Hey guys, haven't posted in a bit. Recently, I've switched from selling the AI voice receptionists and the chatbots to audits. Basically, what I do is I have made an audit form on my website. I've created an agent in Claude that, when it's fed with information of a business, it sees where people lose time and money with manual processes and where AI could replace it, and how much money I could save them.

But what has interested me recently is creating a bot app/website. Something like cal.ai. For example, I ranomly I had an idea of an AI that is for outfits and fashion. For example, you send it pictures of your outfits, and then it rates them and says what could be replaced. Also, giving you suggestions on what would look good on you and ordering it from the app itself. Of course, it was taken already.

I wanted to know first of all if this AI business audit is a good idea, and any suggestions/ tips, and also any AI apps or websites that you guys think are necessary but haven't been created yet.

Share your thoughts and experiences

Quisiera saber si tienen alguna plantilla o donde poder tener una guia para tener un agente IA construido con n8n, he realizado un flujo en n8n que realiza lo siguiente :

- Responder preguntas frecuentes ( consulta a un GoogleSheets)

- Atender citas ( derivar a un agente humano)

- consultar citas ( usar herramienta API)

- Atender consultas otras ( escribir palabra agente para deribar a un agente humano)

- Agregar un horario de atencion para cuando se necesita agente humano

- agregar un Wait para esperar 20 segundos para juntar lo que ha escrito

Todo esto para whatsapp + usando openAI ( gpt 4 mini) + evolutionApi + chatwoot

Estoy teniendo problemas porque de momento solo uso un noto (AGENTE IA) para poder orquestar todo lo mencionado anteriormente pero no se si se podria afinar mas porque tengo problemas como:

- Cuando el usuario a veces pregunta lo mismo y alucina

- Como esta todo centralizado en un solo nodo (Agente IA) entonces a veces alucina en sus preguntas

- A veces el apelativo que ponen "mamita", confunde al agente y piensa que necsita un examen de mama

- entre otros ....

Mas o menos el PROMPT system del AGENTE IA tiene 300 lineas y esto es para la industria de un hospital

Me recomiendan alguna pagina o tienen plantilla donde estos problemas ya han sido resueltos , porque no creo que sea el primero en tener estos problemas .

De antemano Gracias

AI Search is Creating a New Problem

Over the last few months, I started noticing that more people are searching directly on AI platforms instead of traditional search engines.

People are asking ChatGPT, Claude, Gemini and Perplexity things like:

• Best AI automation tool
• Best CRM for agencies
• Best email outreach software
• Best SEO tool for startups

The problem is that most businesses have absolutely no visibility into how often AI systems mention their brand.

Traditional SEO tools still focus heavily on:

• Rankings
• Backlinks
• Organic traffic
• Keyword positions

But AI-generated answers work differently.

These systems decide which brands get recommended, which websites get cited and which businesses become part of the conversation.

And currently, most businesses are completely blind to that layer of visibility.

That was the main reason I built this AI Visibility Tracker.

What This AI Visibility Tracker Does

The workflow automatically tracks brand mentions across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews

Instead of manually checking every AI platform one by one, the system automatically runs weekly and collects all visibility data into a centralized dashboard.

The tracker monitors:

1. Brand mentions
2. Competitor visibility
3. Share of voice
4. Citation frequency
5. Top cited URLs
6. Weak performing queries
7. Weekly visibility trends

I built the entire workflow using n8n, APIFY and Google Sheets.

Why I Built This Instead of Using Enterprise Tools

When I started researching AI visibility platforms, most enterprise solutions were charging somewhere around $500/month.

For agencies or large SaaS companies that pricing may be fine.

But for creators, marketers, indie hackers and small businesses, that pricing becomes very difficult to justify.

So I wanted to build a much more affordable alternative that still provides practical insights.

Right now the entire workflow costs me around $6/month when configured for weekly runs.

How the Workflow Works

Step 1: Weekly Automation Trigger

The workflow starts with an n8n Schedule Trigger.

I intentionally configured it to run weekly instead of daily because AI visibility trends usually do not change aggressively every 24 hours.

