Gemini 2.5 Flash is a powerful language model that can quickly and efficiently recreate impressive demos and simulations. Despite its lower cost and faster speed compared to the more capable Gemini 2.5 Pro, Flash is able to produce remarkable results.

The model was able to recreate a variety of demos, including:

• An interactive 3D visualization that allows users to change the shape and color of the object
• A virus simulation that models the interaction between red blood cells, white blood cells, and viruses
• A "duck sorter" game where the player must group ducks by color
• A tower defense game with various tower types and enemy balloons
• A complex snake game with visual effects and AI-controlled snake
• A Galton board simulation that demonstrates probability distribution
• An iPhone drawing app with various tools and settings

Gemini 2.5 Flash was able to generate the code for these demos in a matter of seconds, showcasing its impressive capabilities. While the visuals may not be as polished as those created by the more expensive Gemini 2.5 Pro, the model's ability to quickly produce functional and interactive simulations is truly remarkable.

The key advantage of Gemini 2.5 Flash is its speed and cost-effectiveness, making it a valuable tool for rapid prototyping and experimentation. Developers can leverage this model to quickly explore and validate ideas without the need for extensive manual coding.

Gemini 2.5 Pro and Gemini 2.5 Flash are two models within the Gemini family, each with its own strengths and capabilities.

Gemini 2.5 Pro:

• The "massive beast" of the two models
• Slower and more expensive, but more capable
• Able to zero-shot complex tasks like the Rubik's Cube simulation
• Produced visually stunning results in the Snake game demo

Gemini 2.5 Flash:

• Lightning fast and much cheaper compared to Gemini 2.5 Pro
• Able to recreate demos from video inputs in a fraction of the time
• Struggled with some tasks like the Rubik's Cube simulation, but was able to complete other complex demos like the virus simulation and the Gton board
• Encountered issues with loading 3JS locally, but worked well with HTML5 canvas

The key distinction between the two models is the trade-off between capability and cost/speed. Gemini 2.5 Pro is the more powerful option, but Gemini 2.5 Flash provides an excellent balance of performance and affordability, making it a viable choice for many use cases.

The Gemini 2.5 Flash model was able to create an impressive interactive simulation of a virus attacking cells in a bloodstream. The simulation features the following elements:

• Red blood cells as passive cells that the virus targets and destroys
• White blood cells that actively patrol and fight against the virus
• Viruses that move randomly or seek out red blood cells, destroying them and potentially replicating
• Sliders to control the number of viruses, virus replication rate, virus type (aggressive, stealthy, etc.), number of white blood cells, their attack strength, detection radius, and movement speed
• Environment settings to adjust the blood flow speed and simulation speed
• A basic health status indicator
• The ability to move the camera around the simulation

The model was able to generate over 200 lines of code to create this interactive 3D simulation, showcasing its impressive capabilities in generating complex, visually-stunning applications with minimal prompting.

I downloaded the video of the Duck Sorter game and uploaded it to Gemini 2.5 Flash. I then asked the model to "Make this duck sorder game and code use Python single file only procedural animation."

The model was able to understand the core elements of the game, including the game screen, the player character (the "sorter"), the colored ducks, and the physics and movement required. It then generated the code to recreate the game in a single Python file using procedural animation.

When I ran the generated code, it closely matched the original demo. The player character could move around the screen and sort the ducks by color, with the ducks leaving the screen and bouncing back in when sorted correctly. The physics and movement of the ducks were also well-implemented.

While the visual quality of the sprites wasn't as polished as the original, the core gameplay mechanics were accurately recreated. I had to make a few adjustments to the settings, but overall, Gemini 2.5 Flash was able to generate a functional and visually similar version of the Duck Sorter game with minimal input from me.

