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Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO

Achievements

Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG

🛠️ What I Do

  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.

🌱 Currently

  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.

🧠 Skills

PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture

📫 Get in Touch

LinkedInX

Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned Loading

  1. tokenriatokenriaPublic

    See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python

  2. civic-shield-mcpcivic-shield-mcpPublic

    An AI-powered Digital Rights Risk Analyzer for Activists

    Python

  3. advent_of_aiadvent_of_aiPublic

    An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript 1

  4. document-analysis-apidocument-analysis-apiPublic

    AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell

  5. gratitude-journalgratitude-journalPublic

    A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript 1

  6. pictures2storiespictures2storiesPublic

    Transforming Images into Short Stories with AI and Azure Services

    Python

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
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UffaModey (Fafa Modey) · GitHub
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56
followers
 · 11
following

Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Highlights

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
Loading
  1. tokenriatokenriaPublic
    
See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python
  2. civic-shield-mcpcivic-shield-mcpPublic
    
An AI-powered Digital Rights Risk Analyzer for Activists

    Python
  3. advent_of_aiadvent_of_aiPublic
    
An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript
1

  4. document-analysis-apidocument-analysis-apiPublic
    
AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell
  5. gratitude-journalgratitude-journalPublic
    
A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript
1

  6. pictures2storiespictures2storiesPublic
    
Transforming Images into Short Stories with AI and Azure Services

    Python

, 'i');
if (__m === '*' || __re.test(location.href)) {
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try {
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var __re = new RegExp('^' + ".*" + '
UffaModey (Fafa Modey) · GitHub
Skip to content
View UffaModey's full-sized avatar
56
followers
 · 11
following

Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Highlights

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
Loading
  1. tokenriatokenriaPublic
    
See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python
  2. civic-shield-mcpcivic-shield-mcpPublic
    
An AI-powered Digital Rights Risk Analyzer for Activists

    Python
  3. advent_of_aiadvent_of_aiPublic
    
An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript
1

  4. document-analysis-apidocument-analysis-apiPublic
    
AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell
  5. gratitude-journalgratitude-journalPublic
    
A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript
1

  6. pictures2storiespictures2storiesPublic
    
Transforming Images into Short Stories with AI and Azure Services

    Python

, '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);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
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style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
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if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
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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('^' + ".*" + '
UffaModey (Fafa Modey) · GitHub
Skip to content
View UffaModey's full-sized avatar
56
followers
 · 11
following

Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Highlights

Block or report UffaModey

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
Loading
  1. tokenriatokenriaPublic
    
See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python
  2. civic-shield-mcpcivic-shield-mcpPublic
    
An AI-powered Digital Rights Risk Analyzer for Activists

    Python
  3. advent_of_aiadvent_of_aiPublic
    
An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript
1

  4. document-analysis-apidocument-analysis-apiPublic
    
AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell
  5. gratitude-journalgratitude-journalPublic
    
A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript
1

  6. pictures2storiespictures2storiesPublic
    
Transforming Images into Short Stories with AI and Azure Services

    Python

, '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) {
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document.querySelectorAll('a[href]').forEach(function(a) {
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observer.observe(document.body, { childList: true, subtree: true });
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
UffaModey (Fafa Modey) · GitHub
Skip to content
View UffaModey's full-sized avatar
56
followers
 · 11
following

Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Highlights

Block or report UffaModey

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
Loading
  1. tokenriatokenriaPublic
    
See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python
  2. civic-shield-mcpcivic-shield-mcpPublic
    
An AI-powered Digital Rights Risk Analyzer for Activists

    Python
  3. advent_of_aiadvent_of_aiPublic
    
An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript
1

  4. document-analysis-apidocument-analysis-apiPublic
    
AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell
  5. gratitude-journalgratitude-journalPublic
    
A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript
1

  6. pictures2storiespictures2storiesPublic
    
Transforming Images into Short Stories with AI and Azure Services

    Python

, '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
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// 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('^' + ".*" + '
UffaModey (Fafa Modey) · GitHub
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 · 11
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Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
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  4. document-analysis-apidocument-analysis-apiPublic
    
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
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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') {
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observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
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} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
UffaModey (Fafa Modey) · GitHub
Skip to content
View UffaModey's full-sized avatar
56
followers
 · 11
following

Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Highlights

Block or report UffaModey

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
Loading
  1. tokenriatokenriaPublic
    
