Official account for High Availability Architecture.
This guide explains how to manage Claude Code's 1 million‑token context window using commands like /rewind, /compact, subagents, and new sessions, offering practical strategies to avoid context rot, improve reliability, and reduce costs during long‑running programming tasks.
The article explains how a Google engineer leveraged Claude Code together with a lightweight .NET application and a curated CLAUDE.md file based on Karpathy’s principles, plus the Everything‑Claude‑Code repository, to automate roughly 80 % of his development tasks, reduce token waste, and achieve massive time and cost savings.
The article critically examines the hype around Agentic Coding by showing that detailed specifications are essentially code, using OpenAI’s Symphony as a case study, and demonstrates that spec‑driven generation is unstable, error‑prone, and often no faster than writing code directly.
In just 18 days after open‑sourcing, the TypeScript‑based Agent Orchestrator built by AI agents themselves amassed 3,800+ GitHub stars, executed 15 parallel sessions, merged six PRs, faced authentication crashes and session deaths, and evolved through a three‑tier escalation protocol with OpenClaw integration to enable real‑time Telegram interaction and continuous self‑improvement.
This article explains the prompt‑caching mechanism used by Claude Code, showing how separating static prefixes from dynamic tails and leveraging KV‑tensor caching reduces the O(n²) complexity of transformer pre‑fill to O(n), achieving a 92% cache hit rate and up to 81% cost savings in long‑running AI agent sessions.
The article details how an AI‑driven Agent Orchestrator autonomously generated 40,000 lines of TypeScript code, managed 17 plugins, ran 3,288 tests, and achieved self‑healing CI in just eight days by orchestrating parallel AI agents, plugin architecture, and a feedback loop.
This tutorial walks you through creating a lightweight, single‑file AI command‑line interface powered by Anthropic's Claude API, covering prerequisites, repository setup, core components, streaming responses, built‑in commands, and the underlying stateless architecture.
The article explains why raw JSON from document‑parsing APIs overwhelms an AI agent's context window and presents a practical workflow that separates readable Markdown content from metadata, uses prompt engineering, and leverages sandboxed code to keep agents efficient and accurate.
This article examines how the design of agent harnesses—simple loops with atomic tools and progressive disclosure—determines the performance ceiling of AI agents, showing that optimized scaffolding can double success rates, cut token usage by up to 47%, and outweigh model selection.
This article shares practical insights on designing AI agents, covering action‑space choices, tool selection strategies, progressive disclosure techniques, and evolving task management, while emphasizing observation, experimentation, and balancing tool complexity with model capabilities.
