Ivan Fioravanti ivanfioravanti

## luxurytravelwebsite.md

      
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                ivanfioravanti
                / luxurytravelwebsite.md
            
            
              Created
              January 18, 2026 07:47
            
              
                luxurytravelwebsite prompt for testing coding agents
              
          
    Create a production-ready, visually stunning website with a futuristic luxury travel theme.
GOAL
Build a single-page (plus optional “Destination” detail route) website for a fictional brand:
“AURORA LUXE TRAVEL” — ultra-premium, concierge-level trips.
TECH STACK (use exactly this unless something breaks)

Next.js (latest stable) + TypeScript
Tailwind CSS
Framer Motion (for scroll/entrance animations)


## sportscarweb.md

      
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                ivanfioravanti
                / sportscarweb.md
            
            
              Created
              January 10, 2026 11:55
            
              
                Prompt: Sports Car Website
              
          
    Here the full prompt used:
Create a stunning, modern sports-car showcase website as a SINGLE self-contained HTML file (one page) with embedded CSS and JavaScript. Output ONLY the final HTML.
HARD REQUIREMENTS

Single file: index.html only. No external files.


## 2025-10-19-225209.txt

Benchmark started on 2025-10-19 22:52:10

** Command line:
/Users/ifioravanti/github/consumer-tflop-database/.venv/bin/python mamf-finder.py --m_range 0 16384 1024 --n_range 0 16384 1024 --k_range 0 16384 1024 --dtype bfloat16 --output_file=2025-10-19-22:52:09.txt

** Dtype: torch.bfloat16

** Platform/Device info:
- Darwin MacStudioIvan 25.1.0 Darwin Kernel Version 25.1.0: Sun Oct  5 21:09:25 PDT 2025; root:xnu-12377.40.120~10/RELEASE_ARM64_T6031 arm64 arm

## mlx_lm_benchmarks.md

      
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                ivanfioravanti
                / mlx_lm_benchmarks.md
            
            
              Last active
              October 14, 2025 19:27
                — forked from awni/mlx_lm_benchmarks.md
            
              
                MLX LM Benchmarks
              
          
    Benchmarks for mlx-lm

The command for evaluating on MMLU Pro:
mlx_lm.evaluate --model model/repo --task mmlu_pro

The command for efficiency benchmarks:

  
## batch_generate_response_multi_batches.py
"""Run four batched generations with varying sampling settings."""

import argparse

import mlx.core as mx

from mlx_lm import batch_generate, load
from mlx_lm.sample_utils import make_sampler

## mlx-bench.py
#!/usr/bin/env python3
"""
MLX benchmark script that replicates llama-bench behavior exactly.
Uses random tokens for both prompt and generation, no sampling.
"""

import mlx.core as mx
import mlx_lm
from mlx_lm.models.cache import make_prompt_cache
import time

## game.py
import pygame
import math
import random

# Initialize pygame
pygame.init()

# Screen dimensions
WIDTH, HEIGHT = 800, 800
screen = pygame.display.set_mode((WIDTH, HEIGHT))

## animation.html
<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>P5.js Particle Animation</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.4.0/p5.min.js"></script>
  <style>
    body {
      margin: 0;

## o1.py
import pygame
import math
import sys

pygame.init()

# Screen setup
WIDTH, HEIGHT = 800, 600
screen = pygame.display.set_mode((WIDTH, HEIGHT))
pygame.display.set_caption("Bouncing Ball in Rotating Square")

## mlx_memory.sh
#!/usr/bin/env bash

# Default values for percentages
DEFAULT_WIRED_LIMIT_PERCENT=85
DEFAULT_WIRED_LWM_PERCENT=75

# Read input parameters or use default values
WIRED_LIMIT_PERCENT=${1:-$DEFAULT_WIRED_LIMIT_PERCENT}
WIRED_LWM_PERCENT=${2:-$DEFAULT_WIRED_LWM_PERCENT}

	Benchmark started on 2025-10-19 22:52:10

	** Command line:
	/Users/ifioravanti/github/consumer-tflop-database/.venv/bin/python mamf-finder.py --m_range 0 16384 1024 --n_range 0 16384 1024 --k_range 0 16384 1024 --dtype bfloat16 --output_file=2025-10-19-22:52:09.txt

	** Dtype: torch.bfloat16

	** Platform/Device info:
	- Darwin MacStudioIvan 25.1.0 Darwin Kernel Version 25.1.0: Sun Oct 5 21:09:25 PDT 2025; root:xnu-12377.40.120~10/RELEASE_ARM64_T6031 arm64 arm
	"""Run four batched generations with varying sampling settings."""

	import argparse

	import mlx.core as mx

	from mlx_lm import batch_generate, load
	from mlx_lm.sample_utils import make_sampler
	#!/usr/bin/env python3
	"""
	MLX benchmark script that replicates llama-bench behavior exactly.
	Uses random tokens for both prompt and generation, no sampling.
	"""

	import mlx.core as mx
	import mlx_lm
	from mlx_lm.models.cache import make_prompt_cache
	import time
	import pygame
	import math
	import random

	# Initialize pygame
	pygame.init()

	# Screen dimensions
	WIDTH, HEIGHT = 800, 800
	screen = pygame.display.set_mode((WIDTH, HEIGHT))
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>P5.js Particle Animation</title>
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/1.4.0/p5.min.js"></script>
	<style>
	body {
	margin: 0;
	import pygame
	import math
	import sys

	pygame.init()

	# Screen setup
	WIDTH, HEIGHT = 800, 600
	screen = pygame.display.set_mode((WIDTH, HEIGHT))
	pygame.display.set_caption("Bouncing Ball in Rotating Square")
	#!/usr/bin/env bash

	# Default values for percentages
	DEFAULT_WIRED_LIMIT_PERCENT=85
	DEFAULT_WIRED_LWM_PERCENT=75

	# Read input parameters or use default values
	WIRED_LIMIT_PERCENT=${1:-$DEFAULT_WIRED_LIMIT_PERCENT}
	WIRED_LWM_PERCENT=${2:-$DEFAULT_WIRED_LWM_PERCENT}