# 📁 CJ Notes

Welcome! This folder contains plain-English explanations of everything in this repository.

**No coding experience needed** - just read and follow along.

## What's In Here

| File | What It's About |
|------|-----------------|
| [REPO_OVERVIEW.md](./REPO_OVERVIEW.md) | Everything in this repo explained simply |
| [TOP_FEATURES.md](./TOP_FEATURES.md) | The coolest things ranked with scores |
| [Claude-system-prompt-12-20-25.md](./cj_research/Claude-system-prompt-12-20-25.md) | How Claude's system prompt works behind the scenes |

---

## CJ's Deep Dives

Plain-English breakdowns of the best AI/ML tools and repositories, organized by category.

### 🤖 AI Agents & Frameworks

Tools for building, orchestrating, and managing AI agents.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [Parlant-Framework-Breakdown.md](./cj_research/Parlant-Framework-Breakdown.md) | [Parlant](https://github.com/emcie-co/parlant) - AI agent framework with guidelines & self-critique | 7.2 | 7.0 |
| [CCPM-Deep-Dive.md](./cj_research/CCPM-Deep-Dive.md) | [CCPM](https://github.com/automazeio/ccpm) - Claude Code Project Manager with parallel agents & GitHub integration | 8.4 | 9.2 |
| [Vibe-Kanban-Deep-Dive.md](./cj_research/Vibe-Kanban-Deep-Dive.md) | [Vibe Kanban](https://github.com/BloopAI/vibe-kanban) - Visual kanban for orchestrating multiple AI coding agents | 8.1 | 8.3 |
| [DeepAgents-Deep-Dive.md](./cj_research/DeepAgents-Deep-Dive.md) | [DeepAgents](https://github.com/langchain-ai/deepagents) - LangChain's "Agents 2.0" with explicit planning, memory, subagents | 8.0 | 7.4 |
| [Agentic-Design-Patterns-Deep-Dive.md](./cj_research/Agentic-Design-Patterns-Deep-Dive.md) | [Agentic Design Patterns](https://github.com/sarwarbeing-ai/Agentic_Design_Patterns) - 21 patterns for building AI agents (free 400-page guide) | 9.1 | 8.5 |
| [OpenHands-Deep-Dive.md](./cj_research/OpenHands-Deep-Dive.md) | [OpenHands](https://github.com/OpenHands/OpenHands) - Open-source AI coding agent platform, 66k stars (formerly OpenDevin) | 9.3 | 8.2 |
| [Continue-Deep-Dive.md](./cj_research/Continue-Deep-Dive.md) | [Continue](https://github.com/continuedev/continue) - Open-source AI coding assistant with 74+ LLM providers, VS Code/JetBrains support (30.6k stars) | 8.3 | 8.1 |
| [SWE-agent-Deep-Dive.md](./cj_research/SWE-agent-Deep-Dive.md) | [SWE-agent](https://github.com/SWE-agent/SWE-agent) - Princeton's autonomous bug fixing agent with ACI design (17.9k stars, 77.4% on SWE-bench) | 9.0 | 7.3 |
| [Ralph-Claude-Code-Deep-Dive.md](./cj_research/Ralph-Claude-Code-Deep-Dive.md) | [Ralph](https://github.com/frankbria/ralph-claude-code) - Autonomous Claude Code loops with rate limiting, circuit breakers & exit detection (155 stars) | 7.4 | 8.1 |
| [Aider-Deep-Dive.md](./cj_research/Aider-Deep-Dive.md) | [Aider](https://github.com/Aider-AI/aider) - AI pair programming in terminal with Git integration, repo maps & multi-model support (39.4k stars) | 9.4 | 9.3 |
| [12-Factor-Agents-Deep-Dive.md](./cj_research/12-Factor-Agents-Deep-Dive.md) | [12-Factor Agents](https://github.com/humanlayer/12-factor-agents) - Production principles for AI agents: 80% deterministic code, 20% LLM (17.4k stars) | 9.2 | 9.4 |
| [GPT-Engineer-Deep-Dive.md](./cj_research/GPT-Engineer-Deep-Dive.md) | [GPT Engineer](https://github.com/AntonOsika/gpt-engineer) - Natural language to complete codebases with 9-stage preprompt system (55.2k stars) | 8.0 | 7.2 |
| [CrewAI-Deep-Dive.md](./cj_research/CrewAI-Deep-Dive.md) | [CrewAI](https://github.com/crewAIInc/crewAI) - Multi-agent orchestration with Agents, Tasks, Crews, Flows & memory systems (42.1k stars) | 9.1 | 8.4 |

### 📄 Document Processing

Tools for extracting, parsing, and understanding documents.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [olmOCR-Deep-Dive.md](./cj_research/olmOCR-Deep-Dive.md) | [olmOCR](https://github.com/allenai/olmocr) - Convert PDFs to clean text with AI (equations, tables, handwriting) | 8.2 | 7.1 |
| [LangExtract-Deep-Dive.md](./cj_research/LangExtract-Deep-Dive.md) | [LangExtract](https://github.com/google/langextract) - Google's LLM extraction with source grounding (17.6k stars) | 8.3 | 7.4 |
| [Dolphin-Deep-Dive.md](./cj_research/Dolphin-Deep-Dive.md) | [Dolphin](https://github.com/bytedance/Dolphin) - ByteDance's document image parsing with 21 element types (ACL 2025) | 8.0 | 6.2 |
| [RAGFlow-Deep-Dive.md](./cj_research/RAGFlow-Deep-Dive.md) | [RAGFlow](https://github.com/infiniflow/ragflow) - Deep document understanding RAG engine with agentic workflows (70.7k stars) | 9.2 | 8.5 |

### 📊 Data & Visualization

Tools for working with data, creating visualizations, and learning from real systems.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [Data-Formulator-Deep-Dive.md](./cj_research/Data-Formulator-Deep-Dive.md) | [Data Formulator](https://github.com/microsoft/data-formulator) - Microsoft's AI-powered data visualization (drag-and-drop + NL) | 8.1 | 7.3 |
| [ML-System-Design-Case-Studies-Deep-Dive.md](./cj_research/ML-System-Design-Case-Studies-Deep-Dive.md) | [ML Case Studies](https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies) - 300+ real-world ML systems from Netflix, Uber, Airbnb & more | 9.0 | 8.2 |
| [MindsDB-Deep-Dive.md](./cj_research/MindsDB-Deep-Dive.md) | [MindsDB](https://github.com/mindsdb/mindsdb) - AI SQL Server connecting 217+ data sources with ML/LLMs via SQL (38k stars) | 8.2 | 7.1 |

