From 4a45a5500bacba4709da049e7b77d35eed3195a0 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 2 Dec 2025 04:53:29 +0000 Subject: [PATCH 1/4] Initial plan From fb7a5da1a2df727c3e222f657f77afe062e1e54d Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 2 Dec 2025 04:57:43 +0000 Subject: [PATCH 2/4] Create LLMNetOps project website with responsive design Co-authored-by: rizalespe <7615996+rizalespe@users.noreply.github.com> --- README.md | 29 +++- index.html | 384 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 412 insertions(+), 1 deletion(-) create mode 100644 index.html diff --git a/README.md b/README.md index c86831d..5400e8c 100644 --- a/README.md +++ b/README.md @@ -1 +1,28 @@ -# llmnetops.github.io \ No newline at end of file +# LLMNetOps + +AI-Powered Network Operations - Leveraging Large Language Models to transform network management. + +## About + +LLMNetOps is a project that combines the power of Large Language Models with network operations. Our mission is to revolutionize how networks are managed, monitored, and optimized by leveraging advanced AI capabilities. + +## Website + +This repository hosts the project website at [https://llmnetops.github.io](https://llmnetops.github.io) + +## Features + +- **Natural Language Interface** - Interact with network infrastructure using natural language +- **Automated Troubleshooting** - AI-driven diagnosis and remediation suggestions +- **Intelligent Analytics** - Predictive insights for network performance +- **Smart Configuration** - AI-generated and validated network configurations +- **Security Enhancement** - Anomaly detection and threat identification +- **Seamless Integration** - Compatible with existing network management tools + +## Contributing + +We welcome contributions! Please feel free to submit issues or pull requests. + +## License + +This project is open source. \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..3818341 --- /dev/null +++ b/index.html @@ -0,0 +1,384 @@ + + + + + + + LLMNetOps - AI-Powered Network Operations + + + + + + + +
+
+

AI-Powered Network Operations

+

Leveraging Large Language Models to transform network management, automate operations, and enable intelligent decision-making.

+ Explore Features +
+
+ + +
+

About LLMNetOps

+

Bridging the gap between AI and network operations

+
+

LLMNetOps is a cutting-edge project that combines the power of Large Language Models with network operations. Our mission is to revolutionize how networks are managed, monitored, and optimized by leveraging advanced AI capabilities.

+
+

By integrating LLMs into network operations workflows, we enable natural language interfaces for network management, automated troubleshooting, intelligent configuration generation, and predictive analytics.

+
+
+ + +
+

Key Features

+

Discover what makes LLMNetOps powerful

+
+
+
🤖
+

Natural Language Interface

+

Interact with your network infrastructure using natural language commands. Query network status, troubleshoot issues, and configure devices conversationally.

+
+
+
⚡
+

Automated Troubleshooting

+

Leverage AI to automatically diagnose network issues, identify root causes, and suggest remediation steps based on historical data and best practices.

+
+
+
📊
+

Intelligent Analytics

+

Gain deep insights into network performance with AI-powered analytics. Predict potential issues before they impact users and optimize resource allocation.

+
+
+
🔧
+

Smart Configuration

+

Generate and validate network configurations using AI. Ensure compliance, reduce human errors, and accelerate deployment processes.

+
+
+
🔒
+

Security Enhancement

+

Detect anomalies and potential security threats using AI-driven analysis. Respond faster to incidents with intelligent alerting and recommendations.

+
+
+
🚀
+

Seamless Integration

+

Integrate with existing network management tools and platforms. Enhance your current workflows without disrupting established processes.

+
+
+
+ + +
+

Get In Touch

+

Interested in LLMNetOps? Connect with us!

