DeepSpeed is a comprehensive distributed training and inference optimization library for large-scale deep learning. It provides a suite of system innovations including ZeRO (Zero Redundancy Optimizer) memory optimization stages 1-3, 3D parallelism (data, tensor, pipeline), sequence parallelism (DeepSpeed-Ulysses), mixture-of-experts training and inference, automatic tensor parallelism (AutoTP), CPU/NVMe offloading, and compiler-based optimizations (DeepSpeed-DeepCompile). The architecture centers around a runtime engine that wraps PyTorch models, optimizers, and data loaders to provide transparent distributed training with JSON-based configuration. Key entry points include `deepspeed.initialize()` for training, `deepspeed.init_inference()` for inference, and CLI launchers (`deepspeed`, `ds_report`). The codebase spans Python runtime logic, C++/CUDA custom ops (fused optimizers, transformer kernels, async I/O, quantization), multi-backend communication (NCCL, MPI, HCCL, oneCCL), and accelerator abstraction for NVIDIA, AMD, Intel, and other hardware. Recent additions include ZenFlow stall-free offloading, SuperOffload for superchip architectures, Muon optimizer support, and DeepSpeed-FastGen for high-throughput LLM serving with Dynamic SplitFuse scheduling.
ztk is a CLI proxy that reduces LLM token consumption by intercepting and compressing shell command output. It sits between AI agents (Claude Code, Cursor, Gemini CLI) and the shell, executing commands normally but returning filtered output through a six-stage pipeline. The tool removes noise like diff metadata, test runner verbosity, and redundant directory listings while preserving essential information such as error messages and exit codes. Built in Zig with zero dependencies, it features a Thompson NFA regex engine for runtime filters, SIMD-accelerated text processing, and an mmap'd session cache to avoid repeated output for unchanged commands.
This repository is a comprehensive educational resource and framework for Context Engineering - the discipline of optimizing how information is structured and presented to Large Language Models within their context window limitations. It moves beyond basic prompt engineering to treat context as a dynamic system that can be engineered for improved AI performance. The repository combines theoretical foundations with practical implementations, offering a structured learning path from basic concepts (atoms, molecules, cells) to advanced applications (neural fields, cognitive tools, multi-agent systems). It provides runnable examples, templates, and reusable components including protocol systems for context operations, multimodal processing frameworks, cognitive architectures, and token budgeting techniques. While primarily educational in nature, the repository contains numerous reusable components and patterns that could be extracted and applied in production AI applications. These include protocol shells for defining context operations, multimodal fusion components, cognitive architecture templates, and specialized agent systems for tasks like research, ethical analysis, and grant writing.