I've learned about automation, but I don't know how to integrate websites with the n8n workflow, and I can't find the resources.

I have recently started university, and I want to learn a skill that can help me earn money alongside my studies. I am considering learning n8n for AI automation, but with tools like Claude Code becoming increasingly powerful, I am wondering whether n8n is still worth learning.

Is there still a future in n8n, and can I realistically earn money by mastering it? Or are AI coding tools like Claude Code making n8n obsolete?

I would really appreciate some honest advice and guidance from people who have experience in AI automation and freelancing. Should I invest my time in learning n8n, or would it be better to focus on something else?

Looking for full SEO blog posting for Shopify. Basically keyword research, competitor research etc. etc.

Hey guys, Thinking of running a local LLM bot on our 20 year old plant nursery business WhatsApp number.

Reply-only, a few dozen messages a day, human-like delays, no broadcasts.

For anyone using Evolution API, WAHA, Baileys, or whatsapp-web.js:
• Banned or fine? What volume, engine, and account age?
• Any warning signs before a ban?
• Did browser-based setups hold up better than Baileys?

to be honest here i don’t want to give meta a cent of my money.

thanks a bunch

Hi everyone! I hope you're all doing well.

I'm relatively new to n8n and I'm looking for some advice on the business and deployment side of things. I'm building an AI chatbot for Instagram DMs, and I want to know how the actual handoff works when you sell it to a client (like a local e-commerce store).

How do you practically connect your n8n workflow to their specific page?

I have a few specific questions about the standard process:

• The Handoff: Do you just send the client the workflow JSON and make them figure out the hosting, or do you host it for them?
• Account Access: Do you ask for their Meta credentials to set up the Developer App and webhooks? Or do you just have them grant you admin access to their Facebook page?
• Meta App Review & Docs: How do you handle the Meta App Review and Business Verification? Do they need to provide official legal documents (like a registre de commerce/business registration) to get the messaging permissions approved, or do you verify a master app on your own developer account and run their page through that?
• I would love if there is a video explaining everything.

I'm sketching a handler for payment-provider webhooks in n8n (for a subscription flow) and want a sanity check before building the rest.

The two things I want to get right:

1. Verify the signature on the raw body, not the parsed JSON.

2. Don't process the same event twice, since providers retry.

Webhook node with "Raw Body" enabled, then a Code node:

const crypto = require('crypto');

const raw = (await this.helpers.getBinaryDataBuffer(0, 'data')).toString('utf8');

const sig = $input.first().json.headers['x-signature'];

const expected = crypto.createHmac('sha256', $env.WEBHOOK_SECRET).update(raw).digest('hex');

const ok = sig && sig.length === expected.length &&

crypto.timingSafeEqual(Buffer.from(sig), Buffer.from(expected));

if (!ok) throw new Error('bad signature');

const event = JSON.parse(raw);

const store = $getWorkflowStaticData('global');

store.seen = store.seen || {};

if (store.seen[event.id]) return []; // already handled

store.seen[event.id] = Date.now();

return [{ json: event }];

Caveats I already know about:

- Header name and signature format differ per provider (some sign a timestamp too).

- Static data only persists for active (production) runs, not manual tests.

- It grows forever, so I'd prune old IDs or use a Data Table instead.

- I haven't run this exact code on n8n Cloud, so crypto/$env may be restricted there.

Questions: is static data or a Data Table the better place for the dedupe list? Anything about retries or out-of-order events I should handle before the "grant access" step?

Hey everyone,

I'm looking for a few agencies and automation builders to work with as design partners. I want to learn your experience with silent AI automation failures today, and shape my product around what you actually need.

The problem it solves is simple: "success," but the AI output is wrong, empty, or garbage. Nobody finds out until a client does.

If you run client automations and have been burned by silent failures, I'd love to chat. Drop a comment or DM me. Also curious how you're handling this today.

I'm curious about workflows that go beyond a simple trigger → action automation.

For people who have been using n8n for a while:

• What's the most complicated workflow you've personally built?
• What made it difficult?
• Did it involve multiple AI agents/models, APIs, databases, or external systems?
• Which parts were reliable and which parts required frequent human intervention?
• Were there tasks where you tried to automate something but found that AI wasn't reliable enough?
• How much of the workflow can realistically run unattended?