+
+ + GitHub + +
+
+ + + + + From 31b0d1a1338be0fc7a82f3fa0e5034c346f2734e Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Tue, 2 Dec 2025 07:02:29 +0000 Subject: [PATCH 3/4] Update website with detailed project context for IDREN Co-authored-by: rizalespe <7615996+rizalespe@users.noreply.github.com> --- README.md | 20 ++++++++++-------- index.html | 62 +++++++++++++++++++++++++++--------------------------- 2 files changed, 42 insertions(+), 40 deletions(-) diff --git a/README.md b/README.md index 5400e8c..20c441f 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,25 @@ # LLMNetOps -AI-Powered Network Operations - Leveraging Large Language Models to transform network management. +**Strengthening Network Operations Knowledge-Base through Locally-Hosted Generative AI** ## About -LLMNetOps is a project that combines the power of Large Language Models with network operations. Our mission is to revolutionize how networks are managed, monitored, and optimized by leveraging advanced AI capabilities. +Many IDREN member institutions struggle to adopt AI-supported network management due to limited staff, uneven expertise, and reliance on undocumented knowledge. Generative AI offers potential for improving documentation and troubleshooting, but most administrators lack training, confidence, and tools that respect data privacy—creating an AI-literacy gap. + +LLMNetOps aims to address this gap by building foundational AI knowledge, fostering peer collaboration, and promoting locally-hosted open-source LLM models as privacy-aware solutions tailored to institutional needs in network operations. ## Website This repository hosts the project website at [https://llmnetops.github.io](https://llmnetops.github.io) -## Features +## Project Goals -- **Natural Language Interface** - Interact with network infrastructure using natural language -- **Automated Troubleshooting** - AI-driven diagnosis and remediation suggestions -- **Intelligent Analytics** - Predictive insights for network performance -- **Smart Configuration** - AI-generated and validated network configurations -- **Security Enhancement** - Anomaly detection and threat identification -- **Seamless Integration** - Compatible with existing network management tools +- **Privacy-First AI Adoption** - Promote locally-hosted open-source LLM models as practical solutions for institutions concerned about data privacy and institutional autonomy +- **Contextual AI for Infrastructure** - Introduce generative AI specifically to support technical teams in managing, understanding, and documenting their own infrastructure +- **Bridging the AI Literacy Gap** - Empower network and IT staff with the knowledge and confidence to use AI in their daily tasks +- **Knowledge-Sharing Community** - Build a culture of learning and shared problem-solving through workshops and peer collaboration +- **Secure LLM Integration** - Investigate how locally-hosted LLMs can be safely integrated into network operations while ensuring security of sensitive data +- **Tailored Solutions** - Empower IDREN members to confidently assess and adopt AI solutions that align with their specific requirements ## Contributing diff --git a/index.html b/index.html index 3818341..34ff800 100644 --- a/index.html +++ b/index.html @@ -3,8 +3,8 @@ - - LLMNetOps - AI-Powered Network Operations + + LLMNetOps - Privacy-First AI for Network Operations + + + @@ -310,67 +39,127 @@
+
+ 🎓 ISIF Asia 2025 + 🏛️ Universitas Brawijaya +

Strengthening Network Operations with Privacy-First AI

Building foundational AI knowledge through locally-hosted open-source LLMs, fostering peer collaboration, and empowering IDREN member institutions with privacy-aware solutions for network operations.

- Explore Our Goals +
+ Learn More + View Workshops +
- -
+ +

About LLMNetOps

Bridging the AI Literacy Gap in Network Operations

Many IDREN member institutions struggle to adopt AI-supported network management due to limited staff, uneven expertise, and reliance on undocumented knowledge. Generative AI offers potential for improving documentation and troubleshooting, but most administrators lack training, confidence, and tools that respect data privacy—creating an AI-literacy gap.


LLMNetOps aims to address this gap by building foundational AI knowledge, fostering peer collaboration, and promoting locally-hosted open-source LLM models as privacy-aware solutions tailored to institutional needs in network operations.

+
+ Read More About Our Mission
- + +
+

Key Pillars

+

Our strategic approach to AI-enabled network operations

+
+
+
🎓
+

Building Capabilities

+

Developing AI literacy and technical skills among network operations professionals through structured training and hands-on workshops.

+
+
+
💡
+

Driving Digital Innovation

+

Introducing contextual AI applications specifically designed for infrastructure operations, documentation, and troubleshooting.

