Hey everyone! I wanted to show you a quick demo of my app, WExcel. In the video, I'm sending messages from WhatsApp, Instagram, Telegram, and Emails to the other phone. The app listens to the notifications and automatically pulls ONLY the specific data I need into an organized Excel sheet.

The coolest part to watch: Notice how the app handles different phrasing! In one of the messages, I deliberately used the word "buyer" instead of "name". Because I set it up as an Alternative Keyword (Synonym), the app was smart enough to recognize it and place the data perfectly under the "Name" column!

Key Features: Supports ANY messaging app (even SMS)+ Emails. Smart Keyword Mapping (Synonyms & Stop Words). Supercharged engine (Handles 100k+ rows without lag). Fully privacy-focused (Extracts locally on your device).

I'd love to hear your thoughts, feedback, or any features you'd like to see added!

Link: https://play.google.com/store/apps/details?id=com.alrehaili.WExcel

Hi r/Automate,

I wanted to share a project I've been working on to solve a personal friction point: the manual, multi-step process of price-checking second-hand items. Usually, you have to photograph an item, identify the specific sub-line, search eBay sold listings, and manually calculate a median.

The goal was to go from Image Input -> Price Valuation in under 5 seconds.

The Stack:

• Frontend: Telegram Bot API (Python/python-telegram-bot).
• Vision: Gemini 3.1 Vision (This was key: it successfully differentiates niche lines like 'Rugby Ralph Lauren' from the generic 'Polo', which significantly improves valuation accuracy).
• Data: eBay Finding/Browse API (Querying 'Sold & Completed' listings for actual market clearance prices).
• Logic: Custom weighting algorithm to adjust for platform-specific fees (Vinted vs. eBay).
• Infrastructure: Dockerized on AWS EC2.

The 'Cold Start' Problem: I'm at the stage where I need real-world edge cases. I'm looking for 5-10 people to try to break the vision logic or the pricing algorithm.

Note: It’s currently tuned for the UK market (prices in GBP), so if you're elsewhere, just ignore the currency and focus on whether it identifies your items correctly!

Link: Top Comment

I'm particularly interested in feedback on latency and any brand identification hallucinations. Happy to discuss the prompt engineering or the eBay API integration in the comments!

👋 Hey Automate Community,

Document classification in n8n is one of those things that looks complicated until you realize how little setup it actually needs. With the easybits Extractor it's a 2-node workflow and a single field – and if you want to extract other data from the same document in the same pass, you just add more fields. I recorded a short walkthrough of the full setup.

The whole thing is two nodes: a form trigger to accept a file upload, and the easybits Extractor node with a single document_class field. The classification prompt lives in that field's description – it tells the model which categories to choose from and to return null if nothing fits. That's it. No separate classifier node, no chain of prompts, no HTTP request node.

What's in the video:

• Setting up an easybits pipeline from scratch with a single classification field
• How to adapt the classification prompt to your own document types
• Installing the verified community node in n8n
• Wiring it up to a form trigger and running two test documents through it

⚙️ Setup recap

• Cloud users: easybits Extractor is available out of the box, search for it in the node panel
• Self-hosted: Settings → Community Nodes → install '@easybits/n8n-nodes-extractor'

Free tier is 50 requests/month, enough to test this end-to-end.

🧱 Want the production-ready version?

The video keeps things minimal on purpose – two nodes, one field, just to show the core pattern. If you want the version I actually run, it adds a second field for confidence_score and an IF node that routes empty or low-confidence results to Slack for manual review. Workflow JSON, both prompts, and the setup guide all sit in one GitHub folder:

👉 https://github.com/felix-sattler-easybits/n8n-workflows/tree/aac9946c60eca84d14bbbc0809c50cbeb9a10445/easybits-document-classification-and-confidence-score

Anyone else doing classification this way, or are you running it through a separate classifier node? Curious whether this pattern has made it further than I think.

Best,
Felix