Not the “replace your entire company with AI agents” stuff.

I mean something small that genuinely stuck.

Maybe:
- summarizing long emails
- brainstorming
- coding help
- voice transcription
- journaling
- research
- automation
- meeting notes
- content drafts

I noticed most AI tools feel exciting for a week, but the ones people keep using are usually the boring tools that quietly save time every day.

Curious what actually survived long term for you.

20 y/o started an AI marketing agency for trades businesses in Edmonton — struggling to land first client, what actually worked for you early on?
I started learning about ai automations about 4 months ago targeting plumbers and home service businesses in Edmonton (missed call text-back, AI chatbot, voice employee, review automation etc.) through GoHighLevel.
I’ve got the system fully built out, website live, pitch deck ready. My main struggle right now is just getting in front of the right people consistently.
Currently trying:
• Cold calling (hate it but doing it )
Is it just a numbers game? Keep dialling and dming?

been thinking about this a fair bit lately. the use cases where LLMs genuinely simplify things seem to cluster around text-heavy, unstructured stuff:, pulling fields out of contracts, triaging support tickets, converting data between formats, that kind of thing. once there's ambiguity or judgment involved, an LLM handles it way better than trying to hardcode rules that break every two weeks. the inbox automation post on here recently kind of nailed it - deterministic tools doing judgment jobs is where things fall over. the pattern that actually works in production seems to be hybrid: traditional workflow engine, for reliability, permissions, and audit trails, LLM for the classification and routing and exception handling. not LLMs replacing everything, just slotting into the parts where rigid logic fails. worth noting though - even in those slots, you still need schema validation and human review on anything high-stakes. LLMs are still probabilistic and will hallucinate, so treating their output as a confident final answer without checks is where things quietly go sideways. workflow decomposition helps heaps too - breaking it into small schema-validated steps rather than one big prompt trying to do everything at once. reduces hallucinations and makes it way easier to debug when something goes wrong. with agentic setups getting more mature now, adding branching, loops, and human-in-the-loop checkpoints at the right spots makes a real difference for reliability. the build vs buy question is where i'm less sure. for anything regulated, high-stakes, or touching data sovereignty concerns, custom pipelines still seem like the, safer move - compliance and privacy requirements alone can rule out a lot of off-the-shelf options. but for most standard stuff n8n or similar gets you there faster. curious whether people here are finding the out-of-the-box tools actually hold up at scale or if you eventually hit a wall and end up building custom anyway.

disclosure up front: I work on Slashy and there's a link at the end. the automation breakdown is the actual point, judge it on that.

for about a year my email/calendar "automation" was the usual pile: a few zaps, a couple of cron scripts, some keyword rules. it worked until anything needed judgment. is this email actually urgent. who is this person. does this meeting request collide with something. every time I tried to automate one of those I ended up hardcoding a dumb rule that broke within a week.

The realization that fixed it: I was trying to make deterministic automation do a judgment job. the fix wasn't a smarter zap, it was giving Claude the actual inbox and calendar so the judgment steps run with real context instead of a keyword guess, and leaving the genuinely deterministic parts as plain automation.

concrete, what's now reliable instead of brittle:

- morning: Claude pulls unread from a defined list of people, summarizes, drafts replies for the ones that need one, queues them for my approval. the old script could fetch mail but had no idea what mattered.

- meeting requests: it checks the actual calendar, proposes times that don't collide, writes the email. I send. the part that used to need babysitting is gone.

- before any call: it assembles who this person is + last thread + calendar into one brief automatically. killed three tabs.

the thing that makes it production-stable: it is not autonomous. it drafts and proposes, I approve anything that leaves the building. an automation that sends the wrong

Thing once is worse than no automation, especially with email where you can't unsend. the scheduled parts run on triggers, not a polling loop burning tokens. setup is OAuth, no API keys sitting in a config file, and it runs over MCP so it works inside Claude (and Cursor/whatever speaks it) rather than being one more siloed app.

The tool is Slashy if you want to look: https://slashy.com genuinely curious what everyone here uses for the judgment-heavy steps of their automations, because "deterministic automation + Claude in the right place with real context" is the pattern that finally held for me and I want to know where it breaks for others.

If you could build an AI automation for the every day office, what would you build? I find that so many offices are still manually handling tasks that can be automated and save them hours, it drives me mad! What would you build so that every work place came with them as standard

Not the overhyped “make $10k overnight” stuff.

I’m curious what AI tools people genuinely ended up using every single day after the hype settled down.