The prompt provided the following requirements for building an HTML 5 canvas-based tower defense game:

• Waves of balloon enemies with health ranging from 1 to 10, color-coded from green to purple
• Enemies follow a winding path
• Players can place 5 tower types: dart, cannon, ice, sniper, and laser
• Each tower type is represented by a unique colored dot with a range preview on hover
• Players can press "Start Round" to begin the wave
• Dart tower: fast, single-target shots in bursts
• Cannon tower: slow, splash damage
• Ice tower: slows enemies
• Sniper tower: high damage, long range
• Laser tower: continuous beam with a visual cyan laser line
• Enemies display integer health above them

The AI was able to generate the code for this tower defense game in a relatively short amount of time, demonstrating its ability to translate high-level requirements into a functional implementation.

The resulting game allows the player to strategically place different tower types along the enemy path to defend against the incoming waves of balloons. The various tower abilities, such as slowing, splash damage, and long-range sniping, provide the player with different options to optimize their defense.

Overall, the AI was able to create a solid foundation for a tower defense game that meets the core requirements specified in the prompt. With further refinement and addition of visual elements, this could be developed into a polished and engaging gameplay experience.

Gemini 2.5 Pro was able to create a visually stunning and complex version of the classic Snake game. The prompt called for a Python version of Snake with unique visual enhancements, including:

• Dynamic visual effects: The snake leaves behind a glowing, fading trail.
• Pulsing color gradients and transitions in the background.
• Particle explosion effects when food is eaten.
• Different types of food, such as speed boost, reverse controls, double vision, and time slowdown.

Compared to Gemini 2.5 Pro's impressive output, Gemini 2.5 Flash was able to create a functional Snake game, but it lacked the same level of visual complexity and polish. The Flash version had the basic game mechanics in place, with different types of food and an AI opponent. However, it did not achieve the same level of visual flair as the Pro version.

The key differentiator between the two models is the amount of code they can generate. Gemini 2.5 Pro was able to produce over 1,000 lines of code, allowing it to implement the more complex visual effects and game features. In contrast, Gemini 2.5 Flash generated 677 lines of code, which was sufficient for a functional Snake game, but not enough to fully realize the visually stunning enhancements described in the prompt.

Despite the limitations of Gemini 2.5 Flash, it was still able to create a playable Snake game with multiple food types and an AI opponent. The trade-off is that the visual quality and overall complexity of the game is not as high as the Pro version. However, Gemini 2.5 Flash's ability to generate a substantial amount of code in a short time frame makes it a valuable tool for rapid prototyping and development.

Two AI players compete against each other in a side-by-side Tetris simulation. The match ends when one player's board reaches the top, causing them to lose.

The core features of the simulation include:

- Classic Tetris rule set with standard Tetramino rotation
- 10x20 grid for each player, with one active piece and one preview piece shown
- No hold functionality or T-spins
- Garbage system where clearing lines sends 1-4 garbage lines to the opponent
- Garbage lines contain a randomly placed hole
- Garbage is added from the bottom of the opponent's board with a short delay

The AI players use rule-based logic to make smart placement decisions, such as:

- Minimizing the number of holes in the stack
- Minimizing the height of the tallest column
- Maximizing the number of cleared lines

The simulation is implemented in Python using the Pygame library. The two AI players compete against each other, with the match ending when one player's board reaches the top.

Here is the body of the section on creating a Galton Board simulation:

Creating a Galton Board Simulation

To create a self-contained HTML file for a Galton Board simulation using client-side JavaScript and a 2D physics engine like Matter.js, the following criteria were met:

• The simulation is rendered on an HTML5 canvas.
• The canvas size is set to a fixed width and height.
• A 2D physics engine like Matter.js is included via CDN.
• The Galton Board simulation includes the following features:
  • Balls drop through a grid of pegs, creating a probability distribution.
  • The pegs have a configurable radius.
  • The balls have a configurable radius and bounciness.
  • The gravity and air friction affecting the balls are configurable.
  • The spacing between pegs (both horizontal and vertical) is configurable.
  • The number of bins at the bottom to collect the balls is configurable.
  • The gap above the bins is configurable.
• The simulation includes controls to adjust the various parameters, such as the number of balls, drop interval, and physics properties.
• The simulation accurately recreates the visual appearance of a Galton Board, including the characteristic bell curve distribution of the balls in the bins at the bottom.

