NTT123

## chrome-webpage-click_SKILL.md

      
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                NTT123
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              January 1, 2026 01:28
            
              
                Chrome Webpage Click Skill
              
          
  name
  description
  allowed-tools
  
  
  chrome-webpage-click
  Click on web page elements with visual verification. Specify the TARGET element description and INITIAL COORDINATES. The skill will iteratively adjust coordinates until the red dot is on the target, then click automatically.
  mcp__claude-in-chrome__javascript_tool, mcp__claude-in-chrome__computer, mcp__claude-in-chrome__tabs_context_mcp, mcp__claude-in-chrome__read_page, mcp__claude-in-chrome__find
  
  
Chrome Webpage Click with Auto-Correction

This skill ensures accurate clicking by iteratively adjusting coordinates until the red dot is visually confirmed on the target element, then clicking directly.

  
## allgather.cu
// All-Gather using Cooperative Groups grid.sync() with vectorized memory access
// RTX 5090: 170 SMs, 1 block per SM, 16 bytes (uint4) per SM to share
// Persistent kernel: multiple rounds of all-gather, each with different buffer

#include <cuda_runtime.h>
#include <cooperative_groups.h>
#include <stdio.h>
#include <climits>

namespace cg = cooperative_groups;

## swiglu-viz.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>SwiGLU 2D Activation</title>
    <style>
        * {
            margin: 0;
            padding: 0;

## short_kernel_timing.svg

      
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## benchmark_matmul.py
"""
Benchmark matrix multiplication with locked GPU clock for stable performance.
Requires: pip install nvidia-ml-py torch numpy
"""
import pynvml
import torch
import random
import os
import numpy as np
from torch.profiler import profile, ProfilerActivity, schedule

## print-cute-tv-layout.ipynb

      
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                print-cute-tv-layout.ipynb
              
          
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## llm-play-chess.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>AI Chess Arena - Gemini API Chess Battle</title>
    <style>
        body {
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
            margin: 0 auto;

## paraxanthine.txt
Caffeine, a methylxanthine alkaloid, stands as the most widely consumed psychoactive substance on the planet. Its presence is woven into the daily rituals of billions, found in coffee, tea, chocolate, and an ever-expanding universe of energy drinks, sodas, and dietary supplements. Consumers turn to it for its well-documented ability to promote wakefulness, enhance cognition, and boost physical performance. For many, it is an indispensable tool for navigating the demands of modern life. Yet, for a significant portion of these users, the benefits of caffeine come with a familiar list of drawbacks: anxiety, jitters, digestive upset, and disrupted sleep.
Into this landscape has emerged paraxanthine, a compound fascinating yet unfamiliar to most people. Scientifically known as 1,7-dimethylxanthine, paraxanthine is not an obscure molecule from a remote plant but is, in fact, the principal metabolite produced by the human body after caffeine is consumed. When an individual drinks a cup of coffee, their liver rapidly

## Paraxanthine.html
<!DOCTYPE html>
<html lang="en" class="scroll-smooth">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Paraxanthine Versus Caffeine: An Evidence-Based Evaluation</title>
    <script src="https://cdn.tailwindcss.com"></script>
    <link rel="preconnect" href="https://fonts.googleapis.com">
    <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
    <link href="https://fonts.googleapis.com/css2?family=Source+Serif+4:opsz,wght@8..60,400;8..60,600;8..60,700&family=Inter:wght@400;500;600&display=swap" rel="stylesheet">

## llama3_model.py
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn
	// All-Gather using Cooperative Groups grid.sync() with vectorized memory access
	// RTX 5090: 170 SMs, 1 block per SM, 16 bytes (uint4) per SM to share
	// Persistent kernel: multiple rounds of all-gather, each with different buffer

	#include <cuda_runtime.h>
	#include <cooperative_groups.h>
	#include <stdio.h>
	#include <climits>

	namespace cg = cooperative_groups;
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>SwiGLU 2D Activation</title>
	<style>
	* {
	margin: 0;
	padding: 0;
	"""
	Benchmark matrix multiplication with locked GPU clock for stable performance.
	Requires: pip install nvidia-ml-py torch numpy
	"""
	import pynvml
	import torch
	import random
	import os
	import numpy as np
	from torch.profiler import profile, ProfilerActivity, schedule
	<!DOCTYPE html>
	<html lang="en" class="scroll-smooth">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Paraxanthine Versus Caffeine: An Evidence-Based Evaluation</title>
	<script src="https://cdn.tailwindcss.com"></script>
	<link rel="preconnect" href="https://fonts.googleapis.com">
	<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
	<link href="https://fonts.googleapis.com/css2?family=Source+Serif+4:opsz,wght@8..60,400;8..60,600;8..60,700&family=Inter:wght@400;500;600&display=swap" rel="stylesheet">
	import json
	from dataclasses import dataclass
	from pathlib import Path
	from typing import Optional, Tuple, Union

	import torch
	import torch.nn.functional as F
	from torch import nn