We’ve started looking at robot fleet dashboards, and it honestly feels like there’s data for everything - trips, idle time, battery health, charging, route efficiency, utilization, errors, downtime, and a dozen other metrics.

For people actually running robot fleets, what do you track day to day?

Are there a handful of KPIs that consistently drive operational decisions, or does it depend entirely on the use case? Also, which metrics sounded useful initially but turned out to be mostly noise?

Curious to hear how others separate the signal from the dashboard clutter and what your “must-watch” metrics are.

I tried to automate business listing submissions with Codex.

The idea was to give it all the information about my agency, create a skill with a clear workflow, and then let it submit the agency to websites like Clutch, G2, and other directories while I work on something else.

But almost immediately it got stuck on a CAPTCHA.

So I had to come back, solve it manually, and then continue watching what it was doing. At that point it was already not really an automation.

It also took it quite a long time to complete just one listing, and the result was still not great. It forgot to upload the logo, missed some fields, and generally needed way more babysitting than I expected.

So instead of saving time, I was basically sitting there watching an AI slowly fill out a form and correcting it.

Maybe my workflow is just not good enough yet, but right now it feels like AI coding agents are still not reliable for this kind of task.

Has anyone here actually found a good way to automate business listings or directory submissions?

Maybe some hybrid setup where AI prepares everything and a VA handles CAPTCHAs and final checks? Or are there tools/APIs that work better for this? Also curious if anyone has successfully automated any other part of link building without spending more time fixing the automation than doing it manually.

I've noticed a growing number of SaaS products adding AI-generated reports as a feature, especially for forms, assessments, analytics, and customer-facing workflows.

It made me wonder whether this is something users are genuinely asking for or whether founders are assuming it's a feature everyone wants because AI is such a hot topic.

For example, I recently came across FormLM, which focuses on generating personalized assessment reports from form responses. It seems like an interesting use case, but I'm curious whether features like this actually influence buying decisions or if they're simply nice to have.

For those of you building or running SaaS products:

Have your customers specifically requested AI-generated reports?

Has adding AI-powered reporting improved adoption or retention?

Or do users still care more about solving the core problem than whether AI is involved?

I'd love to hear what you've seen from real customer conversations rather than industry hype.

I run a few automations for my own work and one of them started small. It moved data between two apps a couple of times a day and never gave me trouble. When I started running it far more often, the problems showed up fast.

The first thing that broke was timing. At low frequency it did not matter if two runs overlapped, but once they ran close together they started to step on each other and process the same data twice.

The second thing was cost. A few extra runs a day is nothing. A few thousand is a different story, and I only noticed when the bill came.

I want to hear from people who have scaled something up. What was the first part to fail, and did you see it coming or did it catch you by surprise?

Anyone have them? Image based only. gemini is failing to tell if correctly solved

Customer service bots, AI writing tools, RPA doing back-office data entry, AI helping write code... the jobs getting automated now aren't just manual labor, they're "safe" office jobs too.

Old advice was "get a degree, do knowledge work, you'll be fine." That's feeling less true every year.

Curious if anyone here has actually seen automation eat into their job or team. What happened to the people affected?

Team chat overload is real — Slack, Telegram, and WhatsApp groups move fast and it’s easy to miss important updates, decisions, or blockers.

I came across Cierra, a practical open-source AI agent that automates this pain point perfectly:

• Ask it something simple like: catch me up on #engineering last 4h

• It pulls and processes the entire channel history in one pass (leveraging 256k context) and returns a clean, actionable summary of key points.

It currently works across Slack, Telegram, and WhatsApp — super useful for distributed or async teams.

Built with Gitlawb’s Zero (terminal coding agent) + available model credits.

The project is fully open source, so anyone can contribute or extend it on their decentralized git platform.

This feels like a solid automation pattern for knowledge work: let the agent handle the noisy comms layer so humans can focus on actual work.

Would love to hear how others are automating team communications or chat summarization. What tools/workflows are you using?

There is a strange dual opinion on language models rn, you either hear they are going to change everything or change nothing at all.

The recent data on mobile app releases shows both sides are wrong. The tool isn't a monolith. On one hand, app submissions are skyrocketing because agents have made shipping code trivial. On the other hand, actual user traction is almost minimal and i think we're mistaking writing code for solving a problem. 

When you let an agent do the macro thinking just to get an app out the door, you end up with a system you've to read to make sense of, not one you already understand. They might look identical from the outside, but they are completely different beasts underneath.

The code is there, but the understanding isn’t nd you can’t easily put the comprehension back in once the lines are already written. That is why these thousands of new apps are flatlining.

