How AI dev tools actually work.

Latest tutorials

• How to Write Acceptance Criteria for LLM Output (Not Just 'Be Accurate')

  Stop writing vague prompts. Learn how to define testable acceptance criteria for LLM output — covering format, edge cases, missing data, and ambiguity.

  prompt engineeringLLM outputacceptance criteriaClaude

• Agent Escalation: When to Hand Off to a Human vs. Keep Handling

  Learn the four exact signals that should trigger agent-to-human escalation, why sentiment is never one of them, and how to structure a clean handoff summary card.

  agentsescalationhuman-in-the-loopClaude

• Building a Customer Support Agent with Claude: Tools, Policy Hooks, and Escalation

  Learn how to build a production Claude agent with scoped tools, code-enforced policy caps, structured errors, clean escalation, and a logging plane.

  Claudeagentstool useescalation

• CCA-F Exam Study Guide: Claude Certified Architect Foundations

  Map every CCA-F exam domain to the Claude stack layers, understand score weights, and build a focused study path using official episode blocks.

  CCA-FClaude APIMCPClaude Code

• Anthropic Agent SDK: Use Claude Code's Engine in Your App

  Learn how the Anthropic Agent SDK exposes Claude Code's agent loop, built-in tools, and MCP support as a library you call from Python or TypeScript.

  ClaudeAgent SDKClaude Codeagents

• Claude Code Hooks Explained: Deterministic Guards for the Agent Loop

  Learn how Claude Code hooks enforce hard rules at every lifecycle event — pre-tool, post-tool, session start/stop — that the model cannot override.

  Claude Codehooksagent looptool use

• Claude Code Extensions: Skills, Subagents, Hooks, and Plugins

  Learn the four extension points in Claude Code — skills, subagents, hooks, and plugins — and when to reach for each one.

  Claude CodeSkillsSubagentsHooks

• How CLAUDE.md File Hierarchy Works: User, Project, Subtree, Local

  Learn how Claude's four-tier CLAUDE.md hierarchy (user, project, subtree, local) controls which rules apply, and how precedence is resolved.

  ClaudeCLAUDE.mdconfigurationprecedence

• How to Run Claude Code Headlessly in CI/CD Pipelines

  Learn how to use claude -p to run Claude Code headlessly in CI/CD: reviewing PRs, generating changelogs, and scripting AI tasks without an interactive shell.

  Claude CodeCI/CDheadlessautomation

• Claude's 5-Layer Stack: MCP, Hooks, Skills, and Subagents Explained

  A structured map of every Claude feature across 5 layers and 2 cross-cutting planes, so you can place any new Anthropic release instantly.

  ClaudeMCPagent loophooks

• Confidence Fields and Human-in-the-Loop Routing for LLM Extraction Pipelines

  Learn how to add a confidence field to LLM tool schemas and route low-confidence extractions to a human review queue automatically.

  structured extractionconfidence scoringhuman-in-the-looptool calling

• Context Engineering: Pin, Summarize, Prune, and Compact

  Learn four techniques to keep long Claude sessions coherent and affordable: pin stable facts, summarize resolved turns, prune tool output, and compact under pressure.

  context windowtokensClaudecontext engineering

• How to Guarantee JSON Output from Claude with Structured Outputs

  Learn how to use tool choice, Pydantic schemas, and Claude's native structured output to get reliable JSON from LLMs every time — no parser explosions.

  structured outputClaudePydantictool use

• How Claude Code Edits Your Repo: Inside the Agentic Edit Loop

  Claude Code doesn't regenerate whole files — it reads, locates, and makes surgical string edits in a verify loop. Here's exactly how an agentic code editor changes your repo, step by step.

  Claude CodeAI coding agentsClaudeagentic editing

• How Claude Code's Agent Loop Works (and Why It Breaks)

  Learn how Claude's agent loop works, what causes infinite loops and premature exits, and the four controls that keep agents on track.

  Claude Codeagent looptool usestop reason

• How Claude Tool Calling Actually Works: The Request-Execute Model

  Learn how Claude's tool calling works under the hood: the model requests, your code executes. Covers tool use blocks, the three executor lanes, and schema tips.

