11:14Jay E | RoboNuggets · Listicle
Claude Fable 5: 5 Use Cases to Try Before Usage Pricing Kicks In
A screen-share tour of five things people are actually doing with Anthropic's newest model, before its free access window closes.
July 2ndThe argument in one line.
Most AI users get mediocre results not because the models are weak but because they ask for narrow tasks instead of outcomes, accept surface-level expertise instead of real depth, and rarely pull in more than one point of view, and all three gaps are fixable with specific habits.
Read if. Skip if.
READ IF YOU ARE…
- You already use Claude or a similar AI agent regularly for work and want to get noticeably more leverage out of the same tool.
- You run a business or team and want a repeatable way to turn one-off AI tasks into durable, hands-off workflows.
- You're comfortable writing detailed prompts and want a mental model for deciding how ambitious to make them.
SKIP IF…
- You haven't started using an AI agent for real work yet. This assumes an existing working relationship with Claude or similar, not a first introduction.
- You want copy-paste prompt templates. Most of this is a mental model and a few technique names, not a script.
The full version, fast.
A lead engineer on Anthropic's Claude team argues most people get mediocre AI results because they ask for too little, not because the models are weak. He names three levers. Altitude means asking for the outcome you want instead of a single task, which turns one-off requests into workflows that stop needing a human in the loop. Depth means pushing a task to the standard a real expert would use, by insisting on expert tools, setting unreasonable goals, and using an outside judge instead of letting the model grade itself. Range means assembling multiple points of view, often as sub-agents with different expertise, to surface options you wouldn't have considered alone. Underneath all three sits self-efficacy, the belief that the outcome is possible, built by seeing proof it's been done and giving yourself permission to fail along the way.
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00:00 – 01:01
01 · Cold open: the 1939 homework mix-up
A quick retelling of the famous George Dantzig story, a student who solves two 'unsolved' statistics problems because he mistook them for homework, used to set up the video's idea about ambition.
01:01 – 01:38
02 · Who Felix Rieseberg is
Introduces the lead engineer behind Anthropic's Claude app, Claude Code, and the Claude Code desktop app, and his viral essay on why some people get far more out of AI agents than others.
01:38 – 03:35
03 · Altitude: ask for the outcome, not the task
Explains the first lever of ambition, operating the model at a higher level of abstraction by stating the end goal instead of handing over task after task, with a crash-free-apps and pitch-deck example.
03:35 – 03:55
04 · Free guide and skill plug
A short pointer to a downloadable guide and a Claude skill built from the video's techniques.
03:55 – 07:16
05 · Depth: push the task to expert standard
Covers three techniques for expert-level output: insisting on the tools real experts use, setting deliberately unreasonable goals, and using an outside judge instead of letting the model grade itself, illustrated with a customer-churn example.
07:16 – 10:35
06 · Range: pull in other points of view
Covers the third lever, splitting a task across multiple sub-agents with different expertise to surface options you wouldn't have considered alone, using a nouns-to-verbs trick with a quarterly-review and real-estate-listing example.
10:35 – 11:43
07 · Self-efficacy: the belief underneath all three
Connects all three levers to self-efficacy, the belief that you can pull something off, and walks through three research-backed ways to build it.
11:43 – 12:42
08 · Ambition still needs a why
Closes with a counterpoint: ambition without a clear reason just means moving faster without direction, so pace it and remember why you're doing it.
Lines worth screenshotting.
- Asking an AI for an outcome instead of a task turns a one-off request into a durable workflow that no longer needs a human in the loop.
- Telling a model to 'act like a professional' usually just changes its vocabulary, not its actual method.
- Insisting an AI use the same tools real experts in a field use is one of the fastest ways to get expert-level output instead of a generic one.
- Setting a goal that sounds unreasonable, like cutting page load time from five seconds to 200 milliseconds, pushes an AI further than asking for a modest improvement.
- AI models are agreeable by default, so letting a model judge its own work rarely catches real problems. Use an outside tool or a separate sub-agent as the judge instead.
- Replacing the noun in a task, a slide deck, a listing page, with the verb it actually serves, getting busy people to decide under uncertainty, makes it obvious which outside expertise would help.