+
+
+
🚀
+

Supporting Digital Transformation

+

Enabling IDREN institutions to confidently adopt privacy-first AI solutions that align with their specific requirements.

+
+
+
+ + +
+
+
+
+ 20 + Target Participants +
+
+ 2 + Workshop Sessions +
+
+ 5 + Training Modules +
+
+ 8 + IDREN Institutions +
+
+
+
+ +

Project Goals

Empowering institutions with privacy-first AI solutions

-
-
-
🔒
+
+
+
🔒

Privacy-First AI Adoption

-

Promote locally-hosted open-source LLM models as a practical solution for institutions concerned about data privacy and institutional autonomy—an issue often overlooked in AI adoption.

+

Promote locally-hosted open-source LLM models as a practical solution for institutions concerned about data privacy and institutional autonomy.

-
-
🖥️
+
+
🖥️

Contextual AI for Infrastructure

-

Introduce the use of generative AI specifically to support technical teams in managing, understanding, and documenting their own infrastructure—not just general productivity.

+

Introduce the use of generative AI specifically to support technical teams in managing, understanding, and documenting their own infrastructure.

-
-
📚
+
+
📚

Bridging the AI Literacy Gap

-

Empower network and IT staff with the knowledge and confidence to use AI in their daily tasks, addressing the training and tool gaps that prevent AI adoption.

+

Empower network and IT staff with the knowledge and confidence to use AI in their daily tasks.

-
-
🤝
+
+
🤝

Knowledge-Sharing Community

-

Build a culture of learning and shared problem-solving through workshops and peer collaboration, rather than one-off training sessions.

+

Build a culture of learning and shared problem-solving through workshops and peer collaboration.

-
-
🛡️
+
+
🛡️

Secure LLM Integration

-

Investigate how locally-hosted LLMs can be safely integrated into network operations while ensuring the security of sensitive data.

+

Investigate how locally-hosted LLMs can be safely integrated into network operations while ensuring data security.

-
-
🎯
+
+
🎯

Tailored Solutions

-

Empower IDREN members to confidently assess and adopt AI solutions that align with their specific institutional requirements and constraints.

+

Empower IDREN members to confidently assess and adopt AI solutions that align with their specific requirements.

- -
-

Get In Touch

-

Interested in LLMNetOps? Connect with us!

-
- + +
+

Get Involved

+

Interested in LLMNetOps? Join our upcoming workshops or connect with us!

+
+ View Workshops + Contact Us +
+
+ GitHub
@@ -378,7 +167,40 @@

Get In Touch

-

© 2024 LLMNetOps. All rights reserved.

+
+
+

LLMNetOps

+

Strengthening Network Operations Knowledge-Base through Locally-Hosted Generative AI

+
+
+

Quick Links

+ +
+
+

Funded By

+

APNIC Foundation
ISIF Asia 2025

+
+
+

Implemented By

+

Universitas Brawijaya
Indonesia

+
+
+
+

© 2024 LLMNetOps. All rights reserved.