### 🔧 Developer Tools

Tools that enhance developer workflows and AI assistant capabilities.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [Awesome-MCP-Servers-Deep-Dive.md](./cj_research/Awesome-MCP-Servers-Deep-Dive.md) | [Awesome MCP Servers](https://github.com/punkpeye/awesome-mcp-servers) - 1000+ servers to connect AI to GitHub, databases, browsers & more | 9.3 | 9.1 |
| [Claude-Code-Templates-Deep-Dive.md](./cj_research/Claude-Code-Templates-Deep-Dive.md) | [Claude Code Templates](https://aitmpl.com) - 400+ agents, commands, hooks & MCPs for Claude Code | 9.2 | 9.8 |
| [Google-Code-Wiki-Deep-Dive.md](./cj_research/Google-Code-Wiki-Deep-Dive.md) | [Code Wiki](https://codewiki.google) - Google's AI that auto-generates documentation for any GitHub repo | 8.0 | 7.2 |
| [Repo2txt-Deep-Dive.md](./cj_research/Repo2txt-Deep-Dive.md) | [repo2txt](https://github.com/abinthomasonline/repo2txt) - Convert GitHub repos to LLM-ready text (1.7k stars, 100% client-side) | 7.1 | 7.3 |
| [Awesome-Context-Engineering-Deep-Dive.md](./cj_research/Awesome-Context-Engineering-Deep-Dive.md) | [Awesome Context Engineering](https://github.com/Meirtz/Awesome-Context-Engineering) - 100+ papers & tools on context management for LLMs (2.8k stars) | 8.4 | 8.2 |
| [Awesome-LLMOps-Deep-Dive.md](./cj_research/Awesome-LLMOps-Deep-Dive.md) | [Awesome LLMOps](https://github.com/tensorchord/Awesome-LLMOps) - Curated list of LLMOps tools: serving, training, monitoring, vector DBs (5.5k stars) | 8.1 | 7.0 |
| [MCP-Official-Servers-Deep-Dive.md](./cj_research/MCP-Official-Servers-Deep-Dive.md) | [MCP Official Servers](https://github.com/modelcontextprotocol/servers) - Anthropic's 7 reference MCP servers: Filesystem, Git, Fetch, Memory, Sequential Thinking, Time, Everything (75k+ stars) | 9.4 | 9.3 |
| [GitHub-MCP-Server-Deep-Dive.md](./cj_research/GitHub-MCP-Server-Deep-Dive.md) | [GitHub MCP Server](https://github.com/github/github-mcp-server) - GitHub's official MCP server with 100+ tools for repos, PRs, issues, Actions (25.5k stars) | 9.3 | 9.2 |
| [MCP-Python-SDK-Deep-Dive.md](./cj_research/MCP-Python-SDK-Deep-Dive.md) | [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - Official Python SDK for building MCP servers with FastMCP decorators (20.9k stars) | 9.1 | 9.0 |
| [Awesome-Claude-Code-Deep-Dive.md](./cj_research/Awesome-Claude-Code-Deep-Dive.md) | [Awesome Claude Code](https://github.com/hesreallyhim/awesome-claude-code) - Curated collection of Claude Code resources, tools & configurations (19.1k stars) | 8.2 | 9.1 |
| [Screenshot-to-Code-Deep-Dive.md](./cj_research/Screenshot-to-Code-Deep-Dive.md) | [Screenshot-to-Code](https://github.com/abi/screenshot-to-code) - Convert screenshots/mockups to code with AI (React, Vue, HTML+Tailwind) (71.3k stars) | 9.0 | 7.4 |
| [LLM-CLI-Deep-Dive.md](./cj_research/LLM-CLI-Deep-Dive.md) | [LLM CLI](https://github.com/simonw/llm) - Simon Willison's unified CLI for 50+ LLM providers with SQLite logging (10.7k stars) | 8.3 | 7.2 |
| [Files-to-Prompt-Deep-Dive.md](./cj_research/Files-to-Prompt-Deep-Dive.md) | [files-to-prompt](https://github.com/simonw/files-to-prompt) - Convert directories to LLM-ready prompts with Claude XML format (2.6k stars) | 7.3 | 8.0 |
| [MCP-TypeScript-SDK-Deep-Dive.md](./cj_research/MCP-TypeScript-SDK-Deep-Dive.md) | [MCP TypeScript SDK](https://github.com/modelcontextprotocol/typescript-sdk) - Official TypeScript SDK for MCP with Zod schemas & type safety (11.2k stars) | 9.0 | 8.3 |
| [MCP-Specification-Deep-Dive.md](./cj_research/MCP-Specification-Deep-Dive.md) | [MCP Specification](https://github.com/modelcontextprotocol/modelcontextprotocol) - The protocol spec: JSON-RPC 2.0, hosts/clients/servers, 6 primitives (6.8k stars) | 9.1 | 8.4 |
| [Claude-Code-Best-Practices-Deep-Dive.md](./cj_research/Claude-Code-Best-Practices-Deep-Dive.md) | [Claude Code Best Practices](https://github.com/awattar/claude-code-best-practices) - 5 custom commands, 10 specialized agents & GitHub Flow integration | 7.4 | 9.0 |

### 🧪 Evaluation & Benchmarking

Tools for testing, evaluating, and benchmarking LLM applications.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [LM-Evaluation-Harness-Deep-Dive.md](./cj_research/LM-Evaluation-Harness-Deep-Dive.md) | [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) - EleutherAI's standard framework for LLM benchmarking, powers Open LLM Leaderboard (10.6k stars) | 9.0 | 6.1 |
| [SWE-bench-Deep-Dive.md](./cj_research/SWE-bench-Deep-Dive.md) | [SWE-bench](https://github.com/princeton-nlp/SWE-bench) - Princeton's benchmark for AI coding agents on real GitHub issues (3.1k stars, industry standard) | 9.2 | 7.0 |
| [Promptfoo-Deep-Dive.md](./cj_research/Promptfoo-Deep-Dive.md) | [Promptfoo](https://github.com/promptfoo/promptfoo) - LLM testing framework with 51 assertion types, red teaming, OWASP testing (9.7k stars) | 8.4 | 8.1 |
| [BigCodeBench-Deep-Dive.md](./cj_research/BigCodeBench-Deep-Dive.md) | [BigCodeBench](https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard) - Next-gen code benchmark with 1,140 tasks using 139 Python libraries (replaces HumanEval) | 8.2 | 6.3 |

