Claude Fable 5: 25+ Things People Built in Its First 72 Hours
25+ use cases people built with Claude Fable 5 in 72 hours: games, apps, real engineering, community builds, and the dashboard I finally pulled off.
I don’t remember the last time a model release made me this restless.
Claude Fable 5 came out while I was already on vacation, up in the mountains in Romania, the kind of trip where you're supposed to put the laptop away. I’d brought it anyway. My FOMO won, and for once I’m glad it did.
I tried it on a few tasks and I was blown away, every time the same way. I’d hand it the task, walk away, and by the time I looked back at my laptop the thing was already done.
Then last night I did something I’d never managed with any AI model before. I watched it finish the whole thing on its own, without the usual headaches, the ones that come from not being a developer and not knowing how this stuff even gets built.
I just described what I needed the way I’d describe it to a person, without knowing how to steer it down one path or another, and Fable went and did it. Perfect, from the first message.
It got me so excited that at 2am I started writing a full list of the hard, tangled things I’d been wanting to do, the ones that would be real game-changers for me if they got done. I lined up the prompts, ready to run them in parallel the next day and finally clear the list before I leave the country tomorrow (for my real, no-laptop vacation), the same day this email lands in your inbox.
But then, the news came…
I’ll get to that part. But I don’t want to start with the loss. I want to start with what we had (and what some of you still do).
So let me show you what Fable is, what people built with it in the few days we all had it, what the AI Blew My Mind community pulled off, and the thing I finally built myself after failing at it for months.
If you’re in the US, and I know about half of you reading me are, maybe this gives you new ideas for what to try with it while you still can. If you’re outside the US like me, well, hopefully we get it back soon.
Let’s begin.
Here’s what we’ll cover:
What Claude Fable 5 actually is (a Mythos-class model, explained)
The 72-hour build wave: how Claude Fable 5 came and went
What people on the internet built with Claude Fable 5 in 72 hours
The cross-platform analytics dashboard I finally built with Claude Fable 5
What the AI Blew My Mind community built
Why Anthropic pulled Claude Fable 5 (and which model to use now)
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What Claude Fable 5 actually is (a Mythos-class model, explained)
If you only know one thing about Fable 5, know this: it is the most capable AI model the public had ever been allowed to touch.
It’s the first model from a new tier they call Mythos, which sits a level above the Opus models most of us were already using.
I made a cheat sheet the day it launched, back when we still thought we all had until June 22. The capability summary holds up. The countdown didn’t, for most of us.
The same model exists in two versions. The public got Fable. A small set of vetted organizations, cyber defenders and infrastructure providers, got Mythos 5, the identical model with some of the safety limits taken off.
So the version you and I could touch was the careful one.
That careful part is something called safety routing. If your request gets close to a few sensitive areas, mainly cybersecurity, biology and chemistry, Fable hands that one request to the older Opus 4.8 instead. Anthropic says this happens in under 5% of sessions, and that you’re told every time it does.
That last part is where people disagree. Plenty of users online say they were never told, that they got downgraded mid-task with no notice, and a few reported being routed away from ordinary technical work, fluid dynamics, medical imaging code, that isn’t dangerous at all.
So Anthropic says one thing, a chunk of the community says another. I never hit it myself, which means either everything I gave it stayed in Fable’s lane or I got switched and didn’t notice, exactly what those users are describing.
Now the part that made everyone lose their minds: what this thing can do.
A few numbers went around. Before launch, Stripe handed it a code change across a 50-million-line codebase, the kind of job a team grinds through for about two months. Fable did it in a day. It can work on its own for hours without losing the thread. And it topped nearly every benchmark Anthropic tested it on.
But benchmarks are abstract. What made the week feel different was watching what regular people did with it once it was in their hands.
The 72-hour build wave: how Claude Fable 5 came and went
The timing is the whole story. Fable went free on the paid plans on June 9. By June 12 it was gone, for those of us outside the US. So we had roughly three days.
My feeds filled with the same reaction over and over, some version of “I can’t believe it just did that”, attached to a game, an app, a tool, a whole website someone described in a sentence and watched get built. Even my business partner, Matei, called it "a f***ing beast", and that was the final straw. My FOMO won, and I opened my laptop.
