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josemanuel22/README.md

José Manuel de Frutos

Hi! I'm José Manuel 👋

I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).

My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.

I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.

Research highlights

  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss

Find me elsewhere

🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn

📫 josemanuel.defrutos22@gmail.com

Pinned Loading

  1. ISLISLPublic

    Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia 1 1

  2. DeepAR.jlDeepAR.jlPublic

    Julia DeepAR implementation

    Julia 5 1

  3. MMD_GAN.jlMMD_GAN.jlPublic

    Julia MMD GAN implementation

    Julia

  4. puppet-boltpuppet-boltPublic

    We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby

  5. ParetoGANParetoGANPublic

    Julia 2

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
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btn.textContent = 'Copied!';
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codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
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addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
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}
} 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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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
Loading
  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

    Julia
5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

    Julia
  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2


, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
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document.head.appendChild(style);
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}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
Achievements
Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
Highlights
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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
Loading
  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

    Julia
5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

    Julia
  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2


, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
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if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
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document.head.appendChild(style);
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var text = node.textContent;
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} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
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});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
Loading
  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

    Julia
5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

    Julia
  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2


, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} 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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Achievements
Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
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Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
Loading
  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

    Julia
5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

    Julia
  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2


, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 · 224
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Achievements
Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
Achievements
Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
Highlights
  • 
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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
Loading
  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

    Julia
5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

    Julia
  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2


, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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followers
 · 224
following

Achievements
Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
Achievements
Achievement: YOLOAchievement: Pull Sharkx2Achievement: Pair Extraordinaire
Highlights
  • 
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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
Loading
  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

    Julia
5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

    Julia
  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2


, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
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josemanuel22/README.md
José Manuel de Frutos
Hi! I'm José Manuel 👋
I'm a mathematician and computer scientist, currently finishing my PhD in Statistical Machine Learning at Universidad Carlos III de Madrid (UC3M).
My research lives somewhere between statistics, machine learning and mathematical modelling. Lately, I have been especially interested in rank-based methods, divergence estimation, and implicit generative models.
I particularly enjoy taking a mathematical idea, understanding why it works, and then turning it into something that can actually be implemented and tested.
Research highlights
  • ICML 2026 — Approximating f-Divergences with Rank Statistics
  • JMLR 2026 — robust training of implicit generative models for multivariate and heavy-tailed distributions
  • AISTATS 2026 Spotlight — explicit density approximation for neural implicit samplers
  • AISTATS 2024 — Training Implicit Generative Models via an Invariant Statistical Loss
Find me elsewhere
🌐 Website · 🎓 Google Scholar · ORCID · LinkedIn
📫 josemanuel.defrutos22@gmail.com

Pinned
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  1. ISLISLPublic
    
Training Implicit Generative Models via an Invariant statistical loss (ISL)

    Julia
1

1

  2. DeepAR.jlDeepAR.jlPublic
    
Julia DeepAR implementation

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5

1

  3. MMD_GAN.jlMMD_GAN.jlPublic
    
Julia MMD GAN implementation

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  4. puppet-boltpuppet-boltPublic
    
We have modified the Bolt code so that it sends the commands that it wants to execute on the remote hosts first to a background process that saves the connections in a hash table. Every time there …

    Ruby
  5. ParetoGANParetoGANPublic
    Julia
2