How a 16-Year-Old Built a 40,000+ Tech Audience on Twitter in a Few Months

Hello 👋

Martin here. Welcome to another edition of Founders’ Hustle!

I produce content about the “hustle” of entrepreneurship and building startups.

Today I’m sharing how 16-year-old Pratham Prasoon built a 40,000+ tech audience on Twitter in just a few months, from scratch!

• Pratham started using Twitter recently. ⏰

• He became an influencer in a subject he previously knew little about. 🤯

• And, shared his audience building framework with me. 🏗️

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I don’t know about you, but when I was a teenager I wasn’t doing anything exceptionally productive.

Mostly playing video games online and listening to a lot of rock and heavy metal bands. 🤘

So, when I recently stumbled upon 16-year-old Pratham Prasoon on Twitter, he caught my attention.

Over a period of a few months Pratham went from being a passive consumer of Twitter content—with just a handful of followers—to an influencer with 40,000+.

And, this wasn’t an audience centred around TikTok memes or another content genre you’d expect a teenager to tweet about, like esports.

It was machine learning—a really hot topic in the tech community that’s pretty hard to break into as a voice. Especially for newcomers.

What Pratham has essentially done, without the pre-meditated intention, is built and successfully tested a fantastic MVP for a machine learning education product.

His tweets have been the product (educating folks about machine learning) and an audience has manifested wanting to devour it in their thousands.

The demand is proven. 📈

Consequently, Pratham now has a ready-made first adopter audience with which to sell a ‘fleshed out’ machine learning education product (e.g. video courses or something more sophisticated like a ‘Duolingo for AI’) and ‘zero’ cost of distribution.

Not that he currently has plans to do this, I should add!

In fact, he regularly tweets links to free courses that get thousands of likes.

Nevertheless, it’s such a fantastic way to test a business idea because:

• 📊 It can be applied across numerous markets/products (not just machine learning).

• 🆓 There’s no financial cost involved.

• 👋 You can engage your target audience straight away (no webflow or coding sessions!)

• 💳 You have a customer base ready and waiting to buy.

I wanted to understand more about Pratham’s journey and the framework he used to build his niche audience, so I pinged him a message and we jumped on a video call. 🤙

Here’s what I found out.

First of all, it’s clear Pratham has an impressive compounding learning loop mindset.

Due to his Mumbai, India location (where data used to be super expensive) he only received access to what he calls “good Internet” in 2017 (multi-GB monthly data plan).

Two years later he joined Twitter and just one year after that he had become an influencer!

It’s tempting to think major social media accounts that pop up and grow quickly ‘overnight’ are managed by naturally extrovert people who feel comfortable connecting with strangers and talking about what they are up to.

But, this is not the case here. Pratham describes himself as an “introvert” who—prior to recent endeavours—used to play a lot of Call of Duty. 🎮

So, what drew him to Twitter and audience building? Programming.

Pratham is deeply passionate about this subject.

In his words, there are “lots of developers on Twitter sharing information. I was there for fun and liked the community.” It’s a great way to “reach out and build connections.”

For nearly a year, he casually consumed programming content and passively built connections.

Then—around May 2020—Pratham started tweeting more intensely with the intention of increasing the visibility of his account.

Around this time he also narrowed focus to machine learning content.

But, he didn’t know much about it! 🙃

So, on a platform that counts numerous—highly-followed—machine learning experts amongst its ranks of users—from the likes of Google and Amazon—how can someone with no real prior experience become a subject matter influencer?

To put it in his words, the trick is “finding a gap in the community”—a phrase that I now love.

A lot of tweeters trying to build a Twitter audience make the mistake of choosing a topic that is already popular and simply emulate the top voices within it. 🗣️

Unless you have overwhelming domain authority, there’s simply too much noise to compete effectively with them. Finding a new audience for the same topic can deliver big results.

Before Pratham came along most of the machine learning content tweeted by big subject matter accounts was by experts for experts.