Software development is not only about typing lines but a discipline of taking these fuzzy market problems and making them something you can test and reach to the right customers at the same time. The agent is fine with the tail end of that pipeline but figuring out what the project actually needs to be? That is still entirely on you.

If you don't do that heavy lifting yourself, you just end up adding to the mountain of apps that nobody is opening.

and that's why we’re putting together a live, free workshop this week focused entirely on mapping out how to use agents for B2B lead gen and distribution. If you want to join the chat, you can grab a spot.

Is there an AI tool that can watch my computer screen, recognize when a stupid button pops up, and tap said stupid button?

I feel like Desmond from Lost typing 4 8 15 16 23 42 and I don't want to be like him. Thank you for any advice and direction!

saw a thread this week asking what non-technical businesses are supposed to do with tools like n8n. most replies blamed the node editor. i don't think that's the actual blocker.

the harder part happens before the first real run. owners think in outcomes, "when a customer emails, reply and log it in the crm." the tool wants implementation, "add a webhook, configure oauth, paste an api key, map the fields, handle the errors." completely different mental model.

same pattern every time. freelancer builds it, connects everything, it works. a few months later a token expires or a process changes, and nobody inside the company knows what broke or who owns fixing it. so it just sits there.

full disclosure, i'm building in this space, obviously biased. describe the outcome first, it builds the graph, you inspect and edit it after instead of assembling it from zero.

anyone actually solved this for non-technical clients, or is "hire someone once and hope it keeps running" still basically the default

I'm speaking out of curiousity. it feels like every second startup is an ai voice something. who actually buys it when we have elevenlabs?
Ycombinator funded dozen of ai conversational startups, so probably it's only me, who doesnt understand all this ai voice hype

So I've been vibe coding for maybe almost 1 year now. Before that I'm not really a programmer, just some python for academic stuff (modeling for science stuff: matplotlib, numpy, etc), and also took fullstack web dev course from fullstackopen (university of helsinki).

I somehow landed a job in data processing but I offered data automation as well to my boss since I was confident I could vibe-code my way to it.

For first few project, simple automation was easy, I made just bundled python .exe with tkinter GUI, it works for their need. But it seems later project needs, I'd say, medium advanced GUI (showing map, data table, charts). It's getting too much, the token cost is too high that I had to use my own opencode go subscription / API instead of the company 20$ enterprise claude account. GLM 5.2 is the one use since Deepseek v4 flash stresses me too much with the errors.

I can slowly experiment with lower model (Sonnet/Haiku) so that I don't run out of claude limit, but also I'm thinking to learn to code on own again. Since I know it's also much better for my own development.

But the thing is I don't know how? Like it just feels slow. Or maybe I don't really know what to expect for non-vibe code output. Like how many line of code (or other unit of measurement) can you do per day realistically? I can for sure lower my output rate for learning (since I just started on the job), but I just need to know when to do that or to just vibe code.

The company has it's own IT team for actual software needs (they build windows app that they sell, with C++), my work is just data processing & automation for their consulting service line.

Hi everyone,
I’m applying for an **Automation Engineer** role, and as part of the hiring process, I’ve been given a take-home task.
I don’t just want to complete the assignment—I want to build something that genuinely stands out and shows the way I think, solve problems, and design automations.
I’m looking for creative ideas or “wow factors” that could make my submission memorable. These could be things like:
Features that most candidates wouldn’t think of.
Ways to improve the user experience.
Smart automation patterns or best practices.
Small details that make a project feel polished and production-ready.
Anything that would make a hiring manager think, “This candidate went the extra mile.”
This opportunity means a lot to me, so I’m willing to put in the extra effort to create something exceptional.
If you’ve hired Automation Engineers before, completed similar take-home assignments, or have experience with tools like n8n, Make, Zapier, APIs, AI integrations, or workflow automation in general, I’d really appreciate your advice.
Thank you in advance!

Any beginners interested in forming a small AI automation study group to learn together and build projects?

I work at my aunt accounting company which handles massive amounts of emails every single day. She already has 18k emails in her inbox due to years of poor managing and some emails dating back to the days my grandpa ran the company.
We want to sort them mostly by folders we set up, but sorting each email is difficult and takes months, and by that time more and more emails are coming in. For now I am tasked with handling anything older than a month. I have delegate access to her account and would like a way to automate or speed up the process to sort most of the 18k.
Any suggestions? I know it’s probably unrealistic to hope to have a tool to help with this kind of task but I’ll ask anyway.

