The argument in one line.
The highest-leverage way to use a temporarily-unlocked frontier model is not general chat but five specific jobs: surfacing your own highest-value work, giving strategic advice grounded in real data, auditing a project for launch-blocking bugs, drafting a detailed plan for another model to execute, and refactoring a large body of code or personal systems.
Read if. Skip if.
READ IF YOU ARE…
- You use Claude Code or a similar AI coding assistant regularly and want concrete, non-obvious use cases beyond writing code.
- You have vibe-coded side projects you intend to ship and want a systematic way to find bugs before launch.
- You maintain a personal knowledge base, plan document, or set of AI skills/prompts and want to see how someone else audits and improves theirs.
- You're deciding how to allocate scarce usage limits on a premium AI subscription tier across different tasks.
SKIP IF…
- You're looking for a general Claude Code tutorial covering setup or basic usage.
- You don't have any existing projects, plan docs, or codebases to point the workflow at -- the value here comes from applying it to your own material.
The full version, fast.
A creator with brief access to Claude's newest model before a usage-limit cutoff demonstrates five ways to get outsized value from it: ask it to mine your own memory and project history for the highest-leverage work worth doing; feed it a personal plan document plus live API/MCP connections and ask for a business strategy review; point it at a shipping-soon codebase and ask it to hunt for real bugs and edge cases; have it draft a detailed implementation plan (including HTML mockups) for a new feature that a cheaper model can later execute; and use it to audit and refactor a large body of code or personal AI skills. He closes with three tips: prep context with cheaper models first, plan with the frontier model but execute with a cheaper one, and use lower effort settings while babysitting long-running tasks to avoid burning through limited usage.
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Create a free account →Where the time goes.
00:00 – 01:14
01 · Cold open: Fable is back, with limits
States the constraint -- access through July 7 only, cut off at 50% of weekly usage, then falls back to paid API credits.
01:14 – 01:30
02 · How to choose Fable 5 in Claude Code
Shows the model picker and effort-level selector in the Claude Code app.
01:30 – 04:13
03 · Use case 1: Find Fable-worthy work
Asks the model to mine his own project memory for the five most valuable tasks worth running on it; walks through its five suggestions.
04:13 – 09:16
04 · Use case 2: Get life and business advice
Demonstrates the plan-doc + APIs/MCPs + ask-for-advice method, including a custom advisor and council skill, then reviews the model's one-pager business assessment.
09:16 – 12:10
05 · Use case 3: Make your project ship-ready
Points the model at a vibe-coded fitness app and asks it to find real bugs before launch; it surfaces 12+ major bugs including a data-leak on sign-out, versus far fewer found by other models on the same prompt.
12:10 – 15:54
06 · Use case 4: Plan the next big thing
Has the model draft a detailed plan (with research and an HTML mockup) for a new nutrition-tracking feature, intended to be handed off to a cheaper model for execution.
15:54 – 18:29
07 · Use case 5: Refactor a large codebase
References Stripe's reported 50M-line Ruby migration as inspiration, then applies the same audit approach to his own 40-skill personal AI OS repository, finding 13 issues to fix.
18:29 – 19:57
08 · Three tips to get the most out of Fable
Prep with cheaper models, plan with Fable and execute elsewhere, use lower effort and babysit long runs.
19:57 – 21:17
09 · Recap and sign-off
Restates the five use cases and reminds viewers of the July 7 deadline.
Lines worth screenshotting.
- Asking a frontier model to review your own memory and project history for 'Fable-worthy work' surfaces high-leverage tasks you might not have prioritized yourself.
- A three-step method for AI-driven strategy advice: build a written plan document, connect live data sources via APIs/MCPs, then ask for advice grounded in both.
- Running the same bug-hunting prompt against three different frontier models on an identical vibe-coded app found over 12 major bugs with the newest model versus only a handful with the others.
- A vibe-coded app that seemed to work fine after a month of personal use still had a critical bug where an involuntary sign-out could leak one user's data into another user's account.
- The most valuable planning output wasn't just a text plan -- the model generated an actual HTML mockup of the proposed UI unprompted, which became the basis for stakeholder review via inline comments.
- Stripe reportedly used a frontier Claude model to migrate a 50-million-line Ruby codebase in roughly a day, a job the blog estimated would take a full team over two months manually.
