BranchesTags

Repository files navigation

AI For Good Hackathon

Challenge 🎯

Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.

  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.

Project Description 🗒️

This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).

The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.

Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.

Tech Stack 💻

  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render

Team Members 👷

  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer

Project Architecture 🖥️

Project architecture diagram

User input flow

  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]
Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question

Project Presentation ▶️

About

ai-for-good-hackathon-chi.vercel.app

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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" + '

Skip to content
BranchesTags
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
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
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
AI For Good Hackathon
Challenge 🎯
Build agentic AI solutions that deliver measurable impact for people, planet, and trust in at least one area.
  • Impact: Increase access, reduce friction, and improve outcomes for end beneficiaries.
  • Sustainability: Measure, reduce, or optimize environmental and operational footprint.
  • Trust & Responsible AI: Build with safety-by-design so the solution can be used responsibly in real settings.
Project Description 🗒️
This project implements a multi-agent pipeline that preprocesses, simplifies, and reviews user prompts before sending them to an LLM—removing redundant detail and sensitive information where possible (data minimization for Trust & Responsible AI).
The app mimics a ChatGPT-style experience with configurable privacy levels (low / medium / high). A simplification agent chain produces a minimized prompt and a final answer for the user.
Benefit: Less sensitive data in transit, while stripping noise from messy prompts so the model can answer more accurately.
Tech Stack 💻
  • Design: Figma
  • Frontend: Vite + React + Tailwind + Shadcn UI
  • Backend: Node.js + TypeScript + Express + Docker
  • AI/LLM: LangGraph + Groq + OpenRouter
  • Deploy: Vercel + Render
Team Members 👷
  • Khoi Do: Lead Developer, Backend Developer, AI Developer
  • Dung Nguyen: Algorithms Developer, AI Developer, Backend Developer
  • Nhi Nguyen: Designer, Frontend Developer
  • Khoa Nguyen: Designer, Frontend Developer
Project Architecture 🖥️
Project architecture diagram
User input flow
  1. Client — The user sends a prompt and chooses a privacy level (low / medium / high), which sets how aggressively the pipeline compresses and redacts input.
  2. Server — The frontend calls the API (e.g. POST /api/pipeline/run) with the raw message and simplify level; the server starts the graph with originalMessage and compressionLevel.
  3. Preprocess agent — Fixes typos and grammar so later steps work on clean text (preprocessedMessage).
  4. Simplify agent — Masks PII, applied SVT -> Top-k -> NoisyKNN algorithms chain at different passing score. (simplifiedMessage).
  5. Review agent — Checks that the simplified prompt still matches the user’s intent (quick rules + optional LLM review). If it fails and retries remain, the pipeline loops back to Simplify agent rewrites the core question (up to three attempts).
  6. Output finalizing agent — When review passes (or max retries are reached), builds the client payload: approved simplified question, final answer (LLM), and review metadata (status, similarity, missing items, retry history).
  7. Client — The server returns simplified prompt and final answer so the user sees what was sent to the model and the reply—without exposing the full raw pipeline on every screen.
flowchart LR
U[User prompt + privacy level] --> C[Client]
C --> S[Server]
S --> P[Preprocess]
P --> SIM[Simplify]
SIM --> REV[Review]
REV -->|passed| OUT[Output finalize]
REV -->|retry| SIM
OUT --> S
S --> C
C --> R[Simplified prompt and final answer]

Loading
StageWhat happens to user input
PreprocessGrammar and typos corrected
SimplifyPII redacted, filler removed, intent-focused question
ReviewValidates meaning; may re-run simplify
OutputAnswer generated from the approved simplified question
Project Presentation ▶️
About
ai-for-good-hackathon-chi.vercel.app
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages