Built a Pinterest affiliate automation system with n8n - AI images, auto-posting, dual cloud/local setup

Blaze — Fri, 14 Aug 2026 09:48:29 +0000

Been running an automated Pinterest affiliate system on n8n for a while, wanted to share the architecture since it's a genuinely fun workflow to build.

The pipeline: AI-generated pin images (Cloudflare Workers AI / FLUX.1) → rendered through a custom Puppeteer server → AI-written titles and descriptions (Groq/Llama 3.3) → posted to Pinterest via Make.com webhooks. Product data lives in Google Sheets.

Infrastructure: runs on both Railway (cloud, handles overnight posting) and a local Windows instance via NSSM, on non-overlapping schedules so they don't collide. Both point at the same Google Sheet, split across three tabs (product data, posting state, performance tracking).

Real numbers from the last 30 days: impressions up 110%, engagements up 250%, engaged audience up 100%. Growing steadily week over week.

A couple of specific bugs I had to solve along the way: Pinterest's API silently truncates descriptions over 800 characters (had to add a safe-truncate function), and an intermittent Pinterest error code that needed retry-on-fail logic to handle reliably.

Happy to go into more detail on any part of the setup if people are curious, particularly the dual cloud/local scheduling or the image generation pipeline.

How I Automated My AI Coding Assistant's Memory Using Git Hooks and n8n

Blaze — Wed, 12 Aug 2026 09:01:12 +0000

I kept running into the same problem: every time I opened a new chat in Cursor, I'd have to re-explain the same architecture decisions, conventions, and gotchas about my codebase. The AI could see my code, but it had no idea why things were built a certain way.

So I automated it.

The idea

Every time you commit code, a small script watches for it, sends the diff to an AI, and asks a simple question: does this commit reveal anything durable worth remembering? If yes, it gets added to a living context file. If it's just a typo fix or formatting change, nothing happens.

How it works

1. A git post-commit hook fires automatically after every commit. It grabs the diff and the current context file, and sends both to a webhook.

2. An n8n workflow receives it, and passes the diff to an AI model (I used Groq's Llama 3.3, mainly for cost and speed) with a system prompt like:

"Given the EXISTING context file and a NEW COMMIT, add any new durable, reusable knowledge this commit reveals. Do not duplicate existing info. If nothing durable, return the file unchanged."

3. The AI's response gets written back to a CONTEXT.md file in the repo, and also copied into Cursor's rules folder (.cursor/rules/) so it auto-loads in every chat without manually tagging it.

The result

Same question, asked before and after a commit that explained a design decision:

Before: generic textbook answer about the concept in general.

After: specific answer referencing the actual reasoning from the commit.

Things that were harder than expected

A few real gotchas from building this:

Windows services often run under a different environment than your normal user account, which broke basic things like PATH resolution for git
Cloud sync folders (OneDrive, etc.) can silently interfere with file writes
Passing large AI-generated content through command-line arguments hits length limits fast, better to write files directly from code
n8n's binary data storage mode isn't always a plain base64 string depending on configuration, worth checking before assuming a simple decode will work

Try it yourself

The whole setup is just a git hook + an n8n workflow, both plain files you can inspect and modify. I packaged a ready-to-import version with a setup guide if you'd rather skip the build:

Happy to answer questions about the node setup, the webhook design, or anything else in the comments.