Claude How To vs TurboOCR

Claude How To fills the gap between Anthropic's feature documentation and what developers actually need to build real workflows with Claude Code. Where official docs describe what features exist, this repository shows how to combine slash commands, memory, subagents, hooks, and MCP servers into automated pipelines for code review, deployment, and documentation generation. The guide contains 10 tutorial modules with Mermaid diagrams, copy-paste configuration templates, and a progressive learning roadmap totaling 11–13 hours of structured content. Each module includes interactive self-assessment quizzes, and the entire guide is actively maintained to track Claude Code releases—currently synced to v2.2.0. Over 25 hook event types are documented with working examples, and there's a complete CLI reference for headless automation in CI/CD pipelines. Built by luongnv89 and released with an MIT license, Claude How To climbed to 18k stars in its first week—mostly organically through HN and X shares from developers frustrated with scattered official documentation. It represents the kind of community-built learning infrastructure that often outlasts the tools it documents.

TurboOCR is a C++20 OCR server that uses CUDA and TensorRT to process documents at speeds that make Python-based OCR look like a fax machine. The headline number: 270 images per second on FUNSD form datasets with approximately 11ms single-request latency — roughly 50x faster than PaddleOCR's standard Python implementation. It uses PP-OCRv5 models (the same underlying tech as PaddleOCR) but squeezes them through TensorRT FP16 optimization for GPU inference. The server exposes both HTTP and gRPC interfaces from a single binary and handles PDFs natively with four extraction strategies: pure OCR, native text layer extraction, hybrid verification mode, and a "best of both" fallback chain. PP-DocLayoutV3 handles layout detection across 25 document region classes — useful for structured documents where you need to know that a bounding box is a table cell vs. a header vs. a figure caption. A Prometheus metrics endpoint tracks throughput, latency, and GPU memory in real time. Deployment is Docker-first: TensorRT engine compilation happens automatically on first startup. The catch is it requires Linux with an NVIDIA Turing GPU (RTX 20-series minimum) and driver 595+, so it's not a laptop tool. But for enterprise document automation — invoices, forms, medical records — the throughput-to-cost ratio is hard to beat.