+
+ + + + + + + + diff --git a/js/animations.js b/js/animations.js new file mode 100644 index 0000000..da6a518 --- /dev/null +++ b/js/animations.js @@ -0,0 +1,200 @@ +// LLMNetOps - Animations JavaScript + +// Fade in on scroll animation +function initScrollReveal() { + const revealElements = document.querySelectorAll('.reveal'); + + const revealOnScroll = () => { + revealElements.forEach(element => { + const elementTop = element.getBoundingClientRect().top; + const elementVisible = 150; + + if (elementTop < window.innerHeight - elementVisible) { + element.classList.add('revealed'); + } + }); + }; + + window.addEventListener('scroll', revealOnScroll); + revealOnScroll(); // Check on initial load +} + +// Staggered animation for card grids +function initStaggerAnimation() { + const grids = document.querySelectorAll('.card-grid, .features-grid'); + + const observer = new IntersectionObserver((entries) => { + entries.forEach(entry => { + if (entry.isIntersecting) { + const cards = entry.target.querySelectorAll('.card, .feature-card'); + cards.forEach((card, index) => { + card.style.animationDelay = `${index * 0.1}s`; + card.classList.add('animate-in'); + }); + observer.unobserve(entry.target); + } + }); + }, { threshold: 0.1 }); + + grids.forEach(grid => observer.observe(grid)); +} + +// Parallax effect for hero section +function initParallax() { + const hero = document.querySelector('.hero'); + + if (!hero) return; + + window.addEventListener('scroll', () => { + const scrolled = window.pageYOffset; + hero.style.backgroundPositionY = `${scrolled * 0.5}px`; + }); +} + +// Typing effect for hero text +function typeWriter(element, text, speed = 50) { + let i = 0; + element.textContent = ''; + + function type() { + if (i < text.length) { + element.textContent += text.charAt(i); + i++; + setTimeout(type, speed); + } + } + + type(); +} + +// Number counting animation +function countUp(element, target, duration = 2000) { + const start = 0; + const startTime = performance.now(); + + function update(currentTime) { + const elapsed = currentTime - startTime; + const progress = Math.min(elapsed / duration, 1); + + // Easing function + const easeOutQuad = progress * (2 - progress); + + const current = Math.floor(easeOutQuad * target); + element.textContent = current.toLocaleString(); + + if (progress < 1) { + requestAnimationFrame(update); + } else { + element.textContent = target.toLocaleString(); + } + } + + requestAnimationFrame(update); +} + +// Initialize stats counter when visible +function initStatsCounter() { + const statNumbers = document.querySelectorAll('.stat-number[data-target]'); + + const observer = new IntersectionObserver((entries) => { + entries.forEach(entry => { + if (entry.isIntersecting) { + const target = parseInt(entry.target.dataset.target); + countUp(entry.target, target); + observer.unobserve(entry.target); + } + }); + }, { threshold: 0.5 }); + + statNumbers.forEach(stat => observer.observe(stat)); +} + +// Progress bar animation +function animateProgressBars() { + const progressBars = document.querySelectorAll('.progress-fill'); + + const observer = new IntersectionObserver((entries) => { + entries.forEach(entry => { + if (entry.isIntersecting) { + const targetWidth = entry.target.dataset.progress || '0'; + entry.target.style.width = targetWidth + '%'; + observer.unobserve(entry.target); + } + }); + }, { threshold: 0.5 }); + + progressBars.forEach(bar => { + bar.style.width = '0%'; + observer.observe(bar); + }); +} + +// Accordion animation +function initAccordion() { + const accordionItems = document.querySelectorAll('.deliverable-card, .faq-item'); + + accordionItems.forEach(item => { + const header = item.querySelector('.deliverable-header, .faq-question'); + const content = item.querySelector('.deliverable-content, .faq-answer'); + + if (header && content) { + header.addEventListener('click', () => { + const isActive = item.classList.contains('active'); + + // Close all other items + accordionItems.forEach(otherItem => { + if (otherItem !