- The plan-then-execute pattern -- use the frontier model to draft a highly detailed plan, then hand it to a cheaper model to implement -- lets you reserve scarce premium usage for the reasoning step, not the mechanical coding step.
- Don't spend a frontier model's limited usage on boring setup work like wiring up an API or MCP connection; do that prep with a cheaper model first.
- Running a coding agent at maximum 'effort' settings burns through usage limits far faster than moderate settings, with little quality gain for most tasks.
- Long-running agent tasks need to be babysat because a smart model can still get stuck looping on a problem and silently burn through a token budget.
Five specific jobs to save your scarce AI usage for
WHAT TO LEARN
Treat a frontier AI model's usage limit like a scarce budget and reserve it for reasoning-heavy work -- self-audit, strategy, bug-hunting, planning, and refactoring -- not routine setup or execution.
- Ask an AI model to review your own project history and memory to surface the highest-leverage tasks worth doing, rather than deciding priorities from scratch yourself.
- Get grounded strategic advice by combining three ingredients: a written plan document, live data connections (APIs/MCPs), and then a direct ask for advice.
- Before shipping any project, ask an AI model to hunt specifically for bugs and edge cases that fall over in front of a user -- a targeted prompt like this can surface critical issues (like data leaking between user sessions) that months of manual use never revealed.
- When planning a new feature, ask for a plan detailed enough that a cheaper or simpler model could execute it step by step -- this separates the expensive reasoning work from the cheap implementation work.
- For any large body of accumulated work -- a codebase, a set of personal AI skills, a document library -- a periodic AI-driven audit pass can find real inconsistencies and dead weight that accumulate silently over time.
- Prep routine setup work (wiring up integrations, drafting rough materials) with a cheaper model, and save the most capable model for the actual reasoning step.
- Lower effort settings and active monitoring of long-running AI tasks prevent a capable model from quietly looping and burning through a limited usage budget.
Terms worth knowing.
- MCP (Model Context Protocol)
- A protocol that lets an AI model connect to external tools and data sources, such as a bank account, analytics dashboard, or document editor, so it can pull live context into its responses.
- Effort setting
- A configurable dial in some AI coding tools that trades off how much reasoning/compute a model spends per task against how quickly it consumes a usage quota.
Things they pointed at.
16:09linkStripe engineering blog: 50M-line Ruby migration with Claude
13:07toolUSDA FoodData Central API
Lines you could clip.
00:06
“Claude Fable five, the world's best AI model, is finally back after being banned by the US government for eighteen days.”
18:33
“If you scroll down here, so it found a bunch of pretty critical bugs -- if you sign out involuntarily, you could potentially leak one user's data into the next user's account.”
18:52
“It found over 12 major bugs and a bunch of minor bugs. And when I run the same prompt on GPT 5.5 or Opus, it does find a few bugs but nowhere near the amount that it found here.”
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.
metaphor
00:00Hey, everyone. So Claude Fable five, the world's best AI model, is finally back after being .
00:09I've been thinking about Fable a lot, and I wanna show you five use cases that I think is actually worth running Fable on. But first, let me catch you guys up. Fable is back, but there's a catch.
00:21It's only available through July 7 on your cloud subscription and you can't use it anymore after you hit 50% of your weekly usage limits.
00:31After that, Fable will only be available through pay as you go API credits, so you have to be very, very careful about how you use Fable for the next few days. Alright.
00:42So without further ado, let me talk about five use cases that I actually think are worth using Fable for. Using Fable to find Fable worthy work, getting life and business advice, making your project ship ready, planning for the next big thing, and also refactoring a large code base.
01:01Now I'm gonna do a live walkthrough of all five use cases and show you real Fable output. Let's get going. Okay.
01:09So this is my Cloudco app and you can select Fable down here to select Fable five and of course, can select, uh, the effort that you want Fable to go with. Now personally, I just stick to high effort because if you go all the way over here to ultra code, your limits are gonna run out super fast. Alright.
01:26So let's start with the first use case which is using Fable to find Fable worthy work. Now Fable is an amazing model and if you use Clot like I do, it already has memory of all your past interactions. So here's what I asked Fable.
01:41You are Fable five, the most capable AI model available. Look through my projects and memory. List the top five tasks that require deep thinking that you think are generally worth running by you.
01:53Please do not share any confidential information because I'm making this video and giving a demo. So here are Fable's five suggestions.