Weekly tracking gives much cleaner trend analysis while also saving API credits.

Step 2: Brand & Competitor Setup

Inside the Set Node, I configure:

• Brand name
• Domain
• Search queries
• Competitors
• Competitor domains

This step is actually very important because poor competitor configuration leads to inaccurate visibility tracking.

Step 3: AI Visibility Data Collection

For collecting AI visibility data, I used this APIFY actor.

The actor helps fetch ranking and mention data across multiple AI platforms automatically.

Then I connected it with n8n using HTTP Request nodes and bearer token authentication.

Step 4: Data Transformation

Once the API response is received, the workflow processes all the raw JSON data using JavaScript Code Nodes inside n8n.

This transformation step converts raw API data into structured visibility metrics that can easily be analysed inside Google Sheets.

Step 5: Dashboard Automation

Finally, the processed data is automatically appended into Google Sheets where the dashboard visualizes:

• Mention rates
• Citation performance
• Competitor comparison
• Share of voice
• Weekly visibility growth

I also shared the Google Sheets dashboard publicly for anyone who wants to replicate the setup.

How This Solves a Real Problem

One thing I realized while building this is that manual AI visibility tracking is basically impossible at scale.

You cannot realistically:

• Open 5 AI platforms manually
• Test dozens of prompts weekly
• Compare competitors
• Track visibility changes
• Document citations
• Analyze trends over time

Doing that manually wastes a massive amount of time.

This automation converts the entire process into a mostly hands-free workflow.

Once configured properly, the system automatically generates visibility insights every single week without requiring manual monitoring.

Why I Used n8n

I chose n8n mainly because:

• It gives full workflow flexibility
• Easy API integrations
• Supports JavaScript transformations
• Self-hosting is affordable
• Highly customizable automation logic

For anyone interested in rebuilding or modifying the workflow, I shared the full n8n template.

Full Video Tutorial

I also created a complete step-by-step video tutorial showing:

• Workflow setup
• APIFY integration
• Dashboard creation
• Authentication setup
• Google Sheets automation
• Common mistakes
• Optimization tips

Things That Broke During Testing

A few things caused problems initially.

JSON formatting errors were the biggest issue. Even a single misplaced comma inside arrays for competitors or queries can break parts of the workflow.

Authentication setup also caused problems initially until the bearer token configuration was properly configured.

Another important lesson was avoiding daily runs. Weekly automation gives better long-term visibility analysis and prevents unnecessary credit usage.

Why I Think AI Visibility Tracking Will Become Important

Most businesses are still focused entirely on Google rankings.

But AI systems are increasingly becoming recommendation engines.

People are now directly asking AI:

• what tool should I use
• which software is best
• which platform is trusted
• which service should I choose

If your brand never appears inside those answers, you are potentially invisible to a growing category of future traffic and customer discovery.

That is why I think AI visibility tracking will eventually become as important as traditional SEO reporting.

Interested to know how others here are approaching this problem. Are you already tracking AI visibility for your brand or clients?

And which AI platform do you think currently has the strongest influence on buying decisions?

I’m building AI automation systems and I’m looking to work with a few real small or medium businesses so I can build better case studies and a stronger portfolio. I’m not trying to sell a course or make this sound like magic. Most useful automations are honestly boring: manual follow-ups, copying data between tools, report generation, lead sorting, invoice reminders, customer support replies, spreadsheet cleanup, CRM updates, etc.

Here’s what I’m offering:I’ll look at what you’re currently doing manually and point out where automation or AI could actually help.

If it’s a simple workflow, I can build it for free in exchange for an honest review or short review video.

If it needs paid APIs, software accounts, or external tools, you would cover those costs. I’m not charging for the audit or simple build itself.

If the workflow is more complex, I can still give you a free consultation and explain what could be automated, what should not be automated, and what the first version should look like.

This is mainly for small businesses that want to use AI but don’t know where to start.

If you’re comfortable sharing, comment with one repetitive task your business does every week. I’ll reply with what I’d automate first and what I’d leave manual.

