Interactive Mouse Canvas

{"id":"gen-1781798910-xtnCDjkj0dvyWkAnigTO","choices":[{"finish_reason":"stop","index":0,"logprobs":null,"message":{"content":"```html\n\n\n\n \n \n Interactive Mouse Canvas\n \n \n\n\n \n\n\n```","refusal":null,"role":"assistant","annotations":null,"audio":null,"function_call":null,"tool_calls":null,"reasoning":"**Exploring Interactive Animation**\n\nI'm currently focusing on how to translate mouse speed into a visually engaging change. My thoughts are revolving around algorithms that can detect velocity and map it to parameters like particle emission, color shifts, or line thickness, aiming for an aesthetic that feels both dynamic and pleasing to the eye.\n\n**Developing Responsive Trails**\n\nMy focus is on creating a dynamic ribbon effect. I'm exploring how mouse velocity can influence the size, color, and opacity of trailing circles, aiming for a fluid, organic feel that visually communicates the speed of interaction.\n\n**Refining Trail Generation**\n\nI'm currently implementing the core logic for generating the trail points. My current focus is on mapping the calculated mouse speed to the size of the shapes at each point, ensuring a visually responsive feedback loop. The hue variable is being incremented each frame to create smooth color transitions.\n\n**Implementing Direct Drawing**\n\nI'm now focusing on a direct drawing approach. My thoughts involve using a translucent background wipe to create trails, directly mapping current mouse speed to visual elements like stroke weight and color for a dynamic, responsive effect. I'm experimenting with hue shifting based on frame count and speed.\n\n**Designing Dynamic Particle Trails**\n\nI'm now shifting my focus to a particle system, aiming for maximum \"coolness\" and responsiveness. My thoughts involve creating `Particle` objects with properties like position, velocity, acceleration, color, size, and lifespan. I plan to spawn these particles based on mouse speed and direction, with color and size also influenced by speed for a visually engaging experience.\n\n**Spawning Animated Particles**\n\nI'm now actively spawning particle objects. Each particle is initialized with properties like position, speed, and I'm influencing its size and color based on the mouse's current velocity. This is aimed at creating a dynamic and visually responsive flow directly tied to user input.\n\n**Updating and Displaying Particles**\n\nI'm now implementing the core particle update and display loop, ensuring each particle's position, velocity, and lifespan are managed correctly. I'm also refining the physics, blending random movement with directional force based on mouse speed for emergent visual patterns.\n\n**Adjusting Particle Properties**\n\nI'm now refining how particle characteristics, like velocity, color, and size, are mapped to mouse speed. I'm experimenting with specific ranges and adding jitter for a more organic feel, ensuring faster movements result in more pronounced visual effects.\n\n**Refining Particle Behavior**\n\nI'm currently tweaking how particles decay and move. I'm implementing subtle upward drift and drag to their velocity, and also a shrinking size over time. Additionally, I'm experimenting with a glowing effect for larger particles to enhance their visual presence.\n\n**Connecting Nearby Particles**\n\nI'm now focusing on enhancing the visual appeal by drawing lines between nearby particles. This effect will be dynamic, influenced by mouse speed to create a \"constellation\" or \"web\" style, with faster movements resulting in tighter, glowing connections. I'm also ensuring particle count is managed for performance.\n\n**Revising Trail Mechanics**\n\nI'm now consolidating my approach, moving away from complex particle systems for the core trail. My focus is on a history-based spline/ribbon effect, coupled with fast-motion \"splatter\" particles. This aims for better performance and a more direct representation of speed.\n\n**Combining Ribbon and Splatter**\n\nI'm now integrating a spline-based ribbon effect with speed-dependent splatter particles. The ribbon's stroke weight tapers and its hue shifts, while high-speed movements trigger multiple splatter emissions. This hybrid approach promises dynamic visuals tied directly to interaction velocity, with a focus on efficient code structure.