02:00Right? Number one is h two strategy stress test. It found out in my memory that I recently left my job to work on my business full time, and it also found a bunch of skills that I built to get advice on my business.
02:14So it wants to do a strategy consultation, which is exactly the second use case that I'm gonna give a live demo of next. Alright.
02:23So let's keep going down the list. So the next one is designing a new offer for a premium tier of the business. And, you know, this is the exact kind of planning work that Fable is good at.
02:34It's great at pulling a bunch of context from various sources to, for example, help me figure out here what my flagship creator offer should be. Number three is content to conversion audit across every platform.
02:48So I publish across my newsletter, YouTube, Shorts, and social channels, and I've also set up APIs and MCPs for Fable to pull data from each source. So what Fable can do is pull all this information and figure out where things are working well and where things still need work and give me the data analysis and insights.
03:09LMs are very good at analyzing large corpus of data and Fable can take this to the next level. Alright. Real quick on the last two, mine your archive into a content roadmap.
03:20So I've been publishing for over three years on a newsletter and about a year on the YouTube, and Fable is suggesting that it post the transcripts and text for all this stuff and figure out what themes and things I can repurpose into Shorts and other types of content. I think it's a great idea but it's probably gonna burn my limit in one shot, so I'm gonna stay away from that.
03:40And last but not least, uh, it wants to audit my personal skill system. So I built over 40 AI skills to streamline my creative workflow and help me build. Having Fable clean up these skills is probably a great idea.
03:53So let's do that at the end of this video. You can kind of see a common theme across these five examples that Fable suggested. It basically wants to look at a large corpus of information and data and extract insights from it and take action from it, right, which is exactly what a state of the art AI model is really good at.
04:13Alright. Now let's go ahead and explore this one, which is using Fable to stress test my business strategy. So before you ask Fable for life and business advice, you have to give it the right context.
04:26And as I shared in my previous video, I like to give it a plan document that has a few different sections. So my overall goal for the year, some principles I like to use to make decisions, like go work work fields like play, keep the main thing and main thing and so on.
04:42The positioning of my business, so who is my target customer? What is my niche? What is the pain point I'm trying to solve?
04:48And how do I differentiate? And then maybe list a few other players and, you know, what the differentiation and positioning is. And then also have a session about what gives me energy and what drains my energy.
05:00And finally, some additional context about my life and my financial situation. So that's number one. Give it a high level plan doc.
05:08And you might have like different sections for this, but these are sections that I like. Number two is you should connect Fable to a bunch of APIs and MCPs so that it can pull in additional information if it needs to.
05:20So for example, I have it connected to Mercury to get my bank information, Substack to get my newsletter stats, YouTube for my YouTube information, Vercel for my website analytics, and last but not least, it's connected to Google Workspace because I like to write plans and stuff that I actually wanna read in Google Docs and Google Sheets, um, and I wanna make sure that Fable can also read this and update the plan as needed.
05:47So this way, Fable has a point of reference. Right? And it can pull in additional information if it needs to.
05:53Alright? After doing all this work, this is the prompt that I gave Fable. Slash adviser, take a detailed look at my plan document and my content schedule.
06:02Feel free to pull other information as well. Then I want you to write a detailed one pager assessing my business and what I should focus on for the next three months. Once again, you know, I'm making this video, so I'm asking you not to share any confidential information, and I'm asking you to pull all this information in the plan document as a new tab.
06:21Now, uh, you notice here that I use the advisor scale. Right?
06:25So the advisor scale is a scale that I built for myself and this is totally optional, but let's take a quick look at what's inside. Okay. So here is my advisor skill, and you can see here I've asked it to use the skill whenever I'm stuck on decision or asking for a gut check, and it has some basic context about my life, and then it has one section about gathering live context.
06:48So I refer it to all the different MCPs and APIs that my personal OS project is connected to so that I can pull in additional information as needed.
06:58And then it has a section about escalating to the council. Now this is very, very optional, but I found it fun to build another council skill so that when my adviser is stuck, it kind of escalates to the council. And the council, uh, basically has three different personas to have a conversation with each other.
07:15So it has the actual customer I'm building for, It has someone who pretends to be a skeptic about my plans, and it has an execution minded operator. And just kind of seeing these three personas talk to each other is very interesting and and kind of causes additional insights.