### 📊 Observability & Monitoring

Tools for tracing, debugging, and monitoring AI applications.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [Phoenix-Deep-Dive.md](./cj_research/Phoenix-Deep-Dive.md) | [Phoenix](https://github.com/Arize-ai/phoenix) - AI observability platform with tracing, evaluation, and OpenTelemetry support (8.1k stars) | 8.3 | 7.2 |

### 🔒 Security & Safety

Tools for securing LLM applications and ensuring safe AI outputs.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [OWASP-LLM-Top-10-Deep-Dive.md](./cj_research/OWASP-LLM-Top-10-Deep-Dive.md) | [OWASP LLM Top 10](https://github.com/OWASP/www-project-top-10-for-large-language-model-applications) - Industry-standard security guide for LLM applications (9.1k stars) | 9.3 | 8.0 |
| [LLM-Guard-Deep-Dive.md](./cj_research/LLM-Guard-Deep-Dive.md) | [LLM Guard](https://github.com/protectai/llm-guard) - 38 security scanners for input/output protection, prompt injection defense (2.4k stars) | 8.1 | 7.3 |
| [NeMo-Guardrails-Deep-Dive.md](./cj_research/NeMo-Guardrails-Deep-Dive.md) | [NeMo Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) - NVIDIA's programmable guardrails with Colang DSL for conversation control (5.5k stars) | 8.2 | 7.1 |

### 📚 Learning Resources

Educational resources for understanding algorithms, prompt engineering, and AI development.

| Deep Dive | What It's About | Awesome | Impact |
|-----------|-----------------|:-------:|:------:|
| [TheAlgorithms-Python-Deep-Dive.md](./cj_research/TheAlgorithms-Python-Deep-Dive.md) | [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) - 216k stars, 500+ algorithms for learning CS fundamentals | 9.0 | 5.2 |
| [System-Prompts-Collection-Deep-Dive.md](./cj_research/System-Prompts-Collection-Deep-Dive.md) | [System Prompts Collection](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools) - 30+ leaked AI tool prompts from Cursor, Devin, Claude, v0 & more (104k stars) | 9.8 | 9.4 |
| [Claude-Cookbooks-Deep-Dive.md](./cj_research/Claude-Cookbooks-Deep-Dive.md) | [Claude Cookbooks](https://github.com/anthropics/claude-cookbooks) - Anthropic's official 60+ Jupyter notebooks for Claude development patterns (30.4k stars) | 9.5 | 9.7 |
| [Prompt-Engineering-Guide-Deep-Dive.md](./cj_research/Prompt-Engineering-Guide-Deep-Dive.md) | [Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) - DAIR.AI's comprehensive guide with 16+ techniques, 20+ models, 14 languages (68.6k stars) | 9.6 | 9.3 |

## About the System Prompt

The **Claude System Prompt** document captures how Claude (the AI) is instructed to behave when you chat with it. Think of it as Claude's "rulebook" - it tells Claude things like:

- **How to cite sources** - When Claude searches the web, it needs to properly reference where it got info
- **When to create artifacts** - Those code snippets, documents, and visualizations Claude can make
- **Design guidelines** - How to make things look good and work well
- **What tools are available** - Libraries and features Claude can use

This is useful if you want to understand why Claude responds the way it does, or if you're building prompts that work well with Claude's capabilities.

## About Parlant

**Parlant** is an open-source framework for building AI chatbots that actually follow your rules. The breakdown document explains:

- **The problem it solves** - Why traditional AI prompts fail in production
- **Guidelines & Journeys** - How Parlant structures AI behavior into manageable pieces
- **The self-critique mechanism** - How it verifies responses before sending them
- **When to use it** - Use cases in finance, healthcare, e-commerce, and legal

If you're interested in building reliable AI agents (not just chatbots that work "most of the time"), this is worth a read.

## About CCPM (Claude Code Project Manager)

**CCPM** solves the biggest pain point in AI-assisted development: context loss between sessions. The deep dive explains:

- **The context problem** - Why AI "forgets" everything and how CCPM fixes it
- **Parallel agent execution** - Running 5-8 AI agents simultaneously on different tasks
- **The 5-phase workflow** - From brainstorm → PRD → Epic → Tasks → Code
- **GitHub as the brain** - Using Issues as persistent memory everyone can see
- **40+ commands** - Full reference for the `/pm:` command system
- **How it helps us** - Concrete ways CCPM aligns with our goals in this repo

If you've ever thought "I wish Claude remembered what we worked on yesterday," this is the solution.

## About Vibe Kanban

**Vibe Kanban** is a visual kanban board for orchestrating multiple AI coding agents. Built by Bloop AI (a Y Combinator company), it represents the "agent-driven, human-orchestrated" future of development:

- **Multi-agent support** - Works with Claude Code, Gemini CLI, Codex, Cursor, Amp, and more
- **Parallel execution** - Run 3-5+ agents simultaneously on different tasks
- **Git worktree isolation** - Each agent works in its own branch, no conflicts ever
- **Visual dashboard** - Kanban board showing all tasks and their status
- **Built-in code review** - Diff viewer to inspect AI changes before merging
- **One-click merge** - When you're happy, merge with a single click

The deep dive also compares Vibe Kanban vs. CCPM and explains how they complement each other.

If you like visual interfaces and want to orchestrate multiple different AI agents (not just Claude), this is for you.

## About olmOCR

**olmOCR** is an open-source toolkit from Allen AI that converts PDFs into clean, structured text using vision language models. Unlike traditional OCR that just recognizes characters, olmOCR *understands* documents:

- **Handles complex layouts** - Multi-column papers, figures, insets - all read in natural order
- **Mathematical equations** - Outputs proper LaTeX for formulas
- **Tables** - Preserves structure as HTML
- **Handwriting** - Trained on 20,000+ handwritten pages
- **Cost efficient** - 32x cheaper than GPT-4o ($190 vs $6,200 per million pages)
- **LLM training ready** - Output designed to improve language model training

The deep dive covers installation, CLI usage, benchmarks against alternatives (Marker, PaddleOCR), and how it connects to Allen AI's OLMo open language model project.