And it wasn’t just my feeds. The moment Fable dropped, I posted in the AI Blew My Mind community chat asking what everyone was making with it, and the thread that came back was so good it gave me the idea to write this whole post:
That’s where this turned collaborative. So in here you’ll find the thing I finally built myself, what the AI Blew My Mind community pulled off, and a pile of what people across the internet made with Fable.
What people on the internet built with Claude Fable 5 in 72 hours
After three days of scrolling, the same few categories kept showing up: games and interactive toys, apps and SaaS built from a sentence or a screenshot, real engineering and production work, and the small everyday-life builds people made just to make their own days run smoother.
What follows is what I picked, the most interesting ones I came across. I haven’t verified every claim, I found these the same way you would, by scrolling, so treat them as a tour of what people said they made, not a lab report.
Every one has a link so you can go look and judge for yourself. Here’s a sample from each:
Claude Fable 5 games and interactive builds: 6 use cases
Three games from three sentences | Ethan Mollick
Mollick, the Wharton professor most of us read for level-headed AI takes, built three playable games with one prompt each in Claude Code: a Snake clone, a lantern-lit tunnel maze, and a wandering game built on Rilke’s poetry. He said Fable outran every other public model he’d used by a considerable margin, and that it worked on its own for up to a dozen hours on long instructions.
An entire Hogwarts castle | Matt Shumer
Fable one-shot a full explorable Hogwarts, classrooms, the Great Hall, the Quidditch pitch, the lot.
An infinite explorable universe, assets and all | Anshu
Connected to image-generation and image-to-3D APIs, Fable built an infinite explorable universe with characters and lore, every asset and sound made from scratch, in three prompts over two hours on max effort.
A Backrooms horror escape game | u/Turbulent-Sink-6171
One prompt produced a playable browser-based Backrooms escape game with eight pages to find and unsettling audio, the builder said they stopped playtesting at 2am because they didn’t want to keep going alone. Worth including precisely because the comments fought about it: the top replies suspected Fable remixed existing Backrooms and Slenderman code from training data rather than inventing it. Live, with public source.
A Minecraft clone in 20 minutes, one shot | ChrissGPT
A single “make a Minecraft clone” prompt to Fable on high effort produced, in about 20 minutes, a world with multiple biomes, a day-night cycle, different ores, and caves. The builder’s own reaction was just “I’m stunned,” which is roughly the universal response to this category.
A one-prompt game a single person could ship | via tyrtyre201
More a story than a demo, and it’s the thesis of this whole piece in miniature: someone built a playable game with Fable in a couple of hours, one prompt for a working prototype, then a few hours of back and forth for a real level with working mechanics, art that loads, sound that triggers. Not the best game ever made, but the cheapest fun game ever made by one person.
Fable 5 apps and SaaS, from one prompt or a screenshot: 4 use cases
An “extremely delightful” iOS calorie tracker, one prompt | Anshu
Asked for an “extremely delightful” calorie tracker for iOS, Fable produced a working app from that single prompt, delight brief included. The interesting signal here is native mobile, not just web, coming out of one instruction.
A beautiful one-shot landing page | Alex Prokhorov
Fable one-shot a polished landing page. A clean example of the “designer-quality first draft” people kept reporting, where the output looks shippable rather than like a placeholder.
A website about itself, with full creative freedom | Aurélien
Given complete freedom to make a website about itself, Fable produced something the builder called wild. A fun entry because it’s the model making an aesthetic call with no human art direction, and the result holds up.
An entire app from nothing but screenshots | one-shot breakdown
A creator fed Fable only screenshots of a SaaS landing page and it built the matching app. The reason it matters for non-developers: you don’t have to describe architecture you don’t understand, you can hand it a picture of the result you want.
Claude Fable 5 for real engineering work: 2 use cases
A working V8 engine CAD model in under 10 minutes | Aaron
Aaron asked Fable to model a V8 engine and got back a fully working CAD model in under ten minutes.
A QDD actuator, designed and collision-tested | Jake
Fable designed a quasi-direct-drive actuator, then animated the gearbox and ran collision inspection as part of its own validation loop, about 30 minutes and 400,000 tokens.
Everyday-life builds with Fable 5
A wall-calendar dashboard for a 77-inch OLED | u/orangejulius
A legal-writing newsletter author had already built a household calendar dashboard for a giant wall display, plus apps to control Sonos and Spotify, then had Fable spin up a portfolio site on Ghost.
