I sent an email last week that I'd been putting off for months. I was terrified of what saying it would mean.
The email I wrote to Tibo, founder of Taplio and Tweet Hunter (sold for $8M, now running $700K+ MRR across 5 products).
My email to Tibo (abridged):
"Is it normal that I'm scared to go public with my ideas? I'm not a dev, by any means, I'm a business guy with a passion for AI, I have deep knowledge of what can sell because I was one of the buyers first. I kinda know that what I'm working on right now has a good 80% of success and hard scaling potential too, but still, I'm very scared to go public with it, my network it's just a bunch of dudes that do law they'll all be "O_o", they'd understand the product but they'll probably be weirded out by the fact that I'm pushing it out."
I hit send before I could overthink it. Then I waited.
The email I was scared to send
Most people would be excited to share what they're building. I was frozen.
The product itself gives me confidence. The gapfeed system I've built has 35,000+ signals, 53 identified gaps, and a pipeline that runs 24/7 without me touching it. The numbers are real.
The fear runs deeper than the product. It's personal.
What helped me move forward was building Run your own agent that work silently in the background. For a deeper look at building without employees, see Building a $1M Business with Zero Employees. Real production results live in the case studies.
When you're a developer, you can hide behind the code. "Here, I built this thing. Judge it on its merits." When you're a business person who uses AI to build things, the line gets blurry. It becomes about you and whether you know what you're doing.
And when your entire network consists of lawyers who've never written a line of code, "I built an AI system that listens to the internet for unsolved problems" gets a very different reaction than if you were pitching to Y Combinator.
Why this specific fear
There's another layer here. I'm building autonomous AI agents that run 24/7 doing market intelligence. I've written about the architecture, what I found scanning 6,000 signals, and why most automation fails.
I know the space. I've evaluated 13 AI models for Big Tech. I've built systems that work. But there's a voice in the back of my head that says: "You're not a real developer. You're playing with tools that real developers built."
The imposter syndrome is specific: I'm not worried my idea is bad. I'm worried people will look at me and think "who does this guy think he is?"
Going public means putting your ideas, your reputation, and your ego on the line. The fact that you're scared means you understand what's at stake.
What Tibo said
Tibo's reply was short:
"going public is taking the risk failing, yeah... scary still need to do it though"
That's it. No pep talk. No "you got this!" Just an acknowledgment that it's scary, and you should do it anyway.
The simplicity hit me harder than any motivational speech could have. He's built and sold multiple 8-figure businesses. He has every reason to have a polished answer. Instead, he gave me the truth: yeah, it's scary. Do it anyway.
How I'm moving forward
I'm going to start with what I know.
- Building autonomous AI systems that work
- Market intelligence and finding gaps nobody else sees
- Why automation fails and how to make it succeed
- How to evaluate AI models for real business use cases
- What it's like to build when you're not a "real" developer
I'm going to start writing about that. One post at a time. If it's useful, great. If not, I'll learn from it and keep going.
If you want to see what building in public looks like, explore business AI agents or start with a workflow diagnostic.
And if you're also scared to put your work out there, maybe this helps: the fear means you're paying attention to what matters.
| Operational layer | Fragile default | Production architecture |
|---|---|---|
| Process design | Automating a broken manual habit as-is | Mapping the data contract and failure modes before writing code |
| Human oversight | Either 100% manual or unguarded autopilot | Autonomous execution on routine cases with approval gates on exceptions |
| Data custody | Operations locked inside third-party SaaS | Code, prompts, logs, and API keys owned inside your accounts |
| Failure recovery | Silent drops when an upstream API changes | Schema validation, retries, and operator alerts on every break |
FAQ
What is the first step to implementing is it normal that i'm scared to go public??
Start by mapping the single workflow that loses the most hours each week, defining the exact input and output data contract, and placing human approval gates before writing code.
How do you measure whether an AI workflow is working in production?
Track time-to-completion, exception queue rate, duplicate or failed writes, and the hours returned to operators each week compared to the manual baseline.
Can our team run the system after the engagement ends?
Yes. Every build includes architecture documentation, alerting, and live operator training so your team owns and runs the system without an ongoing retainer.