For me it’s honestly ChatGPT for brainstorming and research. I thought AI tools would be a phase for me but now I use them almost daily.

What’s yours and what do you actually use it for?

I’m a complete beginner. I can handle basic ChatGPT prompts, but that’s really it. No coding skills, no math degree. I’ve tried teaching myself with YouTube and free tutorials, but I’m stuck. I feel like I’m just copying and pasting prompts without actually understanding how they work. I need structure and someone to answer my questions.

I’ve looked into some programs, but they are expensive. I don't mind paying for something that works, but I’m scared to drop $15k and end up with a useless certificate.

How do you actually start working in AI automation? Was a bootcamp worth the money, or is there a better way to do it?

People keep saying crazy AI agents will take over entire teams. 😅

But honestly the smartest automation I have seen recently was just a business responding to Instagram DMs instantly instead of making people wait hours.

Simple idea, huge difference. Most customers don’t expect perfection more than that, they just hate being ignored.

What are the most useful AI automations you’ve seen recently? Not the flashy demo stuff… real-world useful

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?

Over the last months I've been deep in one problem: why does most AI-generated content still sound like AI wrote it?

I've tested voice extraction workflows, multi-agent pipelines, prompt compression techniques and production setups to close that gap. Some of it worked. Some of it was a waste of time. The stuff that actually works in production looks very different from what gets likes on Twitter.

A recent post about our 100-question voice extraction process got way more traction than expected. So I'm doing an AMA.

What I can talk about:

• voice and tone extraction at scale
• multi-agent workflows vs. single-chat setups
• prompt compression and what it does to output quality
• the line between "sounds human" and "sounds like me specifically"
• mistakes I've made building these systems
• where AI content is actually heading in 2026

I'll start answering questions on: Wednesday, May 20 · 8 AM PT / 11 AM ET / 5 PM CET Drop your questions below.

https://preview.redd.it/ssuilmqo7w1h1.png?width=2048&format=png&auto=webp&s=d3eb3c952fde2daa87ded74bdb20f445b85a7236

I am building a whatsapp automation for real estate. how much i should ask for this:

Feature 1 24/7 AI Chatbot — Answers property questions anytime, no human needed 2 90+ Project Support — Covers all cross Kerala districts 3 Smart Budget Capture — Detects and remembers user budget, never asks twice 4 Lead Scoring — Auto-scores leads as Hot / Warm / Cold based on consent and budget 5 DPDP-Compliant Consent — Separate opt-in for sales calls, no data shared without permission 6 One-Click Delete — User types "delete" and all data is permanently wiped 7 One-Click Withdraw — User types "withdraw" and sales team loses access instantly 8 CRM Integration — consented leads auto-logged to Google Sheets for sales team 9 48-Hour Auto-Delete — Inactive user data automatically purged 10 Permanent Audit Trail — Every consent action logged forever for legal proof

I need feedback for my idea.

I’ve thought of starting an agency where I partner with brands and creat x number of videos and posts to boost their online presence ( basically like new version of personal branding with AI). In this agency, I’ll also be focusing on clipping and building theme pages with that for creators or brands.

I thought of charging $2500 / month for that and in the future, paste an affiliate link and earn money with that too ig.

But for now, partnering with brands and creators to promote products with AI videos and posts (generated with professional industry-grade Nano Banana Pro, Seedance and Kling; not trashy sora and veo)

Is my idea good or what should I do? Should I go all in and start? I have a good knowledge on agency and stuff. Just need to figure out my service and go all in.

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??

I have been using n8n from a long time hosting it locally and have built some automated workflows too like
1st one which reads my mails and notifies me about the important mails by checking if they are replies to my mails, it’s purpose is to check if my leads got replies and notify me in my telegram.
2nd one is where I paste the information from the perplexity research and the workflow pulls out their email and sends them a cold mail through my email using groq api to do the writing work.
3rd I built an automated lead response if someone types their business name and their website my workflow scrapes the data from their website using HTML tags and based on that it gives the response on what can be automated on their websites (not so accurate and works if there is a website only) again I used groq api.
4th Also built some workflows like voice assistant etc. using python script eleven labs and twillio.

After these I got to know about Agentic AI workflows spend some time understanding them. Right now I am looking for ways to earn some side money but I don’t understand where/how to start. Can anyone guide me? It’s been like 4-5 months since I started working on n8n but I want to seriously start earning money and can’t spend much on paid tools too. Can someone tell me how to start?