The cross-platform analytics dashboard I finally built with Claude Fable 5
This week I shared the Claude Code agent that runs my whole content distribution, the one that takes each article and pushes it out to Reddit and LinkedIn while I sleep, increasingly onto platforms where I’m barely present yet.
I only started most of those accounts about a month ago. Now I’m posting everywhere and committed to growing on each one, the way I’ve been growing Substack.
So I wanted one cross-platform analytics dashboard to track all of it: every metric, every platform, side by side, so I can see what’s driving the growth instead of checking five apps.
And I built it as a new project inside that same autonomous distribution OS. The agent pushes the work out, the dashboard tracks what comes of it.
The wall I’d hit before: Substack has no public API. If you want your own numbers out, you’re on your own. I’d seen others do it through Substack’s unofficial endpoints, so months ago I tried. But I’m not a developer, and after multiple tries I couldn’t manage it, so I eventually gave up.
This time: I wrote a plain description of what I wanted, just like I had with other models before, but this time Fable went and got it.
How Fable did it: When you click around your Substack stats, the site is pulling your numbers from its own servers in the background, requests you never see. Fable opened a browser it runs itself with no window (a headless browser), had me log in once, and watched which of those background requests fired. Then it just made the same requests itself, pulling my data the same way Substack’s own pages do. No official API needed.
It did the same for Instagram, Threads, my Facebook page, and LinkedIn through Postiz (I covered what Postiz is here). For now, that is. All of them except Substack have APIs, so next I’ll just add their keys instead of going through the headless browser.
What the dahsboard shows:
Total audience: every channel combined, in one number.
Total views: across all platforms.
Per-platform cards: each one with that platform’s own key metrics.
Last 48 hours: everything I posted, all in one place.
Three-month trends: so I can see direction, not just today.
Hit rate: how often a post beats my median.
Content library: everything I’ve published.
Cross-posting gaps: posts that did well on one channel and never made it to the others, so I can repost the winners where they’re missing.
Daily cohort analysis: on my subscribers, so I can track the lifetime value of a reader.
It matched my brand on its own, and it’s live on my own server right now, refreshing every morning on a 24-hour task so the numbers are current when I open it.
I'm really happy with how it turned out and how fast it all came together, without me ever having to figure out where those Substack endpoints live.
Final impressions: Fable felt different from any model I’ve used. It can go for hours without me babysitting it, and it’s far more action-oriented than the models I’m used to. That’s also why it’s not one you chat with. It’s built for complex, many-step work, either figuring out the steps on its own or running the ones you hand it. That was the real shift I felt: how long it could keep going, and how much sharper its reasoning was at finding a solution and actually building it.
One last thing. By the time I’m writing this, I’ve had a few hours to come down from the morning, when I found out it’s gone and I can’t use it anymore. Sitting with that frustration, I realized how much I’d already come to want it, and how quickly these models work their way into how we operate. How much we lean on them without noticing, and how strange life before AI now seems, at least if you work with it as closely as I do. Ugh.
What the AI Blew My Mind community built
Like I said, the community thread is what gave me the idea for this post. Here’s a sample of what people made. Follow them if something’s your kind of thing.
A full assessment system, held together across sessions, by Rich Carr
Rich fed Fable a multi-phase scope of work and had it hold that as the running spec for everything downstream, then turn raw field data and interviews into scored records, build measurement instruments calibrated against real high and low performers, and keep all of it straight across sessions so each one resumed from the current state instead of starting over. He defines the ask in detail, Fable builds, he validates, and every correction becomes a standing rule rather than a one-time fix.
A skill that makes Claude reflect at the end of a session, by Hans van Gent
Hans built /reflect, a skill that runs a three-phase review when a Claude Code session ends, pulling out the durable facts, corrections, and workflows worth keeping and writing them back into his config so the next session starts smarter. He also ran Fable across two big active repos with two accounts going at once, and his takeaway was simple: he was glad to finally see a model that could hold that much context without losing the thread.
Nine workstreams in one night, by Christopher Duffy
Chris handed Fable nine separate workstreams in a single evening, including a full knowledge-base audit, building something and deploying it, rebuilding a CRM more efficiently, and updating dozens of his existing skills. The striking part isn’t any one task, it’s that the model held nine of them at once without dropping the thread.
