This article analyzes how DeepSeek Harness achieves 99% prompt cache hit rates by enforcing deterministic request assembly, showing that cache hit rates serve as a diagnostic tool for runtime stability rather than just a cost metric.
This article breaks down LLM token billing by explaining what tokens are, how requests are charged, why a single query can consume thousands of tokens, and offers concrete strategies to estimate usage, monitor costs, and reduce expenses across different scenarios.
The article explains that most token cost comes from system‑added context rather than the user query, breaks down cost components, and offers a hierarchy of optimizations—from usage habits and prompt caching to model routing, tool management, and output compression—to dramatically reduce AI coding agent expenses.
The article analyzes why connecting an MCP server can triple response latency and token usage, explains how unstable tool ordering breaks Anthropic's prompt cache, and provides detailed code walkthroughs, design insights, common pitfalls, and concrete best‑practice recommendations for building reliable MCP integrations.
This article dissects Claude Code's AgentTool, explaining how it spawns full‑featured sub‑agents, routes execution through three distinct paths, shares Anthropic Prompt Cache via Fork, and protects against infinite recursion with a dual‑layer safeguard while isolating worktrees for safe parallel file operations.
The article dissects Claude Code’s Speculation system, showing how an AI sub‑agent predicts user intent, runs a full edit‑test pipeline in an overlay filesystem, filters results through twelve safety layers, and only commits changes when the user confirms, effectively turning speculative execution into a safe performance boost.
The article dissects Claude Code's three‑level progressive compression system—MicroCompact, SessionMemoryCompact, and Full Compact—showing how it edits cached prompts, maintains background memory files, and generates a structured nine‑section summary to keep AI agents operating over arbitrarily long conversations within a limited context window.
The article dissects the accidental public release of Anthropic's Claude Code TypeScript source, revealing five concrete AI agent design patterns—including prompt‑cache segmentation, a coordinator‑fork architecture, a four‑layer permission model, file‑system memory, and on‑demand tool loading—while also detailing the underlying tech stack, source‑map exposure, and quirky easter eggs.
OpenClaw v2026.3.7 introduces Feishu webhook compatibility fixes, prompt‑cache cost reductions, smarter model routing, domestic model connectors, and persistent binding for container deployments, turning the tool from a geek toy into a reliable enterprise‑grade AI‑agent platform.
