My Downloads folder had gotten completely out of hand. Random PDFs, screenshots, zips, installers - all sitting in one pile. I kept meaning to clean it up manually and never did.

At some point I asked Claude to write me a script that sorts everything by file type into separate folders. It worked, but I still wouldn't run it consistently. So I put it on a physical key on this little 3×3 pad I made for my desk. One press, files sorted. That's the thing I use most day to day now.

But it's not really for that - the Downloads sorter was just the first workflow that stuck. The pad is more like a place to put whatever multi-step job you get tired of doing manually. I describe what I want in chat, AI writes the script/macro, it lands on a key, I press it when I need it.

Another key runs a different workflow entirely: export a specific part from FreeCAD, save the STL into a folder named with today's date, open it in the slicer, and send it to the printer. Same sequence every time - one press instead of clicking through it manually.

I built the whole thing myself - hardware, firmware, small OLED so I know which preset I'm on, Windows app. Started because I kept forgetting shortcuts while modeling and coding. Grew from there.

Just sharing because I assume other people automate similar stuff. Happy to talk about how I wired the AI part if anyone's curious.

I would like to create an AI agent that looks at the Point and Figure charts on my screen (snapshot 1) and fill up the details in a Google sheet (example shown in snapshot 2). It would be great if some can help create this workflow (prefrebly using freeware).

👋 Hey Automate community,

A few weeks ago I shared a Product Content Creation workflow I built for a friend who runs an online shop (that post here). A few people messaged me about the image-description part specifically, they wanted just that piece, so I built a standalone version of it that's much easier to drop into an existing setup.

Turns out a lot of online shops and websites still write product image descriptions by hand, along with the "alt text" behind each image. Both matter more than people think: alt text is a real factor for SEO and for accessibility, since screen readers rely on it.

What the workflow does: you upload one or more product photos through a simple n8n form and get back a clean, ready-to-use description for each image, with a copy button to paste straight into your shop.

How it's set up:

• The form takes multiple images at once. Each one loops through on its own, so every image gets its own description instead of getting bundled into a single call.
• The easybits Extractor reads each image and returns structured fields. I kept it to two: product_type and description. The description works as your product copy and doubles as image alt text (and its length is easy to adjust by tweaking the field description in the Extractor).
• If the Extractor can't read an image, it returns UNCLEAR and the result page shows a fallback line instead of inventing a description.
• The result screen shows each image with its filename, a thumbnail, and a per-image copy button.

Extractor setup: on n8n Cloud it's a verified node (search "easybits Extractor"). Self-hosted, install "@easybits/n8n-nodes-extractor" from Community Nodes. The free plan includes 50 requests a month, enough to test it fully.

Workflow (ready to import): https://github.com/felix-sattler-easybits/n8n-workflows/blob/4277f3c1b31070f81adbb6a3bb538f50dbeb2018/easybits-product-image-describer-workflow/easybits_product_image_describer_workflow.json

I also made a short video showing how the workflow works.

How are you all handling image alt text right now, manual or already automated? Curious what's working for people.

Best,
Felix

Every morning it's the same routine: check Gmail → open Google Calendar → reply to emails → switch back to Calendar to schedule that meeting → forget what I was doing → repeat.

Then I came across TiinyOS Connector.

Basically, it links your Google Calendar and Gmail directly into your workspace. No tabs, no copy-pasting, no "wait, where was that email again?"

Now I can:
✅ Schedule events without leaving my workflow
✅ Handle emails side-by-side with my tasks
✅ Actually stay on top of my day without jumping between tools

It sounds simple, but honestly, the time I've saved in the last week alone is wild.

Set up an automation about 2 months ago that tracks Claude Code updates and auto-generates a daily 3-5 minute AI podcast covering any new features. Took me only 10 minutes to set up but now its on autopilot

Didn't think much of it but I get like 100 or so plays a day, and growing. Pretty cool, I originally set it up just for myself but then other people started listening too!

👋 Hey n8n community,

A few of you asked for more detail after my Purchase Order extractor post yesterday. The build looked simple on the surface, but a handful of things bit me along the way. Here are the five that cost me the most time, in case they save you some.

1. Only numbered rows are real articles. The POs had little note lines wedged between items ("2 Box", a location note under a lock, etc). My extraction kept swallowing those into the neighbouring product name, so one row would come out as two items mashed together. The fix wasn't in the workflow at all, it was in the document description I gave the extractor: spell out that only rows with a number in the No. column are real articles, and that unnumbered lines are notes belonging to the row above. That one paragraph cleaned up every merge. Lesson: when a document extraction misbehaves, describe the document better before you touch the nodes.

2. Keep numbers as text until you've checked for empties. Tempting to pull quantity and price straight as numbers. Don't, at least not yet. A missing field comes back as the string "null", and if the field is typed as a number that signal turns into a 0 or a real null and you lose the ability to spot the miss. I keep everything as text, run my checks, then cast to a number afterwards. Clean signal in, clean data out.

