretty close. So let me explain.

With Enki, you have this concept of Enki finds, which means that when you're reading information, let's say you're reading a paper, when you see something that strikes your attention, something that you definitely wanna remember for later, you create a card for it. You write down the question for the card, and then you write down the answer for that card based on what you just read or what you just watched.

You leave it there, And then when you go back to it, you can review it, and, hopefully, you you remember it. If you remember it, you click on easy. There's a button there somewhere.

I'm gonna put it on the screen somewhere. And then if you don't remember it, you go again. So you put that card to be reviewed again the same day because, well, you didn't remember it.

So the concept itself is really cool, but what I find people struggle the most is that the process of enquifying is really annoying because it's a lot of work and it's a lot of friction. I mean, you have to do some sort of context switching between the source that you're reading and dealing with and the actual process of that stuff.

That makes the barrier to entry to start memorizing information a little bit harder. Now for me, when I heard about this, I decided that I was gonna jump into this process, and I wasn't gonna care that it was gonna take too long. So what I did was I'm gonna learn all the keyboard shortcuts.

I'm gonna learn and set up my workflows in such a way that Ankify information feels natural. And after a few years, I feel like I've been able to do that. And I I've learned multiple programming languages with Anki.

I've learned new languages. I've practiced martial arts. So I think Anki helped me in a bunch of different ways, but I still struggle with friction of creating the cards.

Sometimes I create too simple cards. Sometimes I don't really memorize the right thing. Sometimes I I feel like I'm not encoding the information properly, and it's not very it's not easy.

Like Michael Nusens says, Enki is a virtuoso skill. You have to develop it with years of practice and experience.

I think I got to a pretty good level, but there's still friction when you wanna learn a lot, when you wanna put in a lot of information. And that's where a tool like ClawCode comes in.

ClawCode is a an agent tool created by a company called Anthropic, and what it does essentially is it allows you to control one or multiple agents inside of your computer. And an agent is essentially just a large language model connected with a few tools that can perform meaningful actions in your computer so it can create files, update sheets, and it can do all sorts of things.

Now I use it primarily in the terminal, but you can also use it with Cloud Desktop, get access to Cowork, which is the nontechnical version of ClawCode, or you can actually access a slightly more limited version of ClawCode directly in a GUI like interface. Now what's awesome about ClawCode is that it allows you to amplify whatever you're already doing across pretty much any domain or anything that you do in a computer.

A couple of months back, I started working on, okay, how do I use ClockCode to memorize information, to help me kinda amplify how I use Enki?

And I got to a really interesting set of conclusions about that. For example, uh, the basic example that for something you can use, any chatbot really, uh, to memorize information is if you take a web article, for example, and you take all the contents of that web article, and then you paste it into Claude or Chhbt, doesn't matter.

And then you paste in this prompt that I'm gonna show you somewhere in the screen out there, uh, which essentially is going to create cards to encode information from the article, and it's going to output that in a format that's easy to import directly into Anki. So you can bulk import this text file that's generated. And when you open Anki, all of the cards are there automatically.

So that's great. That solves the friction problem. Right?

I can just paste in this prompt. Learning is solved. Memorization is solved.

Not really. Because if you're not intentional about the kinds of things you're trying to memorize and then you're creating the cards yourself, at least to some extent, the kinds of cards you're gonna create are not gonna really relate to what you care about.

Because when you read an article, you don't wanna memorize and remember everything. You wanna remember the things that strike your attention. So when you create a when you use a prompt like this, it does help, but it is a bit of a weak way of using AI to with Anki to memorize stuff.

However, I was intrigued by this concept of combining clock code and Anki, so I started experimenting with ways to reduce the friction of creating meaningful Anki flashcards.

So that's the whole thing. I wanted to reduce friction so I could create meaningful cards to help me remember information. And I structured my experimentation by type of source.

For example, when I'm studying a paper, that usually comes in a PDF. So you can find the link to the paper, and you can put that into some AI model and ask it to summarize. But with ClockCode, you can go a little bit beyond that.

You can, for example, create a quiz from that paper with ClockCode and have it render a simple HTML application. HTML is the type of file format that you can open in your browser directly, and you can have a little vibe coded app there that works as a quiz where you can quiz yourself on the contents of that paper. Now when you get to the end of the quiz, you get a score.

And then from that score, you can say, okay. So here's what I don't know about that paper. I didn't know this piece of information, that piece of information.

