Exploring ideas, prompts, and personal insights from curious conversations with AI. This repository contains a collection of in-depth technical guides and prompt engineering templates generated from these explorations.

NEW: The monolithic learning path has been redesigned into 6 focused, digestible journeys that let you master specific domains at your own pace. Each path is 2-8 weeks and can be combined for deeper expertise.

→ Complete Learning Paths Guide

🦀 The Rust Systems Engineer (4-6 weeks)
Build high-performance, memory-safe systems from scratch

🗄️ The Database Architect (3-4 weeks)
Master PostgreSQL from fundamentals to production clusters

⚛️ The Modern Frontend Developer (2-3 weeks)
Build type-safe, performant UIs with React + Tailwind

🤖 The AI Engineer (6-8 weeks)
Build production-ready AI systems from LLMs to multimodal agents

🏗️ The Infrastructure Engineer (3-4 weeks)
Master fault-tolerant messaging and observability

🎯 The System Design Expert (2-3 weeks)
Master distributed patterns across multiple languages

All guides are structured using the Feynman learning method to build deep, intuitive understanding of complex systems.

• The Engineer's Guide to Production-Ready Generative AI
  • Core Concepts: AI Engineering, Transformers, Attention Mechanism, Prompt Engineering, RAG (Retrieval-Augmented Generation), AI Agents, LLMOps, LLM-as-a-Judge.
• Mastering PyTorch for Large Language Models: From Fundamentals to Frontier
  • Core Concepts: Tensors, Autograd, nn.Module, optim, Dataset, DataLoader.
• LLM Reverse-Engineering: A Generalized Framework and Analysis of Modern Architectures
  • Core Concepts: Reverse-Engineering Framework, "Visualize, Hypothesize, Verify", Comparative Triage, Netron, Architectural Analysis (Gemma, DeepSeek, Llama, Qwen).

• Introduction to AI Code Assistants
  • Core Concepts: AI-Powered Development, Code Assistant Fundamentals, Integration Patterns, Best Practices.
• Technical Foundations of AI Code Assistants
  • Core Concepts: Technical Architecture, AI Integration, Development Infrastructure, Implementation Strategies.
• Deep Dive into Claude Code Core Flows
  • Core Concepts: Claude Code Architecture, Core Workflows, Internal Mechanisms, Development Patterns.

• Building a Performant AI Agent Framework in Rust from Scratch
  • Core Concepts: ReAct Paradigm, Modular Design (Traits), Tool Use, State Management, Async Orchestration.
• The AURORA Project: A Production-Focused Guide to Building Multimodal AI Agents in Rust
  • Core Concepts: Rust for AI, ReAct Loop, Model Context Protocol (MCP), Four Pillars (Axum, Qdrant, Tokio, Tracing), Fearless Concurrency.
• Kubrick Course Learning Guide to Multimodal AI Systems
  • Core Concepts: Multimodal Agents, Model Context Protocol (MCP), Four Pillars (Pixeltable, FastMCP, Groq, Opik), Multi-Agent Collaboration, Observability.

• The Complete Guide to Mastering Tailwind CSS: From Foundations to v4 Production Mastery * Core Concepts: Utility-First Philosophy, JIT Compilation, Oxide Engine (v4), CSS-First Configuration (@theme), Semantic Tokens, Container Queries.

• Technical Deep Dive: Kafka Consumer Fault Tolerance and Rebalancing
  • Core Concepts: Fault Tolerance, At-Least-Once Delivery, Rebalancing, Idempotency, Dead Letter Queues (DLQ).

• A Comprehensive Guide to Production-Grade Monitoring with Grafana, Prometheus, and Loki
  • Core Concepts: PLG Stack, Shared Label Philosophy, Metrics & Logs Correlation, Docker Compose, Production Readiness (Security, HA, Performance), Alerting Philosophy, OpenTelemetry.

• PostgreSQL 17: A Feynman-Method Guide from Fundamentals to Production
  • Core Concepts: Architecture (MVCC, WAL), SQL Mastery, Performance Engineering (EXPLAIN, Indexing), Row-Level Security (RLS).
• PostgreSQL High Availability from Scratch: A Practical Guide to Multi-Region Clusters
  • Core Concepts: Multi-Region Architecture, Patroni Cluster Management, etcd Distributed Consensus, HAProxy Load Balancing, Production Security (mTLS), Automated Failover.
• Mastering the PostgreSQL Job Queue: A Comprehensive Guide to High-Throughput Production Systems
  • Core Concepts: Job Queue Implementation, SKIP LOCKED, Lock Contention, Connection Pooling (PgBouncer), Table Partitioning, VACUUM Strategy.