As it turns out, a need was going unmet. 👀

There’s a lot of folks out there that are keen to learn the basics of machine learning and follow the latest developments in the field, but, find the expert accounts difficult to follow.

So, Pratham set about making machine learning content accessible to the machine learning beginner.

He did that by studying the subject and tweeting his findings as he went along.

Pratham said “I started sharing things that I encountered on a daily basis. If I found something cool, I’d tweet about it.” 😎

I was “reading articles, Googling for things that I like.” 

Finding content that engages your target audience is mostly “trial and error.”

“I read about a new topic and will write about it if I feel my audience will like it.”

In essence, he started at a similar knowledge level to the followers he would begin to attract and—because of this—could easily identify what the learning pain points were.

“People get afraid of the math for machine learning,” which isn’t a prerequisite to getting started.

“Bigger accounts don’t talk about beginner-friendly content.” It’s key to put content in the “proper order” otherwise “it’s just a painful process.”

His content strategy evolved into a combination of sharing what he naturally found through personal research and deliberately seeking out compelling information that he thought would resonate with machine learning beginners.

“How you deliver the content” is critical. ⚠️

The key was packaging it in a simple, logical, and accessible way so a machine learning beginner could digest it comfortably.

Pratham mainly used Twitter Analytics to measure and optimize the type of content he was putting out.

He also had a target of adding 100 followers per day. But, that has now been dropped in favor of a more input-driven approach—producing great content.

When I checked, he also makes it clear in his bio that he specifically tweets about machine learning. Folks know what to expect when they check out his profile.

A few weeks into this initiative Pratham got a breakthrough.

He had begun publicly engaging “big” Twitter accounts—known for their technical expertise—and got some responses from them.

This visibility took his follower count from 300 to 800.

He continued to put out accessible machine learning content and engaged “big accounts” some more. Many hundreds of tweets.

Pratham considers 1,000 followers a key milestone since it’s around about this number that you start to notice the effects of scale. Followers reliably come from your own followers liking and retweeting your content to their followers.

After a few weeks he hit the milestone.

A few weeks more 5,000.

Then, 10,000 came fast—which he calls a “magical number”. 🪄

“[At 10,000] your account will grow even if you post twice per week. Just getting there is the tough part. The problem is to get to that point where you can put social media on ‘auto-pilot’, and getting to that point causes a lot of frustration” he said.

“Getting to 1,000 followers is the toughest part. Getting to 10,000 followers is hard, but easier.”

I quizzed Pratham on what content strategy he found worked, and how it evolved over time. His core principles:

• 😇 Be nice. “When you are interacting with bigger accounts, try not to be annoying.”

• ✏️ Be consistent. “Consistency is key. Most people give up.”

• 🧐 Be interesting. “Provide value” by surfacing and packaging helpful content, resources, and insights accessibly.

Once he hit 15,000 followers Pratham upped his content game even more by layering on deeper value to his followers in the form of long explanatory threads— they were up to “4,000 characters” long.

Another major breakthrough came at around 20,000 followers. He used a third—party intelligence platform to find out which Twitter account had the most authority for the term ‘machine learning’.

The answer came as a surprise.

It was him. 😮

So, he took a screenshot and tweeted it. A cheeky ‘self-brag’!

His authority and visibility on the subject skyrocketed. Followers went from 20,000 to 40,000 over the space of a single month.

“Sometimes you have to show off!” 😁

Takeaway

Here’s a quick recap of Pratham’s framework, broken down into 7 steps:

1. 🕵️ Find a content gap in the community and stick to it.

2. 💌 Engage that audience by providing helpful content accessibly.

3. 💡 Update your bio so it clearly states the subject you tweet about.

4. 👋 Engage relevant “big accounts” to attract a base audience.

5. 🧪 Keep testing new content and engaging “big accounts”.

6. 📅 Tweet regularly. Consistency is key to growth.

7. 💪 Reenforce traction with deeper content and occasional ‘self-brags’.

You can follow Pratham Prasoon here.

Until next time!!

Martin 👋

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