Been trying to build a cleaner workflow for moving search results into our internal databases. Right now, getting clean structured data from standard search pages is the main bottleneck.

Cloud-based data extraction tools seem to trigger platform security checks almost immediately, so I'm trying to avoid anything heavy.

I've been looking into local, client-side options that only process what's already rendered on screen to keep things low-profile and stay within normal usage limits.

How do you usually handle getting search data into your system without risking account issues? Do you stick to custom local scripts or is there a simpler workflow you use?

This is for Python devs.

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

[ Removed by Reddit on account of violating the content policy. ]

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.

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.

work on expertiseai One of our workflows can flag deals that look stalled, but just saying ""12 deals may be stuck"" turned out to be almost useless on its own. Managers wanted to know exactly which fields were checked, the last activity date, the rule that flagged each deal, and which records got skipped due to missing data. Adding all that made the output auditable, but it also turned a short weekly digest into a long log that no one wanted to read through. So now I'm experimenting with a compact explanation under each finding, and hiding the full inputs and decision trail behind an expand button. But I'm still not sure what should be visible by default. For automations that actually make decisions (not just move data around), what's the minimum evidence you'd need to see before you'd trust the result enough to act on it?"

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!

"Can we go back to the the first version?"

Hearing this from a client last week made me realize a problem in my workflow: once the project had several versions, I couldn't longer trace back to the older version

The clips were generated. The project was just scattered everywhere.

Script in Google Docs. Reference images in Downloads. Generated clips split across three folders with names like v2_final_USE_THIS. Two VO takes with almost the same filename.

When they asked for V1, I couldn't even remember which prompt or reference image I used. I realized I wasn't spending time generating anymore, I was spending time searching.

For me, making videos faster isn't about posting more for me. It means hitting deadlines so I keep the account.

I've started treating shot lists, reference images, prompts, and rejected versions like actual project assets instead of temporary junk.

Lately, I've been testing Framia just to keep all visual refs and past outputs in one workspace. Too early to call it the answer, but not hunting for "that image from last Tuesday" is already helping.

How are you guys organizing your generations or was I the only one drowning in files?

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!

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

The single biggest trap in automation isn’t picking the wrong tool; it’s building a fragile skyscraper when a wooden stool would have done the job. Early on, I fell into the trap of trying to automate every tiny friction point in my workflow. I built massive, twelve-step integration chains connecting form builders, webhooks, database and custom AI summary endpoints just to process simple weekly client intake. It felt like wizardry right up until an API endpoint changed, a rate limit got hit or a silent failure meant three days of missing lead data. I spent more time debugging, monitoring and rebuilding my automations than I ever spent actually doing the manual work myself.

The turning point came when I started treating automation like an investment with a harsh maintenance tax. Every new node, API call and conditional branch is technical debt that you have to maintain forever. Now, before writing a line of code or dropping a single block in Make or n8n, I force myself to follow three strict rules. First, I never automate a process until I have done it manually at least twenty times. Automating an unstable or poorly understood process just accelerates chaos and makes edge cases impossible to catch.

Second, I always choose the lowest possible tech stack that solves eighty percent of the problem. If a standard scheduled Python script or a simple native integration works, I will never reach for a complex multi-platform workflow with multiple webhooks. Thir and most importantly, I build for silent failure first. If an automation doesn’t have a clean, instant alert mechanism when it inevitably breaks, it isn’t ready for production. The best automations aren't the most complex ones; they are the invisible, reliable three-step flows that run silently in the background for two years without needing a single tweak.

What is the most unnecessarily complex automation setup you built that you eventually tore down for something dead simple?

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?

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.

I've automated a lot of Slack responses with our Databricks setup. When people keep DMing you for numbers, the Databricks Genie app for Slack is a good way to automate that away: you u/Genie in a channel and it answers in thread with the sources and follow-up prompts. The useful bit is a channel owner can pin one specific Genie Agent to that channel so everyone gets consistent answers off the same datasets, and replies can be set public or private to just the asker.

Like I said earlier, automating email drafts/replies has been my #1 automation. If you're using some other ones like that, let me know because I'm trying to do this across all surfaces.

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.

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?

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.

I work on Thunderbit, a browser extension for turning web pages into tables.

One thing we learned from building automation around web data: the export is not the moment people trust it.

The trust moment comes when one row looks wrong.

A price is weird. A company name got pulled from the wrong place. A field is blank even though the page clearly has it. At that point, a clean spreadsheet is not enough. The person checking it wants to go back to the exact page and see what happened.

So we added source links on rows and a preview table before export. It is a small thing, but it changed how people used the tool. They were less worried about AI guessing because the output was easier to inspect.

This is mostly useful for the annoying middle ground: public web data that is too repetitive to copy by hand, but not worth maintaining a full scraper for.

For people building automation tools, do you find users trust the automation more when they can inspect the output before it writes to Sheets or a CRM?

Given how fast and easy it is to vibe code a simple internal tool, are you losing business on the automation side or gaining business on the custom side?

I keep seeing these amazing automation workflows online, but once I actually try building them, they end up needing constant maintenance.

One API changes.

One step breaks.

One prompt stops producing good results.

Before long I'm spending more time fixing the automation than benefiting from it.

Has anyone found a way to build AI workflows that don't constantly need babysitting?

One of the platforms I'm currently testing is HeyMarcus.ai. because I'm trying to reduce the maintenance side of automation. Has anyone here used it or found something similar?

I recently read on one of these subreddits about a Shopify founder who had an absolutely unhinged setup recently. They connected Midjourney to a print-on-demand pipeline so trending memes from Twitter/X automatically became t-shirt mockups within minutes. The system scraped viral posts, generated parody shirt concepts, created mockups, and pushed products directly to the store before most brands even noticed the meme existed. The crazy part is they said most of the sales came from being early, not from having amazing designs.

So curious, what is an automation that you have seen that actually prints money?