07:30But, this is totally optional. If it keeps going down, it kind of just refers to my plan document and so on and so forth.
07:37Right? And last but not least is asking to, uh, proactively update the plan after every conversation I have with AI. And I'll say this again that the skill is optional, but I built it because I'm asking for advice a lot and I found it useful to kind of frame it in these three areas.
07:54So let's go back to Fable and let's see what Fable came up with. Alright. So it said that it created a one pager.
08:01So let's go to the Fable one pager here and let's see what its diagnosis is. Now this is gonna be a little bit weird because I've kind of got rid of all the confidential information, but at a high level, it looks like the business is doing well.
08:14I've already achieved my goal for the year. Uh, sponsorships, newsletter are making money.
08:20Uh, YouTube is growing nicely, and now we gotta figure out how to retain paid subscribers and fulfill all the sponsor commitments that I have. Right?
08:29And the focus for the next three months is fixing annual renewals of, uh, paid subscriptions, uh, shifting the content mix towards more builder tutorials, and so on and so forth.
08:39Um, I feel like this is, like, semi dumped down already because the actual Fable output is extremely detailed with all the stats that I pulled from all my various channels. But the bottom line is I feel like the number one use case for Fable that you should have to try is to just use it to get life and business advice and to do it in three steps.
08:59Right? So first of all, create a high level plan like this, connect Fable to all the various sources of information that are relevant to the plan, and then just ask it for advice.
09:09And this is the highest leverage activity you can do with Fable because it's advice and stuff that can impact what you do over the next three months, six months, or even a year. So definitely give this use case a try.
09:20Alright. Let's go back to our list and now let's talk about making your project ship ready with Fable. So if you're like me, you probably have a bunch of vibe quarter projects that you wanna ship eventually, and Fable, better than any other model, is amazing at finding and fixing bugs and issues to get your project launch ready.
09:40So for example, I've been building this fitness app that let me show you here. Alright? It's a pretty simple fitness app.
09:46It lists my workout. There's a live workout screen where I can check off workouts as I complete them. Um, I can also edit how many sets I wanna do and so on.
09:56And after I check off all the workouts, I can say finish workout.
10:01It will show me an end workout screen with, uh, my progress and new PRs. And then there's also tabs for a calendar view where I can see my past workouts. And finally, if I click into a specific workout, I can see how things are changing over time in terms of weight and volume.
10:19Alright. So I mostly vibecoat this app with Codex and GPT 5.5, and I've been pretty much using this app for my own workouts over the past month.
10:29So it should be in a pretty polished place. Right? It seems to work fine and, uh, you know, things seems to be good.
10:35Alright. So this is what I did. So I asked Fable, I'm about to ship this fitness app that lets users track their workouts.
10:42I want you to find everything wrong with it. Read the whole code base, look for real bugs, uh, broken edge cases, anything that fall over in front of a user and list all of the things that you think needs to be fixed and maintain a high quality bar.
10:58Alright. So Fable worked. It spun off five agents to find bugs.
11:02It ran all my unit tests and everything passed and things look good. But look at this. If I scroll down here, so it found a bunch of pretty critical bugs.
11:12Number one, if you sign out involuntarily by, I guess, just shutting off the app, you could potentially leak one user's data into the next user's account. Know, this would have been disastrous, and it does a bunch of other issues around sign out, around the MCP that I built so I can get agents to pull my workout information, around edge cases like negative weights, permanently breaking cloud sync, and a bunch of other things.
11:37So let's see how many bugs it found. It found over 12 major bugs and a bunch of minor bugs.
11:41And, you know, when I run the same prompt on, uh, g p d 5.5 or Opus, um, it does find a few bugs but nowhere near the amount that it found here. Right?
11:51So if you have a bunch of vibe coded projects or even just real projects lying around, gif able to check the code, find bugs, and fix issues before you launch. I think it's one of the highest leverage activities that you can do with this new model.
12:04Okay. So let's go back to our five use cases and now let's talk about planning the next big thing. You know, I found it really invaluable to use Fable to do planning for a big feature and then hand it off to Opus or GPT to do the actual coding and execution.
12:22So for example, uh, here, I have a conversation with Fable again. I've forked the fitness app conversation, and I basically asked Fable, uh, hey.