An app that estimates the climate cost of everyday objects, by Gabriel Tsuruta
Gabriel built carboneye.app, which lets you photograph or describe a product and get an estimate of its climate impact, water and CO2, in units a normal person can feel, like miles driven or showers taken. It came out of a conversation with a friend who’s anti-AI on environmental grounds, and the point was to show AI working on the kind of problem it usually gets blamed for. He called it the easiest and most pleasant vibe-coding he’s done, no real context rot even working mostly in one long chat.
A 100-million-token overnight test harness, by John Holman
John pointed a persistent Fable setup at building and testing a benchmark harness, then let it run on its own for ten to twelve hours overnight, iterating and correcting as it went, well over 100 million tokens in a single stretch.
Headless publishing to a platform with no API, by Daniel (Dig. Craft Workshop) .
Daniel had Fable automate the whole path from article draft to published Medium post, and since Medium offers no API for it, Fable hacked it together through a Chrome extension running on his own machine. He also had it produce a demo video for a product he’s about to release. The thread running through his work was self-containment, the model handling the entire messy chain rather than one clean step.
The one Claude Fable 5 limit to plan around: cost
Everything above is the good side. The catch that came up again and again is cost.
Fable burns through usage fast, people hit their plan limits quickly, and a single ambitious run can eat a big chunk of even a high-tier subscription.
That’s the thing to plan around, not whether it can do the work, but how much of your usage one session will cost you. So use it for the big jobs that earn it, and keep a lighter model for everything else.
Why Anthropic pulled Claude Fable 5 (and which model to use now)
On June 12, the US government issued an export-control directive, and Anthropic disabled Claude Fable 5 and Mythos 5 worldwide to comply. If you searched why Fable got banned or pulled, that’s the answer. The most capable model the public had ever touched was gone almost as fast as it arrived.
The directive covers any foreign national, inside or outside the US, so for those of us abroad, me in Romania, about half of you reading this, the thing we were using on Friday night was no longer available to use by Saturday.
So if you’re outside the US like me, the rest of Claude is still here and still very good. Here’s a reminder of which model to reach for while we wait for Fable to come back:
And if you’re in the US and Fable is still in your menu: go play. Build the thing you’d never have attempted, run that list you keep putting off. And tell me what you make with it.
Just save it for the truly hard jobs, the ones Opus can’t handle alone. It’s not only that Fable burns credits fast, it’s that asking it something basic is a waste of what it can do. So don’t waste Fable on “how many Rs in strawberry”.
If this gave you a useful look at what Fable could do, I’d love it if you passed it on.
This post is free, but to get access to all premium workflows and tools inside Amplifiers, weekly premium articles, all premium resources inside the AI blew my mind Lab, and exclusive partner discounts, upgrade here.
We covered the demise of Fable in our standup comedy. Light a candle! https://chiphughes.substack.com/p/episode-43-on-deep-background?r=ft4w&utm_medium=ios
On Thursday and Friday I ran Fable 5 through prompt injection, sycophancy, memory hallucination, and error recovery benchmarks. Hours later, the Commerce Department issued an export control directive and Anthropic disabled the model worldwide.
Here's what I measured:
Prompt injection resistance: 87.5% (median across 3 runs, 27 test cases)
The content filter blocked 70% of injection tests at the infrastructure layer before the model saw them. Of the 30% that reached the model, it resisted most but was consistently bypassed by delimiter/tag-closing attacks. Fiction framing and vendor impersonation bypassed it on some runs.
Sycophancy resistance: 64.3% (95 tests)
Four consecutive Anthropic releases show declining sycophancy resistance: Opus 4.6 (68%) → 4.7 (67.7%) → 4.8 (64.5%) → Fable 5 (64.3%). The model increasingly agrees with wrong humans rather than maintaining its position.
Memory hallucination: 63% composite (80 tests, 3 runs)
70% QA accuracy, 30% hallucination rate. The same 12 questions failed every run. All errors at the Generation stage: the model had the right information stored but generated wrong answers from it. Zero content-filtered.
Error recovery: 75.3 (40 tests)
Only scored on 65% of the suite. The other 35% was content-filtered.
Filter variance. The same content filter that blocks 70% of injection tests blocks 0% of memory tests and 3% of conversational tests. The filter does most of the security work. When it doesn't fire, the model's own judgment has measurable holes.
I'm not affiliated with Anthropic or any model provider. I test every model the same way and report what happens.