Created with GPT Image 2.0 on ChatGPT
Prompt: Ultra-detailed miniature diorama of China built into the folds of a giant flowing Chinese flag, photographed in a cinematic tilt-shift style. The red silk fabric of the national flag forms rolling hills and valleys across the landscape, with large golden stars visible in the softly blurred background. At the center stands the magnificent blue-roofed Temple of Heaven, surrounded by intricate stone terraces and tiny visitors walking around the monument.
A winding blue river flows through the fabric landscape, crossed by an elegant traditional stone bridge. Small wooden boats glide across the water, creating a lively scene. Around the Temple of Heaven are iconic Beijing landmarks including Tiananmen Gate, the China Central Television (CCTV) Headquarters building, the Beijing Olympic Stadium (Bird’s Nest), the Central Radio & TV Tower, and modern skyscrapers, all rendered as highly detailed miniature architectural models.
Tiny people stroll along pathways illuminated by warm glowing lantern lights, creating a magical atmosphere. The scene combines traditional Chinese culture, modern architecture, tourism, and national pride in one harmonious composition. Shot from a slightly elevated perspective with shallow depth of field, selective focus, realistic textures, soft bokeh, warm golden lighting, ultra-realistic craftsmanship, vibrant red and blue colors, photorealistic miniature world, travel-poster quality, hyper-detailed, 8K resolution, masterpiece, cinematic storytelling, breathtaking visual impact.
This prompt structure can generate 100s of unique, gorgeous images. I simply asked Ai to pick 4 famous brands and nothing else but food type and other parts can be customized
2x2 grid, do this for 4 famous Fortune 500 brands, 16:9 def plate_dish(): brand_dna = "[$BRAND]" base_dish = "[$FOOD_TYPE, e.g., Deconstructed Cheesecake, Sushi Roll, Macaron]" # Semantic Material Mapping texture_profile = infer_brand_packaging(brand_dna) # e.g., cardboard, glossy plastic, brushed aluminum color_palette = infer_brand_hex_codes(brand_dna) # Culinary Transformation edible_materials = map_to_molecular_gastronomy(texture_profile) # e.g., transforming "matte plastic" into "matte fondant" or "aluminum" into "edible silver leaf" print(f"A hyper-macro food photography shot of a Michelin-star dessert based on {brand_dna}.") print(f"The {base_dish} is constructed using {edible_materials} matching {color_palette}.") print("Lighting: Dramatic restaurant spotlight, black slate plate, smoke/dry ice effects.") print("Garnish: A perfect, delicate sugar-glass rendering of the brand's logo.") execute(plate_dish)
Wow Claude Fable 5 is wild!!
I just told it to make 3 apps with 1 prompt.
1. Jarvis Dashboard
2. Build Apple fitness app
3. Clone my hotel website pixel perfect
results have me speechless
BREAKING:
Anthropic just dropped Claude Fable 5—this is Mythos, made safe for public release. It is the best coding model in the world.
We've been testing it internally @every for the last week or so across coding, writing, marketing, editing, and more—here's our vibe check:
- It broke our benchmarks. Fable scored a 91/100 on our Senior Engineer benchmark—this is human senior engineer level. The previous high score was Opus 4.8 at 63. GPT-5.5 is a 62.
- It's a one-shot wonder. You can set it and forget for hours or overnight on huge coding tasks, and come back to completed work. It cleared entire production bug backlogs, built a playable 3D, and even made a 2-minute animated film—all one-shot.
- Taste and attention to detail. In coding and knowledge work tasks, it has much better taste and attention to detail than we've ever seen. It gets subtle things right, adds little features you might not have thought of, and generally understands the assignment in ways that surprised us.
- Great use of context. We set it loose analyzing customer feedback surveys and our website data and it came back with a crisp, clean report that identified a. our biggest problem and b. a concrete testable solution—and then we sent it off to build that.
- It's best for power users. If you're already used to orchestrating multiple agents in your work, this model can do things that you've never seen before. If you're a knowledge worker or vibe coder with a more basic setup, you're not going to notice a huge difference—in fact, it probably isn't the right model for you.
- It's very slow, token-hungry. Using this thing for regular knowledge work is like squashing an ant with a rocket launcher. It also routinely uses 500k to 1M tokens on tasks. That's why it's best for your heaviest jobs—but not as good for tasks like collaborative writing.
- It's expensive. It's about twice as expensive as Opus, and it's also incredibly token hungry—so expect it to be something you'll use sparingly unless your company pays for it.
Overall, I think of it like a warp drive for coding: It can get you across the galaxy in a few hours, when it used to take months or years. But it's not appropriate for getting around town—you need something faster, cheaper, and more maneuverable.
The ceiling is extraordinarily high on this model though. Even our most advanced testers like @kieranklaassen felt like they were only scratching the surface of it.
Want our full vibe check with all of our testing and benchmarks? Read it on @every: https://t.co/MgJLZszJUB
Claude Fable just built this entire product from one prompt. I'm blown away.
Shotblock is a 3D shot-planning tool for AI filmmakers — real lens math, actor blocking, 180 ° rule warnings, animatics, storyboard + prompt export. It researched storyboard conventions, wrote its own test suite, verified everything in a browser, and deployed it.
Then I asked for a promo video. It scripted, recorded, and edited the one below with minimal direction.
Give it a try. Free, no signup. Feedback button in the app for any issues or feature requests.
https://t.co/EEx9ClAC9m