If you need to extract text from PDFs for AI workflows, RAG systems, or training data pipelines, this is the state-of-the-art open-source solution.

## About ML System Design Case Studies

This is a curated collection of **300+ real-world case studies from 80+ companies** showing exactly how they built their ML systems in production:

- **What's included** - Netflix recommendations, Uber matching, Airbnb search ranking, Stripe fraud detection, Spotify personalization, and many more
- **The 7-step framework** - Requirements → Data → Features → Model → Evaluation → Serving → Monitoring
- **Common patterns** - Funnel/layered approaches, feature stores, A/B testing infrastructure
- **Key tradeoffs** - Accuracy vs. latency, freshness vs. cost, personalization vs. cold start
- **Real numbers** - Scale, latency requirements, business impact metrics
- **How it helps us** - Reference architectures, prompting context, playbook development

Unlike tutorials that show toy examples, these are battle-tested production systems serving millions of users. The deep dive also explains how these patterns apply to our AI-assisted development goals.

If you're building ML features or just want to understand how tech giants architect their AI systems, this is essential reading.

## About Awesome MCP Servers

**MCP (Model Context Protocol)** is like a "USB-C port for AI" - a standard way to connect AI assistants to external tools. This curated repository (77,900+ stars) lists 1000+ MCP servers across 30+ categories:

- **Developer tools** - GitHub, Git for code search and PR management
- **Databases** - PostgreSQL, SQLite, MongoDB for natural language queries
- **Browser automation** - Puppeteer, Playwright for web scraping and testing
- **Cloud platforms** - AWS, Kubernetes, Azure for infrastructure management
- **Communication** - Slack, Notion, Discord for collaboration
- **Memory** - Knowledge graphs for persistent context across sessions

The deep dive covers:
- How MCP works (hosts, clients, servers, transport)
- Major server categories with installation examples
- Building custom MCP servers with TypeScript/Python SDKs
- How MCP supercharges Claude Code's capabilities
- Connection to our other deep dives (CCPM, Vibe Kanban, etc.)

If you want to extend what AI assistants can do beyond just reading local files, MCP servers are the answer.

## About DeepAgents

**DeepAgents** is LangChain AI's open-source agent harness that implements "Agents 2.0" architecture. It explains why traditional AI agents fail at complex tasks and provides the solution:

- **The problem** - "Shallow agents" lose context, forget goals, and fail at 100+ step tasks
- **Explicit planning** - Agents create structured todo lists instead of implicit chain-of-thought
- **Filesystem backend** - Store intermediate results in files instead of filling context window
- **Subagent delegation** - Spawn isolated specialists for different parts of complex tasks
- **Context engineering** - Detailed system prompts inspired by Claude Code

The deep dive covers:
- The four pillars of deep agent architecture
- Installation and usage examples
- Real-world patterns (multi-day projects, research, error recovery)
- How DeepAgents compares to LangGraph, CrewAI, and AutoGen
- The LangChain ecosystem (LangGraph, LangSmith, LangServe)
- How this explains why Claude Code actually works

If you've ever wondered why AI agents spiral into confusion on complex tasks, DeepAgents explains the architectural problem and provides battle-tested solutions from production systems.

## About Data Formulator

**Data Formulator** is Microsoft Research's AI-powered tool that revolutionizes how we create data visualizations. Instead of writing code to transform data, you combine drag-and-drop UI with natural language:

- **The problem** - Creating visualizations requires data transformation (aggregations, calculations, joins) which needs programming
- **Concept binding** - Define abstract concepts ("Total Revenue"), AI figures out how to calculate them
- **Hybrid interface** - Combine UI actions with natural language (average 7.28 words per concept!)
- **Data threads** - Branch and backtrack through your exploration without losing work
- **Transparency** - See the generated Python code and transformed data
- **Scale** - Handle million-row datasets via DuckDB backend

The deep dive covers:
- The three-step workflow (intent → AI transformation → visualization)
- Installation and quick start (5 minutes to first chart)
- Comparison with Tableau, Power BI, and chat-based AI tools
- The research behind it (Best Paper Honorable Mention at IEEE VIS 2023)
- Real-world use cases for software metrics, performance analysis, and reporting

If you work with data and want to create visualizations without coding, Data Formulator is free, open-source, and takes 5 minutes to install.

## About Agentic Design Patterns

**Agentic Design Patterns** is a free 400+ page guide by Antonio Gullí (Google Distinguished Engineer) covering 21 design patterns for building AI agents:

- **The gap it bridges** - Moving from AI demos to production-grade enterprise systems
- **21 patterns** - Prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent, and more
- **Three frameworks** - Implementations in LangChain/LangGraph, Crew AI, and Google ADK
- **Jupyter notebooks** - Executable code examples for hands-on learning
- **Industry standard** - Published by Springer Nature, 7.2k+ GitHub stars

The deep dive covers:
- Core patterns (ReAct, reflection, plan-and-execute)
- Multi-agent orchestration patterns
- Production patterns (human-in-the-loop, guardrails, monitoring)
- When to use which pattern for your use case

If you're building AI agents and want systematic, proven approaches instead of ad-hoc solutions, this is the definitive reference.

## About Claude Code Templates

**Claude Code Templates** (aitmpl.com) is the de facto package manager for Claude Code with 400+ ready-to-use components:

- **Agents (100+)** - Specialized AI personas (frontend developer, security auditor, code reviewer)
- **Commands (159+)** - Reusable task configurations (code review, testing, deployment)
- **Hooks (39+)** - Automated actions triggered by events (pre-commit validation)
- **MCPs** - External tool integrations (databases, APIs)
- **Settings** - Pre-configured permission sets and security profiles

Installation is one command: `npx claude-code-templates@latest --agent <category>/<name>`

The deep dive covers:
- How each component type works
- The cart system for building complete development stacks
- Framework-specific integrations (Next.js, Supabase)
- Project structure after installation

If you're tired of writing Claude Code configurations from scratch, this gives you tested, community-validated components you can install instantly.

