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New Opus 4.8 crashed Opus 4.7 at physics on canvas! We gave both models the same three prompts: simulate a real physics phenomenon on raw HTML5 canvas. Prompt 1: "A triple pendulum swings into chaos and paints glowing trails with its tip" Prompt 2: "A 1 kg block bounces... show more

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635,191 次观看 • 1 个月前 •via X (Twitter)

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New Z.ai GLM-5.2 beats Kimi K2.7 Code on physics contest! We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Pool break 2. Block on a bed of springs 3. Galton board Outputs: GLM-5.2: 12,640 tokens Kimi K2.7 Code: 7,420 tokens GLM 5.2 nailed all three, and it did it with way more detail and polish. The break conserved momentum, the block bounced off the springs and the Galton beads spread into a clean bell curve. Kimi struggled on every scene: its block fell straight through the springs, its break didn't look realistic with the balls colliding all wrong, and on the Galton board its balls overlapped and piled into each other instead of spreading out

New Z.ai GLM-5.2 beats Kimi K2.7 Code on physics contest! We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Pool break 2. Block on a bed of springs 3. Galton board Outputs: GLM-5.2: 12,640 tokens Kimi K2.7 Code: 7,420 tokens GLM 5.2 nailed all three, and it did it with way more detail and polish. The break conserved momentum, the block bounced off the springs and the Galton beads spread into a clean bell curve. Kimi struggled on every scene: its block fell straight through the springs, its break didn't look realistic with the balls colliding all wrong, and on the Galton board its balls overlapped and piled into each other instead of spreading out

atomic.chat

60,899 次观看 • 25 天前

New Fable 5 beats Opus 4.8 on real world physics simulations We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Chaotic double pendulum 2. Galton board 3. Water in a spinning drum (WCSPH) Generation cost Fable 5: $3.35 on 68.7k tokens, time 14m 47s Opus 4.8: $0.93 on 38.9k tokens, time 8m 10s Fable clearly did better on the water simulation, producing a much more solid and continuous body of water. Opus left larger gaps near the walls, scattered particles around the scene, and struggled to keep the fluid stable.

New Fable 5 beats Opus 4.8 on real world physics simulations We gave both models the same three prompts and asked them to build self contained HTML5 sims with real physics and no libraries: 1. Chaotic double pendulum 2. Galton board 3. Water in a spinning drum (WCSPH) Generation cost Fable 5: $3.35 on 68.7k tokens, time 14m 47s Opus 4.8: $0.93 on 38.9k tokens, time 8m 10s Fable clearly did better on the water simulation, producing a much more solid and continuous body of water. Opus left larger gaps near the walls, scattered particles around the scene, and struggled to keep the fluid stable.

atomic.chat

1,585,905 次观看 • 1 个月前

Opus 4.8 vs MiniMax M3 tested both on default settings with the same prompt > Opus one shotted everything in 7 minutes > M3 needed an extra prompt to fix the "break block" feature and took 20+ minutes both got super close, judge both and lemme know which one looks better?

Opus 4.8 vs MiniMax M3 tested both on default settings with the same prompt > Opus one shotted everything in 7 minutes > M3 needed an extra prompt to fix the "break block" feature and took 20+ minutes both got super close, judge both and lemme know which one looks better?

J A Z I I

67,862 次观看 • 1 个月前

1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5 We gave 3 models the same prompt and compared one-shot outputs. The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s. Which output do you like best? GGUF:

1-bit GLM-5.2 GGUF vs. Claude 4.8 Opus vs. GPT-5.5 We gave 3 models the same prompt and compared one-shot outputs. The 1-bit GLM-5.2 GGUF ran locally on a Mac Studio M3 Ultra with 256GB RAM at ~21.6 tok/s. Which output do you like best? GGUF:

Unsloth AI

1,532,900 次观看 • 18 天前

GPT 5.5 just buried opus 4.7 I had it build a pokemon style battle app. 1 prompt. the opus game crashes 90% of the time and Ui is absolute garbage. GPT 5.5 one shotted a masterpiece. Time to cancel anthropic sub?

GPT 5.5 just buried opus 4.7 I had it build a pokemon style battle app. 1 prompt. the opus game crashes 90% of the time and Ui is absolute garbage. GPT 5.5 one shotted a masterpiece. Time to cancel anthropic sub?