12:32I want you to draft a detailed plan for a new nutrition check-in tab in this app. Please use searches online to find relevant resources and then lay out a full plan, the key phases, the decisions we need to make, the risks, and the open questions that I need to answer.
12:48Flag anything that could sync the project if I get it wrong. Give me a plan clear enough that a cheaper or simpler model could execute step by step.
12:57And, also, uh, please write out the plan in HTML so that I can review it. So Fable worked, uh, through the plan. It looks like it found an API to pull food and nutrition data from the USDA, from the government, and it went through everything.
13:12Now let's take a look at the HTML that I created. Alright. So this is the HTML that I created, nutrition checking tab, a fourth bottom tab that lets you log meals in under thirty seconds.
13:25Alright? So it even mapped out what the tab could look like with the apple. Let's see.
13:30Oh, it even created a proposed experience. That's pretty amazing. Right?
13:33It actually created the design for the nutrition tab, which is, like, a delightful surprise to me. Alright.
13:39So it has a daily nutrition tracker. It's got food logging here. Pretty standard stuff.
13:44And let's keep going down here. And it has the architecture of how this thing should be designed.
13:51So we're gonna use super base for a database again and the data model of what should be included. And let's keep going down here.
14:00Some key decisions that we need to make. Alright. So where are we gonna get the food data source from?
14:05Right? So we can get it from the USDA FTC only as a free, we can get it from some commercial thing, or we can get it from Open Food Facts.
14:13It looks like it's recommending USDA FTC only. It's asking what is a day and what is our cloud sync strategy and how we can log everything.
14:23And by the way, the HTML plan that I made here is made with Lavish editor. If you just tell Fable to make HTML, it can make a pretty good one too, but I'm using Lavish editor from my friend Kun. So let me pull it up.
14:36GitHub Lavish and let's see if it found it. Yeah.
14:40Here we are. And this is like a free GitHub repo for my friend to get the HTML editor. So it just, like, pastes URL into Claude or Fable and asks it to use it.
14:50And the benefit of this is now I can leave comments. So for example, if I wanna say use US FTC only, can just I don't think it's the same. I say use a FTC only.
15:01Or I can leave comments throughout just like a Google Doc and any kind of queue comments will be sent to Fable to, uh, work through. Alright?
15:10Okay? And, um, you know, everyone talks about loops and goals these days. If you wanna take this plan to the next level, you could potentially ask Fable to list some specific acceptance criteria.
15:20Like, for example, give it some images to work towards and just use slash go to have it keep working until it actually reproduces the exact design that's showing here. Now, of course, if you do this, you risk burning out your limits.
15:34So personally, I will not do it, but if you wanna try it, you can. Okay? So that is number four, which is planning the next big thing, like the next big feature, next big app, or the next big product that you wanna build.
15:47Again, once Fable drafts such a detailed plan, you can hand it over to GPT or Opus for actual implementation. So last but not least, let's talk about refactoring a large code base.
16:02For example, I came across this article about how Stripe used Fable to refactor 50,000,000 lines of Ruby code and migrate it in a day where it could have taken a whole team over two months to do.
16:15Now I don't have a repo with 50,000,000 lines of code. Okay? But I do have my personal OS folder and it has over 40 skills that I built to streamline all my creator work, to build better apps and so on.
16:31So let's take a look at this thing. So here is the readme file for my personal OS repo and you can see here that I have a bunch of skills to help me edit my newsletter, remove AI slop, to prepare YouTube tutorials like this, to make shorts, and, uh, prep the podcast and so on and so forth.
16:50Right? There's a lot of different skills here that I built for myself. And let's go back to our very first Fable thread.
16:56So remember Fable here suggested that it should audit and consolidate my skill system. So that's exactly what I did. I asked it to take a look at all my user level skills and find opportunities, clean them up, and make them more effective, do a rigorous pass with skill editor, which is actually a skill that I built to edit my skills to basically, like, cut out redundancies and make it more concise.
17:19And, you know, just go ahead and find everything wrong with this personal OS system that I have. So Fable, again, spun five different agents to look through all this, and it did a bunch of auditing, and here is the fixes that I recommend.
17:33So it looks like for my counsel skill, there's a bunch of dead escalation block that's not really being used anymore.
17:41For my edit document skill, there is a bunch of ambiguities. There's this pointer in my sponsor skill, and there's a bunch of, like, temporary files that haven't been deleted, and there's a bunch of other stuff around routing and missing evals.