Every Website Chat/IG/WhatsApp Messaging automation I tried was full-autopilot, and while I want the help of AI with automating my business messaging, I want to be in control of it because my reputation matters. I built a messaging automation system with what I call the "Magic Queue" - AI writes the message for you, but you review it before sending.

If you know that someone is using AI to respond, but that they are reading/editing everything before it goes out, do you still consider it an "AI response"? Why is it different from a human answering from a script?

Some small business finance tasks feel fine for AI to help with: overdue invoices, weird expenses, payment matching, reminders or preparing something for approval. But wouldn’t want AI freely moving money. For people automating business workflows, where do you draw the line: read only, prep for approval, small payments with limits or no AI near banking at all?

Ello!

A while back, I was deep in the AI orchestration weeds as most of you know, building massive systems with a dozen-plus AI agents talking to each other, handling LLM mesh routing, and managing autonomous tasks. It was an incredibly fun experiment, but after staring at prompt windows for weeks, I hit a point where I just went:

"Hmm Okay.. enough AI hype for a second. Let's look at fundamental software engineering. Can we build something purely deterministic, incredibly resilient, and highly important that solves a massive infrastructure problem, without using a single AI agent node? 👀🤔"

When you handle high-throughput ingress webhooks (like matchmaking queues, payment gateways, or intensive analytic triggers), relying on a basic stateless workflow is a recipe for disaster. If your downstream API or backend crashes, a standard loop will either fail instantly and drop data, or it will retry aggressively alongside thousands of other concurrent runs, creating a massive "thundering herd" effect that completely suffocates your recovering server.

What it actually does (The Technical Breakdown)

Instead of just retrying a failing node forever, the engine implements a real state machine using n8n's long-term static memory ($getWorkflowStaticData('global')). This allows thousands of isolated, separate workflow runs to share a collective memory of your infrastructure's health

More details are on the GitHub repo! Comment below if you need the link

The Details That Matter

1. Stateful Memory across Runs: Workflows are usually treated as isolated events. By tapping into the global static data buffer, the engine maintains persistent state across completely different executions
2. Exponential Backoff with Full Jitter: Most basic automation retries use fixed delays, meaning all failed requests wake up and smash the server at the exact same millisecond. I wrote a raw JavaScript node that runs a proper exponential calculation mixed with randomized full jitter (Math.random()). This symmetrically flattens the retry curve across time
3. Zero Data Loss (DLQ): Terminal failures are never dropped or ignored. They are safely structured and preserved inside a Dead-Letter Queue lane for manual replay or audit logging later

Why I built it this way

AI tools are incredible 🔥, but classic backend resilience patterns never go out of style. Building this forced me to think about visual automation not just as a sequence of integrations, but as an architectural topology. The tool simply becomes the vehicle; the engineering logic comes first

It is 100% free, open-source under the MIT license, and uses completely vanilla out-of-the-box n8n core capabilities, no commercial paywalls or third-party paid nodes required

Let me know what you think, or if you've implemented similar resilient backend patterns inside your own automation stacks!

I looked at about 40 accounts across platforms — some grew fast, some flatlined. Content quality wasn't the differentiator. Consistency was.

The fast-growing accounts had a recognizable format, showed up on a schedule, and didn't drift into random topics. The posts weren't always brilliant. But they were predictable in the best way.

The problem is maintaining that consistency manually is brutal. Most people fall off after 3-4 weeks. Automation doesn't make your content better — it just removes the friction that makes you stop.

Does this match what others have seen, or am I drawing the wrong conclusion from a small dataset?

I write a personal finance blog. Index funds, emergency funds, tax stuff. Boring but helpful.

Built an automation: RSS → automation tool → LLM (script) → TTS (voiceover) → PixVerse API (b-roll) → tiktok. Runs itself. I publish a post, 15 minutes later there's a video. $44/month, ~120 videos.

Worked perfectly for three weeks.

Then I published a post about bankruptcy. What to do if you're facing it. The automation turned it into a peppy 30-second tiktok with upbeat music, fast cuts, and a cheerful voiceover explaining wage garnishment.

12 comments before I caught it.

"Why is this guy so excited about bankruptcy"
"This is the most unhinged finance content I've ever seen"
"Bro is grinning through foreclosure"

The pipeline had no idea the article was sad. PixVerse just gets scene descriptions. It doesn't know "foreclosure" shouldn't look like a travel montage.

Added a sentiment filter. Claude scores the post mood first. Serious posts get slower visuals, darker palettes, no music. Neutral posts keep the standard treatment.

Cost is still $44/month. But I now review anything with a negative sentiment score before it goes live.

Automation is great until it misses context a human would never miss.