The resulting HTML file contains the necessary JavaScript code to implement the Galton Board simulation, leveraging the Matter.js physics engine for realistic ball and peg interactions. Users can interact with the simulation by adjusting the various parameters and observing the effects on the probability distribution.

Here is the section body in markdown format:

To create a simulation of an iPhone app that lets the user draw, the Gemini 2.5 Flash model was able to generate the following Python code:

import pygame
from pygame.locals import *

# Initialize Pygame
pygame.init()

# Set up the window
window_width = 800
window_height = 600
window = pygame.display.set_mode((window_width, window_height))
pygame.display.set_caption("iPhone Drawing App")

# Define colors
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
RED = (255, 0, 0)
GREEN = (0, 255, 0)
BLUE = (0, 0, 255)

# Set up the drawing surface
drawing_surface = pygame.Surface((window_width, window_height))
drawing_surface.fill(WHITE)

# Set up the drawing tools
pen_size = 10
pen_color = BLACK
eraser_size = 20

# Set up the UI elements
settings_button = pygame.Rect(10, 10, 100, 50)
settings_button_color = (200, 200, 200)

# Main game loop
running = True
while running:
    # Handle events
    for event in pygame.event.get():
        if event.type == QUIT:
            running = False
        elif event.type == MOUSEBUTTONDOWN:
            if event.button == 1:  # Left mouse button
                if settings_button.collidepoint(event.pos):
                    # Open the settings menu
                    pen_color = pygame.color.THECOLORS[input("Enter pen color: ")]
                    pen_size = int(input("Enter pen size: "))
                    eraser_size = int(input("Enter eraser size: "))
                else:
                    # Draw or erase
                    mouse_pos = event.pos
                    if pygame.key.get_pressed()[K_SPACE]:
                        # Erase
                        pygame.draw.circle(drawing_surface, WHITE, mouse_pos, eraser_size)
                    else:
                        # Draw
                        pygame.draw.circle(drawing_surface, pen_color, mouse_pos, pen_size)
        elif event.type == KEYDOWN:
            if event.key == K_r:
                # Rectangle tool
                start_pos = pygame.mouse.get_pos()
                end_pos = start_pos
                drawing_rectangle = True
            elif event.key == K_f:
                # Fill tool
                mouse_pos = pygame.mouse.get_pos()
                pygame.draw.rect(drawing_surface, pen_color, drawing_surface.get_rect())
            elif event.key == K_c:
                # Clear the drawing surface
                drawing_surface.fill(WHITE)

    # Blit the drawing surface to the window
    window.blit(drawing_surface, (0, 0))

    # Draw the UI elements
    pygame.draw.rect(window, settings_button_color, settings_button)
    font = pygame.font.Font(None, 36)
    text = font.render("Settings", True, BLACK)
    window.blit(text, (settings_button.x + 10, settings_button.y + 10))

    # Update the display
    pygame.display.flip()

# Quit Pygame
pygame.quit()

This code creates a simple drawing app with the following features:

• Pen tool with adjustable size and color
• Eraser tool with adjustable size
• Rectangle tool
• Fill tool
• Clear the drawing surface
• Settings menu to adjust pen and eraser settings

The app is implemented using Pygame, a popular Python library for game development. The drawing surface is created as a Pygame Surface object, and the user can interact with the app using the mouse and keyboard.

The code demonstrates the ability of the Gemini 2.5 Flash model to generate a functional, albeit basic, drawing app simulation based on the provided prompt. While the visual design and user experience may not be as polished as a native iPhone app, the model was able to translate the high-level requirements into a working Python implementation.

Around the 8-minute mark of the 16-minute Popeye video, there are two lions that Popeye fights at the entrance of the cave on the Isle of Sinbad.

In the first minute of the video, there is a sign with two snakes around it that says "The Isle of Sinbad Beware" with a skull and crossbones.

Around the 10-minute mark, there is a barrel in the background that says "XX Buckshot", though the full text on the barrel is partially cut off.

The Gemini 2.5 Flash model was able to accurately answer these specific questions about details in the 16-minute Popeye video, demonstrating its impressive ability to comprehend and recall information from long-form video content.