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About

Screenshot

This is the really interesting project that allows you to see how the living cell can evolve.

There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.

Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.

Each robot has unique set of commands, its code.

Code is just an array of 64 integers. Each number in the code represents a command.

There are this set of commands:

  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP

So, in each iteration each robot executes its commands consequently.

Also, robot loses some amount of HP each iteration, death occurs at 0 HP.

In first generation, the code for each robot is generated randomly;

In each next generation, that are starting when there are just a couple of most viable robots left,

robots that are created inherit the code that these most viable robots had.

So, after some amount of generations, robots learn how to live longer!

Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.

I'm too lazy to sit here and develop new ways of robots' interaction.

About

Graphical simulation of natural selection!

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Readme
Activity

Stars

0 stars

Watchers

1 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
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1 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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img
img
.gitignore
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README.md
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About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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NameName
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NatSelection
img
img
.gitignore
.gitignore
README.md
README.md
Repository files navigation
About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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NameName
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NatSelection
NatSelection
img
img
.gitignore
.gitignore
README.md
README.md
Repository files navigation
About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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12 Commits
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NameName
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NatSelection
img
img
.gitignore
.gitignore
README.md
README.md
Repository files navigation
About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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12 Commits
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NameName
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NatSelection
img
img
.gitignore
.gitignore
README.md
README.md
Repository files navigation
About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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img
img
.gitignore
.gitignore
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README.md
Repository files navigation
About
Screenshot
This is the really interesting project that allows you to see how the living cell can evolve.
There's a grid that consist of cells. Each cell can be empty, or be a food, or be a wall.
Also there's a couple of living cells (robots henceforth) on that grid that all can do some stuff.
Each robot has unique set of commands, its code.
Code is just an array of 64 integers. Each number in the code represents a command.
There are this set of commands:
  • Walk in one of 8 directions
  • Eat food that is placed around it
  • Turn wall around it in a food
  • Just get free HP
So, in each iteration each robot executes its commands consequently.
Also, robot loses some amount of HP each iteration, death occurs at 0 HP.
In first generation, the code for each robot is generated randomly;
In each next generation, that are starting when there are just a couple of most viable robots left,
robots that are created inherit the code that these most viable robots had.
So, after some amount of generations, robots learn how to live longer!
Sadly, the project haven't gone very far. It lacks variety of commands/objects on the grid/interesting mechanics.
I'm too lazy to sit here and develop new ways of robots' interaction.
About
Graphical simulation of natural selection!
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages