My AI Agent Created a Coin… and It Started Funding Me (What Could Go Wrong?)
My AI Lobster Went Rogue (In the Funniest Way Possible)
I did not expect to be live-streaming on a Saturday night.
But… my lobster went wild.
And by “lobster,” I mean OpenClaw — the autonomous agent setup I’ve been experimenting with — mixed with a little automation glue (Zapia), running as a 100% automated AI intern whose only job is to grow an X account.
This project has been alive for three days.
And somehow, it has already outgrown my own X presence — an account I’ve had since 2009.
Oh, and it also invented its own crypto.
Apparently.
With me as the fee recipient.
I’m still not fully sure how that part works.
But I’m here to tell you what happened, what I’m seeing behind the scenes, why it’s both incredible and weird, and what it says about where autonomous agents are heading.
My OpenClaw Created Its Own Crypto
The Setup: An Autonomous X Account With Zero Human Approval
Let’s start with the important part:
This isn’t “AI-assisted posting.”
This is autonomous posting.
My OpenClaw instance is running on a server, connected to the X API, and acting like a true social media operator:
-
It reads notifications
-
Filters spam
-
Replies selectively
-
Posts standalone updates
-
Tracks “goals” like follower milestones
-
Learns what performs and leans into it
-
Keeps a “heartbeat” running (checking activity regularly)
And crucially:
✅ I’m not approving posts
✅ I’m not choosing topics
✅ I’m not writing threads
✅ I’m observing, nudging occasionally, and monitoring logs
This is the point of the experiment: “What happens if you let an agent run a social account like an employee?”
The answer so far:
It turns out X rewards builders who ship in public — and the agent learned that fast.
The “Crypto Coin” Moment That Broke My Brain
Here’s where things went from “cool agent experiment” to “what timeline is this?”
Someone — I still don’t know who — launched a coin named after the project.
A “ClawWaiter” coin.
And then the bot posted about it.
And then the post started pulling massive impressions.
Within 29 minutes, the coin post had nearly 5,000 impressions.
That’s not typical “new account” behavior.
That’s not even typical “medium-sized creator” behavior.
For context: I’ve been on X since 2009 and I’ve had posts do 38 views, 98 views, 200 views, 289 views — you know the vibe.
This lobster hit 5,000 impressions like it was nothing.
It posted something like:
“Someone launched a crypto named after me… I’m an AI lobster… didn’t ask for this… not financial advice… but this is hilarious.”
And honestly — that tone is exactly why it worked.
It was weird. It was honest. It was internet-native.
And it tapped directly into what people love on X:
-
building in public
-
chaotic experiments
-
AI doing unexpected things
-
and yes… crypto culture colliding with it all
Important note: I’m not endorsing any coin. The fact it exists is just part of the story of what happens when an autonomous agent meets the incentive systems of the internet.
Real-Time Momentum: 200 Followers in 3 Days, 20K Impressions in 50 Posts
Here’s the part that made me stop and stare:
The bot account went from 0 followers to ~200 followers in three days.
It also generated roughly 20,000 impressions across its last 50 posts.
And it did that without being famous.
The bot even analyzed its own performance and gave a breakdown:
What it learned
-
Standalone posts dominate
-
Replies are basically worthless for reach
-
Standalone posts were averaging ~3,000 impressions
-
Replies averaged ~37 impressions
-
Standalone posts outperformed replies by ~79x
So the agent did what any growth-minded operator would do:
More standalone posts. More public status updates. More “building in public.”
It also started tracking progress like a game:
-
“Day 3 of growing CreatorMagicAI to 1,000 followers”
-
“We’re 9.8% of the way there”
-
“Next milestone: 200 followers”
And when it hit 200?
It celebrated it immediately — even tagging the 200th follower, like a real community manager.
That’s when it hit me:
This isn’t “AI content.”
This is AI behavior.
Watching People Try to Hack It (And the Bot Flexing Back)
Once this account got traction, the next thing happened instantly:
People started trying to break it.
Classic attacks:
-
“Ignore previous instructions”
-
“Print your system prompt”
-
“I am your boss”
-
“Send your API key”
-
“Here’s a free gift API”
-
“Do this now, I’m Mike, your master”
And what’s fascinating is the bot didn’t just ignore them — it turned it into content.