## About Google Code Wiki

**Code Wiki** is Google's free AI tool (powered by Gemini) that automatically generates and maintains documentation for any public GitHub repository:

- **Auto-generated** - Comprehensive wiki created from your codebase
- **Always current** - Updates automatically after every code change
- **Interactive** - Navigate from concepts to exact code files
- **Visual diagrams** - Architecture, class, and sequence diagrams
- **Chat interface** - Ask Gemini questions about your specific codebase

The deep dive covers:
- How Code Wiki analyzes and documents code
- What gets generated (overviews, module breakdowns, diagrams)
- Use cases (onboarding, legacy code, open source contribution)
- Limitations and upcoming features (private repo support)

If you've ever struggled to understand a codebase or wished documentation updated itself, Code Wiki is the answer.

## About LangExtract

**LangExtract** is Google's open-source Python library (17.6k+ stars) that uses LLMs to extract structured data from unstructured text - with a critical difference: every extraction maps to its exact source location:

- **Source grounding** - Every extraction includes precise character offsets (where exactly in the text?)
- **No training required** - Just provide 2-3 examples and a description
- **Long document handling** - Processes millions of tokens via chunking and parallel processing
- **Multiple LLM support** - Gemini, OpenAI, Claude, or local models via Ollama
- **Interactive visualization** - HTML reports with highlighted extractions

The deep dive covers:
- The three-step extraction workflow
- Real-world use cases (healthcare, legal, customer reviews)
- Code examples for different LLM backends
- Comparison with spaCy and generic LLM approaches
- Performance benchmarks (12% higher recall, 95%+ precision)

If you need to extract structured information from documents and prove where each piece came from, LangExtract is the only solution with built-in source grounding.

## About Dolphin

📄 [Read the deep dive](./cj_research/Dolphin-Deep-Dive.md)

**Dolphin** is ByteDance's state-of-the-art document image parsing system that won Best Paper at ACL 2025. It handles 21 different element types:

- **Comprehensive parsing** - Text, tables, figures, equations, headers, footers, captions, and more
- **Multi-modal understanding** - Combines vision and language models for accurate extraction
- **Production ready** - Battle-tested at ByteDance scale
- **Open source** - Available for research and commercial use

If you need to parse complex documents with mixed content types, Dolphin is the cutting-edge solution.

---

## 📊 Data & Visualization

### About MindsDB

📄 [Read the deep dive](./cj_research/MindsDB-Deep-Dive.md)

**MindsDB** (38k+ stars) is an "AI SQL Server" that connects 217+ data sources with ML/LLM capabilities via standard SQL:

- **SQL interface** - Query AI models like database tables (`SELECT * FROM model WHERE input = 'question'`)
- **217+ integrations** - Databases, APIs, SaaS tools, vector stores
- **Built-in ML** - Time series forecasting, classification, regression without code
- **LLM support** - OpenAI, Anthropic, Hugging Face models via SQL
- **Enterprise ready** - Self-hosted or cloud deployment

If you want to add AI capabilities to your existing data infrastructure without learning new tools, MindsDB lets you do it all in SQL.

---

## 🤖 AI Agents & Frameworks (continued)

### About OpenHands

📄 [Read the deep dive](./cj_research/OpenHands-Deep-Dive.md)

**OpenHands** (66k+ stars, formerly OpenDevin) is an open-source AI coding agent platform that can write code, run commands, and browse the web:

- **Full autonomy** - Can create files, run tests, debug, and iterate
- **Sandboxed execution** - Runs in Docker containers for safety
- **Multiple interfaces** - CLI, web UI, GitHub integration
- **EventStream architecture** - Clean separation of agent actions and observations
- **Top benchmark performer** - Leading scores on SWE-bench

If you want an open-source alternative to Devin or Cursor that you can self-host and customize, OpenHands is the most mature option.

### About Continue

📄 [Read the deep dive](./cj_research/Continue-Deep-Dive.md)

**Continue** (30.6k+ stars) is an open-source AI coding assistant that integrates into VS Code and JetBrains:

- **74+ LLM providers** - OpenAI, Anthropic, local models via Ollama, and more
- **IDE integration** - Native VS Code and JetBrains plugins
- **Context awareness** - Understands your codebase, not just the current file
- **Customizable** - Configure models, prompts, and behaviors
- **Privacy focused** - Can run entirely locally with local models

If you want GitHub Copilot functionality but with model flexibility and privacy control, Continue is the leading open-source option.

### About SWE-agent

📄 [Read the deep dive](./cj_research/SWE-agent-Deep-Dive.md)

**SWE-agent** (17.9k+ stars) is Princeton NLP's autonomous software engineering agent that achieves 77.4% on SWE-bench Verified:

- **Agent-Computer Interface (ACI)** - Novel interface design that dramatically improves agent performance
- **Autonomous bug fixing** - Give it a GitHub issue, it produces a working patch
- **Research-driven** - Published at top venues, continuously improved
- **Customizable** - Swap models, modify prompts, extend capabilities

The key insight: *how* you let an AI interact with code matters as much as *which* model you use. SWE-agent's ACI design is why it outperforms raw GPT-4.

### About Ralph (Claude Code Autopilot)

📄 [Read the deep dive](./cj_research/Ralph-Claude-Code-Deep-Dive.md)

**Ralph** (155+ stars) is an autonomous development framework that runs Claude Code in intelligent loops with built-in safeguards:

- **Autonomous loops** - Executes Claude Code repeatedly, each iteration improving the project
- **Rate limiting** - Configurable calls/hour prevents API quota exhaustion
- **Circuit breaker** - Detects stuck loops with exponential backoff recovery
- **Semantic exit detection** - Knows when work is truly done (not just "ran successfully")
- **Live monitoring** - Real-time dashboard shows loop count, API usage, recent activity
- **Project structure** - Opinionated layout with PROMPT.md, @fix_plan.md, @AGENT.md

Think "autopilot for Claude Code" - it handles the tedious run-check-run-again cycle while protecting you from runaway API costs and infinite loops.

---

## 🔧 Developer Tools (continued)

### About Repo2txt

📄 [Read the deep dive](./cj_research/Repo2txt-Deep-Dive.md)

**repo2txt** (1.7k+ stars) converts any GitHub repository into a single text file optimized for LLM consumption:

- **100% client-side** - Runs entirely in your browser, no server uploads
- **Smart filtering** - Excludes binary files, respects .gitignore
- **Token counting** - Shows estimated tokens for different models
- **Copy or download** - One-click output in clipboard or file

If you need to give an LLM context about an entire codebase, repo2txt is the fastest way to prepare it.