== item) { + otherItem.classList.remove('active'); + const otherContent = otherItem.querySelector('.deliverable-content, .faq-answer'); + if (otherContent) { + otherContent.style.maxHeight = null; + } + } + }); + + // Toggle current item + item.classList.toggle('active'); + + if (!isActive) { + content.style.maxHeight = content.scrollHeight + 'px'; + } else { + content.style.maxHeight = null; + } + }); + } + }); +} + +// Smooth scroll with offset for fixed header +function smoothScrollTo(targetId) { + const target = document.querySelector(targetId); + if (target) { + const navHeight = document.querySelector('nav').offsetHeight; + const targetPosition = target.offsetTop - navHeight - 20; + + window.scrollTo({ + top: targetPosition, + behavior: 'smooth' + }); + } +} + +// Initialize all animations +function initAnimations() { + initScrollReveal(); + initStaggerAnimation(); + initStatsCounter(); + animateProgressBars(); + initAccordion(); +} + +// Run when DOM is loaded +document.addEventListener('DOMContentLoaded', initAnimations); + +// Export for module use +window.Animations = { + typeWriter, + countUp, + smoothScrollTo, + initAccordion +}; diff --git a/js/main.js b/js/main.js new file mode 100644 index 0000000..9ee0bc4 --- /dev/null +++ b/js/main.js @@ -0,0 +1,139 @@ +// LLMNetOps - Main JavaScript + +document.addEventListener('DOMContentLoaded', function() { + // Initialize all components + initNavigation(); + initScrollAnimations(); + initBackToTop(); + initCurrentYear(); +}); + +// Update copyright year +function initCurrentYear() { + const yearElements = document.querySelectorAll('.current-year'); + const currentYear = new Date().getFullYear(); + yearElements.forEach(el => { + el.textContent = currentYear; + }); +} + +// Initialize scroll-based animations +function initScrollAnimations() { + const fadeElements = document.querySelectorAll('.fade-in'); + + const observerOptions = { + root: null, + rootMargin: '0px', + threshold: 0.1 + }; + + const observer = new IntersectionObserver((entries) => { + entries.forEach(entry => { + if (entry.isIntersecting) { + entry.target.classList.add('visible'); + observer.unobserve(entry.target); + } + }); + }, observerOptions); + + fadeElements.forEach(element => { + observer.observe(element); + }); +} + +// Back to top button +function initBackToTop() { + const backToTopBtn = document.querySelector('.back-to-top'); + + if (!backToTopBtn) return; + + window.addEventListener('scroll', () => { + if (window.pageYOffset > 300) { + backToTopBtn.classList.add('visible'); + } else { + backToTopBtn.classList.remove('visible'); + } + }); + + backToTopBtn.addEventListener('click', () => { + window.scrollTo({ + top: 0, + behavior: 'smooth' + }); + }); +} + +// Smooth scroll for anchor links +document.querySelectorAll('a[href^="#"]').forEach(anchor => { + anchor.addEventListener('click', function(e) { + const href = this.getAttribute('href'); + if (href === '#') return; + + e.preventDefault(); + const target = document.querySelector(href); + if (target) { + const navHeight = document.querySelector('nav').offsetHeight; + const targetPosition = target.offsetTop - navHeight; + + window.scrollTo({ + top: targetPosition, + behavior: 'smooth' + }); + } + }); +}); + +// Utility function for debouncing +function debounce(func, wait) { + let timeout; + return function executedFunction(...args) { + const later = () => { + clearTimeout(timeout); + func(...args); + }; + clearTimeout(timeout); + timeout = setTimeout(later, wait); + }; +} + +// Counter animation for statistics +function animateCounter(element, target, duration = 2000) { + let start = 0; + const increment = target / (duration / 16); + + function updateCounter() { + start += increment; + if (start < target) { + element.textContent = Math.floor(start); + requestAnimationFrame(updateCounter); + } else { + element.textContent = target; + } + } + + updateCounter(); +} + +// Initialize counters when they come into view +function initCounters() { + const counters = document.querySelectorAll('[data-counter]'); + + const observer = new IntersectionObserver((entries) => { + entries.forEach(entry => { + if (entry.isIntersecting) { + const target = parseInt(entry.target.dataset.counter); + animateCounter(entry.target, target); + observer.unobserve(entry.target); + } + }); + }, { threshold: 0.5 }); + + counters.forEach(counter => observer.observe(counter)); +} + +// Export functions for use in other modules +window.LLMNetOps = { + debounce, + animateCounter, + initCounters +}; diff --git a/js/navigation.js b/js/navigation.js new file mode 100644 index 0000000..fa4d965 --- /dev/null +++ b/js/navigation.js @@ -0,0 +1,114 @@ +// LLMNetOps - Navigation JavaScript + +function initNavigation() { + const mobileMenuBtn = document.querySelector('.mobile-menu-btn'); + const navLinks = document.querySelector('.nav-links'); + const nav = document.querySelector('nav'); + + // Mobile menu toggle + if (mobileMenuBtn && navLinks) { + mobileMenuBtn.addEventListener('click', function() { + navLinks.classList.toggle('active'); + this.classList.toggle('active'); + + // Animate hamburger to X + const spans = this.querySelectorAll('span'); + if (this.classList.contains('active')) { + spans[0].style.transform = 'rotate(45deg) translate(5px, 5px)'; + spans[1].style.opacity = '0'; + spans[2].style.transform = 'rotate(-45deg) translate(7px, -6px)'; + } else { + spans[0].style.transform = 'none'; + spans[1].style.opacity = '1'; + spans[2].style.transform = 'none'; + } + }); + + // Close mobile menu when clicking a link + navLinks.querySelectorAll('a').forEach(link => { + link.addEventListener('click', () => { + navLinks.classList.remove('active'); + mobileMenuBtn.classList.remove('active'); + const spans = mobileMenuBtn.querySelectorAll('span'); + spans[0].style.transform = 'none'; + spans[1].style.opacity = '1'; + spans[2].style.transform = 'none'; + }); + }); + + // Close mobile menu when clicking outside + document.addEventListener('click', (e) => { + if (!nav.contains(e.target) && navLinks.classList.contains('active')) { + navLinks.classList.remove('active'); + mobileMenuBtn.classList.remove('active'); + const spans = mobileMenuBtn.querySelectorAll('span'); + spans[0].style.transform = 'none'; + spans[1].style.opacity = '1'; + spans[2].style.transform = 'none'; + } + }); + } + + // Navbar scroll effect + let lastScroll = 0; + window.addEventListener('scroll', () => { + const currentScroll = window.pageYOffset; + + // Add shadow on scroll + if (currentScroll > 50) { + nav.style.boxShadow = '0 4px 20px rgba(0, 0, 0, 0.1)'; + } else { + nav.style.boxShadow = '0 2px 10px rgba(0, 0, 0, 0.1)'; + } + + lastScroll = currentScroll; + }); + + // Set active nav link based on current page + setActiveNavLink(); +} + +function setActiveNavLink() { + const currentPage = window.location.pathname.split('/').pop() || 'index.html'; + const navLinks = document.querySelectorAll('.nav-links a'); + + navLinks.forEach(link => { + const href = link.getAttribute('href'); + if (href === currentPage || + (currentPage === 'index.html' && href === 'index.html') || + (currentPage === '' && href === 'index.html')) { + link.classList.add('active'); + } else { + link.classList.remove('active'); + } + }); +} + +// Dropdown menu support (if needed) +function initDropdowns() { + const dropdowns = document.querySelectorAll('.nav-dropdown'); + + dropdowns.forEach(dropdown => { + const toggle = dropdown.querySelector('.dropdown-toggle'); + const menu = dropdown.querySelector('.dropdown-menu'); + + if (toggle && menu) { + toggle.addEventListener('click', (e) => { + e.preventDefault(); + dropdown.classList.toggle('active'); + }); + + // Close when clicking outside + document.addEventListener('click', (e) => { + if (!dropdown.contains(e.target)) { + dropdown.classList.remove('active'); + } + }); + } + }); +} + +// Export for module use +if (typeof module !== 'undefined' && module.exports) { + module.exports = { initNavigation, setActiveNavLink, initDropdowns }; +} diff --git a/resources.html b/resources.html new file mode 100644 index 0000000..01e0848 --- /dev/null +++ b/resources.html @@ -0,0 +1,273 @@ + + + + + + + Resources - LLMNetOps + + + + + + + + + +
+
+ Home + / + Resources +
+