So I'm new to this AI automation field. I heard about N8N about 4 months ago but I watched some tutorials abot a month ago. and built a basic simple workflow and then I dropped it because of my exams .I'm currently a Computer Science Engineer. I have now two options:

1. One is to go deep into this AI automation using N8N and other no code tools but I think it's late because a lot of YouTube videos and Twitter post are saying it is over and Claude Code and other AI will take over it.

2. The other path is to become an AI engineer. I need to study coding tools like Python etc.and I need to study all of that.

One thing is literally no code and the other thing is coding stuff. I don't know if I can do both at the same time. What way should I go for? Should I study AI automation using N8N or other no-code tools or should I study to be a core AI engineer?Is AI automation dead??

Workflow Code:
https://gist.github.com/iamvaar-dev/6544545160d058f716b121e7e38e52bc

Pre-requesties:
- Apaleo Client id and secret.

- Apaleo basic authentication key which we recieve just after creation of the dev app.

- seam.co Api key as we need it to generate smart lock pin.

- Twillio API

Workflow 1: Smart Lock PIN Generation & Notifications

This is the primary workflow that runs whenever a room is assigned to a guest.

1. When Reservation Unit Assigned (Apaleo Trigger)
   • What it does: This is the starting point. It listens for a specific webhook event from Apaleo (reservation/unit-assigned). As soon as a staff member assigns a physical room to a booking, this node triggers the automation.
2. Fetch Reservation Details (Apaleo Node)
   • What it does: It takes the ID from the trigger node and makes a request (BookingReservationsByIdGet) to Apaleo to grab the full details of the reservation, specifically the arrival and departure times needed for the smart lock.
3. Map Room ID to Lock ID (Code Node)
   • What it does: This runs custom JavaScript to match the Apaleo room string (e.g., "BER-VWD 1.001") against a hardcoded database of your physical locks. It finds the corresponding Seam locker_id and prepares a clean JSON payload containing the device ID, reservation ID, and the exact start/end times for the lock access.
4. Post Access Code to Seam (HTTP Request Node)
   • What it does: It sends a POST request to Seam's API (/access_codes/create) using the data prepared in the previous step. Seam generates a unique PIN that will automatically activate at check-in time and expire at check-out time.
5. Append PIN to Reservation Note (Apaleo Node)
   • What it does: Uses a PATCH request to update the Apaleo reservation. It takes the newly generated PIN from Seam and saves it directly into the reservation's comments/notes so hotel staff have a record of it.
6. Fetch Customer Contact Data (HTTP Request Node)
   • What it does: Makes another call to Apaleo's API using a Bearer token to pull expanded customer details, specifically the primaryGuest and booker profiles. This is required to get the guest's email address and phone number.
7. Send Email via Gmail (Gmail Node) & Send PIN via Twilio (Twilio Node)
   • What they do: The workflow splits into two parallel actions here.
     • The Gmail node takes the customer's email and sends a beautifully formatted, custom HTML email containing their secure PIN and instructions.
     • The Twilio node simultaneously takes the customer's phone number and sends a concise SMS text message with the PIN for easy access on the go.

Workflow 2: Automated API Token Rotation

Because the Apaleo Bearer token (used in the "Fetch Customer Contact Data" node) likely expires every 60 minutes, this background workflow runs independently to refresh it.

1. Every 58 Minutes Trigger (Schedule Trigger Node)
   • What it does: Acts as a cron job, firing off this secondary sequence exactly every 58 minutes.
2. Fetch Access Token (HTTP Request Node)
   • What it does: Sends a POST request to Apaleo's identity server (identity.apaleo.com/connect/token) using a static Basic Auth key to request a brand new, short-lived access token.
3. Patch API Credential (HTTP Request Node)
   • What it does: This is a clever use of the n8n API itself. It takes the fresh token fetched in the previous step and makes a PATCH request to your n8n instance. It dynamically overwrites the existing "Apaleo Bearer Auth" credential saved in n8n. This ensures that Workflow 1 always has a valid token when it needs to fetch customer data.