I'm particularly interested in workflows involving things like research, analysis, decision-making, execution, and review rather than just straightforward integrations.

Would be interested to hear about real workflows you've built and what you've learned from them.

I'm building a vendor due-diligence and monitoring tool for UK companies. I'm not a risk professional, so I'd value input from people who are.

Inputs: Companies House (status, overdue accounts and confirmation statements, charges, insolvency, officer resignations, disqualified officers), UK Sanctions List fuzzy matching on the company, directors and PSCs, and financial figures from iXBRL/PDF (net assets, profit/loss, current ratio).

Current logic:

• Six categories, each starting at 100 and losing points for specific red flags: registration, filing, governance, insolvency, financial health, sanctions.
• Composite = weighted average (20/15/10/10/25/20%).
• Grades: A ≥ 90, B ≥ 75, C ≥ 55, D ≥ 30, E below.
• Any "critical" flag (sanctions match, dissolved, active insolvency, disqualified officer) forces grade E.
• Categories with no data are excluded and the weights renormalized.

​

score = sum(category_score * weight) / sum(weights of assessed categories)
grade = "E" if any critical flag else band(score)

The problem: almost everything I screen comes out 100/A. Working through the numbers, the maximum deduction without a critical flag is about 20 points, so C and D can't happen. It's effectively binary: A/B or E.

What I've tried: I screened through 13 different companies, while also providing incomplete data on bulk uploads, still each company I screen comes out with an almost perfect score with all the data intact so, either companies house or gazette connections are not working properly or I am freaking out for having unrealistic expectations.

Questions:

1. Is blending hard compliance gates (sanctions, dissolution) and probabilistic financial risk into one number a design mistake? Should they be separate outputs?
2. How do practitioners treat "no data" versus "clean"? Is a separate confidence indicator standard?
3. Should I calibrate weights against outcomes, such as back-testing on companies that later went insolvent or dissolved? Is Companies House data enough for that?
4. Any frameworks, papers, or open-source examples for third-party risk scoring, especially UK or SME-focused?

Not looking for code review. Happy to share more detail on the logic :)

My scariest failure was never a crash. Crashes are loud, crashes page you, crashes are almost a relief honestly. It was a dedupe rule that just... stopped firing, and kept running green for weeks.

A working dedupe rule produces zero output. No errors, no logs, nothing to alert on. So success and breakage looked exactly the same, and the first signal came weeks later when I was staring at data that had been quietly piling up the whole time. I used to check my runs from my phone in the morning and everything said green, which made it worse somehow. Cost me a weekend of cleanup and a very awkward conversation.

Anyway, I used to burn real time on this. My ritual was expected-count checks per silent step ("this rule should have matched N rows today"), plus replaying a period against a copy before cutover and diffing outputs. Half of it worked, half of it was vibes. And the quiet fear never fully went away.

So how do you catch the silent ones? Do you write heartbeat rows even on no-op runs? Assert expected counts on steps that should produce zero output? Something completely different? What was the last silent failure that bit you, and how long did it run green before anyone noticed?

Genuinely curious what these jobs are for, too. Own business workflows or client work? Who eats the cost when a silent one breaks, you or the client?

Last one, since I'm sketching something: what if something watched your scheduled jobs for exactly this kind of silent drift, in the cloud over MCP, same context across every agent and tool so decisions, failed attempts, and run history all cross over. You never touch a file again. Would you guys pay for something that fixes this, or does your own heartbeat setup already cover it?

The more I think about adding AI to a member portal, the less I like the idea of just dropping a chat bubble in the corner. If someone is already looking at an event, a certification page or their membership account, the assistant should probably understand that context and help from there.

I could see customgpt.ai handling questions from the association’s approved content, algolia keeping regular search useful and n8n pulling in live things like renewal status or upcoming events when needed. The interesting part to me isn’t really the chatbot. It’s making the portal feel like it actually knows where the member is and what they’re trying to do. Has anyone approached it this way instead of building a separate “AI assistant” experience?