Learning Resources

+

Training modules, case studies, and documentation for privacy-aware AI deployment

+
+ + +
+
+
+ + + + + +
+
+
+ + +
+
+

Training Modules

+

Comprehensive curriculum on locally-hosted LLMs for network operations

+ +
+ 📚 +
+ Coming Soon
+ Training modules are currently in development and will be available after the first workshop session. +
+
+ +
+
+ Training Module +

Module 1: Introduction to Large Language Models

+

Comprehensive introduction to LLMs covering architecture, capabilities, and applications in network operations.

+
+ 🌐 Bilingual (ID/EN) + Coming Soon +
+
+ +
+ Training Module +

Module 2: Privacy-Aware AI Deployment

+

Deep dive into privacy considerations, data protection, and institutional autonomy in AI deployment.

+
+ 🌐 Bilingual (ID/EN) + Coming Soon +
+
+ +
+ Training Module +

Module 3: RAG Pipelines for Network Documentation

+

Practical guide to building Retrieval-Augmented Generation pipelines for network documentation and troubleshooting.

+
+ 🌐 Bilingual (ID/EN) + Coming Soon +
+
+ +
+ Training Module +

Module 4: AI Agentic Workflows

+

Advanced module on building AI agents for automated network operations tasks.

+
+ 🌐 Bilingual (ID/EN) + Coming Soon +
+
+ +
+ Training Module +

Module 5: Infrastructure Integration

+

Guide to integrating locally-hosted LLMs with existing network infrastructure and monitoring systems.

+
+ 🌐 Bilingual (ID/EN) + Coming Soon +
+
+
+
+
+ + +
+
+

Reports & White Papers

+

Research outputs and policy insights

+ +
+
+ Report +

Initial Needs Assessment Report

+

Survey results and analysis of AI adoption challenges across IDREN member institutions.

+
+ 📄 PDF + Coming Soon +
+
+ +
+ White Paper +

NetOps with AI: Policy Insights for IDREN

+

Comprehensive whitepaper on AI adoption policies and recommendations for network operations in educational institutions.

+
+ 📄 PDF + Coming Soon +
+
+
+
+
+ + +
+
+

Case Studies

+

Real-world implementation examples from IDREN institutions

+ +
+ 📋 +
+ Case Studies Coming Soon
+ Case studies will be developed after workshop completion, documenting real-world implementation experiences from participating institutions. +
+
+ +
+

Contribute a Case Study

+

Are you implementing locally-hosted LLMs in your institution? We welcome case study contributions from IDREN member institutions. Your experience could help others on their AI adoption journey.

+ Contact Us to Contribute +
+
+
+ + +
+
+

Helpful External Resources

+

Recommended resources for learning about LLMs and AI deployment

+ +
+
+
🦙
+

Ollama

+

Run large language models locally. An easy way to get started with locally-hosted LLMs.

+ Visit Website → +
+ +
+
🤗
+

Hugging Face

+

The AI community building the future. Access thousands of open-source models and datasets.

+ Visit Website → +
+ +
+
🔗
+

LangChain

+

Build applications with LLMs through composability. Framework for RAG pipelines and AI agents.

+ Visit Website → +
+ +
+
📖
+

APNIC Blog

+

Latest news and insights from the Asia-Pacific networking community.

+ Visit Website → +
+
+
+
+ + +
+

Need Help?

+

Questions about resources or looking for specific materials?

+
+ Contact Us + View Workshops +
+
+ + +
+
+
+

LLMNetOps

+

Strengthening Network Operations Knowledge-Base through Locally-Hosted Generative AI

+
+
+

Quick Links

+ +
+
+

Funded By

+

APNIC Foundation
ISIF Asia 2025

+
+
+

Implemented By

+

Universitas Brawijaya
Indonesia

+
+
+
+

© 2024 LLMNetOps. All rights reserved.

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Workshops

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Hands-on Training on Privacy-Aware LLM Deployment for Network Operations

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+ Registration Opening Soon
+ Workshop dates and registration details will be announced shortly. Join our mailing list to be notified! +
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+ 2 + Workshop Sessions +
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+ 20+ + Target Participants +
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+ 70% + Knowledge Improvement +
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+ Free + No Registration Fee +
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Workshop Sessions

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Two comprehensive sessions designed for network operations professionals

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Session 1: Foundations of Local LLM Deployment

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Introduction to Large Language Models and Privacy-Aware AI

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+ 📅 Date: TBA + ⏱️ Duration: 4 hours + 📍 Format: Hybrid +
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This foundational session introduces participants to Large Language Models, covering core concepts, privacy considerations, and the fundamentals of local deployment. Participants will learn why locally-hosted LLMs are crucial for institutional data privacy.

