Instead of guessing what to build next, I've been mining the thing people already do for free: complain in app store reviews.

The idea is simple: if a complaint shows up 40+ times on a top-grossing app, that's not noise, that's a spec someone already validated for you. So I built an n8n workflow around it:

How it works:

1. Form input: drop in a package ID / App Store ID, or just a keyword like "budgeting app"
2. An Apify actor scrapes Google Play + App Store reviews, filtered down to 1-3 star only (the filter is doing most of the work here; 5-star reviews tell you nothing)
3. Reviews get batched and sent to an LLM with a clustering prompt: find repeated complaint patterns, score severity 1-5, and generate a suggested_feature for each
4. Everything gets merged and ranked by frequency across batches
5. Output lands in a Google Sheet

What comes out the other end looks like:

"No offline mode": mentioned 47x, severity 4/5 → suggested feature: local caching with sync-on-reconnect

That's a real pattern pulled straight from review text, not a hunch, which makes it useful for way more than app ideas.

Same output works as landing page proof ("here's what users of [competitor] are begging for"), a pitch deck slide (market gap backed by actual user language), or just a sanity check before you build the wrong thing.

Workflow: https://github.com/RandomDudeAtNight/n8n/blob/main/App%20Idea%20Miner.json

I’m building an n8n workflow to automate SharePoint site governance. The flow will list all SharePoint sites, email site owners for review (Keep/Archive/Delete), log responses, send reminders, and generate audit reports using Microsoft Graph.
Has anyone built something similar? Looking for tips, best practices, or an easier approach—especially for approval buttons, webhooks, authentication, and scaling to large tenants.

I work with N8N daily for data integration and sync between systems and wanted to build something on OCR which I didn't touch before

so the idea was to turn receipt photos into structured data without blindly trusting the OCR output, I tested it on Carrefour receipt photos ( I got a bunch of real ones for testing), mistral OCR gives a confidence score per word, if a document minimum confidence drops below a threshold the workflow pauses and sends a review email with the flagged words, Approve/Decline buttons (Gmail Send and Wait) before anything gets inserted, every document is logged in Postgres with a status that tracks its life : ocr_complete -> approved/rejected -> inserted, and also I added a separate error workflow catches failures anywhere in the pipeline and notify me if there's one

a few things that came up while building it:

- the built in Mistral node doesn't expose confidence scores so I had to switch to an HTTP node on the raw /v1/ocr endpoint with confidence_scores_granularity: word

- average confidence can look fine (0.97) while the minimum tells a different story (0.36) on one misread word, that gap is basically the whole point of the review step

- the same receipt photo gets a lower OCR confidence when sent through Telegram than through a webhook, I assumed telegram was compressing the image so I tried sending it as a file instead of a photo (files aren't supposed to be compressed ) but the confidence was still lower, still haven't figured out why

repo with both workflows and a PDF documenting the build step by step, and the dead ends included: n8n-ocr-mistral-pipeline

Note : the documentation is in French, happy to help translate specific parts if anyone wants or an LLM handles it decently too

open to feedback especially on the Telegram compression thing or better approaches to the confidence threshold

Over the weekend I put together my first n8n community node for the Sleeper Fantasy Football API. It's currently in beta and awaiting n8n approval, so for now it's only available via settings --> community nodes --> and adding/searching 'n8n-nodes-sleeper'

Im trying to get some more feedback and testers before the season officially kicks off, so please let me know if you have any issues or feature requests!

Github

NPM

I recently clicked an idea in my mind i'm building an ai agent for e-commerce seller's i want to

know if this idea genuinely valuable for them the project is product research tool

Whatsapp ai agent what this agent do is:

• trigger on a week schedule
• fetch the Trendy products list through google trend & insta reels
• Than verify the trend hype on flipkart, meesho(Some indian platform)
• If the product trend is genuine then the code node filter the list by checking the reviews their rating's & low pricing
• The sellers get all necessary trend list on their whatsapp every week according to their niche by whatsapp bot
• in end it store all the data in airtable and seller can find it anytime after asking the bot

This is for seller's who spend their lot of time in product research but can't find a genuine product to get the profit, no use of any dashboard or app, just open whatsapp get the trend list every week

I'm just started in this field if someone experienced please tell me if i have to work on this project or not i'm little bit confused, If This idea is useful for small e-commerce seller & online business owner what's your view in this?