This repository is a comprehensive, production-focused AI system design guide, not a software application. It contains over 170 markdown files organized into 19 chapters covering the full lifecycle of building and operating AI systems: foundations (transformers, tokenization), model landscape (selection, pricing), training and adaptation (fine-tuning, LoRA, RLHF), inference optimization (KV cache, speculative decoding, batching), prompting and context engineering, retrieval systems (RAG, chunking, vector databases, hybrid search, GraphRAG), agentic systems (ReAct, MCP, multi-agent orchestration, durable execution), memory and state management, frameworks and tools (LangChain, LangGraph, LlamaIndex, DSPy, Semantic Kernel), document processing, infrastructure and MLOps (CI/CD, AI gateways, FinOps), security and access control, reliability and safety (guardrails, ensemble methods, governance), evaluation and observability (LLM evaluation, benchmarks), design patterns and anti-patterns, and 20+ real-world case studies. It also includes interview preparation materials (question bank, answer frameworks, whiteboard exercises, job market trends) and transition guides for engineers moving into AI roles. The content is current as of mid-2026, referencing specific model versions, pricing, and protocols (MCP 2.0, A2A). While it contains no executable code, it serves as an authoritative reference for engineers designing and deploying AI systems in production.
This repository maintains a comprehensive Python cheatsheet available as a web application and printable PDF. The core content resides in README.md, which is converted to a multi-page website via parse.js. The web version features interactive elements like theme switching and a table of contents, and includes live-updating data visualizations (e.g., COVID-19 trends) refreshed by update_plots.py. Supporting scripts handle PDF generation, index creation, and table format conversion. While the cheatsheet content is exceptionally well-curated, the supporting codebase consists of specific-purpose scripts with moderate coupling, limiting broad reuse outside this project.
This repository is a community-curated knowledge base of Git troubleshooting procedures, structured as a collection of markdown documents in multiple languages. It is not a software application, library, or data pipeline in the traditional sense, but rather a reference guide organized around common Git failure scenarios and their solutions. The content covers repository operations, commit editing, branching, merging, rebasing, stashing, and configuration, with each section providing step-by-step command-line instructions. The architecture is purely documentation-oriented: there are 9 translated README files (English, Spanish, Russian, Traditional Chinese, Simplified Chinese, Korean, Vietnamese, French, Japanese), a package.json with minimal build tooling for table-of-contents generation via doctoc, and a Travis CI configuration for link checking and TOC validation. The actual 'code' consists entirely of markdown prose and shell command examples. There is no application logic, no runtime, and no data processing. Maintenance appears stale: the last commit was June 4, 2026, but the repository has open issues and relies on community contributions via GitLocalize for translations. The Travis CI config uses Ruby 2.2 (EOL) and doctoc 1.3.0, indicating some dependency aging, though this is trivial given the repo's nature. The content itself is static documentation with no runtime dependencies to drift. There are no reusable software components here. The value is entirely in the curated Git knowledge, not in any code patterns or engineering craftsmanship. The markdown structure is conventional, and the shell commands are standard Git usage examples, not novel implementations.