\n\n**Integrating Splatter Elements**\n\nI'm now focusing on adding speed-dependent \"splatter\" elements that originate from the ribbon. These will appear during faster mouse movements, adding a burst of dynamic energy. My thoughts involve carefully balancing their emission rate and visual intensity with the main ribbon effect.\n\n**Drawing Smooth Trails**\n\nI am currently implementing a history-based spline to create a smooth ribbon trail. My focus is on mapping the mouse's speed to the stroke weight and hue of this ribbon, aiming for a fluid and responsive visual output. The background is subtly fading each frame to maintain a sense of motion.\n\n**Refining Ribbon and Splatter**\n\nI'm now focusing on the ribbon's stroke weight, making it thicker with higher mouse speeds and tapered at the ends. I'm also cycling through hues based on trail position and frame count for smooth color transitions. Separately, I'm implementing a speed threshold to trigger the emission of splatter particles.\n\n**Adjusting Particle Spread**\n\nI'm currently refining how particles are initially dispersed. My focus is on adjusting the angle of spread based on mouse speed, aiming for a tighter, more directed burst at higher velocities and a wider puff at lower speeds. I'm also enhancing their initial velocity magnitude with speed.\n\n**Enhancing Particle Visuals**\n\nI'm now refining the particle display, adding a brighter inner core and an outer glow effect, both of which fade and shrink over time. This aims to create a more dynamic and ethereal appearance for the particles.\n\n**Finalizing Visuals**\n\nI'm now integrating all elements into a single HTML file, ensuring the ribbon thickens and its color shifts with speed. Particles now emit outwards, their size directly proportional to velocity, creating a more impactful, dynamic experience.\n\n","reasoning_details":[{"type":"reasoning.text","text":"**Exploring Interactive Animation**\n\nI'm currently focusing on how to translate mouse speed into a visually engaging change. My thoughts are revolving around algorithms that can detect velocity and map it to parameters like particle emission, color shifts, or line thickness, aiming for an aesthetic that feels both dynamic and pleasing to the eye.\n\n**Developing Responsive Trails**\n\nMy focus is on creating a dynamic ribbon effect. I'm exploring how mouse velocity can influence the size, color, and opacity of trailing circles, aiming for a fluid, organic feel that visually communicates the speed of interaction.\n\n**Refining Trail Generation**\n\nI'm currently implementing the core logic for generating the trail points. My current focus is on mapping the calculated mouse speed to the size of the shapes at each point, ensuring a visually responsive feedback loop. The hue variable is being incremented each frame to create smooth color transitions.\n\n**Implementing Direct Drawing**\n\nI'm now focusing on a direct drawing approach. My thoughts involve using a translucent background wipe to create trails, directly mapping current mouse speed to visual elements like stroke weight and color for a dynamic, responsive effect. I'm experimenting with hue shifting based on frame count and speed.\n\n**Designing Dynamic Particle Trails**\n\nI'm now shifting my focus to a particle system, aiming for maximum \"coolness\" and responsiveness. My thoughts involve creating `Particle` objects with properties like position, velocity, acceleration, color, size, and lifespan. I plan to spawn these particles based on mouse speed and direction, with color and size also influenced by speed for a visually engaging experience.\n\n**Spawning Animated Particles**\n\nI'm now actively spawning particle objects. Each particle is initialized with properties like position, speed, and I'm influencing its size and color based on the mouse's current velocity. This is aimed at creating a dynamic and visually responsive flow directly tied to user input.\n\n**Updating and Displaying Particles**\n\nI'm now implementing the core particle update and display loop, ensuring each particle's position, velocity, and lifespan are managed correctly. I'm also refining the physics, blending random movement with directional force based on mouse speed for emergent visual patterns.