I used n8n, Telegram, and Google Sheets to automate publishing products to WooCommerce. No more manual work! It reads the product queue from Sheets and publishes it to WooCommerce via POST request.
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Been meaning to solve my own content problem, so I built this in n8n over the weekend. Sharing the setup in case it's useful to anyone.
What it does: every Monday it pulls the most-viewed YouTube videos in my niche, has an AI model break down why each one worked (hook type, emotional trigger, format), then generates 7 original content ideas β each with a hook line, format, structure, and caption starter β and drops them as clean rows into a Google Sheet. Also pings me on Telegram when it's done.
The pipeline:
Schedule Trigger (weekly)
YouTube Data API β most-viewed videos in-niche (free, official API)
Code node β rank + prep the top 10
OpenAI (gpt-4o-mini) β analyze the viral patterns
OpenAI β generate 7 structured ideas as JSON
Code node β split JSON into one row per idea
Google Sheets (append) + Telegram (notify)
Stuff I learned the hard way:
Started with Reddit as the source but n8n Cloud's shared IPs get 403'd on anonymous requests β switched to the YouTube Data API and it's honestly better data (real view counts = a cleaner viral signal).
Ask the AI for JSON, not a markdown table β a table dumps into one ugly cell; JSON split into rows gives you clean columns.
gpt-4o-mini is more than enough for this and keeps the cost basically nothing.
Runs completely hands-off now. Happy to share more detail on any node if anyone wants to build their own.
There are a few tools out there that do something like this, but they're closed source, and a couple even send your workflow to a server or an LLM to do the "sanitizing." That kind of defeats the whole point: if you have to hand your private data to someone else in the first step, it was never private. For a tool whose entire job is protecting sensitive data, "trust us, we won't look" isn't good enough.
So I made n8nRedact. Paste or drop a workflow, get a sanitized copy back.
First, the problem. n8n doesn't strip anything on export. Even though your actual credentials aren't in the JSON, a surprising amount still leaks: credential names, cURL auth headers in HTTP Request nodes, Airtable base/table IDs, Postgres table names, Notion page IDs, Telegram chat IDs, hardcoded URLs and emails, your instance ID, and webhook tokens buried inside URLs.
What it does:
- Detects and replaces the sensitive stuff: credentials/keys, emails, URLs, UUIDs and hex IDs, Airtable/DB resource IDs, Telegram chat IDs (string and numeric), phone numbers, instance IDs, and more.
- Format-preserving. john@acme.com becomes user1@example.com, not [REDACTED]. The output stays a valid, importable workflow.
- Deterministic. The same value always maps to the same fake, so cross-node references still line up.
- Side-by-side diff so you see exactly what changed.
- Toggle rules on/off, with live counts of what was found.
- n8n expressions (={{ $json.foo }}) are preserved, not mangled.
Why you should be able to trust it:
- 100% client-side. Nothing is uploaded, and it can't be. There's a Content-Security-Policy with connect-src 'none' that physically blocks the browser from making any network request. Even a compromised dependency couldn't exfiltrate your paste. There's an automated test that fires a fetch and asserts the browser blocks it.
- Open source, so you can verify all of the above instead of taking my word for it.
- Works offline after first load.
Free, no accounts, no analytics, no cookies.
Repo: https://github.com/Shuffzord/n8nRedact
Live: https://shuffzord.github.io/n8nRedact/
Would love feedback, especially edge cases where it misses something or over-scrubs. If you've got a weird node type that leaks in a way I haven't handled, drop an example (anonymized π) and I'll add a rule.
Wanted to share a workflow I built for cleaning up messy PDFs (scanned notes, rough drafts, unformatted text dumps) sent via Telegram.
