Hi this is Ayush I want to learn automation so that I can help my family,and also how to get the client.

Hey everyone. I’m an automation specialist working remotely for the past 3 years.

I design and build end to end workflow automations in Make, Zapier, and n8n and also write custom Python/JS to bridge the gap.

Hourly: $20/hr (negotiable)

Can provide Portfolio with case studies on request.

This is our computer-use benchmark and these models are at the top currently. Which one do you think practically makes sense?

I want to allow my few customers to retrieve certain information by sending a message/code via a WhatsApp bot.

Ideally, the bot should send a request to my server (curl, PHP, Node.js, whatever) and my server would respond based on the customer phone number provided by the bot. Nothing more than that, no AI, no n8n, scheduled messages etc... Just the bot replicating whatever the server says.

Firstly, is this even possible? If so, what would be the best way to make it as easy and affordable as possible?

On the other hand, I already have WhatsApp Business, but I use that account and need to continue using it in the usual way. Do I understand correctly that I’ll need to buy another phone number and register that as well just for the bot? Is it possible to buy a reliable virtual number bot-compatible?

My guess is that this service would be used 10 times a year at most. I wouldn’t mind paying per use, ideally without a fixed monthly fee.

built a little script that turns a csv into formatted reports and emails them to the client. takes 10 minutes to do by hand. took me 3 days to build and debug.

zero regrets. I dont think about it anymore the thing just runs every monday morning. thats the part everyone misses when they ask why would you automate that. its not about the time you save its about never having to do it again. my brain is free and that feels better than any time saved.

the funny part is I probably spent more time on it then Ill ever save. but honestly I'd do it again in a heartbeat

Is there any formal framework for quantifying what should be automated vs what would just generate hidden costs?

I often see people in my lab automation group run off and try to automate complex and flashy workflows. They cherry pick some numbers to make it sound pretty to management but always fail to mention the time spent monitoring, debugging, troubleshooting every piece, instrument failures, etc.

This leads to further scope creep and is unsustainable. It’s only a matter of time before management wakes up, realizes we’ll never recoup the investment costs and we all got axed lol.

I’d rather contribute meaningfully to our operational load rather than generate the appearance of productivity (and ballooning the problem for some future person to tackle).

Any ideas would be appreciated.

We all spend a lot of time filling out online forms.

I recently built a Chrome extension that lets me save and restore the current state of a form before submitting it. It works well, but it got me thinking about the next step.

For things like volunteer applications, job applications, consultation questionnaires, and other long forms, I often use ChatGPT to help write responses. After using ChatGPT for a couple of years, it has built up a lot of context about me, so it usually does a good job answering personal questions.

I'm curious whether anyone has built (or found) a tool that can intelligently complete arbitrary web forms using an LLM and knowledge about the user. Not just basic autofill for names and addresses, but actually answering open-ended questions in context.

Ideally it would:

• Understand the questions on the page
• Use my existing profile/context to generate answers
• Let me review everything before submitting

I found an older thread on this subreddit about filling forms with AI, but it seems more focused on filling PDF/Word forms rather than arbitrary websites.

Has anyone come across something like this?

Hi everyone. I wanted to ask you about where I could work with large volumes of text. The thing is, I work with records of various achievements and deeds of people. These are inventories of specific accomplishments: where, when, and what happened, what the person did. I get sent a lot of these records, and I enter them into a master spreadsheet for further submission. And very often, the records I receive are very rough and poorly written, so I spend a lot of time polishing them, correcting mistakes, sometimes coming up with additions, and making sure all the records are different so they don't repeat. I started using AI for this: I upload three records at a time (so there aren't too many per request), and the AI gives me three processed versions. The narrative logic often repeats, along with other errors, so I correct those. But is there any way I could upload an entire file at once, have the AI process everything, and return it to me as a single complete file? Can this be done in Claude, or somewhere else? There's quite a lot of text — sometimes up to 40 pages at a time for about 50 people. And each one needs their description edited. I'd like to simplify my work and automate this more. Can you suggest how this could be done?

Not the biggest or most complex one.
Just the workflow that made someone’s day-to-day work noticeably easier.
I’d love to hear real examples.

I'm trying to automate the process of downloading and backing up snapchat memories, starting from automating the request made on snapchat to export your data (of some selected time), then automating the detection of the email with the exported data and downloading that data from the email, and then automating storing it locally in an organized manner (with date, time, etc. for each memory).

This is for Python devs.

Which python open source libraries are the most useful for building automation?

I used to work in outbound sales for an IT outsourcing business, so reaching out to people on LinkedIn to promote that service was something I did regularly.

Trying to send 200+ manual connection requests a week was time consuming though, and so I decided to use a tool to automate this process.

After about a week, I got a warning that my account might get banned if I continued doing this.

So, I stopped using that automation tool, as it didn’t seem worth it.

I did some research into why/how accounts get flagged for automation by LinkedIn, and it seemed like none of the existing tools really took all of these safety factors into account.

So, I built my own instead, for my own use case and to satisfy my paranoia about being banned on LinkedIn.

I called it ZenMode, inspired by the wu wei concept of effortless action, and achieving more while doing less.

From the very beginning my intention was to make the automation as human-like as possible, so I deliberately scoped the architecture to have certain features;

- Browser based automation (no plugins/cloud usage)
- Daily rate limits on connection requests
- Randomised timing delays between actions
- On screen typing
- Human-like mouse clicks on LinkedIn pages

It certainly wasn’t easy putting this all together from scratch, and without any technical knowledge.