Max Blade

459,591 次观看 • 2 个月前

Opus 4.6 vs Gemini Pro 3.1 (7/9) I wanted to do a physics simulation test, overall both models did a good job in this department, however Opus added nice animations to things like "Fan", "Magnet" whilst gemini just rendered them out as a red circle. Opus also included way more effects in its toolbox, like rocket, connecting shapes with rope, springs etc. Should note that in all of these tests, Opus one shotted all of them, whilst gemini took an extra 3-5 prompts to get the project compile :) prompt: Build a polished physics sandbox toy using React + Vite with a real physics engine (Matter.js or Rapier), where users can spawn, grab, throw, and connect objects with springs, hinges, and ropes, plus interactive gadgets like fans and thrusters.

Opus 4.6 vs Gemini Pro 3.1 (7/9) I wanted to do a physics simulation test, overall both models did a good job in this department, however Opus added nice animations to things like "Fan", "Magnet" whilst gemini just rendered them out as a red circle. Opus also included way more effects in its toolbox, like rocket, connecting shapes with rope, springs etc. Should note that in all of these tests, Opus one shotted all of them, whilst gemini took an extra 3-5 prompts to get the project compile :) prompt: Build a polished physics sandbox toy using React + Vite with a real physics engine (Matter.js or Rapier), where users can spawn, grab, throw, and connect objects with springs, hinges, and ropes, plus interactive gadgets like fans and thrusters.

Dev Ed

28,179 次观看 • 4 个月前

Claude Opus 4.8 beats Opus 4.7, GPT-5.5, and Gemini 3.1 Pro !! We ran a multimodal gaming comparison across these four closed source models. Opus 4.8 took the longest to generate, but it came out ahead, capturing the most elements from the prompt and successfully hitting the rival cars. GPT 5.5 is still good, but Gemini is no longer the best multimodal model now

Claude Opus 4.8 beats Opus 4.7, GPT-5.5, and Gemini 3.1 Pro !! We ran a multimodal gaming comparison across these four closed source models. Opus 4.8 took the longest to generate, but it came out ahead, capturing the most elements from the prompt and successfully hitting the rival cars. GPT 5.5 is still good, but Gemini is no longer the best multimodal model now

GMI Cloud

116,249 次观看 • 1 个月前

Prompts shouldn’t be static. Introducing Prompt Variables. A way to build prompts that adapt to your inputs. Think of it as a text compositor: drop dynamic variables directly into your prompt, reorder them with drag-and-drop, and create new ones instantly with an @ sign. It means a cleaner workflow, and a cleaner canvas. 👉 See it in action:

Prompts shouldn’t be static. Introducing Prompt Variables. A way to build prompts that adapt to your inputs. Think of it as a text compositor: drop dynamic variables directly into your prompt, reorder them with drag-and-drop, and create new ones instantly with an @ sign. It means a cleaner workflow, and a cleaner canvas. 👉 See it in action:

Figma Weave

24,524 次观看 • 4 个月前

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

New Google Gemma 4 12B claims near-26B performance - we tested both! We ran both models locally on one RTX 4090 and gave each the same task: write a self-contained HTML5 canvas animation with real physics in one file without libraries. Three scenes - a Galton board, two blocks colliding off a wall, and a chaotic triple pendulum Outputs: Gemma 4 26B-A4B: 15 GB VRAM usage, 6.9k tokens, 138 tok/s Gemma 4 12B: 9 GB VRAM usage, 8.9k tokens, 80 tok/s Same Gemma 4 family, but the 26B-A4B won every scene and ran ~1.7x faster - on just 4B active params. The 12B stayed very close though, on almost half the VRAM - which makes it the ideal model for a 16 GB laptop

atomic.chat

151,491 次观看 • 1 个月前

🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

🎨 Introducing Prompt Canvas — a novel UX for prompt engineering Building LLM applications requires new and dedicated tools for prompt engineering. With Prompt Canvas in LangSmith, you can: • Collaborate with an AI agent to draft, refine, and edit your prompts • Define custom quick actions to standardize prompting strategies used across the org ✍️ Learn more: ➡️ Try it out:

LangChain

43,733 次观看 • 1 年前

I made $40K on YouTube last month and never turned on a camera, it was 7 ChatGPT prompts Here's what I typed > Prompt 1: generate 10 viral video ideas in my niche from the latest trends > Prompt 2: write a full script with a hook, story arc, and CTA that maximizes watch time > Prompt 3: build me a faceless channel, script it, voice it, edit it, make it run without me > Prompt 4: generate a clickworthy, SEO-optimized title, description, and tags > Prompt 5: write a 15-second hook people physically can't stop watching > Prompt 6: create a 30-day growth and monetization plan from zero > Prompt 7: turn the video into a blog post, a Reel, a TikTok, and a thread 7 prompts, 1 laptop, 0 cameras the channel never needed my face, it needed my prompts

