Pillar
Step-by-step builds of useful tools using AI and automation.
This tutorial shows how to connect Notion MCP to Claude Code or Cursor in 2026, using Notion’s hosted remote MCP server and OAuth-based authorisation. It walks through setup, scope checks, and safe read/write testing so you can use a real Notion knowledge base without overexposing content or jumping straight to risky edits.
A practical Perplexity-to-brief workflow separates discovery, verification, and synthesis. Use Perplexity for source-first research, NotebookLM to check load-bearing claims, and Claude to turn the verified Research Pack into a client-ready memo, outline, or slides. The key is not a better prompt, but a repeatable SOP with scope, sub-questions, source rules, and clear labels for facts, claims, and inference.
A small VPS can run n8n well enough for a solo operator if you treat it like a real server, not a throwaway experiment. The reliable pattern in 2026 is Docker + PostgreSQL + HTTPS behind a reverse proxy, then one focused workflow first: Gmail triage, Notion task capture, or Slack alerts.
In this calm, practical ai lead scoring workflow tutorial, you’ll build a composite Fit/Intent scoring model in your CRM, then use Make and Claude (or Perplexity) to enrich scores and route hot leads automatically. The workflow runs end-to-end in under a day: trigger on new leads, call AI for structured scoring, update properties, and alert sales when thresholds are crossed, giving you measurable pipeline lift this quarter.
In about 90 minutes you can turn Notion into a grounded research engine: one structured Research Notes database, a memo template with embedded AI blocks, and Q&A over your own workspace. Use external tools like Perplexity or GPT‑4 for web research, then let Notion AI organise, summarise, and draft memos from tagged notes. Human review and tight prompts keep the workflow reliable for teams and solo operators alike.
This tutorial shows consultants how to build a Claude literature review workflow they can comfortably defend in a partner or client meeting. You’ll separate source gathering from AI synthesis, use Google Scholar, Perplexity, and Claude Projects in a clear pipeline, lock evidence tables, apply grounded prompts to control hallucinations, and add a claim-level verification loop and AI-disclosure note that align with 2025–2026 research best practices.
This tutorial walks consultants and operators through a practical AI research to slides workflow using Perplexity for sources, Claude or GPT‑4.1 for narrative, and Canva/Pitch for design. You’ll set up a structured research and slide-ready brief, chain the tools cleanly, control slide density at the outline stage, and add guardrails for citations, brand alignment, and manual polish so decks stay client-safe and on-story.
In one afternoon, you can wire Stripe, Notion, and Claude via Make or n8n to auto-generate monthly analytics reports. Stripe streams subscription and churn data into Notion, Notion stores client and cohort tables, and Claude turns those rows into client-ready summaries. The first setup takes about 45 minutes; after that, your stripe notion ai analytics workflow runs in minutes and quietly replaces manual Monday reporting.
This track AI workflow costs tutorial walks you through building a pragmatic “AI ops console” in Airtable or Google Sheets. You’ll design a schema for runs, costs, and failures, ingest execution logs from n8n, Make, and Zapier, join them to pricing tables, and add alerts for anomalies and budget breaches. The result: a single place to keep AI workflows compliant, efficient, and under budget without another subscription.
You can build a focused internal tool in a weekend in 2026 if you pair Cursor (editor-first) with Claude Code (agent-first), keep scope to one core workflow, and use a simple repo layout. This tutorial walks through a lead-enrichment dashboard example, shows prompt patterns that work, and outlines guardrails—tests, linting, and security habits—so AI speed translates into a stable, deployable internal tool rather than fragile prototype code.
Self hosting n8n on a $5 VPS is viable for solo builders if you accept tighter RAM, some Linux/Docker work, and ongoing maintenance. This guide walks through choosing Hetzner/DigitalOcean, setting up Docker Compose, putting Caddy in front for automatic HTTPS, adding basic firewall and backups, and then doing honest cost math versus n8n Cloud so you know when to upgrade or switch.
This tutorial shows you how to build a practical inbox AI agent in n8n: a scheduled workflow that fetches Gmail messages, sends them to an AI Agent node (Gemini or OpenAI), and emails you a structured daily digest with summaries and priorities. We stay beginner-friendly: strict prompts, JSON output, small test batches, and light cost control so your first agent is useful without being fragile.
You can build a functioning voice AI booking assistant in one afternoon by pairing a voice platform like Retell or Vapi with your own LLM backend. Keep scope tight—new bookings and reschedules—wire a single endpoint for booking logic, configure the agent’s prompt and tools, attach a real phone number, and test with live calls. Treat it as a workflow appliance, not a human replacement.
You can build a working perplexity api research agent in one evening by scoping the problem, using Perplexity’s Agent API presets, and wiring a simple plan→search→read→extract→verify→cite loop in a single script. This tutorial walks through setup, the Agent API “define the run” flow, a minimal Python implementation, and how to extend it with background runs and internal knowledge bases.
This tutorial shows how to build a practical perplexity claude research pipeline: Perplexity Sonar Deep Research gathers sources, Claude 3 or Claude Code synthesises them into a structured report, and a simple script converts Claude’s Markdown output into a PDF. The focus is on a predictable, evidence-backed workflow solo operators can run for client research or internal briefings.
This tutorial shows how to automate invoicing with Stripe using a ~50‑line Python script, wired to a simple CSV or Airtable export. You’ll see how to validate customer data, create invoice items, generate and finalize invoices, and optionally send them automatically. We’ll keep Stripe as the billing engine, your spreadsheet as the source of record, and avoid brittle one‑off hacks that break as you grow.
This tutorial shows how to build an AI agent from scratch with TypeScript and the Vercel AI SDK, focusing on a single Next.js API route, AI Gateway, a tool-calling loop with `stopWhen`, structured JSON output, and honest error handling. You’ll get a runnable repo pattern that’s production-friendly for single-request workflows, without jumping straight into heavyweight agent frameworks.
This tutorial walks you through building an AI inbox second brain: a single capture-first inbox powered by Readwise, Notion, Claude, and Make. You’ll set up one-shortcut capture, auto-tagging at ingest, and a weekly digest that surfaces themes and actions. The goal isn’t more notes, but a quiet system that compounds, turning what you read and think into reusable knowledge over time.