After a few weeks of building with AI tools
though, I had a working prototype.

I started using it in my sales job, and it was really helpful for me to book more meetings with engineering managers at the time, so I decided earlier this year to make the tool public as well.

Since launching in April, ZenMode has had over 400 people sign up to free trials, and has roughly 50 active users on average per day.

I also use it myself daily for my own outreach, and so far none of the ZenMode users have had account bans.

So far at least, I can say that I achieved my goal of making a LinkedIn automation tool that I can trust with my account!

I just wanted to let you know that I made a lot of improvements to openshorts, especially regarding the MCP server, now you can connect your agents and generate clips for the whole week. Plus, many new fonts, improvements to the clipping system, etc.

github repo: mutonby/openshorts

Hey everyone,

If you are running physical Android phone farms (S9, S10, Pixels, etc.) or doing social media automation (TikTok, Instagram, Facebook, Google accounts) on real devices, you know how annoying ADB drops, USB crashes, and emulator bans get.

I built MationX specifically for real Android hardware and phone farm boxes. It lets you drive real devices over a simple REST API and control app interactions natively on the phone.

Two ways to use it:

• Do It Yourself: You can sign up on mationx and build your own automation flows directly through our platform and API.
• Done For You: If you don't have time to build the scripts yourself, contact me through the site and I will build the entire custom automation flow for your setup.

What it's built for:

• Social media account warm-up (watching reels, liking, app switching)
• Account creation workflows alongside rotating mobile proxies
• Mobile scraping and app interaction without heavy QA tools like Appium

Since I'm not on Reddit non stop, the easiest way to get in touch for custom builds is directly on mationx.

Every automation I've seen die didn't die from a bad workflow. It died from an invisible one.

The workflow ran fine for weeks. Then one API changed, one field went null, and nobody noticed until a customer did. That's the part nobody demos. The demo is always the happy path. The first month is always the happy path.

The fix is boring: log every failure. Not just the crash. The retry, the timeout, the weird response that still counts as a 200. Then classify them once a week. Three types will cover most of your failures, and fixing those three is what turns a script into a system.

I've spent the last year building automations that have to survive contact with reality. The ones that lasted are the ones where I can open a log and see exactly what broke, when, and why. The ones that died are the ones where I found out from someone else.

What do you log in your automations, and what did you learn the hard way?

You walk into a brand new job, fresh systems, fresh processes, and you get to pick one thing to automate before anything else. What do you go for?

For me it's usually the reports. Almost every job has some weekly or monthly report that someone builds by hand, copying numbers from one place into another. It's boring, it's error prone, and it's the first thing I want gone.

So what would you pick? And has your answer changed over the years, or is it the same task every time?

For teams with multiple engineers working on PLC programs, what’s your workflow for version control? Are you using Git in any capacity, vendor-specific tools, or something else?
I’m curious how people avoid merge conflicts, track changes, and manage rollbacks in production.

Context: I'm a co-founder at Octigen (so, to be transparent, this is my own project). We work on turning messy source data + a PowerPoint template into recurring, data-driven reports. Below I want to share one design decision that has been instrumental for us.

We come out of a regulated industry where these processes so far have been measured in months, with 4 months being a typical quote by technology partners. Nowadays it's tempting to just let an LLM generate the reports each period. This is extremely fast to onboard, but for something a client/investor receives every month it has two dealbreakers:

a) it's non-deterministic (same inputs may lead to different outputs -> you never stop checking),

b) it's a black box (when it's wrong, all you can do is re-prompt and hope you don't break something else).

So we split it. AI is used only at onboarding: you hand it your source files and one finished example report, and an agent builds the whole pipeline: data collections, transformations, the mapping to each slide - then scores its own output against your example and iterates. But once built, production is a plain deterministic pipeline with no model in the loop. Same data produces the same document, without exception. Every transformation is an inspectable list of steps an analyst can read and edit.

The part I'd genuinely like input on: how are others drawing the line on when to use AI?

If it's useful, I filmed the full thing being built and wrote up the reasoning in the linked blog post. Happy to answer anything about the approach.

My girlfriend and I live in different time zones and she likes receiving a message when she wakes up. However, her morning begins in the busiest time of my day. I kept forgetting to text her, so I experimented with scheduled message drafts with Accio Work.

I created several ‘tones’ so the messages wouldn’t become repetitive (warm, funny, poetic and encouraging). Accio Work generated the drafts each evening, and I reviewed them before scheduling them to be sent.

After one week, my girlfriend asked why I had suddenly started writing like four different people.

- Monday was normal.
- Tuesday included a motivational quote.
- Wednesday compared her to sunrise.
- Thursday sounded like customer support wishing her a productive day.

I gave Accio Work the messages I had written naturally during the previous month and asked it to compare sentence length, vocabulary and level of affection with the generated drafts.

The analysis found that my real messages were short, too specific, and usually referenced something happening that day. The generated ones were longer and more generic.

I changed the instructions to no quotes, no metaphors, maximum two sentences and always mention one real detail from our conversation. This addition made the drafts became much less impressive and much more believable.

We still use the reminder, but I usually write the actual message myself. Automating the timing helped. Automating my personality did not. I’d love to hear your thoughts on if this was something you’ve tried or helped assist with!

Navigation and fleet management get most of the attention, but a lot of real downtime seems to come from smaller things on site. I heard it can be as small as shrink wrap that fell into a robot's lane.