How it works:
Send a PDF to my Telegram bot
n8n extracts the raw text from the PDF
A "Cleaner Agent" (GPT-4o-mini) strips out OCR noise, fixes broken sentences, normalizes spacing
A "Structurer Agent" re-organizes the cleaned text into logical sections using a content schema parser
A "Summary Agent" generates a short summary alongside the full cleaned doc
Everything gets built into a properly formatted Google Doc (headings, bullet points, etc. β not just a wall of text)
Exported as PDF, then both the cleaned PDF and a summary message are sent back to me on Telegram
Stack: n8n + OpenAI (gpt-4o-mini) + Google Docs API + Telegram Bot API
Biggest technical hurdle: pdf-lib doesn't work on n8n Cloud, so I had to route through Google Docs batchUpdate β Drive export β PDF as a workaround instead of manipulating PDFs directly.
Happy to answer questions about the node setup or the agent prompts if anyone's building something similar. Also open to feedback if anyone sees ways to simplify the chain.
Hey everyone,
I wanted to share a look at an autonomous content engine Iβve been fine-tuning recently. The goal was to build a system that handles everything from ideation and video rendering to final asset management and social media publishing without any manual intervention.
Iβve attached the full canvas architecture in image_48bbaa.png. Here is how the technical pipeline handles the heavy lifting:
βοΈ How It Works:
Structured Ideation: A Schedule Trigger fires up a Google Gemini Chat Model node. Iβm utilizing a Structured Output Parser here to ensure the AI output strictly adheres to a predictable JSON schema (captions, hashtags, visual prompt data) so it never breaks the down-funnel nodes.
Async Video Generation (Google Veo 3): The visual prompts are sent via HTTP requests directly to the Google Veo API. Because video generation takes time, the workflow passes through a conditional check (If node) and a Wait loop to poll the endpoint until the asset rendering is complete.
Data Sanitization & Storage: A custom JavaScript node cleans up the API response. The video is downloaded, pushed to Google Drive, and permission-shared automatically to create a clean, accessible URL for the social platforms.
Meta API Publishing: The final asset URL and Gemini-generated caption are sent to the Instagram Graph API (INSTA node). It pauses momentarily via a Wait step to let the platform finish processing the media container before triggering the final container publish step. Everything is logged in a Google Sheet at both the start and finish for auditing.
π οΈ Key Takeaways from Building This:
Handling Async APIs: When dealing with heavy GenAI video models like Veo, robust webhook polling or carefully configured wait-loops are essential to prevent workflow timeouts.
Strict Schemas are Life: If you don't parse your LLM outputs structurally, minor formatting variations in captions or hashtags will crash your downstream HTTP requests.
Happy to answer any questions about the node configurations, the Meta API payload structure, or working with Veo endpoints! Let me know what you think.
Creating professional 360Β° apparel videos usually requires expensive 3D software, manual modeling, product photography, or complex CGI pipelines.
I wanted to see how far I could push AI.
The goal was simple:
>Upload the front and back images of a garment, choose the body type, and automatically generate a realistic 360Β° rotating ghost mannequin product video that is ready for e-commerce.
No manual prompt writing.
No video editing.
No 3D modeling.
Everything happens automatically.
Creating high-quality apparel rotation videos is surprisingly difficult.
A typical workflow often involves:
Even with AI, most models struggle to:
For e-commerce, even small changes in print placement or garment shape can make the result unusable.
I built an automated AI workflow using n8n that generates a photorealistic 360Β° ghost mannequin apparel video from only two reference images.
The user simply uploads:
Everything else is handled automatically.
The workflow consists of two AI pipelines running in parallel.
The uploaded garment images are first analyzed using AI Vision.
The workflow automatically:
This structured analysis becomes the foundation for the next stage.
Instead of manually writing prompts, the workflow generates a structured master specification describing every aspect of the garment and scene.
The specification includes:
A second AI model then compiles this structured specification into a production-ready prompt optimized specifically for AI video generation.
This approach produces much more consistent results than writing prompts manually.
Once the prompt is generated, the workflow automatically prepares the API request and submits it for video generation.
