Ai

I pointed a team of Claude Code agents at one of my codebases and asked them to find bugs. Here is the prompt I used, how I tightened it, and what came back.

How to build production-ready wrappers around Claude CLI. Input/output formats, structured JSON schemas, permission modes, and everything you need for CI/CD and agent pipelines.

Configure Claude Code and the Anthropic Agent SDK to work with Z.AI, OpenRouter, or other providers instead of the default Anthropic API.

Build an autonomous development loop that picks up GitHub issues, implements them with TDD, runs QA, and closes issues automatically using Claude Code Agent Teams.

AI-generated text has tells. Wikipedia editors catalogued 28 of them. Here is what they found, why it happens, and how to make AI writing sound human again.

A structured skill that transforms how AI agents create implementation plans. Learn why each component exists and how it prevents common planning failures.

A technical deep dive into OpenClaw agent internals — 15+ AI providers with auto-discovery, a 9-layer tool policy system, context window management with cache-aware pruning, the skills loading pipeline, and Docker sandboxing with zero-trust defaults.

A technical deep dive into how OpenClaw controls browsers via Playwright and CDP, processes media through a multi-provider AI pipeline, renders interactive canvases on mobile nodes, and synthesizes speech with three TTS backends.

A technical deep dive into OpenClaw channel internals — the two-tier dock/plugin abstraction, plugin registry architecture, inbound normalization to MsgContext, outbound adapters with lazy loading, and platform-specific handling across Telegram, Discord, WhatsApp, Signal, Slack, iMessage, and LINE.

A technical deep dive into OpenClaw CLI internals — fast-path routing that bypasses Commander entirely, lazy subcommand loading via placeholder-and-swap, the full command registry, profile isolation, the component-based TUI with WebSocket streaming, the onboarding wizard with its prompter abstraction, and plugin CLI extensibility.

A technical deep dive into OpenClaw core architecture — the Gateway daemon as a WebSocket control plane, the bootstrap sequence, protocol framing with AJV-validated schemas, JSON5 configuration with hot-reload, connection management with backpressure, and the single-user local-first design philosophy.

A deep dive into OpenClaw infrastructure — cron scheduling with stuck job detection, 7 security subsystems including Ed25519 device identity and SSRF prevention, the plugin SDK with 15 lifecycle hooks, node pairing with crypto verification, and daemon management across platforms.

A technical deep dive into OpenClaw session management — hierarchical key resolution, file-based persistence with JSONL transcripts, sub-agent spawning with lifecycle tracking, cross-session communication via A2A ping-pong, and the security model that keeps it all isolated.

An end-user guide and technical deep dive into OpenClaw memory system — how plain Markdown files, hybrid vector search, atomic reindexing, and pre-compaction flushes give an AI assistant persistent, searchable memory across sessions.

Set up a custom status line in Claude Code that displays context window usage, session costs, model name, and git branch - all color-coded for quick visibility.

An exploration of how Test-Driven Development and Spec-Driven Development create lossless feedback loops in AI-assisted coding, with practical examples using Claude Code.

A comprehensive comparison of OpenSpec and GitHub Spec Kit. Two open-source frameworks for structured AI-assisted software development.

An in-depth technical analysis of Claude Skills architecture, progressive disclosure mechanism, and how Skills complement the Model Context Protocol for building autonomous AI agents.

Create a metaskill that automatically converts documentation files into structured Claude Code skills, enabling instant access to any framework or library documentation.

Learn how to transcribe audio input with Firebase Firestore and Storage through

An evidence-based analysis of LLM context utilization based on 11 academic studies. Learn why your 200K context window might only be 50% effective, and how to engineer prompts that actually work.