I made $40K on YouTube last month and never turned on a camera, it was 7 ChatGPT prompts Here's what I typed > Prompt 1: generate 10 viral video ideas in my niche from the latest trends > Prompt 2: write a full script with a hook, story arc, and CTA that maximizes watch time > Prompt 3: build me a faceless channel, script it, voice it, edit it, make it run without me > Prompt 4: generate a clickworthy, SEO-optimized title, description, and tags > Prompt 5: write a 15-second hook people physically can't stop watching > Prompt 6: create a 30-day growth and monetization plan from zero > Prompt 7: turn the video into a blog post, a Reel, a TikTok, and a thread 7 prompts, 1 laptop, 0 cameras the channel never needed my face, it needed my prompts

Woody

69,715 次观看 • 1 个月前

Stop saying that Opus 4.6 is better than Opus 4.8. I just tested Claude Opus 4.8 (Right) and Claude Opus 4.6 (Left) on the same exact prompt. Claude Opus 4.8 completed it in less than 2 minutes. Claude Opus 4.6 "Garnished" for over 7 minutes. Stop using Opus 4.6.

Stop saying that Opus 4.6 is better than Opus 4.8. I just tested Claude Opus 4.8 (Right) and Claude Opus 4.6 (Left) on the same exact prompt. Claude Opus 4.8 completed it in less than 2 minutes. Claude Opus 4.6 "Garnished" for over 7 minutes. Stop using Opus 4.6.

BridgeMind

52,868 次观看 • 1 个月前

this is f**king dangerous someone just figured out how to get fable 5 reasoning in opus 4.8 with one prompt you have less than 24 hours to set this up as this takes away 20% of your usage limit here is how: 1) Paste the prompt and ask it to "make an operating manual" 2) Create a New Claude Project and "Add this .md in Project instructions 3) Use Opus 4.8 High/Max and now you have Fable 5 reasoning at 1/5th the price save and bookmark it no matter what

this is f**king dangerous someone just figured out how to get fable 5 reasoning in opus 4.8 with one prompt you have less than 24 hours to set this up as this takes away 20% of your usage limit here is how: 1) Paste the prompt and ask it to "make an operating manual" 2) Create a New Claude Project and "Add this .md in Project instructions 3) Use Opus 4.8 High/Max and now you have Fable 5 reasoning at 1/5th the price save and bookmark it no matter what

Avid

902,585 次观看 • 4 天前

Opus 4.6 vs GPT-5.4 (High) (6/9) Testing out how well these models to with cloth physics simulation, and honestly both where pretty close here but my god I am shocked how good GPT 5.4 is with optimizing this scene whilst opus 4.6 felt quite janky and lower fps. GPT 5.4 also got the cutting working properly whilst Opus was just floating about. I do want to heavily stress the fact that making this prompt, Opus 4.6 cost the whole 100% of the session ($20 pro plan) + $3 in credits whilst GPT 5.4 only cost 10% of the usage. prompt: Build a premium Vite + React interactive cloth simulation of a cozy room curtain using real particle/constraint physics, with structural + shear springs, damping, collisions, and stable real-time deformation. Users should be able to drag the curtain, add wind, and cut constraints, while the experience stays polished, visually elegant, and numerically stable.

Opus 4.6 vs GPT-5.4 (High) (6/9) Testing out how well these models to with cloth physics simulation, and honestly both where pretty close here but my god I am shocked how good GPT 5.4 is with optimizing this scene whilst opus 4.6 felt quite janky and lower fps. GPT 5.4 also got the cutting working properly whilst Opus was just floating about. I do want to heavily stress the fact that making this prompt, Opus 4.6 cost the whole 100% of the session ($20 pro plan) + $3 in credits whilst GPT 5.4 only cost 10% of the usage. prompt: Build a premium Vite + React interactive cloth simulation of a cozy room curtain using real particle/constraint physics, with structural + shear springs, damping, collisions, and stable real-time deformation. Users should be able to drag the curtain, add wind, and cut constraints, while the experience stays polished, visually elegant, and numerically stable.

Dev Ed

414,134 次观看 • 4 个月前

I gave Kimi K2.6 and K2.7 Code the exact same prompt to animate a letter burning to ash. Pure HTML canvas, zero libraries.