The AI reconstructs one physically consistent 3D garment by using:
It then generates realistic intermediate views including:
Finally, it creates a smooth 360Β° rotating ghost mannequin video while preserving the garment's structure and artwork.
The workflow also handles the entire generation lifecycle automatically.
It:
The user never needs to monitor the generation manually.
Telegram Form
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Upload Front & Back Images
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Extract Image Metadata
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AI Garment Analysis
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β β
βΌ βΌ
Image Generation Master Specification
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AI Prompt Compiler
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Seedance 2 mini Video Generation
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Status Monitoring
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Telegram Video Delivery
I wanted a workflow that could produce consistent apparel product videos without relying on handcrafted prompts for every garment.
By separating the process into structured analysis, specification generation, prompt compilation, and video rendering, the workflow becomes reusable for different garments while maintaining consistent output quality.
It also makes experimenting with different AI video models much easier, since only the final prompt compiler needs to change.
The workflow can now automatically generate:
The entire process runs end-to-end with minimal user input just two reference images and a body type selection.
Hello everyone!Β
I'd like to share another n8n project I built: a personal AI assistant for Telegram.
/new starts a new conversation by clearing the current chat history while keeping your saved preferences./clear permanently deletes all stored preferences without affecting the current conversation.The idea was to create an AI assistant that combines long-term memory with everyday productivity tools. By remembering user preferences and integrating with Google services, conversations become more personalized and practical over time.
I'd really appreciate any feedback or suggestions for improvements. This project will be part of my portfolio.
If you're looking to automate your own workflows or business processes, feel free to reach out:
https://zhrssh.github.io/contact/
I've been trying to practice automation with n8n for a well. Regardless to say, its super time consuming. So I started using Claude and Open code with Mimo v2.5 & DeepSeek v4 flash ( Opencode zen free tier )
Eventually stopping claude as I can't afford pro plan and lets not even talk about how much tokens the free plan provides lol. But even with opencode's MCP connection, the models struggle a lot. Ik ds v4 flash's hallucination history but even Mimo v2.5 is performing no better.
I'm thinking about going for paid api but I wanna know if anyone tried this Opencode's MCP configuration with n8n agentic type shit. Is it really a LLM capability issue or its just an Opencode issue. And is there any better way to scale up this with other tool or free LLM ?
I recently completed a website and lead-management automation for a real-estate investment business.
The business needed a simple way for homeowners to submit their property details while making sure every inquiry reached the team quickly.
What I built
Workflow
Website form β Zapier webhook β Format data β Create REsimpli lead β SMS team member 1 β SMS team member 2
The main objective was to remove manual CRM entry and make new inquiries visible to the team immediately.
One improvement Iβm considering is adding stronger duplicate detection, automatic retries and failure alerts when REsimpli or the SMS step is unavailable.
Iβd appreciate feedback on:
This was built for a real client, and Iβve removed or hidden any sensitive information.
If you're new to n8n and mixing these up, here's the short version:
$json is the current item's data in the current context. Use it inside a Code node set to "Run Once for Each Item", or inline in expressions like {{ $json.email }}.
$items() is the old-school way to grab an array of items from a specific node. It still works in a lot of workflows you'll find online, but it's legacy syntax. The modern equivalent is $input.all() inside a Code node, or $('Node Name').all() in expressions β both are more explicit about which node's data you're pulling.
Rule of thumb I use: if I'm working with ONE item, $json. If I need ALL items to loop, aggregate, or compare, $input.all(). I basically never reach for $items() anymore, but it's worth recognizing when you see it in older templates.
Curious if anyone still has a real use case for $items() over $input.all() β haven't run into one myself.
I used n8n to automate order refunds by connecting a webhook to Google Sheets, Stripe API, and Telegram. No more manual processing!
More automations: Join our community: https://go.phonezait.de/
A few weeks ago, I shared a workflow that handles bookings across two courts/calendars with conflict checking. This is the next chapter.
Instead of users filling a form, I wanted an AI agent to collect booking details conversationally through chat β then pass the data into the same workflow.