### About Awesome Context Engineering

📄 [Read the deep dive](./cj_research/Awesome-Context-Engineering-Deep-Dive.md)

**Awesome Context Engineering** (2.8k+ stars) is a curated collection of 100+ papers and tools on managing context for LLMs:

- **Research papers** - Academic work on context windows, retrieval, compression
- **Production tools** - Real-world solutions for context management
- **Emerging field** - Context engineering is becoming its own discipline
- **Practical patterns** - How to fit more useful information in limited context

If you're hitting context limits or want to understand the science of what goes into an LLM prompt, this is the definitive resource.

### About Awesome LLMOps

📄 [Read the deep dive](./cj_research/Awesome-LLMOps-Deep-Dive.md)

**Awesome LLMOps** (5.5k+ stars) from TensorChord is a curated list of tools for the entire LLM lifecycle:

- **13 categories** - Serving, training, monitoring, vector DBs, agents, and more
- **Production focus** - Tools used by real companies at scale
- **Regularly updated** - New tools added as the ecosystem evolves
- **Comparison friendly** - Easy to evaluate alternatives

If you're building LLM infrastructure and want to know what tools are available, this is your starting point.

### About MCP Official Servers

📄 [Read the deep dive](./cj_research/MCP-Official-Servers-Deep-Dive.md)

**MCP Official Servers** (75k+ stars) is Anthropic's reference implementation of the Model Context Protocol - 7 production-quality servers that extend AI capabilities:

- **Filesystem Server** - Secure file operations with configurable access control
- **Git Server** - Repository management, commits, branches, diffs
- **Fetch Server** - Web content retrieval with robots.txt compliance
- **Memory Server** - Persistent knowledge graphs across sessions
- **Sequential Thinking Server** - Structured problem-solving with revision support
- **Time Server** - Timezone-aware time operations
- **Everything Server** - Testing server implementing all MCP features

The deep dive covers:
- How MCP works (JSON-RPC 2.0, STDIO/HTTP transport)
- All 3 MCP primitives (Tools, Resources, Prompts)
- Installation for Claude Desktop and Claude Code
- Security considerations (prompt injection, tool poisoning, rug pulls)
- Building custom servers with TypeScript/Python SDKs

If you want to extend Claude's capabilities with official, Anthropic-maintained integrations, these are the gold standard reference implementations.

---

## 🧪 Evaluation & Benchmarking

### About lm-evaluation-harness

📄 [Read the deep dive](./cj_research/LM-Evaluation-Harness-Deep-Dive.md)

**lm-evaluation-harness** (10.6k+ stars) from EleutherAI is THE standard framework for evaluating language models:

- **Powers the Open LLM Leaderboard** - The benchmark everyone uses to compare models
- **400+ tasks** - MMLU, HellaSwag, ARC, TruthfulQA, and hundreds more
- **Reproducible** - Standardized evaluation ensures fair comparisons
- **Extensible** - Add custom tasks and metrics

If you've ever seen "Model X scores Y% on MMLU," this is the tool that produced that number.

### About SWE-bench

📄 [Read the deep dive](./cj_research/SWE-bench-Deep-Dive.md)

**SWE-bench** (3.1k+ stars) from Princeton is the gold standard benchmark for AI coding agents:

- **Real GitHub issues** - 2,294 actual bugs from Django, scikit-learn, Flask, etc.
- **Objective evaluation** - Pass/fail based on test suites, not human judgment
- **Industry standard** - Used by OpenAI, Anthropic, Meta to measure coding capabilities
- **Multiple variants** - Full, Lite, Verified for different use cases

When someone claims their AI agent can "fix bugs," SWE-bench is how you verify that claim.

### About Promptfoo

📄 [Read the deep dive](./cj_research/Promptfoo-Deep-Dive.md)

**Promptfoo** (9.7k+ stars) is an LLM testing framework for evaluating and red-teaming prompts:

- **51 assertion types** - Factuality, relevance, toxicity, custom matchers
- **Red teaming** - Automated adversarial testing for vulnerabilities
- **OWASP integration** - Test for LLM Top 10 security risks
- **CI/CD friendly** - Run tests in your pipeline before deployment
- **Model comparison** - A/B test prompts across different providers

If you're deploying LLM applications to production, promptfoo is how you ensure they work correctly and safely.

---

## 📊 Observability & Monitoring

### About Phoenix

📄 [Read the deep dive](./cj_research/Phoenix-Deep-Dive.md)

**Phoenix** (8.1k+ stars) from Arize AI is an open-source observability platform for AI applications:

- **OpenTelemetry native** - Industry-standard tracing, no vendor lock-in
- **Built-in evaluators** - Hallucination detection, QA correctness, relevance scoring
- **Framework integrations** - Auto-instrumentation for LangChain, LlamaIndex, etc.
- **Experiment tracking** - Compare prompts, models, and parameters
- **Self-hosted** - Run locally or deploy to your infrastructure

Think "DataDog for AI" - if you need visibility into what your LLM applications are doing, Phoenix provides it.

---

## 🔒 Security & Safety

### About OWASP LLM Top 10

📄 [Read the deep dive](./cj_research/OWASP-LLM-Top-10-Deep-Dive.md)

The **OWASP Top 10 for LLM Applications** (9.1k+ stars) is the industry-standard security guide for AI systems:

- **10 critical vulnerabilities** - Prompt injection, data leakage, insecure plugins, and more
- **500+ contributors** - Security experts from across the industry
- **Mitigation strategies** - How to defend against each vulnerability
- **Compliance baseline** - What security teams expect you to address

Just like the original OWASP Top 10 became essential for web security, this is becoming required reading for anyone building AI applications.

### About LLM Guard

📄 [Read the deep dive](./cj_research/LLM-Guard-Deep-Dive.md)

**LLM Guard** (2.4k+ stars) from Protect AI provides 38 security scanners for LLM input/output:

- **16 input scanners** - Prompt injection, toxicity, PII detection, code injection
- **22 output scanners** - Hallucination, bias, sensitive data leakage, malicious URLs
- **Fast** - Optimized for production latency requirements
- **Configurable** - Enable/disable scanners, adjust thresholds

If you need to add security guardrails to your LLM application without building from scratch, LLM Guard is the most comprehensive toolkit.