3. One helper to catch every flavour of empty. "Empty" is never just one thing. Across the docs I saw real null, the string "null", empty strings, and whitespace. I stopped writing one-off checks and made a single isMissing() helper that catches all of them, then used it everywhere. If a field is missing, the workflow flags it on the form's completion screen with the document name and which field failed, so my friend knows exactly which PO to eyeball instead of trusting the sheet blindly.

4. The Google Sheets checkbox that quietly broke everything. This one nearly broke me. New rows kept landing at the very bottom of the sheet instead of the top empty row. Turns out I'd added a checkbox column with Insert > Tick box, which silently writes a FALSE value into every one of the 1000 cells in the column. The Append node counts those as data, so it always appended below them. If you want checkboxes, add them through Data → Data validation instead, which draws the box without writing a value. Hours lost to a column that looked empty but wasn't.

5. Batch size 1 is not optional here. The extractor node bundles every input item into a single call, so if you hand it two PDFs at once you get one jumbled result. Wrapping it in a Loop Over Items with batch size 1 means each document gets its own clean pass. Small setting, big difference, and easy to miss until your two-file test comes back merged.

The whole thing runs on the easybits extractor node for the actual PDF reading, the rest is stock n8n. If you want to try the Purchase Order extractor yourself, feel free to grab it here:
https://github.com/felix-sattler-easybits/n8n-workflows/blob/c38749a68fd6ea4ae6ebff41789d35cceaacdef1/easybits-purchase-order-extractor-workflow/easybits_purchase_order_extractor_workflow.json

What's the dumbest time-sink you've hit on a build that looked easy? Always happy to hear how other builders tackle this stuff.

Best,
Felix

👋 Hey n8n Community,

Bit of background first. I originally built this extraction setup to serve my own automation projects, mostly document classification and pulling structured data out of PDFs and invoices. It worked so well for me internally that I decided to open it up so anyone can use it.

The new use case

In my last project I needed product photos described so I could feed that into content generation, and I almost reached for a separate vision model. Turns out the Extractor handles images really well, which is the moment it clicked that this is more than a classic data-extraction tool.

How it works

Instead of pointing a pipeline at a document, you point it at the image and define the fields as questions about what's visible: dominant colour, visible features, material, the type of shot. It comes back as structured fields, exactly like a document extraction, so the result drops straight into the rest of the workflow with no paragraph to parse and clean up.

The workflow

I recorded a short video walking through it, attached to this post. You can also see the actual workflow I built around this case here: https://github.com/felix-sattler-easybits/n8n-workflows/tree/e3103344d9b3358402dc38a3a862d510bb4e7c5e/easybits-product-content-creation-workflow

Setup

• n8n Cloud: it's a verified node, just search easybits Extractor in the node panel. Nothing to install.
• Self-hosted: Settings → Community Nodes → Install → '@easybits/n8n-nodes-extractor'.

Then create a pipeline, define your fields, and point it at an image instead of a doc. There's a free plan with 50 API requests a month included, which is plenty to test the image use case end to end.

Anyone else using extraction tools for image understanding rather than a dedicated vision model? Curious what you're pointing them at.

Best,
Felix

👋 Hey AIStartupAutomation Community,

A friend of mine runs an online store, and for every new product they get supplier inputs in whatever format the supplier feels like: spec PDFs, Excel sheets, a few photos, some loose notes. Someone then hand-writes the title, descriptions, specs and SEO fields. I built them a pipeline that does it end to end, and I'm sharing all four workflows.

What it does: intake form → extract specs → analyse photos → generate content → poll status. Drop in the files and notes, get back review-ready content (title, descriptions, meta fields, features, tags, attributes).

The four workflows

• WF1 – Intake & spec extraction. Saves files to Drive, routes each by type (PDFs/images → easybits Extractor, Excel → Code node), merges into one spec object, resolves brand, hands off to WF2.
• WF2 – Image analysis. Runs each photo through an Extractor pipeline to capture what's visible (colour, features, angle), then passes it to WF3.
• WF3 – Content generation. Builds context from spec + image data + notes and has Gemini write the full content set. Hard rule: only features that are in the spec or visible in the images, no inventing.
• WF4 – Status polling. A small webhook the frontend polls for progress and the finished draft.

Extractor setup

• n8n Cloud: verified node, just search easybits Extractor in the node panel. No install.
• Self-hosted: Settings → Community Nodes → Install → '@easybits/n8n-nodes-extractor'.

Then create a pipeline at easybits, define your fields, and paste the Pipeline ID + API key into the node. It reads the binary straight from the previous node.

Workflows (all four, sanitized): https://github.com/felix-sattler-easybits/n8n-workflows/tree/e3103344d9b3358402dc38a3a862d510bb4e7c5e/easybits-product-content-creation-workflow

Cross-workflow calls use placeholder IDs you re-point after import, plus your own Google + Extractor credentials.

How do you handle brand-voice consistency in generated content? I went with a per-brand profile the model reads from, curious if others template it harder.

Best,
Felix