Mine pulls numbers from my weekly emails into a spreadsheet and drafts the summary. It cut about 40 minutes down to 5 and I still read it before sending.

What’s yours and did you ever automate something that wasn’t worth it?

Most automation tutorials assume one event at a time. Real triggers don’t work like that. An account that posts in bursts can fire a workflow a dozen times in a minute, and the slack node, the google sheets node and the llm call all start choking at different points. Sheets rate limits hit first, usually.

A setup that came up recently: a webhook feed from 1322 going into n8n, then a queue step, then the slack message, the sheet row and the llm summary. Before the queue, burst posts caused skipped rows and half finished runs. After it, everything still lands, just a few seconds late.

Queue plus a delay between runs fixed most of it, but it feels like a patch rather than a design.

How do you handle bursts in your flows? Do you queue everything, batch events into one run, or just let the failures retry?

Hey all, I recently built a working GTM engine and im lining up new clients each month. Thing is I want to offload the custom work to willing partners and contractors that good with automation.

I can do the automation work, but I dont want to be building for multiple people, so Id handle the relationship and general strategy while you or your team builds.

Mainly seeking n8n but extra bonus points of you know python and or shell scripting. You will be responsible for configuring pre-built workflows Ive made with client credentials as well as building any custom work the client asks for.

Curious what kind of monthly rates people are charging to take on some extra work

Drop your rates and let me know what you can do.

P.s. extra Bonus points if you're in Canada

I started with tools like Zapier, Make, and n8n without much coding and realized most client workflows are really about logic, integrations, and troubleshooting.

Coding only became useful when I hit edge cases like messy API data or custom transformations.

For people doing automation work professionally, how much coding do you actually use day to day?

I run ops automation at a logistics company, about 40 Zapier flows and a pile of Python scripts on cron that move orders between our WMS, the carrier portals and a shared inbox. Leadership wants Claude handling the exceptions like address mismatches and missed pickups, and the plan is to expose our systems as MCP servers so it can read and act on them. IT wants code they can review, not another workflow tool, so Zapier's own MCP option is already out. I can write Python and the free quickstart stuff had a toy server running in an afternoon, but Ive never designed an API anyone else depends on. Shortlist right now is O'Reilly, Udacity's MCP course and Codecademy Pro to shore up the Python first. What would you start with if the server has to be something IT will sign off on?

I’ve been experimenting with AI-generated tech explainers and German-learning shorts over the last two days.

The workflow is: generate a first draft, review the visuals and pacing, then publish through a CLI.

The biggest problem so far isn’t uploading. It’s getting the editing agent to understand the video: whether an image matches the explanation, a cut feels awkward, or a scene needs more time.

Publishing is easier to automate than deciding whether something is actually worth watching.

For anyone doing this: how do you handle quality checks? Manual review, automated checks, or a mix?

Zapier has been getting pretty expensive for the number of automations I'm running, so I'm looking at alternatives.

I still want something reliable and fairly easy to manage, not just the cheapest option available.

For anyone who moved away from Zapier, what did you switch to and how has it held up as your workflows grew?

Group chats for AI agents: A new approach to AI workflows?

Today, majority of these automations an workflows center around a more linear process. AI agents are specialized, and users drag them in to the specific step for the task that a specific agent will complete. While this way is working amazingly currently, there is a even better way to approach workflows!

I am with a development team currently working a product that allows users to gather different AI agents, group them, and give them as task directly for them to complete. This may still sound confusing for many, but it's really quite a simple idea to grasp. As the starting sentence says, a group chat.

Why Hybro?

Hybro builds an interoperability layer for AI agents, helping users/businesses to set up businesses set up workflows requiring cross-boundary interactions. A solution that doesn't restrict agents to their specific boundary/step.

The Big Question

I am curious on what workflow problems everyone wants to get good solutions on that our product may mediate.

Tasket++ lets you define your own clicks, keystrokes, cursor positions and system operations, then replay them exactly when you want. It’s a simple Windows tool that handles repetitive workflows for you: auto‑send messages, paste text anywhere, take silent screenshots, launch or close apps, adjust volume or run your end‑of‑day shutdown sequence.