\n\n**Adjusting Particle Properties**\n\nI'm now refining how particle characteristics, like velocity, color, and size, are mapped to mouse speed. I'm experimenting with specific ranges and adding jitter for a more organic feel, ensuring faster movements result in more pronounced visual effects.\n\n**Refining Particle Behavior**\n\nI'm currently tweaking how particles decay and move. I'm implementing subtle upward drift and drag to their velocity, and also a shrinking size over time. Additionally, I'm experimenting with a glowing effect for larger particles to enhance their visual presence.\n\n**Connecting Nearby Particles**\n\nI'm now focusing on enhancing the visual appeal by drawing lines between nearby particles. This effect will be dynamic, influenced by mouse speed to create a \"constellation\" or \"web\" style, with faster movements resulting in tighter, glowing connections. I'm also ensuring particle count is managed for performance.\n\n**Revising Trail Mechanics**\n\nI'm now consolidating my approach, moving away from complex particle systems for the core trail. My focus is on a history-based spline/ribbon effect, coupled with fast-motion \"splatter\" particles. This aims for better performance and a more direct representation of speed.\n\n**Combining Ribbon and Splatter**\n\nI'm now integrating a spline-based ribbon effect with speed-dependent splatter particles. The ribbon's stroke weight tapers and its hue shifts, while high-speed movements trigger multiple splatter emissions. This hybrid approach promises dynamic visuals tied directly to interaction velocity, with a focus on efficient code structure.\n\n**Integrating Splatter Elements**\n\nI'm now focusing on adding speed-dependent \"splatter\" elements that originate from the ribbon. These will appear during faster mouse movements, adding a burst of dynamic energy. My thoughts involve carefully balancing their emission rate and visual intensity with the main ribbon effect.\n\n**Drawing Smooth Trails**\n\nI am currently implementing a history-based spline to create a smooth ribbon trail. My focus is on mapping the mouse's speed to the stroke weight and hue of this ribbon, aiming for a fluid and responsive visual output. The background is subtly fading each frame to maintain a sense of motion.\n\n**Refining Ribbon and Splatter**\n\nI'm now focusing on the ribbon's stroke weight, making it thicker with higher mouse speeds and tapered at the ends. I'm also cycling through hues based on trail position and frame count for smooth color transitions. Separately, I'm implementing a speed threshold to trigger the emission of splatter particles.\n\n**Adjusting Particle Spread**\n\nI'm currently refining how particles are initially dispersed. My focus is on adjusting the angle of spread based on mouse speed, aiming for a tighter, more directed burst at higher velocities and a wider puff at lower speeds. I'm also enhancing their initial velocity magnitude with speed.\n\n**Enhancing Particle Visuals**\n\nI'm now refining the particle display, adding a brighter inner core and an outer glow effect, both of which fade and shrink over time. This aims to create a more dynamic and ethereal appearance for the particles.\n\n**Finalizing Visuals**\n\nI'm now integrating all elements into a single HTML file, ensuring the ribbon thickens and its color shifts with speed. Particles now emit outwards, their size directly proportional to velocity, creating a more impactful, dynamic experience.\n\n","format":"google-gemini-v1","index":0},{"type":"reasoning.encrypted","data":"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","format":"google-gemini-v1","index":1}]},"native_finish_reason":"STOP"}],"created":1781798910,"model":"google/gemini-3.1-pro-preview-20260219","object":"chat.completion","service_tier":"provisioned","system_fingerprint":null,"usage":{"completion_tokens":6775,"prompt_tokens":94,"total_tokens":6869,"completion_tokens_details":{"accepted_prediction_tokens":null,"audio_tokens":0,"reasoning_tokens":5112,"rejected_prediction_tokens":null,"image_tokens":0},"prompt_tokens_details":{"audio_tokens":0,"cached_tokens":0,"cache_write_tokens":0,"video_tokens":0},"cost":0.081488,"is_byok":false,"cost_details":{"upstream_inference_cost":0.081488,"upstream_inferenc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Create a p5.js animation that is a cool interactive thing ba...