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AI Automation library.

Practical AI automation guides covering workflow design, model integration, structured outputs, evaluation, guardrails, and human review.

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Quick answer

AI automation combines language models with deterministic workflow steps for tasks such as classification, drafting, extraction, routing, and reporting. Reliable systems define inputs and outputs, validate model responses, protect sensitive data, log failures, and retain human review where errors carry meaningful risk.

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The guides that connect everything else in this library. Read these first, then go deeper by theme below.

01 · 10 min read

n8n Workflows: 25 Ideas and Where to Get Templates

n8n workflows explained: 25 n8n workflow ideas with suggested stacks, where real templates live, how to import one and what to check before you publish.

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02 · 11 min read

Agentic AI Workflows: 5 Patterns and When to Use Each

Agentic AI workflows explained: what makes a workflow agentic, the five documented patterns, a worked support inbox example and when a fixed workflow wins.

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03 · 8 min read

LLM API Pricing Compared 2026

LLM API pricing comparison for 2026: OpenAI, Claude, Gemini, Mistral and open models per million tokens, with batch, caching and a worked monthly cost estimate.

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04 · 14 min read

Claude Code Complete Guide (2026 Edition)

Claude Code guide for 2026: install it, learn the agent loop, then set up CLAUDE.md, permission modes, subagents, hooks, skills and MCP, plus plans and costs.

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05 · 8 min read

What Is the Model Context Protocol (MCP)?

What is an MCP server? The Model Context Protocol in plain terms: hosts, clients, servers, tools and resources, MCP vs an API, and how to add a server safely.

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06 · 13 min read

Building AI Phone Agents in 2026

How to build an AI phone agent in 2026: the reference architecture, per-minute costs from published rates, a worked receptionist, and which platform fits.

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The full library

Every guide

n8n workflows and integrations

AI coding tools

Questions

Frequently asked

Should I use N8N or Zapier for AI workflows?

N8N for complex/AI-heavy workflows (better LLM nodes, lower per-execution cost, self-host option). Zapier for simple CRM-style automations where you need pre-built apps. Make.com sits in between with visual flow design and reasonable LLM support.

Which LLM is best for automation: Claude, GPT, or Gemini?

There is no universal winner. Compare current model documentation, privacy terms, tool support, latency, cost, context limits, and measured performance on your own evaluation set. Model capabilities and prices change frequently.

What can AI automation actually replace?

High-leverage, well-defined repeatable tasks: email triage, content drafting, lead qualification, customer support tier-1, data extraction, social media scheduling. It does NOT replace high-judgment work, novel problem solving, or relationship-driven sales.

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Tool comparisons

n8n vs Zapier

n8n vs Zapier: executions against tasks

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n8n vs Make

n8n vs Make: executions against credits

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Claude vs GPT

Claude vs GPT: Opus 5.5 against GPT-6 Astra

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Cursor vs Devin Desktop

Cursor vs Windsurf: Cursor against Devin Desktop

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Claude vs Gemini

Claude vs Gemini: Opus 5.5 against Gemini 3.8 Flash

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Figures from named publishers, each with its date and a link. IImagined did not collect them.

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