See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python
  2. civic-shield-mcpcivic-shield-mcpPublic
    
An AI-powered Digital Rights Risk Analyzer for Activists

    Python
  3. advent_of_aiadvent_of_aiPublic
    
An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript
1

  4. document-analysis-apidocument-analysis-apiPublic
    
AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell
  5. gratitude-journalgratitude-journalPublic
    
A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript
1

  6. pictures2storiespictures2storiesPublic
    
Transforming Images into Short Stories with AI and Azure Services

    Python

, '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; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
UffaModey (Fafa Modey) · GitHub
Skip to content
View UffaModey's full-sized avatar
56
followers
 · 11
following

Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Achievements
Achievement: QuickdrawAchievement: Pull Sharkx3Achievement: Pair Extraordinairex2Achievement: YOLO
Highlights

Block or report UffaModey

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UffaModey/README.md
Fafa Modey (@UffaModey) — Platform Engineer, Agentic AI Engineer, Python Software Engineer, Data EngineerTyping SVG
🛠️ What I Do
  • Agentic AI Engineering — RAG pipelines (LangChain, vector search, hybrid retrieval + reranking), structured LLM output with schema validation and retry-on-failure, confidence-based grounding/hallucination filtering, content-safety guardrails, and agentic automation (custom Claude Code agents, MCP tooling, LLM-driven PR review and issue triage).
  • Platform Engineering — Infrastructure-as-code with Terraform across multi-environment deployments, containerized services on Docker/Kubernetes/Azure Container Apps, event-driven workers (Azure Functions, Service Bus, Celery), CI/CD (GitHub Actions, Bitbucket Pipelines), and OpenTelemetry-based observability.
  • Python Software Engineering — Async-first APIs with FastAPI and Django/DRF, strict typing (mypy), SQLAlchemy + Alembic migrations, test-driven backend design.
  • AI Engineering — LLM integration across OpenAI, Azure OpenAI, and Claude; embeddings and vector search (Azure AI Search, MongoDB Atlas $vectorSearch, FAISS); token usage and cost accounting.
  • Data Engineering — Relational + NoSQL data modeling (Postgres, MySQL, Cosmos DB, MongoDB), message-queue-driven ingestion pipelines, ETL for document/knowledge stores feeding retrieval systems.
🌱 Currently
  • Building Tokenria — a local tool that breaks down LLM token spend by structural category (cache, tools, history, output) and separately tracks how much of what a model generates actually gets used, tagged by hand rather than auto-scored.
  • Deepening production RAG patterns: retrieval grounding, hallucination confidence-filtering, and guardrail testing for LLM-backed platforms.
  • Growing a Claude Code / MCP-based agentic tooling practice — custom agents for PR review, technical-debt triage, and dev workflow automation.
🧠 Skills
PythonFastAPI · Django · AsyncIO
Cloud PlatformsAzure · AWS · Azure OpenAI
Docker & Containerization
Kubernetes & Container Orchestration
Git, CI/CD & DevOpsGitHub Actions · Azure DevOps · GitLab CI
SQL, NoSQL & DatabasesPostgreSQL · MySQL · Redis · Cosmos DB · Vector Databases
Infrastructure as CodeTerraform
AI & LLM FrameworksLangChain · OpenAI SDKs · Azure OpenAI
Agentic AIMCP · Tool Calling · RAG · LLM-driven automation
API & Distributed SystemsREST APIs · GraphQL · Message Queues · Event-Driven Architecture
📫 Get in Touch
LinkedInX
Open to collaborating on agentic AI systems, RAG infrastructure, and platform engineering for AI-native products.

Pinned
Loading
  1. tokenriatokenriaPublic
    
See where your LLM tokens actually go, and how much of what you generate is actually worth using.

    Python
  2. civic-shield-mcpcivic-shield-mcpPublic
    
An AI-powered Digital Rights Risk Analyzer for Activists

    Python
  3. advent_of_aiadvent_of_aiPublic
    
An collection of AI engineering challenges completed as part of Goose’s Advent of AI.

    JavaScript
1

  4. document-analysis-apidocument-analysis-apiPublic
    
AI Document Analysis API with AWS Textract and AWS App Runner

    PowerShell
  5. gratitude-journalgratitude-journalPublic
    
A full stack Figma to Code project for AWS Amplify and AWS Cognito

    JavaScript
1

  6. pictures2storiespictures2storiesPublic
    
Transforming Images into Short Stories with AI and Azure Services

    Python