Founder | CEO @ GrowMeOrganic 🚀

When I was 19, I accidentally built my first SaaS while learning how to code. It was a simple email scraper that pulled leads from Instagram.

It wasn’t perfect. It broke often. But it worked well enough to catch the attention of some top cold email communities.

I didn’t know much about marketing or product scaling. So I started talking to users, wrote every single email, and patched things together late at night.

Over time, I built GrowMeOrganic. Then came SendMails. Then WhatChimp.

Now, I’m building GrowthToolkit. A prospecting platform focused on speed, accuracy, and verified contact data that actually works when it matters.

Built for people who want results without getting slowed down.

Backed by a team that believes in moving fast, staying curious, and solving problems that others avoid.

💌 Anant Gupta

AGI for Vibe Coding 

Not really, but I am getting closer to it…

I was frustrated with AI turning passive after five or ten minutes and asking me to “run this” or “check that.”

So I stopped treating AI like a code generator and started treating it like an engineer with tools.

I built an internal system where AI has full operational control over the project.

First, I created a remote browser control, similar to Puppeteer or Selenium, but without requiring any installation.

The AI can take over a real user session in my app, interact with the UI, trigger flows, capture screenshots, and send those screenshots back to my local project. 

Claude can read them and understand exactly what’s happening in the browser.

On top of that, I built a lightweight Sentry-like system, so the AI can inspect logs whenever something breaks instead of guessing.

If needed, it can also run JavaScript directly inside the browser, inspect network requests, analyze APIs, and even check the database.

If nothing is clear from logs or APIs, it can always fall back to screenshots or direct file inspection.

So the AI has visibility across:
	•	Browser state
	•	Frontend behavior
	•	Backend logs
	•	APIs
	•	Database
	•	Project files

This allows it to code and test end-to-end autonomously, without stopping every few minutes.

The final missing piece was memory.

So I added structured documentation using MD files, plus my own short-term and long-term memory layer. 

This extends the effective context window and lets the AI retain what it learned across modules and dependencies.

Because of this, when the AI switches context; say from one module to another, it already understands the historical decisions, dependencies, and intent.

The result is an AI system that behaves less like a chat model and more like a senior engineer:
observe → inspect → reason → test → fix → remember → continue.

Why this is different from “autonomous agents”

Most “AI agents”:

	•	Still rely on humans to execute
	•	Simulate environments
	•	Die when context is lost

My system:

	•	Operates on real infrastructure
	•	Has feedback loops
	•	Has memory
	•	Has tools + authority

That’s the difference between automation and agency.

CEO @ GrowthToolkit.io | 🏆#1 Platform to Find Accurate Email & Phone Numbers of Decision Makers from any Company | Sign Up for FREE!

graphical user interface, text, application, email

taplio