- Splitting one important task across several AI sub-agents with different points of view can surface unknown unknowns, options you didn't know you had.
- People believed a mile couldn't be run in under four minutes until Roger Bannister did it in 1954, and other runners broke the barrier within days.
- A student who copied two problems off the board thinking they were homework solved two previously unsolved statistics problems because he didn't know they were considered impossible.
- Self-efficacy, the belief that you can pull something off, can be built on purpose: by seeing proof it's possible, by watching someone else do it, and by giving yourself permission to fail.
Three levers that make AI agents ambitious
AI AMBITION LEVERS
Most AI output is mediocre not because the models are weak, but because people ask for narrow tasks instead of outcomes, accept surface-level expertise, and never bring in more than one point of view.
03Altitude: ask for the outcome, not the task
- State the outcome you actually want instead of handing over one task at a time, so the AI can build a durable process instead of waiting for your next instruction.
- Ask yourself what the goal beyond the goal is before you write the prompt. A request to fill in pitch-deck slides can become a request to impress the client, which produces a different result entirely.
- Altitude works best for a clearly definable outcome, like keeping an app crash-free. For creative or open-ended work it works less well right now, so you still have to bring the judgment yourself.
05Depth: push the task to expert standard
- Telling a model to 'act like a professional' usually just changes its vocabulary, not its method, so depth has to be forced with specific techniques, not a persona instruction.
- Insist the AI use the same tools and roles a real expert would reach for, a customer success manager, a pricing strategist, a data scientist, and push back if it defaults to a simple table.
- Set a goal that sounds unreasonable, like a 200-millisecond load time instead of five seconds, rather than a modest target, and tell the model to keep going if it claims that's impossible.
- Never let a model grade its own work on anything that matters. Use an outside tool it can call, like a speed test, or a separate sub-agent as judge instead.
06Range: pull in other points of view
- Replace the noun in your request, a slide deck, a real estate listing, with the verb it actually serves, which makes it obvious which outside expertise would actually help.
- Split an important task across multiple sub-agents playing different roles, analyst, sales manager, interior designer, local expert, instead of asking a single AI persona for everything.
- Deliberately include a point of view meant to find holes in the work, like a red team, since the most useful outside opinions are the ones you wouldn't have thought to ask for.
- Reserve multi-agent range for your most important work. Deploying several helpers at once is more token-intensive than a single request.
07Self-efficacy: the belief underneath all three
- Believing a goal is achievable is itself a lever, not just a mindset. A student solved two statistics problems considered unsolved because he mistook them for ordinary homework.
- Watching someone else do the thing first makes it easier to do yourself. No one ran a mile in under four minutes for decades, then others broke the barrier within days of Roger Bannister.
- Give yourself permission to fail. Shipping work and improving it each time is what eventually gets you to success, not getting it right on the first attempt.
08Ambition still needs a why
- More ambitious AI use needs a reason behind it, or you end up moving faster without a clear direction.
- Pace your ambition deliberately and give yourself permission to pause, instead of treating 'go faster' as the goal on its own.
Terms worth knowing.
- Sub-agent
- A secondary AI instance spawned by a primary AI agent to handle one piece of a task, often with a distinct persona or area of expertise, which then reports back to the main agent.
- Self-efficacy
- A psychological term for how much a person believes they're capable of succeeding at a specific task or goal, which research links to actually achieving it.
- QBR (Quarterly Business Review)
- A recurring meeting where a team presents results, trends, and plans for the past and upcoming quarter, usually to leadership or stakeholders.
- Outside judge
- A separate tool, script, or AI instance used to evaluate whether an AI's output actually meets the goal, instead of letting the same model grade its own work.
- Unknown unknowns
- Options, risks, or approaches you don't know exist because you've never been exposed to the field or person who would introduce them.
Things they pointed at.
08:08toolBlender (free 3D software)
Lines you could clip.
00:09
“They were two unsolved problems in statistics, and he solved them because he didn't know that they weren't solved.”
02:11
“He just asked Claude to keep the apps crash-free.”
05:05
“Push back and ask it if that's really what the best in the field would use.”
11:43
“Self-efficacy isn't really a new concept, but it's a more powerful one now.”
Word for word.
