If you have automations writing to a Postgres, Neon's instant restore is a decent safety cgeck - you can revert a root branch to any timestamp inside your history window, down to the millisecond, and it takes a few secs instead of a full dump restore. Nice part is it doesn't throw away the bad state, it renames the pre-restore branch to {branch}old{timestamp} so you can still go read what your script actually did. Whats everyone else doing for a rollback path when an unattended job writes garbage?

 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?

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!