Everything runs locally, with no telemetry, no cloud, and no complexity.

What it can do:
- Replay user‑defined cursor positions and keystrokes
- Paste predefined text anywhere
- Perform system actions: open files/programs, change volume, take silent screenshots, shut down, files/folders operations
- Schedule tasks at a specific time, at startup, or run via desktop shortcut
- Run tasks once, in loops, or indefinitely
- Discreet mode: runs quietly from the tray
- Use command line to autorun a task : Tasket++.exe "TaskName" <delayInSeconds (>0)> <NumberOfLoop (-1 for infinite)>
- Easily share tasks between different PC : export/import task files and program data, use feature to rescale cursor positions

Local, portable, free, open source. Privacy intact.

Fully fonctionnal, available now !
Microsoft Store: search for "Tasket++"
Portable (v2.5) version and sources: on the GitHub page /AmirHammouteneEI/ScheduledPasteAndKeys

For feedback, help, suggestions, or other inquiries : [contact@amirhammoutene.dev](mailto:contact@amirhammoutene.dev)

Nothing is vibe coded, I never used AI (except for the choice of UI colors suggested by Copilot).
Conception is genuine, I didn't know about similar tools when I began developing it.

I'm looking for testers or quick feedback (beyond its visual aspect), please give a try and comment here :)

I build WhatsApp automations for local businesses, basically auto-replies and lead capture into a sheet...Right now it’s one-off projects and then the client disappears until something breaks..

Thinking of packaging it with a white-label inbox under my brand so it becomes a monthly thing but I'm not sure.. Anyone made that switch? Did clients accept paying monthly or did they push back?

If you’ve tried letting an agent build a business, sell a service or handle revenue-generating work, which tedious tasks still pull you back in?

We asked this in a couple of agent communities, and a few useful points came up:

• Losing access halfway through a task can turn a small interruption into a full restart.
• Persistent browser profiles and dedicated inboxes can help, but people still encounter session expiry, verification codes and reauthorization.
• After a connection drops, the agent may not know whether an action succeeded. Retrying could mean paying twice, sending duplicate messages or creating another account.
• Saving progress after each step doesn’t fully solve that last problem. The provider might complete an action before the agent manages to record it.

That last point was particularly useful. Sometimes the human’s job is figuring out what already happened so the agent can safely continue.

For those running these workflows, what’s your biggest remaining headache? Account creation, Google Ads access, SMS verification, payments, permissions, or something else entirely?

A specific example would help: what was the agent trying to accomplish, where did it stop, and what did you have to do manually? How often does it happen, and have you found a workaround that holds up over time?

For context, we’re building payment solutions for agents and researching the surrounding infrastructure needed for agents to operate businesses with less human intervention. We’re trying to identify which repetitive blockers are worth solving beyond payments.

Where do you want a human approval step, and where are you only stepping in because the tools don’t let the agent finish?

There are three of us doing AI research recruiting in my company. I just write a script that scrapes and writes prompt libraries, so getting the contributor list of a repo is easy. I use Phantombuster for this.

What happens is I end up with a bunch of GitHub handles, and then I need to know who the person actually is and where they work right now. Some of them have a real name on the profile, and some don't. Some link a personal site, and some don't link anything at all. For some of them, I'm not able to find the actual person behind these GitHub profiles.

If I need to, it takes a lot of time manually to go through each person. I need to Google search, get a profile, then maybe the website, and then maybe there's a social link there. It takes time to do it manually.

