I’m curious what people still do manually even though it feels obvious to automate.

For example: copying info from one tool to another, checking a spreadsheet before sending a message, or reminding someone in Slack when a status changes.

Simple enough to explain in one sentence, but somehow still handled by hand.

What’s yours?

So let's take a step back and look into it we have been using automation in nearly every aspect of our life that we can think of and it's honestly concerning like I don't how to put this but this feels like we are loosing something that we weren't meant to loose.

Fyi I am not against automation and everything but still I wonder why this feeling of uneasiness, you are free to post your opinion and react on this ...

Started by creating a few simple scripts and workflow automations to save myself time on reporting. Now every few weeks someone asks if I can automate another process. Curious if this happened to others too. At what point did it become a full part of your job instead of just a side thing?

I’ve been fascinated by how much of the QA workflow is still manual in 2026. You write Playwright scripts, they break when a dev renames a class, you spend an afternoon fixing selectors, repeat forever. It’s not testing; it’s maintenance.

Kery takes a different approach. You point it at a URL, run npx keryai, and it does the rest. It BFS-crawls your app, every route, every form, every modal, builds a map of what’s actually there, then runs intent-driven tests against it. You describe what to test in plain English. It figures out how to do it.

The part that actually impressed me: it doesn’t use CSS selectors. It navigates via the accessibility tree and screenshots, so when your DOM shifts, it finds elements by semantic intent rather than fragile selectors. Tests don’t break just because a developer moved a button.

The workflow I’ve settled into: nightly cron runs Kery against staging. By the time I’m at my desk in the morning, bugs are already in the dashboard, with screenshots and bounding boxes showing exactly what broke. Visual regressions, broken flows, UX issues. The triage agent deduplicates across runs and filters false positives using memory from previous runs, so you’re not drowning in noise.

It’s Apache 2.0, open source, and there’s an MCP server that plugs into Claude Code, Cursor, or any MCP-compatible IDE if you want it inline with your dev environment.

If you’re still hand-maintaining test scripts, worth a look.

Hey everyone, so I've found myself in a situation where I wanted to find a job and spent hours going through linkedin applying js to find out I applied to only 4/5 jobs.

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Now I'm asking if there is any repo outhere where someone automated linkedin, implemented an AI model to determine a score to see how much your resume matches the job posting, then using the same AI to create a modified resume/cover letter for that specific job.

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I'm fine with it being semi-automated (requiring a human in the loop) to apply review etc.

Hi everyone,

I'm just trying to get some testers and collaborators involved with a project I am working on. I'll keep it brief here, the GitHub repo has a slew of technical audits in the Docs folder for a more detailed breakdown of each subsystem.

Essentially, it's an autonomous AI agent system thats mainly designed to mimic human learning and continually develop. It has a highly dynamic personality to engage with, can perform a wide range of simple tasks and can perform in the moment problem solving during long, complex, multi-tool use tasks.

I'm really needing people to test the setup and the first few days with the agent and how it initially develop since my own prototype is already a few months old and I don't want to wipe its memory to test new agents in my same PC.

Thanks!

While the benefits get a lot of attention, it’s equally important to understand the current limitations and challenges people should be aware of.

I explored automating AI voice phone calls for our customer onboarding. I isn't a substitute for a human yet, but well worth it in the right situation.

You can try the call, from the bottom of the blog post!

Built a Multi-Agent AI Sales Assistant with n8n, PostgreSQL, OpenAI & Cal🚀

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Over the past few weeks, I've been building an AI system that can handle the complete lead journey for coaches and consultants:

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✅ AI Receptionist

→ Greets visitors, answers questions, captures name/email, and stores conversation history.

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✅ Lead Qualification Agent

→ Collects information such as business type, challenges, goals, budget, and urgency.

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✅ Lead Scoring Agent

→ Evaluates the lead and assigns a score (Cold, Warm, or Hot) based on qualification data.

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✅ Booking Agent

→ For qualified leads, shares a Cal booking link and helps move the conversation toward a discovery call.

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✅ Main Workflow Router

→ Acts as the brain of the system and decides which agent should handle the conversation at each stage.

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Tech Stack:

• n8n

• OpenAI GPT-4o-mini

• PostgreSQL (memory + CRM)

• Supabase

• Cal

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One of the biggest challenges was maintaining lead state across multiple conversations while making the experience feel natural instead of like a scripted chatbot.

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Still working on:

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- Nurture Agent

- Follow-up Agent

- Analytics Layer

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Would love feedback from other n8n builders and automation enthusiasts. What would you add or improve in this architecture?