So you can create a comprehensive quiz first to see the stuff that maybe after you read the paper, you didn't actually remember. So now those things become the candidates for an Enke flashcard. So I can paste that paste the link to the PDF into ClockCode.

I can then say, quiz me on this, and then it will render the HTML app, uh, automatic. And it does that because besides having prompts to do different things for me, I have ClockCode use something called skills.

Now if you don't know what skills are, skills are absolutely fascinating. Skills are essentially a way for us to package expertise so that an agent like ClockCode or Codex, so that it can actually do something a little bit more complex, I can say, quiz me on this.

ClawCode will then render this very nice little quiz app. I will then take the quiz, identify the gaps, and then take that information back into ClawCode to create Inkey cards directly. Now the first example of a prompt I set was using this text based format that you can directly import to Enki.

The problem is there's still the friction of every time you create such a file, you have to import it into Enki. Right? And, I mean, I do get very weird about this kind of stuff, and I wanna reduce friction to the max.

So I found out that you can actually create a skill for ClawCode that creates the cards directly in your Anki database. Now what's cool about this is that now it's all about giving the source to Clocko and then saying, look.

I wanna learn and understand this. I've already read it once, but now I want you to quiz me so I can identify some gaps. It will then quiz me with a very nice little vibe coded HTML app.

I will identify the gaps. I will feedback that information back into ClockCode and then say, create the cards about these topics where I missed, and then it will automatically create. Now now that was a great example of a way of using an agent to learn and memorize information that I don't think bypasses us as the main active agents of our knowledge.

I know it sounds kind of cringey to say, but what I mean is I feel like using AI like this doesn't feel so much as I'm offloading the the hard work to the AI tool, but I'm offloading the stuff that I really don't think I should be doing. So after some early experiments with PDFs and web articles, I was like, okay.

This is really cool. But I noticed that a lot of the stuff that I've learned recently is actually with YouTube. I watch a lot of YouTube videos about different things, and I this video that you're watching right now is on YouTube.

Right? So I was wondering, could there be hacks and ways of making it easy for me to learn information from YouTube videos using this approach, combining this skill, this this idea of, like, creating the cards automatically using ClockCode to speed up the process?

And I realized that I wanna remember specific parts of the video. So YouTube itself has a really nice little feature where if you right click on any part of a video, you have this very nice copy the link to the current time stamp thing that you can click on, and then you have a link that goes directly to that part of the video that you that has the information you're looking for.

So for example, in my early days of using Anki, I used to create cards about information that was inside of a YouTube video, and the front of the card would have the question, the back of the card would have the answer, and then I would always have a source section on my cards where that link with the timestamp would be placed.

So that works really well, and I've been using that for many years. But I was like, okay. Let's let's make this a little bit fancier with Clockode.

How could I automate this? And one of the things I kept coming back to when it comes to I just spoke like a real AI now.

One of the things I keep coming back to, but but one of the things that I that I realized were a pain point for me were, for example, if you have a lecture about LLMs or the amazing lectures from Andrush Karpathy that are, like, three hours long. If you don't know who who that is, go look him up.

If I have a lecture like that and I know I want specific things from specific parts of the video, I have to actually go and navigate through the whole video to find those specific parts. And that felt to me a little bit like that shouldn't be like that. So what I did was I the first thing was, okay.

I wanna be able to obtain the transcript of the video in YouTube and then paste that into ClockCode and then obtain the parts that I care about. That's the thing you can do right now with a simple chatbot interface. For example, I can go to the YouTube video.

Uh, if you scroll down in the video's description, you can click on more. And then from there, you're gonna see show transcript at the bottom of the video description. If you click on show transcript, you can, in a very hacky and weird way, copy paste the entire transcript.

Now there are some Chrome extensions that allow you to click a button and then copy the full transcript, but it just didn't feel like I should like, Chrome extensions can be weird sometimes and and generate weird behaviors, so it didn't feel like I should have a Chrome extension for that. However, uh, I also felt like copying it manually was a bit annoying.

Now there is a package that allows you to extract the transcripts from videos. It's in Python programming language, if you don't know. And I asked Claude, write a little script that gives me the entire transcript and the link at the top.

