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ArtificialAlgorithms

This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:

  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.

  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.

This is part of an effort to create safe and hallucination-free coding AIs.

Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:

  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.

Feel free to send in PRs. Please document your process in the open comments of the source file.

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Verified algorithms in Lean, implemented and proved by AIs

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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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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
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12 stars
Watchers
0 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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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
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12 stars
Watchers
0 watching
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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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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
Stars
12 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
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Contributors
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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" + '

Skip to content
BranchesTags
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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
Stars
12 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Contributors
Languages

, '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('^' + ".*" + '

Skip to content
BranchesTags
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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
Stars
12 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Contributors
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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
BranchesTags
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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
Stars
12 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Contributors
Languages

, '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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ArtificialAlgorithms
This repo is a collection of verified algorithms in Lean, implemented and proved by AIs. Our main goals:
  • Be instructive. We aim to create a curated set of examples, of using AIs to produce code with proofs of correctness. We hope this can serve as recipes for creating agentic systems for coding, sources of examples for multi-shot prompting, and/or data set for fine-tuning.
  • Be useful. A secondary goal is to serve as a library of verified algorithms, that can be used to build more complex projects with provable guarantees.
This is part of an effort to create safe and hallucination-free coding AIs.
Contributions welcome! We are interested in algorithms that are partially or wholly produced by AIs. Methods of production may include but are not limited to:
  • prompting, including with worked examples;
  • translation / autoformalization, from natural languages, other programming languages and/or formal languages;
  • tool calling, including LeanTool, LeanExplore, lean-lsp-mcp;
  • custom models, including fine-tuning via SFT or RL;
  • other custom scaffolding, such as multi-agent workflows.
Feel free to send in PRs. Please document your process in the open comments of the source file.
About
Verified algorithms in Lean, implemented and proved by AIs
Resources
Readme
Apache-2.0 license
Activity
Stars
12 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Sponsor this project
Packages
Contributors
Languages