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metaphoranalogystory
In 1939, a student turned up late to a statistics class, copied two problems off the board thinking they were homework, and handed in his answers the next day. They were two unsolved problems in statistics, and he solved them because he didn't know that they weren't solved. And according to the lead engineer of the Claude app at Anthropic, that is the mindset that the top 1 % of agentic AI users have.
His article on it called How to Be More Ambitious, a tutorial for humans with models, now has over 4 .5 million views on X. So today I'll share with you all the insights he shared and go through the three methods by which you can become more ambitious. with AI.
And for each one, I'll show you what most people do right now and what the top 1 % of AI users' version looks like so that you can apply it on your own to become even more effective when you're using Claude for your business. And if you're new, my name's Jay. I spent over a decade working with brands you probably know, have been in AI since my master's in data science, and now I'm leading our AI business in one of the largest AI communities globally.
So Felix Riesberg is a lead engineer at Entropic, and he's led the engineering for the Cloud app, CodeWork, and the Cloud Code desktop app, which means he's worked on a lot of the agents that we use every day. And what he noticed is that, as he puts it, Some people seem to be flying with AI, while many others are still .
Now, his fix, in just three words, is to be more ambitious. But he admits that's pretty tricky and general advice to follow, so he broke it down into types of ambition. And today we'll go through three of them.
So we'll go through each of those three, and to keep it concrete, I'll also give you examples from different kinds of businesses for each one. So let's start with the first method, which is altitude. Felix says people often flippantly call this letting the model operate at a higher abstraction, which basically means that instead of asking for the task, you ask for the outcome.
Because when you give Claude a task, it's only carrying out your best guess at how to get what you want. So the question Felix says to ask yourself is, what do you actually want to happen next? Or what's the goal beyond the goal?
His own example is that at the start of the last year, he was still asking Claude to fix the crashes in his apps one at a time. And eventually, he just asked Claude to keep the apps crash -free.
And what that resulted in is what Felix calls a durable workflow, one that is more effective at taking the human out of the loop. So he no longer has to hand Claude task after task. So in your own business, what you can do is to look at the tasks that you're about to hand Claude and ask what it's actually for.
For example, let's say you're putting together a pitch deck for a client, and maybe you already have a slide template for this. Now, what most people do is ask Claude to just fill in the slides. And to be fair, that works for most use cases.
But if the real goal is to impress the client, then if you give clog that goal instead, it might come back with a completely different approach. If the pitch involves a venue, for example, maybe it builds an explorable 3D world of the space that you can walk the client through.
Or instead of a boring slide pack made in PowerPoint, it now builds you beautiful HTML slides that are interactive, can animate, and clearly show what you're going to deliver. Now, altitude works best when you really are after some ultimate definable outcome, like keeping the apps crash -free, for example. However, at least with the models right now, it works less well for creative work.
So, for example, if you just ask Claude to go make money and not make money, any mistakes, that doesn't really work for most businesses by itself in 2026 because that part still takes a lot of creativity from you. Although Felix also does say that models seem to be able to handle higher and higher levels of abstraction the more capable they get.
So we'll see if that changes down the line. And also, as we go deeper into the next two types, just know that I've put every resource, prompt, and learning from this video into this free guide that includes a skill, which you can just send to your agent. so that if you want your agent to be more ambitious with a certain task, then it invokes all the key points and best practices from this video.
That's just in the description below if you need it. So now let's take it to a second level with depth. Because sometimes you just want a task done really, really well.
And being ambitious in depth means getting the task done the way that a top expert in that field would. And the trap that Felix points out is that when you just tell a model to act like a professional, what it often does is just change its vocabulary. It uses the jargon without really changing its methods.
So how do you get your agent to work like an expert and not just sound like one? Well, Felix actually uses three techniques to show them. use his example, a company called Acme that's losing customers every month, which is called churn, and it needs to get ready for its quarterly review.
The first technique is to insist on the same tools that the best in the industry use. Because to work on Acme's churn properly, Claude would need the tools that the experts themselves use. So at least for this problem, maybe you're thinking of a customer success manager or a pricing strategist, or for large scale churn reduction work, you would also need a data scientist.
So if Claude just reaches for a simple table in the chat and just goes ahead and does the task, push back and ask it if that's really what the best in the field would use. And the good news is you don't even need to know or understand what the experts use because these models, including Claude, usually does. Second is that you need to pick goals that sound unreasonable and just tell the model to have a crack at it.
For example, if you need your website to load in five seconds instead of 10, don't just ask for five and see if Claude can get it down to load in one second or even 200 milliseconds. And if Claude says that's impossible, Felix says that you can just tell it that it's very much solvable and it just needs to keep going. Another technique is to use an outside judge, which means never asking the model to judge his own work for your most important tasks.
And this is important because if you notice, a lot of the AI models that we have now are quite agreeable. And it actually doesn't take a few prompts to convince them that what you are saying is right. Now, Felix does note that he expects this will get solved as the models get smarter.
But for now, he's zest to insist on outside validation. Ideally, this is a tool that the model can call. Like let's say for that website example, it can be a speed test that it runs after every change.
Or if it's more creative work, spawning a sub -agent which acts as a judge is proven by multiple independent tests to be quite effective in refining the final output. And the good news is you can get most of the way there for this method with just a single prompt. And Felix actually shares the one that he uses himself to get a deeper version of any task.
It asks Claude four things before it starts. What would a top professional do here that you're not asking for? What's the rawest material they would start from?
Which methods and tools would they use? And what other than you or Claude would show that the result is right? Then it rewrites your prompt to ask for exactly that.
And this prompt, you can just screenshot it, or you can also access that skill that I mentioned below so that it's always one short command away. And by the way, if you want to learn how to build and sell AI systems that businesses actually pay for, then that's pretty much all we do over at the RoboNuggets community, where not only do you get access to the Cloud Living Masterclass, which we update every week and takes you from zero to mastery with the latest on AI, but you also get access to our Agents as a Service course, which walks you through how to actually get paid for all these AI skills that you are learning.
You also get to be part of a genuinely great community of AI builders. In fact, you can see just some of the recent wins our members are getting from the program right here. So if you want to start earning from AI, then check that just in the pinned comment below.
Now back to the video. And finally, the third component of being more ambitious is range. Because if altitude is looking at things at a thousand feet view, and depth is pushing each task to its maximum possible output, range goes sideways, which means looking at your work from the point of view of other fields.
And probably more points of view than would have ever made sense to do versus the past. Now, this isn't a new idea, because every single heist movie that you think about would spend the first 30 minutes putting together a crew of very different people and talents, right? So that's basically the same principle.
But now it's really to have your agents split a job across several helpers at once. And for a very important task, it helps if those sub -agents have varying points of view so that you can figure out what we call unknown unknowns, which means the part of your work that you didn't even know is an option to begin with. Now, Felix admits this one isn't easy because it takes some creativity.
For example, he said that until he saw his colleague use Blender, which is this free 3D software to render interior designs, he never thought of using Claude to build 3D environments for his own website. So that's an example of an option that he didn't even consider in the beginning. An unknown unknown.
So given this challenge, how do you actually do it? Well, the first trick Felix uses is an old one from a book called The Art of Innovation, which is to try replacing the nouns with verbs. So if we talk about an example of a QBR, which is a quarterly business review that a lot of companies do, some people may describe that output as a PowerPoint presentation, which is the noun in this case.
The verb, and I'll read verbatim from what Felix wrote here, is getting busy people to believe a story about cause and effect and to decide under uncertainty in 30 minutes. And having the verbs makes it much easier for not just your agent, but also yourself to see what the actual goal is and which fields could bring in a unique and useful point of view.
Which leads us to a follow -up trick, which is to assemble the crew. Meaning that you can insist that Claude bring in several roles and points of view and not just its own. So for the quarterly review example, Claude would then bring an analyst, a sales manager, and if it's being presented, it can even bring in a graphic designer too.
And a good practice, as we mentioned, the earlier point is to bring in the point of view of a red team which if in case you don't know that red teaming is basically this practice where their job is to find holes in the work sort of like a devil's advocate group now obviously deploying helpers can be token intensive so you can do this for just your most important work and you can get started with a prompt like this one which you can just screenshot Now, just to provide an example before we leave this point, say you're a real estate agent with a house to sell.