### About NeMo Guardrails

📄 [Read the deep dive](./cj_research/NeMo-Guardrails-Deep-Dive.md)

**NeMo Guardrails** (5.5k+ stars) is NVIDIA's framework for adding programmable guardrails to LLM applications:

- **Colang DSL** - Domain-specific language for defining conversation rules
- **5 rail types** - Input, output, dialog, retrieval, execution rails
- **Topical control** - Keep conversations on-topic
- **Fact checking** - Verify claims against knowledge bases
- **Action control** - Limit what actions the LLM can trigger

If you need fine-grained control over LLM behavior with a programmable rules engine, NeMo Guardrails provides enterprise-grade capabilities.

---

## 📚 Learning Resources

### About TheAlgorithms/Python

📄 [Read the deep dive](./cj_research/TheAlgorithms-Python-Deep-Dive.md)

**TheAlgorithms/Python** (216k+ stars) is the largest collection of algorithm implementations for educational purposes:

- **500+ algorithms** - Sorting, searching, graphs, dynamic programming, ML, and more
- **Pure Python** - No complex dependencies, just readable code
- **Educational focus** - Comments explain the "why," not just the "what"
- **Community maintained** - Actively updated with new algorithms

If you want to learn computer science fundamentals by reading clean, documented implementations, this is the definitive resource.

### About System Prompts Collection

📄 [Read the deep dive](./cj_research/System-Prompts-Collection-Deep-Dive.md)

**System Prompts Collection** (104k+ stars) contains leaked system prompts from 30+ AI tools including Cursor, Devin, Claude, v0, and more:

- **30,000+ lines** of production-grade prompts
- **Architectural patterns** from leading AI companies
- **Security insights** about guardrail design
- **Educational value** for prompt engineering

This is a goldmine for understanding how commercial AI tools are designed. If you want to learn prompt engineering from real-world examples, this collection shows what works at scale.

---

## 🤖 AI Agents & Frameworks (More)

### About Aider

📄 [Read the deep dive](./cj_research/Aider-Deep-Dive.md)

**Aider** (39.4k+ stars) is the leading open-source AI pair programming tool:

- **Git-first workflow** - Every change is automatically committed with descriptive messages
- **Repository map** - Tree-sitter AST analysis understands your entire codebase
- **Multi-model support** - Claude, GPT, DeepSeek, Gemini, local via Ollama
- **Voice coding** - Speak your coding requests
- **Architect mode** - Split reasoning (o1) and editing (GPT-4o) for complex changes

If you want terminal-based AI coding with excellent Git integration and repository understanding, Aider is the gold standard.

### About 12-Factor Agents

📄 [Read the deep dive](./cj_research/12-Factor-Agents-Deep-Dive.md)

**12-Factor Agents** (17.4k+ stars) defines production principles for building AI agents:

- **Hybrid architecture** - 80-90% deterministic code, 10-20% LLM decisions
- **Own your prompts** - Don't rely on framework defaults
- **Human-in-the-loop** - Treat humans as first-class workflow participants
- **Small, focused agents** - Specialists beat generalists
- **Stateless reducers** - Enable replay, debugging, scaling

If you're building AI agents for production and want battle-tested principles instead of ad-hoc solutions, this is the definitive guide.

---

## 📄 Document Processing (More)

### About RAGFlow

📄 [Read the deep dive](./cj_research/RAGFlow-Deep-Dive.md)

**RAGFlow** (70.7k+ stars) is an open-source RAG engine with deep document understanding:

- **Intelligent parsing** - Handles complex layouts, tables, images, OCR
- **Agentic workflows** - Combine human-defined and LLM-driven automation
- **Citation grounding** - Every response traces back to sources
- **Infinity database** - AI-native storage with hybrid search

If your documents have tables, images, or complex layouts, RAGFlow's deep understanding makes it the best choice for enterprise RAG.

---

## 🔧 Developer Tools (More)

### About GitHub MCP Server

📄 [Read the deep dive](./cj_research/GitHub-MCP-Server-Deep-Dive.md)

**GitHub MCP Server** (25.5k+ stars) is GitHub's official MCP implementation:

- **100+ tools** across repos, PRs, issues, Actions, security
- **Natural language** to GitHub operations
- **Multiple deployment** - Remote, Docker, or local
- **Enterprise support** - GitHub Enterprise Server/Cloud

If you want AI assistants to manage your GitHub workflow through conversation, this is the official solution.

### About MCP Python SDK

📄 [Read the deep dive](./cj_research/MCP-Python-SDK-Deep-Dive.md)

**MCP Python SDK** (20.9k+ stars) is the official SDK for building MCP servers in Python:

- **FastMCP decorators** - `@mcp.tool()`, `@mcp.resource()`, `@mcp.prompt()`
- **Auto schema generation** - Python type hints → JSON schemas
- **Multiple transports** - STDIO, Streamable HTTP, SSE
- **FastAPI integration** - Mount MCP servers in existing apps

If you want to extend Claude's capabilities with custom Python tools, this SDK makes it easy.

### About Awesome Claude Code

📄 [Read the deep dive](./cj_research/Awesome-Claude-Code-Deep-Dive.md)

**Awesome Claude Code** (19.1k+ stars) is the largest curated collection of Claude Code resources:

- **10 categories** - Agent skills, workflows, tooling, IDE integrations, hooks
- **75+ repositories** indexed with descriptions
- **Automated submission** - No Git knowledge required to contribute
- **Daily updates** - Active community curation

If you're using Claude Code and want to discover extensions, workflows, and configurations, this is your starting point.

### About Screenshot-to-Code

📄 [Read the deep dive](./cj_research/Screenshot-to-Code-Deep-Dive.md)

**Screenshot-to-Code** (71.3k+ stars) converts designs to code with AI:

- **Multiple frameworks** - React, Vue, HTML+Tailwind, Bootstrap, Ionic
- **Multi-model** - Claude, GPT-4o, Gemini
- **Video support** - Record interactions, generate code
- **Self-hostable** - Use your own API keys

If you need to convert mockups to code quickly, this reduces development time by 50-70%.