• The Modern React Stack: A Feynman Guide from First Principles to Production
  • Core Concepts: TanStack Router, TanStack Query, Zustand, Zod, Type-Safe Patterns.

• A Deep Dive into Rust: From Fundamentals to Production Patterns
  • Core Concepts: Structs and Enums, Traits, Concurrency with Tokio and Rayon, Design Patterns.
• 10 Essential Rust Patterns for Robust Code
  • Core Concepts: RAII, Result Pattern, Builder Pattern, Newtype, Typestate, Dynamic Dispatch, Send/Sync, GATs.

• Type-Safe Full-Stack Development: Rust & TypeScript
  • Core Concepts: SSoT, JSON Schema, schemars, serde, Enum Strategies, Generics, Automation, Ecosystem Benefits.
• Building Production-Ready Telegram Bots in Rust: From Zero to Deployment
  • Core Concepts: Axum Webhooks, Teloxide FSM, SQLx for Persistent State, Modular Monolith Architecture.
• Rust Architecture Patterns
  • Core Concepts: Integration Test Architecture, Repository Pattern, Domain Events, Event Sourcing, CQRS, Observability, Error Handling, Validation Rules Engine.

• Distributed Task Execution Workflow Patterns (Python) | Go | Rust | TypeScript
  • Core Concepts: Task Lifecycle, Priority Scheduling, Worker Assignment, Retry Strategies, Circuit Breaker, Dead Letter Queues, DAGs, Sagas.

• The Ultimate MCP Crash Course - Build From Scratch
  • Core Concepts: Model Context Protocol (MCP), MCP Server/Client Architecture, Tools, Resources, Prompts, Sampling, TypeScript SDK, VS Code Integration, GitHub Copilot Integration.

• Financial Literacy for Dummies (Like Me) with JL Collins
  • Core Concepts: The Simple Path to Wealth, Spend Less Than You Earn, Index Fund Investing (VTSAX), Avoiding Debt, Financial Independence (FI), F.U. Money, Market Crashes.

Powerful, protocol-based prompts with a MANDATORY PROCESSING PROTOCOL that forces AI systems to think systematically and deliver consistent, professional results. Each prompt follows a proven 4-stage approach: Data Ingestion → Execution Planning → Synthesis & Generation → Finalization. → Complete Guide

• Comprehensive Learning: Creates comprehensive learning guides that take you from zero to mastery on any topic. Accepts any input format, auto-detects learning level, and creates 6-section guides with diagrams and hands-on exercises.
• Learning Path Generator: Combines multiple technical resources into cohesive, narrative-driven learning journeys with engaging themes and visual learning maps.
• Feynman Article Generator: Transforms technical discussions, documentation, or research into clear, standalone articles using the Feynman teaching method with 6-section structure.
• Feynman Article Enhancer: Takes existing educational articles and enhances them with deeper examples, better diagrams, and clearer explanations.
• Feynman Article Refiner: Systematically applies specific improvement suggestions to refine technical articles for iterative content development.

• Audio Transcript Refiner: Corrects errors in machine-generated transcripts while preserving the original speaker's intent and style.
• Audio Transcript Summarizer: Extracts key information from transcripts and presents it as concise, time-stamped summaries with decisions and action items.
• Video Transcription Enhancer: Takes raw, messy video transcripts and reformats them for clarity and professional presentation.

• Documentation Generator: Analyzes codebases comprehensively and generates complete, production-grade documentation ecosystems. Deep analysis beyond surface-level explanations with essential docs, tutorial series, and visual diagrams.
• Tutorials Generator: Creates detailed, step-by-step tutorial content with hands-on instructions for technical topics and implementations.
• Example to Tutorial Converter: Analyzes project example folders and generates comprehensive tutorial series using the Feynman method. Creates one tutorial per example with deep understanding from first principles.
• Idea to Tutorial Generator: Takes a list of tutorial topic ideas and creates detailed plans for each tutorial using the Feynman approach. Structures tutorials into progressive sections with core concepts, practical guides, and deep dives.
• PoC Implementation Plan Generator: Creates comprehensive Proof of Concept implementation plans with timelines, milestones, and success criteria for validating technical approaches.
• System Design Expert: Provides expert-level system design analysis and architectural recommendations. Accepts problems, scenarios, or challenges and generates critical questions with trade-off analysis.
• Technical Feature Request Document: Transforms informal discussions into formal, comprehensive technical feature request documents with architecture, APIs, and rollout plans.