  Claudetool callingMCPAnthropic API

• How LLM Tokens Work — And Why They Explain Your AI Bill

  Claude never sees your words — it sees tokens. Here's what tokenization actually is, why it drives every dollar of your AI bill, and how to reason about token cost.

  tokenstokenizationLLMClaude

• How Claude's Context Window Works: Limits, Costs, and Overflow

  Learn what fills Claude's context window, which models support 1M tokens, when pricing changes, and what happens when you overflow it.

  Claudecontext windowtokenspricing

• How to Write LLM Evals: Testing AI Apps with Real Data

  Learn how to replace gut-feel LLM testing with a real eval harness: datasets, graders, CI score gates, and LLM-as-judge caveats.

  evalsLLM testingCIClaude

• How MCP Apps Work: Tools That Return Interactive UI

  MCP tools can return real interactive UI — not just text. Here's how MCP Apps render forms, dashboards, and widgets inside Claude and ChatGPT, with a minimal working example.

  MCPModel Context ProtocolClaudeMCP Apps

• Anthropic Managed Agents: Claude Runs the Loop for You

  Learn how Anthropic's managed agents API offloads the agent loop, tool sandbox, and persistent state to Anthropic's cloud, replacing hand-rolled loops.

  Claudeagentsmanaged agentssessions

• The 6 Multi-Agent Patterns That Actually Work in 2026

  Orchestrator-worker, routing, pipeline, parallel fan-out, reflection, and debate — the six multi-agent patterns worth using, when each one pays off, and when a single agent still wins.

  AI agentsmulti-agent systemsorchestrationLLM

• How to Structure a Production Claude Agent: All Layers Explained

  Learn the full layered architecture of a production Claude agent: gateway, model router, agent loop, scoped tools, policy hooks, prompt caching, and clean escalation.

  Claudeagent architectureMCPprompt caching

• How Prompt Caching Cuts Your AI Bill ~90% (and the Floor Trap)

  Prompt caching reuses a stable prompt prefix so you stop paying full price to resend the same context. Here's how it works, the real numbers, and the floor trap that quietly costs you money.

  prompt cachingClaude APIAI costAnthropic

• Prompt Injection Attacks Explained: How to Defend Your AI Agent

  Learn how prompt injection turns untrusted text into commands, why indirect injection is the dangerous case, and three layered defenses every agent needs.

  prompt injectionClaudeAI securityagents

• How to Build a Structured Data Extraction Pipeline with Claude

  Learn how to wire forced schemas, few-shot examples, validation retry, confidence routing, and prompt caching into one production extraction flow.

  structured outputtool useprompt cachingdata extraction

• Temperature, Top-P, and Top-K Explained: Controlling LLM Randomness

  Learn how temperature, top-p, and top-k sampling parameters shape an LLM's output distribution and when to tune each for your use case.

  temperaturesamplingtop-ptop-k

• How to Pin Model Output Format Using Few-Shot Examples

  Learn why few-shot examples in the messages array beat temperature tweaks for fixing format, locale, and edge-case failures in LLM output.

  few-shot promptingClaudeprompt engineeringoutput format

• How Claude Works: A 5-Layer Mental Model for Developers

  Learn the 5-layer Claude Stack — model, Messages API, MCP tools, agent loop, and surfaces — and how they compose into real AI-powered apps.

  ClaudeMCPAgentsClaude Code

• Building a Multi-Agent Research System with Isolated Contexts

  Learn how to build a multi-agent research pipeline with a coordinator, scoped sub-agents, and provenance-preserving synthesis — without context overflow.

  multi-agentClaudecontext managementresearch pipeline

• Claude Architecture Explained: 5 Layers and 2 Cross-Cutting Planes

  A complete recap of the Claude stack: model, protocol, reach, orchestration, and surfaces — plus the two planes that cut through every layer.

  Claudeagent architectureMCPorchestration

• How Claude Token Billing Works: Input, Output, and Cache Costs

  Learn how Claude breaks API costs into input, output, and cached tokens, why output tokens cost 5x more, and how to fix the three most common cost leaks in agents.

  tokenscost optimizationClaude APIcaching

• Understanding stop_reason in the Claude Messages API

  Learn how stop_reason controls agent loop branching in the Claude API — end_turn, tool_use, max_tokens, pause_turn, and more explained.

  ClaudeMessages APIstop_reasonagent loop

• What Is MCP (Model Context Protocol) and How It Works

  Learn how MCP reduces AI tool integrations from N×M to N+M by giving every client and tool a single shared protocol to speak.

  MCPClaudeAI toolstool use