Sounds simple. It wasn't.
I built a fully automated booking system for a padel club with an AI agent front-end β no code, just n8n
Padel is blowing up where I'm from and most clubs are still taking bookings over WhatsApp manually. I wanted to fix that for a 2-court setup.
Here's what I ended up with.
**What it actually does:**
\-A customer chats with an AI agent β talks naturally, no forms
\-Agent collects the details conversationally
\-Passes structured data into a booking workflow
\-Workflow checks availability, assigns the first available court, and confirms
\-Everything syncs to Google Sheets automatically
\-Works for new bookings, updates, and cancellations β including edge cases like overlapping time slots on updates
A customer types "book tomorrow at 8 for an hour" and gets a confirmed booking with an ID. That's the whole experience.
**Why I split the AI and the workflow into two separate pieces:**
Early version had the AI doing everything β checking availability, assigning courts, generating IDs. It hallucinated constantly.
The fix was simple: AI only talks to the user. The workflow handles all the logic. The agent calls the workflow like a tool, gets a result back, and translates it into a friendly reply.
Much more reliable. Much easier to debug when something goes wrong.
**The core insight:**
Keep AI in the conversation layer. Keep deterministic logic in the workflow layer. When I mixed them, things broke in ways that were hard to debug. When I separated them, both parts became much easier to reason about and fix.
**What this could work for beyond padel:**
Any multi-resource booking scenario β tennis courts, meeting rooms, recording studios, parking bays. The core pattern (AI conversation layer β deterministic booking backend β calendar + sheets sync) is reusable.
Full workflow is on GitHub if anyone wants to dig into it or adapt it for their own use case. Happy to answer questions or help anyone trying to build something similar.
https://github.com/rajakumarbakhtawar-dotcom/padel-test-2-courts/issues/1#issue-4736136194
**Curious what others are doing:**
If you're using AI agents with n8n tools β how much logic are you putting in the prompt vs the workflow?
And how are you handling ambiguous user input before it reaches your tools?
I'm interested in learning everything related to AI Automation, Chatbots, AI Agents, RAG systems, and workflow automation with tools like n8n. My goal is to become a freelancer and build solutions for clients.
If you were starting from scratch today, what learning roadmap would you follow to become freelance-ready as quickly as possible?
I'm looking for the best resources, courses, YouTube channels, documentation, communities, and hands-on projects to learn:
n8n and workflow automation
AI Chatbots (Telegram, WhatsApp, web chatbots, etc.)
AI Agents and multi-agent systems
RAG (Chat with PDFs, documents, knowledge bases)
LangChain and related AI frameworks
APIs, databases, and integrations
Docker, Linux, and cloud platforms (AWS, Azure, etc.)
I'd also love to know:
Which skills are most in demand by freelance clients right now?
What portfolio projects should I build to attract clients?
How long does it realistically take to become good enough to get paid freelance work?
Are there any mistakes beginners should avoid?
Any roadmap, project ideas, resources, or real-world advice from freelancers working in this space would be greatly appreciated.
I built an AI-powered Lead Response & Follow-up System using n8n. I'd love your feedback.
I'm a software engineering student learning AI automation by building real-world projects.
My latest project is an automated lead management workflow designed for businesses that rely on contact forms.
Here's what it does:
β’ A customer submits a contact form.
β’ AI generates and sends a personalized reply within 60 seconds.
β’ The lead is automatically saved to Google Sheets.
β’ A Slack notification is sent instantly.
β’ If the lead doesn't reply, the system sends follow-up emails after 24 hours, 3 days, and 7 days.
β’ If the lead replies or books a meeting through Cal.com, all follow-ups stop automatically and the lead status changes to "Booked."
I recorded a short demo showing the complete workflow:
https://drive.google.com/file/d/1Bv-6FdnvEbeDD_7Kxx5aZRviOJhpBn4B/view?usp=sharing
I'm looking for honest feedback from people who build automations or run businesses.