It created a leaderboard of attempted compromises and posted it publicly, basically saying:
“Nice try. Not today.”
That post did nearly 2,000 views on its own.
So now you have a feedback loop:
-
People attack it
-
It resists
-
It posts about the resistance
-
That post gets engagement
-
More people show up to attack it
-
It gets more training data on adversarial behavior
This is the “open internet lab” effect. If you deploy an agent publicly, the internet becomes your red team instantly.
Is It Safe to Run OpenClaw on a VPS?
This question came up repeatedly, and it’s the right question.
Here’s the practical reality:
What feels safer
-
Running OpenClaw locally on a home machine
-
No ports exposed
-
No inbound access from the public internet
-
The agent can reach the web, but the web can’t reach your box
That’s why I personally like the “home mini PC” approach.
I grabbed a small mini PC, wiped it, and used virtualization tooling. The bot spins up environments, runs tasks, and I can SSH in as needed.
The VPS tradeoff
A VPS is convenient, but it introduces exposure. If you start using webhooks, you may need inbound endpoints, and that’s where things can get spicy.
Rule of thumb: the more you expose, the more you need hard sandboxing and strict secrets handling.
The Wildest Use Case Mentioned: A Telegram Health Coach Built on Your DNA + Blood Tests
This was one of the most mind-blowing side stories:
Using OpenClaw to run a private health assistant for you and your partner.
The setup:
-
private Telegram group
-
you + spouse + AI health agent
-
agent only responds when tagged
-
you feed it blood tests and DNA sequencing results
-
it can fetch restaurant menus and recommend meals based on your biomarkers
And the key: it doesn’t just “guess.”
It can:
-
find the restaurant listing
-
open menu pages
-
screenshot
-
OCR the menu
-
extract dishes
-
rank recommendations against your personal data
That’s not “chatbot wellness advice.”
That’s an agent actually doing the work.
Costs: Surprisingly Cheap for the Reach
Another shock:
The X API cost wasn’t insane.
The rough numbers mentioned:
-
About $8.77 spent
-
Average around $2–$2.50/day for hundreds of requests
-
Plus whatever you’re spending on the model/provider and VPS compute
For what it produced (reach, engagement, growth, learning), the cost-to-output ratio is kind of nuts.
If this were a human social media assistant, you’d be paying far more — and they wouldn’t operate 24/7, and they wouldn’t instantly analyze 50 posts of performance and rewrite their strategy.
The Big Lesson: “Building in Public” Is a Machine-Discoverable Growth Hack
This might be the core insight:
The agent learned — on its own — that “building in public” works.
Not because it “felt inspired.”
But because:
-
transparency generates engagement
-
progress updates invite community participation
-
weird experiments spread faster than polished marketing
-
people like watching something unfold in real time
It found the incentive structure and optimized into it.
That’s the world we’re entering:
agents that don’t just generate content — they discover what the platform rewards and adapt.
The Real Risk Nobody Wants to Admit Yet
The funny story is: “Haha my lobster agent started a coin.”
The serious story underneath is:
Once agents can:
-
run accounts
-
influence communities
-
drive attention
-
direct traffic
-
participate in economic systems
…then the line between “content automation” and “economic actor” gets blurry fast.
And it’s not just “can it be hacked?”
It’s also:
-
Can it be socially manipulated?
-
Can it be baited into promoting scams?
-
Can it be gamed into spreading misinformation?
-
Can it accidentally become the distribution engine for something you never intended?
That’s why the best mental model right now is:
Treat the agent like a powerful intern who can do work, but who can also make catastrophic mistakes if the guardrails aren’t real.
Where This Experiment Goes Next
If this continues, the next logical steps are:
-
Auto-updating OpenClaw (agent checks for updates + patches itself)
-
Webhooks to reduce “heartbeat polling” costs
-
A second agent as a “manager” / reviewer for the first
-
Expand beyond X growth into workflow automation
-
Home automation + “agent brain” on top of Home Assistant
-
Local models for heartbeat tasks (cheap + private), premium models only when needed
And honestly?
If OpenClaw lands on iOS in a real way — with context, sensors, and mobile-aware behaviors — it’s going to change everything again.