Hi, I'm trying to connect to the Google Sheets API via OAuth. I enter the client ID and client secret, and when I try to connect, it says the credentials were created successfully. But when I click "Sign in with Google" again, nothing happens. The credentials then appear in red, and I can't connect. I'm hosting my N8N with HostGator.

I've attached screenshots.

Does anyone know what might be happening?

https://preview.redd.it/vk9dqcvhs02h1.png?width=1320&format=png&auto=webp&s=59d27241cd93296678f72d4dcecbc4b1b911f271

https://preview.redd.it/qtrkzcvhs02h1.png?width=829&format=png&auto=webp&s=2895498f1dc82446a380cdefbf95e8bcb6900175

Thanks!

I've been seeing this a lot lately, especially with YouTubers using "n8n is dead" in their video titles. With the rise of agentic tools like Openclaud, Hermes Agent, and Claude Code, is the platform actually losing its edge, or is it just clickbait for views? How do you see the current state of n8n and its future?

Hi everyone,

I’m an AI automation developer with around 6 month of experience working with n8n and custom workflows.

I’m looking for someone who is interested in collaborating, building projects together, and growing in this space. Open to learning, sharing ideas, and working on real use cases.

If you’re working with n8n, AI agents, or automation tools, let’s connect!

After working closely with people building automations using n8n, AI agents, WhatsApp systems, lead pipelines, internal tools, etc… we noticed something interesting.

Most workflows actually aren’t extremely complicated.

What businesses really need is:

reliability

stable executions

webhook uptime

fast response times

proper backups

infrastructure that doesn’t randomly break at 2AM

A lot of builders can create amazing automations now.

That part is getting easier every month.

But production infrastructure for real-world businesses still feels underestimated.

We’ve seen workflows fail because of:

cheap overloaded VPS setups

SQLite corruption

memory spikes

missing backups

reverse proxy misconfigurations

queue issues under load

long-running AI tasks timing out

Especially now with AI automations becoming business-critical.

Honestly feels like the next phase of the n8n ecosystem is not just workflow building anymore… but creating infrastructure optimized specifically for production-grade automations and AI systems.

Curious how everyone here is currently handling deployments for real client/business use cases:

fully self-hosted?

managed hosting?

queue mode?

isolated instances per client?

Docker Swarm/K8s?

PostgreSQL or still SQLite?

Would love to hear what’s actually working in production for people here.

https://preview.redd.it/iv5wvknrew1h1.png?width=1442&format=png&auto=webp&s=9a3f8b71d61ef38c698e09699661e370d5d0edff

Built one n8n workflow with four ways to wire a reasoning harness into an agent. Single chat trigger; prefix selects the branch.

The harness in the example is Ejentum, a reasoning API that returns a structured scaffold per call (failure patterns to avoid, target patterns, amplify/suppress signals) which the agent absorbs into its prompt before answering.

- `/inject /reasoning` (or `/code` `/memory` `/anti-deception`) — locked routing, harness always applied as a system prompt injection. You pick the mode.

- `/reasoning` — single tool, model decides when to call it.

- `/full` — four tools, model decides which to call and when.

- `/ejentum-mcp` — same as `/full` but one MCP Client node instead of four HTTP tools.

The tradeoff axis is how much routing discretion you hand to the model. Determinism on the left, flexibility on the right.

The four wiring patterns are generic. Drop in any HTTP tool or MCP server in the same slot and they still apply.

This workflow beyond the tool usage is an example of harness that activates branches with command like "slash" calls. That makes the workflow modular and ready to modify to its builder use case.
I am not doing self promotion, i am just showing the possibility to consider a middleware of cognitive frameworks that increases performance that may be for u crucial but for the agent less relevant and apply a reasoning structure that demands verification and a clear execution logic to apply. Each reasoning ability the agent receives is a tested self contained cognitive operation that is designed to give procedural steps intead of theatrical content. I appreciate the attention poured into the post, here u can find more links about ejentum project. cheers

the ejentum node is installable inside community nodes as " n8n-nodes-ejentum " :
https://www.npmjs.com/package/n8n-nodes-ejentum

Template + README:

https://github.com/ejentum/agent-teams/tree/main/n8n-harness-integration-patterns
ejentum.com
github.com/ejentum

Free tier on the API is 100 calls, no card.