I gave Kimi K2.6 and K2.7 Code the exact same prompt to animate a letter burning to ash. Pure HTML canvas, zero libraries.

stevibe

89,828 次观看 • 29 天前

So Runway Gen 4.5 finally adds image-to-video, the workflow most pros rely on for consistency. We put it head to head with Kling AI and Flow by Google VEO using the same reference images and prompts (below) to evaluate motion quality, stability, and cinematic realism. 1. Action/WaterPhysics Test Prompt: Cinematic, wide-shot of a man running in a shallow river. The camera is tracking the man from behind as he runs up the river. Handheld camera shake as the camera follows the man. 2. Fire Physics Test Prompt: Cinematic, wide-shot of terrified woman running towards her burning barn. She abruptly stops, and puts in hands on her head as she watches her barn burn down. 3. VFX test prompt: Cinematic, wide-shot of a hooded figure. Flashes of purple magic and smoke whirl around the figure. The figure lifts its arms as the purple magic and smoke intensifies. 4. 2D Animation Test Prompt: 2D animated shot of a waiting at a bus stop in a thunderstorm. The man turns, walks to the bench, and sits down. 5. 3D Animation Test Prompt: 3D animated shot of an octopus. The octopus reaches into a coral and picks up a glowing white gem. 6. Conversation Test Prompt: slow camera push-in as two friends are having a conversation at a coffee shop Overall verdict: Despite the “world’s best” claim, Runway Gen 4.5 is not there yet. Prompt adherence is solid, but motion, physics, and cinematic realism still lag behind tools like Kling and VEO. Great platform, mid-tier model for now.

So Runway Gen 4.5 finally adds image-to-video, the workflow most pros rely on for consistency. We put it head to head with Kling AI and Flow by Google VEO using the same reference images and prompts (below) to evaluate motion quality, stability, and cinematic realism. 1. Action/WaterPhysics Test Prompt: Cinematic, wide-shot of a man running in a shallow river. The camera is tracking the man from behind as he runs up the river. Handheld camera shake as the camera follows the man. 2. Fire Physics Test Prompt: Cinematic, wide-shot of terrified woman running towards her burning barn. She abruptly stops, and puts in hands on her head as she watches her barn burn down. 3. VFX test prompt: Cinematic, wide-shot of a hooded figure. Flashes of purple magic and smoke whirl around the figure. The figure lifts its arms as the purple magic and smoke intensifies. 4. 2D Animation Test Prompt: 2D animated shot of a waiting at a bus stop in a thunderstorm. The man turns, walks to the bench, and sits down. 5. 3D Animation Test Prompt: 3D animated shot of an octopus. The octopus reaches into a coral and picks up a glowing white gem. 6. Conversation Test Prompt: slow camera push-in as two friends are having a conversation at a coffee shop Overall verdict: Despite the “world’s best” claim, Runway Gen 4.5 is not there yet. Prompt adherence is solid, but motion, physics, and cinematic realism still lag behind tools like Kling and VEO. Great platform, mid-tier model for now.

Curious Refuge

25,538 次观看 • 5 个月前

Generated this e-com website with 2 v0 Opus prompts + 1 @lumalabsai prompt for the video 🤘

Generated this e-com website with 2 v0 Opus prompts + 1 @lumalabsai prompt for the video 🤘

Guillermo Rauch

54,128 次观看 • 7 个月前

GROK IMAGINE PROMPT TIP: There's a very simple way to improve your prompts: use the prompts from the templates offered by Grok Imagine. These templates provide a wealth of prompts available to everyone. Take a prompt and analyze it to understand the purpose of each word. For example, I took the fish template and added a color change in the prompt using the video extension.

GROK IMAGINE PROMPT TIP: There's a very simple way to improve your prompts: use the prompts from the templates offered by Grok Imagine. These templates provide a wealth of prompts available to everyone. Take a prompt and analyze it to understand the purpose of each word. For example, I took the fish template and added a color change in the prompt using the video extension.