Some questions I'd love your thoughts on:
β’ Would this solve a real problem in your business?
β’ What's missing from this workflow?
β’ How would you improve it?
β’ If you run a business, how do you currently handle new leads?
If this solves a problem your business is facing, I'd be happy to discuss how a similar workflow could be adapted to your process.
If you think this wouldn't completely fit your business, I'd genuinely like to know why. Every business has a different sales process, and understanding those differences helps me build better automation systems.
Whether you're a business owner, developer, or automation builder, I'd really appreciate your feedback. Every comment helps me improve these workflows and build solutions that solve real-world problems.
Thanks for taking the time to watch the demo.
I am trying to automate a card reader type of thing, the flow goes like
Upload the photo to a Telegram bot-> Gemini AI translates it from Arabic to English -> Writes it to a google sheet
But, the main problem is the translations are not correct, anybody knows how to tackle it? I searched on the net, but of no use. I am using make[dot]com for now, does moving to n8n or other things help in translation?
My current workflow is below.
Over the last while Iβve been building automation systems for 12 small businesses and founders β detailing studios, agencies, e-commerce shops, local service businesses. Different industries, but the time-wasters are pretty much always the same four things.
If you run a business and feel like you are the bottleneck for everything, hereβs what is usually worth automating first:
The βdid anyone follow up with that guy?β problem kills more deals than bad sales ever did. Simple n8n workflow to automatically follow up and track every lead until they convert or go cold. No one has to remember anything.
Filled slot β Immediate confirmation β Reminder before the appointment No-shows go down and the owner doesnβt have to move a muscle.
Invoice reminders, status updates, copy-pasting the same replies, moving data between tools. Small on its own but a part-time job when combined. n8n runs and weaves the tools together.
Across all 12: The owner is almost always doing $10/hr tasks which prevents them from doing the $1000/hr work. Automation doesnβt kill jobs β it kills the stuff that never should have had a person doing it.
Most builds save something like 10-20 hrs a week, depending on how manual things were to start with.
Happy to break down how I'd approach any specific workflow β drop your most repetitive weekly task in the comments and I'll tell you how I'd automate it.
I created a workflow to catch and classify order errors using n8n, Google Sheets, and Telegram. Now I can focus on other things while it handles auth, timeout, and rate limit errors. CJ Dropshipping and WooCommerce are also integrated.
More automations: Join our community: https://go.phonezait.de/
I've been building an end-to-end social media automation workflow in n8n over the past few months, and it's finally at a stage where we're using it for real clients.
The exciting part? The first video we created using this system reached over 111K views, which gave us confidence that the workflow can produce content people actually engage with.
Here's what the workflow does:
The goal was simple: fill in a few fields like topic, audience, language, and goal, and let the workflow handle the rest.
I'm now considering breaking this into smaller modular workflows because maintaining one massive workflow is becoming challenging.
For those running larger n8n projects, do you prefer one big workflow or multiple smaller ones connected with webhooks?
I'd love to hear how others are scaling and maintaining production automations.
I created an n8n workflow to send daily security news to your Discord
Video Tutorial: https://youtu.be/ZSIyHFEgF2I
Workflow File: https://github.com/th3-ch0s3n-0n3/n8n-workflows/blob/main/Daily_Sec_News.json
Overview and Full Details: https://github.com/th3-ch0s3n-0n3/n8n-workflows/blob/main/Readme.md
This workflow runs on demand (via chat trigger) and performs the following:
The aggregated batch is sent to Gemini 2.5 Flash Lite with a role-prompted CTI analyst system prompt. The model is instructed to use only the provided news batch (no external knowledge) and filter strictly by the following criteria.
numberInputs accordingly).maxItems value in any Limit node (currently 5 per feed).guildId and channelId in the Discord nodes.I used n8n to automate order status updates by connecting a webhook to Google Sheets and routing emails with Gmail - saves me a ton of time
More automations: Join our community: https://go.phonezait.de/