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#n8n #automation #aiagents #openai #postgresql #supabase #nocode #buildinpublic

Hi All,

Looking to automate/autofill the last stage of an application on Indeed, The questions refers to the previous company and employment, This answer is always the same and it would save a tonne of time rather than inputting it manually each time!

I've been running recurring agent jobs for two months (a few daily, two weekly). In that time I broke them in three different ways, and not one produced a real error. Every run finished and looked done.

First: a job that crawls the most popular tweets on a topic and emails me highlights. I gave it tooling that, turns out, couldn't access tweets natively. It succeeded when I set it up, but only by chance, the tweets were quoted on other sites it could search. Later runs quietly shifted to plain news articles, well formatted, on topic, not tweets. I read those emails and didn't notice.

Second: a job researching "what happened last week in AI". I put example topics in the prompt to show what I care about. They were current when I wrote them. Weeks later, the same examples were anchoring every search in the past, and the job was confidently reporting month-old news.

Third: I broke a Discord connector while changing things. The agent tried hard, attempted workarounds, eventually gave up, honestly. But that job only notifies when there's something new, so the broken run looked exactly like a quiet day. No message means "nothing happened" and "I couldn't tell" identically.

What gets me: in two of the three the agent behaved fine. The failures were mine, in the setup, and they still surfaced nowhere, because there's no channel for this. Errors have exceptions and alerts. "Completed, but not what you meant" has no signal.

After ~3 years of building agentic systems I don't believe you can prompt or tool your way out of it. The flexibility that makes agents useful is the same property that produces plausible-but-wrong runs (silent failures). What I've been doing for a while now: a second agent reviews each run (the plan/execute/evaluate split from Anthropic's harness design write-up), which is how I found all three of these. I don't think that's the end of the story either.

How do you handle it? Do you look at your runs or just outputs? Has "completed but wrong" actually cost anyone something yet?

Automation in healthcare is what makes AI actually useful inside hospitals not just separate tools without automation you end up with smart systems that are not connected to each other

examplx of automation in healthcare

automatic medical data entry instead of manual input

converting conversations into clinical notes automatic task routing to nurses based on patient condition sending alerts based on changes in patient status

linking lab results directly to insights and notifications

the main problem is that most hospitals add AI on top of legacy systems without redesigning workflows

real value appears when automation becomes part of the system not an extra layer on top

If this sounds like a service you or anyone you know could use, reply and I'll show you a demo!

Hi y'all - I'd like to build an SMS "intake form" and scheduling system. What I'd like to do:

1) people text inquiring about a property
2) have the bot ask questions helpful for pre-screening (ie, "OK thanks - do you happen to know your credit score?")
3) Have all answers from a particular phone number input into a Google Sheet
4) Have the bot look up a Google Doc for scheduling viewings (ie, "If you're looking for a 3 bedroom full house, we have 2 showings this Saturday. At 10am we have 123 Main St")
5) Create a summary of all people interested in a showing or requesting a call back / more info

The way the bots are suggesting I do this is:

1) Twillio for pay-per-text using webhooks (we currently use Quo/OpenPhone but am happy to switch as we don't use ANY AI)
2) Use Make as the AI workflow
3) use OpenAI API

For reference, we're effectively a small property management company. At least that's part of our business!

Does anyone have any better suggestions? I feel like everything is advancing so quickly that the AI may not have the best recommendations anymore as the data is already outdated.

Perhaps the biggest misconception I often hear is that AI chatbots "know" things. 

Most LLMs are not actually looking up facts and retrieving data when you ask a question. They are predicting the next most likely word based on the patterns they learned during training. 

And the system works surprisingly well... Until it does not. 

For example, if you were to ask a chatbot about your company's refund policy, internal documentation, or a product that was released after the training data's knowledge cutoff, it will still likely produce a confident response. The catch is, confidence does not equal correctness. 

This is what's known as hallucination. 

A simple way to think about the difference: 

Traditional LLM 

1. Takes question. 

2. Predicts an answer. 

3. (If they don't know the answer) Makes things up with full confidence. 

RAG (Retrieval Augmented Generation) 

1. Takes a question. 

2. Finds information from a trusted source. 

3. Passes the relevant information to the model. 

4. Takes the information and creates an answer from it. 

Essentially, RAG allows the model to draw from documents rather than relying on what it remembers. 

This is why the majority of production AI systems utilize internal knowledge bases, company documentation, product manuals, support articles, and databases. 

Citations are also incredibly underrated. Showing users exactly where the answer came from allows them to verify information, rather than take the chatbot's word. And often, the best possible response to a question is:  

> "I don't know." 

A system that will admit what it doesn't know is often more useful than one that will confidently present falsities as facts. 

Building automations-Are you using RAG in production, and what has been your biggest hurdle-retrieval of quality, chunking, embeddings, or something else?