Déborah

550,955 次观看 • 3 个月前

Gemini 2.5 Flash demolishes my Galton Board test, I could not get 4omini, 4o mini high, or 03 to produce this. I found that Gemini 2.5 Flash understands my intents almost instantly, code produced is tight and neat. The prompt is a merging of various steps. It took me 5 steps to achieve this in Gemini 2.5 Flash, I gave up on OpenAI models after about half an hour. My iterations are obviously not exact. But people can test with this one prompt for more objective comparison. Please try this prompt on your end to confirm: -------------------------------------------------- Create a self-contained HTML file for a Galton board simulation using client-side JavaScript and a 2D physics engine (like Matter.js, included via CDN). The simulation should be rendered on an HTML5 canvas and meet the following criteria: 1. **Single File:** All necessary HTML, CSS, and JavaScript code must be within this single `.html` file. 2. **Canvas Size:** The overall simulation area (canvas) should be reasonably sized to fit on a standard screen without requiring extensive scrolling or zooming (e.g., around 500x700 pixels). 3. **Physics:** Utilize a 2D rigid body physics engine for realistic ball-peg and ball-wall interactions. 4. **Obstacles (Pegs):** Create static, circular pegs arranged in full-width horizontal rows extending across the usable width of the board (not just a triangle). The pegs should be small enough and spaced appropriately for balls to navigate and bounce between them. 5. **Containment:** * Include static, sufficiently thick side walls and a ground at the bottom to contain the balls within the board. * Implement *physical* static dividers between the collection bins at the bottom. These dividers must be thick enough to prevent balls from passing through them, ensuring accurate accumulation in each bin. 6. **Ball Dropping:** Balls should be dropped from a controlled, narrow area near the horizontal center at the top of the board to ensure they enter the peg field consistently. 7. **Bins:** The collection area at the bottom should be divided into distinct bins by the physical dividers. The height of the bins should be sufficient to clearly visualize the accumulation of balls. 8. **Visualization:** Use a high-contrast color scheme to clearly distinguish between elements. Specifically, use yellow for the structural elements (walls, top guides, physical bin dividers, ground), a contrasting color (like red) for the pegs, and a highly contrasting color (like dark grey or black) for the balls. 9. **Demonstration:** The simulation should visually demonstrate the formation of the normal (or binomial) distribution as multiple balls fall through the pegs and collect in the bins. Ensure the physics parameters (restitution, friction, density) and ball drop rate are tuned for a smooth and clear demonstration of the distribution. #OpenAI Sam Altman Greg Brockman AshutoshShrivastava Aidan McLaughlin

Gemini 2.5 Flash demolishes my Galton Board test, I could not get 4omini, 4o mini high, or 03 to produce this. I found that Gemini 2.5 Flash understands my intents almost instantly, code produced is tight and neat. The prompt is a merging of various steps. It took me 5 steps to achieve this in Gemini 2.5 Flash, I gave up on OpenAI models after about half an hour. My iterations are obviously not exact. But people can test with this one prompt for more objective comparison. Please try this prompt on your end to confirm: -------------------------------------------------- Create a self-contained HTML file for a Galton board simulation using client-side JavaScript and a 2D physics engine (like Matter.js, included via CDN). The simulation should be rendered on an HTML5 canvas and meet the following criteria: 1. Single File: All necessary HTML, CSS, and JavaScript code must be within this single `.html` file. 2. Canvas Size: The overall simulation area (canvas) should be reasonably sized to fit on a standard screen without requiring extensive scrolling or zooming (e.g., around 500x700 pixels). 3. Physics: Utilize a 2D rigid body physics engine for realistic ball-peg and ball-wall interactions. 4. Obstacles (Pegs): Create static, circular pegs arranged in full-width horizontal rows extending across the usable width of the board (not just a triangle). The pegs should be small enough and spaced appropriately for balls to navigate and bounce between them. 5. Containment: * Include static, sufficiently thick side walls and a ground at the bottom to contain the balls within the board. * Implement physical static dividers between the collection bins at the bottom. These dividers must be thick enough to prevent balls from passing through them, ensuring accurate accumulation in each bin. 6. Ball Dropping: Balls should be dropped from a controlled, narrow area near the horizontal center at the top of the board to ensure they enter the peg field consistently. 7. Bins: The collection area at the bottom should be divided into distinct bins by the physical dividers. The height of the bins should be sufficient to clearly visualize the accumulation of balls. 8. Visualization: Use a high-contrast color scheme to clearly distinguish between elements. Specifically, use yellow for the structural elements (walls, top guides, physical bin dividers, ground), a contrasting color (like red) for the pegs, and a highly contrasting color (like dark grey or black) for the balls. 9. Demonstration: The simulation should visually demonstrate the formation of the normal (or binomial) distribution as multiple balls fall through the pegs and collect in the bins. Ensure the physics parameters (restitution, friction, density) and ball drop rate are tuned for a smooth and clear demonstration of the distribution. #OpenAI Sam Altman Greg Brockman AshutoshShrivastava Aidan McLaughlin

RameshR

247,923 次观看 • 1 年前

One shotted by Opus 4.7 The full prompt: "Make a website for This Week in AI podcast with great branding."

One shotted by Opus 4.7 The full prompt: "Make a website for This Week in AI podcast with great branding."

This Week in AI

659,577 次观看 • 2 个月前