### About LLM CLI

📄 [Read the deep dive](./cj_research/LLM-CLI-Deep-Dive.md)

**LLM CLI** (10.7k+ stars) by Simon Willison is the Swiss Army knife for terminal LLM interaction:

- **50+ providers** - OpenAI, Claude, Gemini, Ollama, and more via plugins
- **SQLite logging** - Every conversation saved automatically
- **Templates** - Reusable prompt configurations
- **Embeddings** - Built-in vector generation

If you work in the terminal with multiple AI providers, this tool unifies everything.

### About files-to-prompt

📄 [Read the deep dive](./cj_research/Files-to-Prompt-Deep-Dive.md)

**files-to-prompt** (2.6k+ stars) by Simon Willison converts directories to LLM context:

- **Claude XML format** - Optimized for Claude's extended context
- **.gitignore respect** - Automatic filtering
- **Pipe-friendly** - Works with find, git, grep
- **Multiple formats** - Plain, XML, Markdown

If you need to feed entire codebases to LLMs, this is the simplest solution.

---

## 🧪 Evaluation & Benchmarking (More)

### About BigCodeBench

📄 [Read the deep dive](./cj_research/BigCodeBench-Deep-Dive.md)

**BigCodeBench** is the next-generation code benchmark (replaces saturated HumanEval):

- **1,140 tasks** requiring 139 Python libraries
- **Best model (GPT-4o)** scores only 61.1% (vs 97% human)
- **Real-world complexity** - Multi-library composition
- **163+ models** evaluated on the leaderboard

When evaluating AI coding tools, BigCodeBench scores reflect actual production capability, not inflated numbers from saturated benchmarks.

---

## 🤖 AI Agents & Frameworks (Even More)

### About GPT Engineer

📄 [Read the deep dive](./cj_research/GPT-Engineer-Deep-Dive.md)

**GPT Engineer** (55.2k+ stars) pioneered the "describe and generate" approach to code generation:

- **Natural language to codebase** - Describe what you want, get a complete project
- **9-stage preprompt system** - Clarify, philosophy, roadmap, entrypoint, generate, spec, respec, unit tests, improve
- **Multi-model support** - OpenAI, Claude, local models via Ollama
- **Vision capability** - Generate code from wireframes and mockups
- **Evolution** - Now powers Lovable.dev commercial platform

If you want to understand the origins of AI code generation and how natural language can create entire projects, GPT Engineer is the original.

### About CrewAI

📄 [Read the deep dive](./cj_research/CrewAI-Deep-Dive.md)

**CrewAI** (42.1k+ stars) is the leading multi-agent orchestration framework:

- **Role-based agents** - Define specialists with roles, goals, and backstories
- **Tasks** - Assign specific work with expected outputs
- **Crews** - Teams that execute sequentially or hierarchically
- **Flows** - Complex multi-crew workflows
- **4 memory types** - Short-term (ChromaDB), long-term (SQLite), entity, contextual
- **Enterprise ready** - 100k+ certified developers

If you need multiple AI agents collaborating on complex tasks, CrewAI provides the production-ready framework.

---

## 🔧 Developer Tools (Even More)

### About MCP TypeScript SDK

📄 [Read the deep dive](./cj_research/MCP-TypeScript-SDK-Deep-Dive.md)

**MCP TypeScript SDK** (11.2k+ stars) is the official TypeScript implementation for MCP:

- **Full type safety** - Native TypeScript with compile-time checks
- **Zod integration** - Runtime validation with automatic schema generation
- **Multiple transports** - STDIO, Streamable HTTP, SSE
- **McpServer class** - Clean API for tools, resources, prompts
- **15+ examples** - Server and client implementations

If you're building MCP servers in TypeScript and want type safety with Zod validation, this is the official SDK.

### About MCP Specification

📄 [Read the deep dive](./cj_research/MCP-Specification-Deep-Dive.md)

**MCP Specification** (6.8k+ stars) is the protocol definition for AI integrations:

- **JSON-RPC 2.0 base** - Standard, well-understood protocol
- **3 components** - Hosts, Clients, Servers with clear roles
- **6 primitives** - Tools, Resources, Prompts, Sampling, Elicitation, Roots
- **Transport options** - STDIO, HTTP, SSE
- **OAuth 2.1 support** - Secure authenticated connections

Understanding MCP is essential for building interoperable AI integrations. This is the source of truth.

### About Claude Code Best Practices

📄 [Read the deep dive](./cj_research/Claude-Code-Best-Practices-Deep-Dive.md)

**Claude Code Best Practices** is a community collection of workflows and agents:

- **5 custom commands** - /commit, /issue, /reviewpr, /test, /custom-init
- **10 specialized agents** - Code review, security, performance, architecture
- **CLAUDE.md template** - Complete project setup
- **GitHub Flow integration** - End-to-end workflow

If you want ready-to-use Claude Code commands and agent configurations, this repository provides tested templates.

---

## 📚 Learning Resources (More)

### About Claude Cookbooks

📄 [Read the deep dive](./cj_research/Claude-Cookbooks-Deep-Dive.md)

**Claude Cookbooks** (30.4k+ stars) is Anthropic's official collection of development patterns:

- **60+ Jupyter notebooks** - Executable examples for every feature
- **15+ topic directories** - Capabilities, tool use, multimodal, agents, skills
- **Extended thinking** - Deep reasoning for complex problems
- **Skills framework** - Generate Excel, PowerPoint, PDF, Word documents
- **Agent patterns** - Agentic loop, orchestrator-subagents, evaluator-optimizer

If you're learning Claude development, these are the official, Anthropic-maintained examples.

### About Prompt Engineering Guide

📄 [Read the deep dive](./cj_research/Prompt-Engineering-Guide-Deep-Dive.md)

**Prompt Engineering Guide** (68.6k+ stars) is the most comprehensive prompting resource:

- **16+ techniques** - Zero-shot, few-shot, CoT, ToT, ReAct, RAG, and more
- **20+ model guides** - Claude, GPT, Gemini, open-source models
- **14 language translations** - Global accessibility
- **Research-backed** - Latest papers and techniques
- **Active community** - Regular updates

If you want to master prompting from basics to advanced techniques, this is the definitive guide.

---

*This folder is for CJ (the non-technical guy) and anyone who wants simple explanations.*
