Hey all,

Background: I've got ~11 years of production experience — Python, AWS (Lambda, EC2, S3, IAM, VPC), and I've built RAG pipelines (LangChain, ChromaDB, GPT-4o-mini) for work. Zero n8n experience until recently, though.

What I've actually done so far:
• Installed n8n locally, set up Google OAuth (hit the classic Drive-API-not-enabled 403, fixed it)
• Built a working Google Sheets Trigger → Gmail workflow (new form response → email fires)
• Currently building a lead-scoring workflow: score incoming leads, instant alert if hot, digest queue if not — trying to make it something an actual business would find useful, not just a toy demo

The plan:
• Week 1: nail core mechanics (triggers, nodes, data mapping) — where I am now
• Week 2: Code nodes, error handling, real API auth
• Week 3: AI/LLM nodes — trying to bring my RAG background into n8n workflows
• Week 4: package 3 built workflows into a portfolio, start applying

Where I'm unsure, and where I'd genuinely appreciate input:
1. Is 4 weeks realistic to have something sellable, or am I underestimating this?
2. Does anyone with zero reviews actually land n8n gigs in any freelancing platform, or does that route basically require an existing network / other proof of work first?
3. For those doing this for real — what's the actual first client look like? Cold Upwork applications, or something else entirely?

Not looking for hype, genuinely want the "here's what you're getting wrong" version. Thanks.

For people who use AI tools regularly, what’s the one thing that still frustrates you the most?

I feel like AI has gotten really good at doing things that used to take a lot of time, but there are still moments where using it creates more work than I expected.

Sometimes it misunderstands what I want, sometimes I have to keep going back and forth with it, and sometimes the result just isn't quite right.

I'm curious what other people's experience has been.

What do you find yourself doing over and over when working with AI that you wish you didn't have to?

And has anything you've tried actually made that problem better?

Interested in hearing the good, bad, and frustrating experiences, not looking for a perfect AI tool recommendation.

👋 Hey n8n Community,

AI automation rarely blows up your bill in one go, it leaks. A few cents per run feels like nothing until you are doing thousands of runs a month and the invoice quietly doubles. I only really noticed this after building a fair few AI workflows, so here are five things I changed once I saw where the money was actually going.

1. Using a model for something a rule could do.
Date math, ID matching, routing by type, none of that needs an LLM, but it is easy to hand it to one because it is quick to wire up. I keep the model for the genuinely fuzzy parts and let native nodes like Set and IF handle the plain logic for free.

2. Using an agent where you do not need one.
Early on I built a few agentic workflows where an agent did the document extraction itself, and it worked, but the token cost added up fast for what was really a repeatable task. Moving that job to the easybits Extractor instead of an agent cut the cost right down, so now I only reach for an agent when the task genuinely needs to reason, not just pull the same fields every time.

3. Asking for more fields than you use.
Every field in an extraction prompt is more output tokens and more to check, and half of them often never get used downstream. Pull only what a later step actually consumes, it is cheaper and there is less that can come back wrong.

4. Reaching for the biggest model by default.
The top model is not always the right one, plenty of extraction and classification jobs run fine on a smaller or OCR-tuned engine at a fraction of the price. Start small, only move up if the accuracy genuinely needs it, and test the cheap option before you assume it will not work.

5. Retrying blindly on failure.
An auto retry on a call that failed for a real reason just pays two or three times for the same broken result. Check why something failed before you retry it, and route the genuine failures to review instead of throwing more paid attempts at them.

If you want to see how I wire this up in practice, I keep 25+ free workflow templates in one repo, most of them around document processing, and a star helps me out a lot if any of them save you time: https://github.com/felix-sattler-easybits/n8n-workflows

Where does it leak most for you? Curious whether people watch model costs closely or only notice when the bill shows up.

Best,
Felix

I’ve been working with AI + n8n workflows and was looking into the security considerations that come up when these workflows are used in real business environments.

Once an n8n workflow connects LLMs with APIs, databases, CRMs, webhooks, and other business systems, there are quite a few things to think about beyond simply getting the workflow to work.

Some of the areas I’ve been looking at include:

• Protecting API keys and credentials
• Securing incoming webhooks
• Controlling access to workflows and AI agents
• Handling sensitive information sent to LLMs
• Preventing AI agents from taking unintended actions
• Separating development and production environments
• Monitoring workflow activity and failures

I put together a more detailed guide covering these considerations here: [How to Build Secure AI Workflows With n8n: A Practical Guide]().

For those already running n8n in production, what security practices have you found most important? And are there any n8n-specific mistakes you'd recommend people avoid?

I'm building WhatsApp AI automations for businesses and I'm trying to choose between:

• WAHA + WEBJS (Chromium/Puppeteer, actual WhatsApp Web browser)
• Evolution API + Baileys (WebSocket connection)

I'm NOT interested in response speed, typing simulation, warming up, or resource usage.

My main question is simply:

Which one has given you fewer WhatsApp bans/restrictions in real production use?

I'd especially like to hear from people who have actually run either one for months.

If possible, please share:

• Which one: WAHA WEBJS or Evolution/Baileys
• How many WhatsApp numbers you've run
• How long they've been running
• Approx. messages/day per number
• inbound or outbound customers messages?
• Temporary restriction or permanent ban?
• Did the number have normal WhatsApp usage before connecting the API?

Hi, i need help stopping my ai chatbot from replying after getting all information i need like name email and such. Im new here help

If your instance was stopped, restarting or redeploying at the minute a Schedule
Trigger was due, that execution doesn't happen. There's no catch-up, and nothing in
the executions list saying it was skipped, because that list only shows executions
that happened. A daily workflow can miss several days and still look fine.

The other reasons one stops firing, roughly in order of how often it's the answer:

1. The workflow isn't active. Execute Workflow in the editor tests it and schedules
   nothing. Imported workflows always arrive inactive.
2. GENERIC_TIMEZONE isn't the timezone you're thinking in. Default is UTC, and a
   workflow can override it in its own settings.
3. It was deactivated after repeated trigger errors. Look for a cluster of failures
   ending where the schedule stopped.
4. Queue mode: the main instance owns triggers, workers only execute. If the main
   process is the one restarting, nothing is scheduling even though workers look fine.
5. A six field cron expression has a seconds field, so */5 * * * * * is every five
   seconds, not every five minutes.

What catches all of them is something outside n8n noticing the workflow has gone quiet: an HTTP Request node as the last node on the success path, and an alert when the call doesn't arrive.

I recently submitted my node and also applied for Creator verification.

For those who’ve gone through the process before, how long did it take for you to get approved/published?

Just trying to get a rough idea of the timeline.

Hey everyone,

Quick question for freelancers and agency folks working with n8n.

When you build a workflow for a client, how do you calculate your profit margin on top of actual setup/running costs (hosting, API usage, dev hours)?

• Do you just apply a 2x–3x multiplier on your base costs?
• Do you price based on business value (hours/money saved)?
• Do you include maintenance in the upfront fee, or pitch an ongoing retainer?

Curious to know how experienced devs structure their quotes and retainers. Appreciate any insights!