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Topics Covered

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  • Introduction to Large Language Models (LLMs)
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  • Understanding AI privacy concerns
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  • Local vs. cloud-based AI deployment
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  • Privacy-aware AI principles
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  • Introduction to open-source LLM models
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  • Basic RAG (Retrieval-Augmented Generation) concepts
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  • Infrastructure requirements overview
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Prerequisites

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  • Basic understanding of networking concepts
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  • Familiarity with command-line interfaces
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  • No prior AI/ML experience required
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Learning Outcomes

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  • Understand core LLM concepts and terminology
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  • Recognize privacy implications of AI deployment
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  • Evaluate local vs. cloud AI solutions
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  • Identify suitable open-source LLM models
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Session 2: AI Agentic Workflow for Network Operations

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Practical Implementation and Hands-on Exercises

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+ 📅 Date: TBA + ⏱️ Duration: 4 hours + 📍 Format: Hybrid +
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Building on Session 1, this advanced session focuses on practical implementation of AI agentic workflows for network operations. Participants will work with live prototypes and learn to integrate LLMs into their network management workflows.

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Topics Covered

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  • AI Agentic workflow fundamentals
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  • Building RAG pipelines for network documentation
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  • Live prototype demonstration
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  • Contextual AI for network troubleshooting
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  • Integration with existing infrastructure
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  • Best practices for production deployment
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  • Security considerations
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Prerequisites

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  • Completion of Session 1 (or equivalent knowledge)
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  • Basic Python knowledge (helpful but not required)
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  • Access to a test environment
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Learning Outcomes

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  • Build basic RAG pipelines
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  • Deploy LLMs for network operations use cases
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  • Create contextual AI assistants for documentation
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  • Implement security best practices
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Workshop Information

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Target Audience

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Network operations professionals, IT administrators, and technical staff from IDREN member institutions. Open to other educational and research institutions in Indonesia.

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Registration Fee

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The workshops are free of charge, funded by APNIC Foundation through the ISIF Asia 2025 grant program.

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Language

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Instruction will be primarily in Indonesian with materials available in both Indonesian and English. English support available.

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Certificate

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Participants who complete both sessions will receive a certificate of completion from Universitas Brawijaya.

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Frequently Asked Questions

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Common questions about our workshops

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Who can participate in the workshops?
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The workshops are primarily designed for network operations professionals, IT administrators, and technical staff from IDREN member institutions. However, we welcome participants from other educational and research institutions in Indonesia who are interested in privacy-aware AI deployment.

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Is there a registration fee?
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No, the workshops are free of charge. This project is funded by APNIC Foundation through the ISIF Asia 2025 grant program.

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What equipment do I need?
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Participants should have a laptop with at least 8GB RAM. For hands-on exercises, we'll provide access to cloud-based environments. Specific setup instructions will be shared before each workshop.

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Will workshop materials be available afterwards?
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Yes, all workshop materials including slides, recordings (when available), and hands-on guides will be made available through our Resources page. Materials are provided in both Indonesian and English.

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What language will instruction be in?
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Instruction will be primarily in Indonesian with materials available in both Indonesian and English. English support will be available for non-Indonesian speakers.

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Do I need prior AI/ML experience?
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No prior AI or machine learning experience is required for Session 1. Basic familiarity with networking concepts and command-line interfaces is helpful. Session 2 builds on Session 1 content.

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Stay Updated

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Be the first to know when registration opens

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LLMNetOps

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Strengthening Network Operations Knowledge-Base through Locally-Hosted Generative AI

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Quick Links

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Funded By

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APNIC Foundation
ISIF Asia 2025

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Implemented By

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Universitas Brawijaya
Indonesia

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© 2024 LLMNetOps. All rights reserved.

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