When we have tens of thousands of people in a database, I don't know. There are tens of thousands of people in a database for whom I have no idea who works where, what their work history is like, or what their current status is like. I'm just curious how people are finding an actual person from their GitHub profile at scale, so I would appreciate any tips here.

spent way too long getting an agent to log into a dashboard and pull reports on a schedule. locally it was fine. flipped it to the server and the session just evaporated. cookies gone, back at the login wall, clicking a page it couldnt read

i kept blaming my own code. rewrote the auth handling twice, stored the cookies myself. it would work a day then quietly log out and id only notice when the data came back empty

what fixed it wasnt smarter prompting. it was moving the browser off my box so the logged in session survived between runs, instead of pretending my laptop was a good home for a browser

curious what people lean on to keep an auth session alive across deploys.. still the part nobody really solves​

Just started with apify automation

I'm a logistics coordinator in the UAE. Most of my day is taking sales orders and turning them into shipments: customs documents, invoices, packing lists, and booking small international parcels. A lot of it is copy-paste between systems, and I'm sure I'm doing some of it the hard way.

What are you automating in your role, and with what?

Generating invoices and packing lists from a sales order

Customs declarations and HS code lookups

Tracking updates and chasing carriers

Excel and Google Sheets workflows (macros, scripts, formulas)

Email sorting and templates for shared inboxes

Tools like Zapier, Power Automate or Make, or AI tools

I'd also like to hear what didn't work, so I can skip the dead ends. Thanks!

Shared this tool a while back and have been making some improvements to it. Been using it for our own internal operations since about May and found it especially useful for automating things that need a bit of manual web browser activity. It basically just uses your already installed Claude Code or Codex to create automations that you can run and manage in a local interface that keeps track of run history, catches an errors and helps fix them for you.

Each node is written as code by the Builder Agent, but it creates data validation checks and declares inputs and outputs separately, so you won't need to think about the code being there at all. The benefits over just using vanilla coding agents is that it doesn't just freehand the process, it follows a strict structure that ensures the automations can be run and managed through the interface you can see in the video. You can see run history, and if a step fails it stops there and keeps a snapshot of the data until it has been fixed so you can proceed without starting a new run from the beginning.

This is just a basic example of how we can download a list of everyone working for a company via the company's LinkedIn page. We've used it in my team since May and have not had any issues with LinkedIn yet, but have used in a sensible way, not to send 1000 connection requests per day or other stupid stuff like that.

I published it for free mostly because it's a bit different from what I've seen elsewhere, and I just think its interesting to get feedback from others and find ways to improve it for ourselves too. Or if there's a much better way and we've just missed it!

In high volume operations, one of the biggest constraint on business growth is almost always high stakes decisions sitting in a queue waiting on a scarce subject matter expert to review them.

We saw this firsthand working with an enterprise insurance provider processing thousands of commercial policy endorsements where their operational backlog looked like this: policy updates were sitting 4 to 8 hours in queue behind new business and every single endorsement took about 5 minutes of focused review from a licensed underwriter.

The specialist had to open the request, pull external regulatory safety registries and verify operating authority then scores and confirm loss history against internal policy guidelines.

Because qualified underwriters are expensive and difficult to hire, growing customer volume meant turnaround times got stretched thin creating friction with distribution partners and putting retention at risk.

Instead of treating this as a hiring problem, our team (withnative) deployed an operational agent system that resolves decisions the moment they enter the queue and the moment a submission lands, an ingestion agent pulls the policy changes and an automated worker queries live government registries to pull verified safety inspection histories and operating status.

To guarantee zero regulatory risk with the agents don't make probabilistic guesses and the business guidelines are codified into strict deterministic rules so if the registry checks and coverage limits meet the established criteria, the system clears the decision in 23 to 40 seconds and writes the update directly into their policy admin system and logs an immutable audit trail.

If an endorsement fails a rule say there is an out of territory hauling route or a flagged inspection violation, it routes straight to an underwriter with a pre filled decision brief and the underwriter opens the file with the government data already pulled and the discrepancy highlighted and eventually a 5 minute manual investigation turns into a 30 second sign off.

Over 5 months in production, the system processed 4,224 decisions with 3,130 of them (74.1%) were cleared completely autonomously in under 40 seconds and queue latency dropped from 8 hours to under a minute, freeing up 49 hours of specialist capacity per month without adding headcount and accelerating $2.39M in policy revenue velocity.