And now I can use that to, for example, paste that into ClockCode or paste that into a chatbot, and I use a custom prompt to extract the sections of the video that I care about. But with the addition that I wrote down a little special prompt that will be showing up somewhere here that extracts those time stamps. So now when I get the output from the video, I get the answers, and I get the answers with the links where I can click and go directly to that part of the video, which makes for, like, a super granular extraction of the information that I actually care about.

And I have a custom prompt in ClawCode. ClawCode makes it super easy for you to create slash commands and custom prompt. And I have one that's specifically to extract, to summarize a video with these YouTube URL time stamps.

So it makes it very, very easy. If all this stuff is maybe sounding a little bit too technical and too many things to remember and shortcuts, in Gemini, you can do that for free. The only thing in Gemini is that when you generate the actual contents, can summarize with time stamps that are clickable directly in Gemini.

And NotebookLM has like a similar feature, if I'm not mistaken. But if you go to copy those contents, links don't come in the source raw material that you copy.

Problem then becomes you can't really reuse it. That data of the clickable things is not really yours. Now since I use ClawCode, what I do usually is that I save these outputs with the nice extracted information and the YouTube links to my Obsidian, which is a note taking application.

So it is geeky. It is a bit overengineered, but it's something that works very well for me.

So this is just a layer that makes the search process for the right type of information much easier. And a final note I'm gonna make in this video is that you don't have to do all this stuff.

You can just use an a smart combination of prompts and a simple chat based interface. You don't necessarily need ClockCode. I just think that ClockCode plus Obsidian plus Anki, it feels kind of futuristic because it's simple, it's easy.

I can directly go to the source materials. I know where things are. So when I'm writing, for example, I combine bytes of information extracted with these approaches And I have like a single note in my Obsidian file, in my Obsidian vault, that contains these linked notes with specific sections of a video so I can when I'm writing, I can go back to specific sections that I cared about, and it makes the process feel super flow natural, and I just I just enjoy it.

Feels like a Brain OS type of situation Reduces friction to a point where you can really just work with ideas directly Now there are many more ways that I use Anki and if you want to check out, I have a course about combining Clockcode and Anki.

I'll put a link somewhere in here that you can click. And in that course, I break down not just this approach, but a bunch of other approaches, and I share source materials and prompts and folders and all that stuff. So if you wanna check that out, I'll leave a link in the description.

Like and subscribe if you like the video, and thanks for watching. Cheers.

The Hook

The bait, then the rug-pull.

Seven words into the video, the claim is on the table: a perfect formula. What follows is not hype — it is a methodical walkthrough from a practitioner who has used Anki for seven years and AI agents professionally, and who spent months narrowing the gap between 'I read this' and 'I remember this.'

Frameworks

Named ideas worth stealing.

02:00concept

Ankify-as-you-go

When you notice something worth remembering while reading or watching, create a card for it immediately. The spaced-repetition algorithm handles scheduling from there.

Steal forAny knowledge-intensive workflow where you want active retention rather than passive notes

07:00model

Quiz-first, card-second

Read/watch the source
Have AI generate a quiz
Take the quiz to surface genuine gaps
Only gaps become Anki card candidates

Quizzing yourself before creating cards filters out false confidence and ensures every card addresses a real knowledge gap.

Steal forStudying technical papers, dense tutorials, or any material where you want to test comprehension before committing to memory

06:00model

Source-type pipelines

PDF -> Claude Code quiz skill -> HTML quiz app -> gap cards to Anki DB
Web article -> paste + AI prompt -> bulk import to Anki (weak form)
YouTube -> transcript extraction -> timestamp-linked summaries -> Obsidian -> Anki cards

Each source type has a different friction point requiring a different automation. Treating them as separate pipelines lets you optimize each one.

Steal forBuilding a personal learning system across mixed media types

08:00concept

Claude Code Skills

Packaged expertise files that let Claude Code execute complex domain-specific workflows from a single slash command, such as rendering a quiz HTML app or writing directly to a database.

Steal forAny repeatable multi-step agentic workflow you want to invoke without re-explaining the context each time

CTA Breakdown

How they asked for the click.

VERBAL ASK

15:00product

“I have a course about combining Claude Code and Anki. I'll put a link somewhere in here that you can click. And in that course, I break down not just this approach, but a bunch of other approaches, and I share source materials and prompts and folders and all that stuff.”

Verbal mention at the very end with a visual link indicator. Low-pressure, informative tone. No urgency or discount framing.

MENTIONED ON CAMERA