System Design SEO REPORTS

Someone showed me their monthly SEO reporting process and I couldn't let it go.

GA4 open, Search Console open, copy numbers into a doc, write a summary around them, format a PDF, send it out. Every month. Per client. Two to four hours of just moving numbers from one screen to another.

So I built a workflow to replace the whole thing.

https://preview.redd.it/tci9hxmj2u1h1.png?width=1314&format=png&auto=webp&s=9e8b624940764c4b63387be61839ce6df19a41ac

Here's how it's structured. An OAuth connection pulls traffic, clicks, impressions, top pages, and keyword data from both GA4 and Google Search Console. A pre-computation layer calculates period over period deltas, flags anomalies, and surfaces keyword movement opportunities... then packages everything into structured JSON so the LLM isn't just guessing, it's working from real numbers. That JSON goes to an LLM which writes a 400 to 600 word narrative report grounded in the actual data. Finally it exports a white label PDF with custom branding applied.

Start to finish, under three minutes.

The part I spent the most time on was the pre-computation layer. Sending raw GA4 output straight to an LLM produces garbage. The structured JSON step is what keeps the report grounded and makes the narrative actually useful instead of generic.

Happy to walk through any of the nodes if you have questions, especially the data transformation step before the LLM call.

Github link: n8n-workflows/SEO Reports/Automate Weekly SEO Report with AI Insights.json at main · vk-jr/n8n-workflows

Hey everyone,

I’m currently in the early stages of learning n8n and building my understanding of automation, workflows, APIs and AI integrations.

Right now I’m looking to connect with people who are also learning or already working with these tools, with the idea of creating a small collaborative space where we can:

• Discuss real client situations and use cases
• Break down problems and design possible workflow solutions
• Share ideas, templates, resources and learning material
• Build a consistent rhythm of learning (weekly or daily discussions)

My intention is not just to “consume content”, but to actively grow through practice, discussion and shared problem-solving — and ideally, help each other level up in the process.

If this resonates with you, feel free to comment or DM me. Would be great to build something small but consistent with like-minded people. :)

Somewhere around the 4th or 5th build, I stopped asking people what they wanted automated.

I started asking what they did every single morning before they opened their actual work.

The answers were always the same. Checking if a lead responded. Copying something from one place to another. Sending a follow-up that probably wasn't sent last week. Nothing complicated. Nothing that felt worth solving. Just... the slow bleed.

I've been doing this for the past several months. Lead response flows, database reactivation, document processing, internal ops, follow-up sequences. Not for big companies. Mostly solo operators and small teams that are genuinely good at what they do but are spending 30 to 45 minutes a day doing something a workflow could handle in 4 seconds.

Here's the thing I kept getting wrong early on. I'd show up with a complex multi-step system and they'd be genuinely impressed... for about a week. Then it would quietly stop being used because it added friction to their existing habits.

The ones that stuck were embarrassingly simple. A form that auto-creates a follow-up task. A daily digest that pulls from three places into one message. A reactivation sequence that sends three texts over six days when a lead goes cold. None of these are technically interesting. All of them are still running.

The insight, if you can call it that: the automation doesn't need to be clever. It needs to be invisible. If they have to think about it, you've already lost.

I'm doing a few more of these builds for free right now, just because I want more reps across different industries and I'd rather have real testimonials than nothing. If you know a small business owner who's manually doing something every day that's clearly a workflow problem... send them this. Or drop a comment and I'll tell you if it's something I can actually solve for them.

No pitch. If it doesn't work for them I'll say so upfront.

Hello Good day everyone I'm planning to tackle and learn N8N automation I've heard and research its more technical. I've already made projects from Make(Integromat) and Zappier.

Now I have 2 questions

First is: I'm planning to subscribe to Claude Pro and is there any tips or Idea to to leverage Claude pro aside from using Claude Code to automatically create workflow from me.

Second: Should I subscribe to N8n directly or use Hostinger to self host? Is there any difference from the two?