OpenClaw has been blowing up lately, and for good reason. I've been running it for just a few days—here's what it actually looks like in production for a small team.
I run SubEasy.ai, a transcription/translation/voiceover platform. Good reviews, users worldwide, but perpetually understaffed. I'm not the type who enjoys managing people, so I've always been looking for ways to get more done without hiring more.
I set up OpenClaw on a cloud server with a Telegram bot, running Claude Opus. What happened next genuinely surprised me.
## Day 1: It Built Its Own Workflow
I started simple—asked it to send me a daily briefing with news and stock prices. It came back as a wall of plain text, unreadable in Telegram.
So I asked for HTML. But then it gave me local file paths I couldn't access.
I suggested: "Can you set up a repo, connect it to auto-deployment, and just send me links?"
It did. Created the GitHub repo, configured Vercel, and now every report is a clean webpage I can actually read. The whole pipeline was essentially self-built.
## Day 2: It Fixed a Production Emergency
While setting up Gmail API integration, our YouTube downloader's core component (yt-dlp) broke. Alerts everywhere.
I asked it to check for fixes. It found a patch that hadn't been released as a binary yet.
"Just compile it then."
It pulled the source, compiled it, and we deployed. Problem solved in maybe 20 minutes.
That's when I realized: this thing can actually do real work.
## What It Does Now
We gave it read access to our user database, payment system (Stripe), and email. Now it handles a lot of the daily grind:
*Customer complaints*: When a user emails us, it automatically pulls their payment history, subscription status, and usage data, drafts a response, and sends it to us for one-click approval.
*Influencer outreach*: Finding YouTube/TikTok creators to partner with used to be a manual slog—searching, evaluating content fit, checking follower counts. Lots of judgment calls that kept getting deprioritized. Now it does the initial screening automatically, dumps qualified candidates into a Notion database, and we just make final decisions.
*Review monitoring*: It checks for negative reviews daily, cross-references user data to understand what went wrong, drafts response emails. We just review and send.
## The Actual Insight
The interesting part isn't that AI can do individual tasks—we knew that. It's that AI can now handle the glue work between tasks.
Most of our jobs are really just moving between tools: check email, look up user in database, cross-reference with payment system, write response. The automation tools we had before could handle single steps, but the stitching was always manual.
Now the stitching is automated too. The more systems you connect, the more powerful it gets.
## Team Use
Our small team shares a Discord server with it. Everyone can talk to it individually or in group channels. Knowledge it learns from one person benefits everyone. It's like a shared team member that keeps growing.
terryops•1h ago
I run SubEasy.ai, a transcription/translation/voiceover platform. Good reviews, users worldwide, but perpetually understaffed. I'm not the type who enjoys managing people, so I've always been looking for ways to get more done without hiring more.
I set up OpenClaw on a cloud server with a Telegram bot, running Claude Opus. What happened next genuinely surprised me.
## Day 1: It Built Its Own Workflow
I started simple—asked it to send me a daily briefing with news and stock prices. It came back as a wall of plain text, unreadable in Telegram.
So I asked for HTML. But then it gave me local file paths I couldn't access.
I suggested: "Can you set up a repo, connect it to auto-deployment, and just send me links?"
It did. Created the GitHub repo, configured Vercel, and now every report is a clean webpage I can actually read. The whole pipeline was essentially self-built.
## Day 2: It Fixed a Production Emergency
While setting up Gmail API integration, our YouTube downloader's core component (yt-dlp) broke. Alerts everywhere.
I asked it to check for fixes. It found a patch that hadn't been released as a binary yet.
"Just compile it then."
It pulled the source, compiled it, and we deployed. Problem solved in maybe 20 minutes.
That's when I realized: this thing can actually do real work.
## What It Does Now
We gave it read access to our user database, payment system (Stripe), and email. Now it handles a lot of the daily grind:
*Customer complaints*: When a user emails us, it automatically pulls their payment history, subscription status, and usage data, drafts a response, and sends it to us for one-click approval.
*Influencer outreach*: Finding YouTube/TikTok creators to partner with used to be a manual slog—searching, evaluating content fit, checking follower counts. Lots of judgment calls that kept getting deprioritized. Now it does the initial screening automatically, dumps qualified candidates into a Notion database, and we just make final decisions.
*Review monitoring*: It checks for negative reviews daily, cross-references user data to understand what went wrong, drafts response emails. We just review and send.
## The Actual Insight
The interesting part isn't that AI can do individual tasks—we knew that. It's that AI can now handle the glue work between tasks.
Most of our jobs are really just moving between tools: check email, look up user in database, cross-reference with payment system, write response. The automation tools we had before could handle single steps, but the stitching was always manual.
Now the stitching is automated too. The more systems you connect, the more powerful it gets.
## Team Use
Our small team shares a Discord server with it. Everyone can talk to it individually or in group channels. Knowledge it learns from one person benefits everyone. It's like a shared team member that keeps growing.