And selling this house is extremely important to meet your revenue target for the year. Now, what most people do is just ask Claude to output the real estate listing, which is the noun in this case. But the verb is, for example, helping a family picture their life in this house before they ever visit.
And once you say it like that, then the mixture of experts that Claude can bring in can change and can become clearer. You can have it assume the role of an interior designer. designer to stage each room, maybe a 3D artist to build a walkthrough, a bit like Felix's blender example.
You can even assume the role of a local who knows the best restaurants nearby, so that the final listing page that you get in the end is not just some generic real estate page, but one that is customized for that project given its importance to you. So those are the three lovers of ambition. But what I really also like is how Felix ends, because it all leads up to one idea, which he mentions, which is this term called self -efficacy.
And basically, that means how much you believe that you can pull something off. And if you can increase it, especially now that we have AI, you also increase your chances of success in whatever field or game you're playing in. The great thing is that self -efficacy can actually be hacked in some way.
There's apparently more than 35 years of goal -setting studies behind this. And there's actually... three hacks that are pretty well documented.
So very quickly, I'll just highlight each one because it is quite interesting. The first is believing it's possible. Like that student, George Danzig, who solved two never -before -solved problems in statistics back in the 1930s because he thought they were homework.
The second is watching somebody else do it. For decades, for example, nobody could run a mile in under four minutes. Then Roger Bannister did it in the 1950s.
And only after a few days, someone else did it too. And the third is giving yourself permission to fail, which is just related to the idea that if you just ship things and put your work out there improving each time, then you will eventually succeed. And as Felix Enzen puts it, self -efficacy isn't really a new concept, but it's a more powerful one now.
Because in a world where our abilities make leaps every couple of weeks, we can actually be more ambitious in the things that we can bring the world. But I think this also needs a counterpoint. Because when we talk about AI, we also need to talk about pacing that ambition and even finding the why behind that ambition.
Or else you just end up going faster and faster and getting more and more ambitious without a clear reason why. So really, definitely take in these techniques and methods to become more ambitious, given how powerful AI agents have become. But at the end of the day, remember why you're even doing it and feel free to pause when you need to.
As usual, thanks for watching till the end. I usually do sessions like these to help you use AI better for your business. So if that's your thing, feel free to subscribe.
I'll see you next time. Cheers.
The Hook
The bait, then the rug-pull.
The video opens with the famous George Dantzig story, a student who solved two 'impossible' statistics problems because he thought they were homework, before pivoting to Anthropic's Claude lead engineer's framework for getting dramatically more out of an AI agent: altitude, depth, and range.
Named ideas worth stealing.
01:38model
Three Types of AI Ambition
- Altitude — ask for the outcome, not the task
- Depth — push the task to expert standard
- Range — pull in multiple points of view
The framework used as the spine of the video for getting more out of an AI agent.
Steal forany internal prompt-writing checklist or team AI-onboarding doc
03:55list
Three Depth Techniques
- Insist on the same tools/instruments the best in the industry use
- Set goals that sound unreasonable
- Use an outside judge instead of letting the model grade itself
How to get an AI to actually work like an expert instead of just sounding like one.
Steal fora prompt checklist for any task you want done to a professional standard
08:56concept
Nouns to Verbs
Replace the deliverable noun, a slide deck, a listing page, with the verb it actually serves, getting busy people to decide under uncertainty, which makes it clear which outside expertise would help.
Steal forany assignment brief or creative request handed to an agent
11:04list
Three Self-Efficacy Hacks
- Believe it's possible
- Watch someone else do it
- Give yourself permission to fail
Three research-backed ways to build the belief that you can actually pull off an ambitious goal.
Steal forany goal-setting or team-motivation context
How they asked for the click.
VERBAL ASK
06:43product
“check that just in the pinned comment below”
Mid-roll pitch for the paid RoboNuggets community (Claude Living Masterclass + Agents as a Service course), dropped right after the Depth section closes, with a quick return line, 'Now back to the video,' to resume the main content.
MENTIONED ON CAMERA
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
AFFILIATECommission earned if you click.
OTHER LINKSAlso linked in the description.
Visual structure at a glance.
