Research & Curriculum

AI Engineering Curriculum & Technical Papers

Welcome to our research collection featuring academic papers, technical studies, and in-depth analysis in AI development and systems architecture.

NLWeb: Building the AI Web with Natural Language Interfaces - Microsoft’s open-source framework for building conversational interfaces using MCP and Schema.org standards
Agentic Context Engineering: Evolving Contexts for Self-Improving LMs - Stanford/Princeton research on ACE framework treating AI contexts as evolving playbooks (+10.6% agent performance, +8.6% finance tasks)
Tiny Recursive Models: Less is More - Samsung research achieving 45% on ARC-AGI-1 with only 7M parameters through recursive reasoning
Model Context Protocol (MCP) for Beginners - Comprehensive Microsoft curriculum with hands-on labs in C#, Java, JavaScript, Rust, Python, and TypeScript
Hybrid AI Stack: Reduce AI Costs by 60-80% - Production-ready system that intelligently routes between local CPU models and cloud APIs to slash costs
Complete AI Engineering Curriculum - Comprehensive guide from zero to $200K+ AI engineering salary
Multi-Agent Architecture - Exploring distributed AI system design patterns
Scaling AI Inference to Billions - Google Cloud’s decade-long journey in AI infrastructure
Bob’s Brain: Open Source AI Assistant - Slack AI assistant template with enterprise integration
Imbalanced-learn ML Toolkit - Essential toolkit for handling imbalanced datasets

Terraform for AI Infrastructure: Complete Guide - From zero to production: comprehensive Terraform learning resource for AI and cloud infrastructure
Distributed Systems Architecture Patterns - Comprehensive cheat sheet for scalable system design
Modern AI Transformer Deployment - End-to-end guide for production AI model serving

Building 254-Table BigQuery Schema in 72 Hours - Enterprise data platform architecture with 254 BigQuery tables
DiagnosticPro Evolution Analysis - Complete forensic analysis of 13,597 files in AI platform development
DiagnosticPro Platform Case Study - AI-powered vehicle diagnostics platform
Building DiagnosticPro Platform - Complete technical breakdown of AI diagnostic system
DiagnosticPro Feature Rollouts - Preview of advanced AI diagnostic features
From Zero to AI Empire - Complete project evolution timeline and architecture decisions
Season 2025 Recap - Complete year overview of platform development

AI-Dev Transformation Part 1 - From chaos to organized AI-powered development
AI-Dev Transformation Part 2 - Building enterprise prompt libraries
AI-Dev Transformation Part 3 - One-paste magic template system
AI-Dev Transformation Part 4 - Dual AI workflows with Claude and Cursor
Building AI-Friendly Documentation - Professional documentation toolkit with Claude
Repository Transformation Guide - From chaos to professional prompt engineering toolkit

Advanced Linux Security - SSH, Debian package management, and security best practices
Comprehensive SSH, Debian & Grep Guide - Technical guide to SSH, Debian packages, and text processing
Linux Security Glossary - Comprehensive reference for systems administration
Security Audit & Python Environment Battle - 3-hour battle with Python environments and security audits
Waygate MCP v2.1.0 Production Release - Enterprise MCP server with TaskWarrior

Founder’s Log: September 2025 - Daily operations and strategic thinking in AI platform development
Day One Tech Journey - Starting the AI engineering journey
A Day of Steel Beams and Soccer - Balancing life and tech
The Perfect Developer Workspace - Lessons from a real-world cleanup

Our research methodology emphasizes:

Real-world Implementation - All papers include actual production code and deployment examples
Performance Metrics - Detailed benchmarks and scalability analysis
Open Source - Code repositories and templates available on GitHub
Practical Application - Focus on immediately actionable insights for practitioners

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