17:58And so so it found, like, actually 13 things to improve through my skill system. And of course, I wanna add this a little bit more deeply to make sure that all these improvements actually make sense. But with all this personal OS stuff, you wanna build a system to help you do work.
18:14Right? So make sure that the system, like all these skills and all these plugins actually work properly and actually give the AI the correct instructions is really important.
18:23So using a advanced model like Fable to fix all this stuff, think is definitely worthwhile.
18:29Alright. So let's close with three tips to get the most out of Fable. So tip number one is to prep with cheaper models.
18:38We talked about getting a personal adviser. Right? So use Opus or GPT to prep the right plan document and hook up all the APIs and MCPs to get things ready for Fable to work with you to explore the solution space.
18:52Don't use Fable for boring work like hooking up an MCP or API or like trying to get to draft a document. Alright? And number two is plan with Fable and execute with another model.
19:04So as we saw with the nutrition tab plan, Fable is really great at making detailed plans. You can even make designs and architectural diagrams to supplement these plans.
19:14And once you have a detailed plan, you can then pass this plan to another model that's cheaper to do the actual execution. Right? And last but not least, consider using lower effort with Fable and also babysit what Fable is doing.
19:29So you really don't have to run Fable with ultra high effort all the time. In fact, if you do that, I feel like your limit's gonna run out almost immediately. So consider using lower efforts, like high effort or even lower, and also babysit what Fable is doing.
19:43You know, Fable is really smart, but sometimes it can get into a rat hole and just keep looping and burn your tokens, and you don't want that to happen since the tokens are so limited here on your subscription. Okay. So that's about it.
19:55Let me quickly recap the five use cases again. Use Fable to find Fable worthy work. Just ask it.
20:00You're a very smart model. Look at my memory. Look at my files.
20:03Tell me what are some things that you think you can fix for me. Number two, getting life and business advice. Make a plan doc, hook up all the APIs and MCPs, and then ask Fable for long term advice.
20:15Number three, make your project ship ready. So you saw how many bugs, uh, Fable found in my Vibe Coded fitness app. Definitely get it to fix all the stuff before you ship anything.
20:25And number four, plan for the next big thing. Fable is really good at writing detailed HTML or markdown plans for another model to execute. And number five, refactoring a large code base.
20:37For your personal work like you saw from me, it doesn't have to be a large code base. It can just be like your personal OS or something or like all of your skills that you can fix using Fable.
20:47Now I just wanna remind all of you that Fable is back only until July 7 on your cloud subscription, which is actually two days before my birthday and that usage can run out super fast, right?
20:59So you have to make sure that you use it wisely, don't YOLO it with Favourable and don't waste this opportunity.
21:06And as always, my goal is to share practical no hype AI tutorials and interviews with you all. Now go out there and get the most out of Fable, and I'll see you next time.
The Hook
The bait, then the rug-pull.
A brand-new, temporarily-unlocked frontier model and a hard deadline: use it wisely before the usage window closes, or lose the access entirely.
Named ideas worth stealing.
01:30list
5 Fable use cases
- Find Fable-worthy work
- Get life and business advice
- Make your project ship-ready
- Plan the next big thing
- Refactor a large codebase
The video's core structure -- five categories of task the creator considers worth spending a frontier model's limited usage on.
Steal forany evaluation of how to prioritize scarce premium-AI usage across a backlog of personal or business tasks
04:13list
3-step advice method
- Write a high-level plan document
- Connect APIs/MCPs for live context
- Ask the model for advice
A repeatable pattern for getting grounded strategic advice from an AI model rather than generic suggestions.
Steal forbuilding a personal or business advisor workflow on top of any AI assistant
18:29list
3 tips to get the most out of Fable
- Prep with cheaper models
- Plan with Fable, execute with another model
- Use lower effort and babysit what it's doing
Usage-management tips for working within a constrained quota on a premium model tier.
Steal forany workflow mixing multiple AI models of different cost tiers
How they asked for the click.
VERBAL ASK
00:00product
“Get my personal AI OS with useful AI skills, $270 in AI tool credits, live monthly workshops, and courses on Hermes and Codex agents (behindthecraft.com)”
Soft plug, description-only -- not voiced on camera, pinned as a link rather than a mid-roll pitch.
MENTIONED ON CAMERA
Visual structure at a glance.
