So you think I’m “non-technical”?

Think again

Hey friend, you’re receiving this as a subscriber of the databeats newsletter – which is now the Semitechnicals newsletter!

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I’m not an engineer.

I don’t know how to code.

Even though I learned programming in high school and aced the computer exam, I wasn’t quite interested in writing code.

Today, I don’t pretend to be even remotely interested in dabbling with code.

I’m too impatient. I love interacting with well-designed interfaces. And I’ve got nothing to prove to anybody.

Growing up, I had two very different kinds of role models – besides my mother, of course, who was relentless in her pursuit of making me excel at everything I did.

My father, who worked independently and used his math skills to make money. And my mother’s brother, a renowned engineer (the real kind) who has dedicated his life to building technology in India for the benefit of the masses.

Both of these men have held the highest possible standard for integrity – they’ve only cared about making an impact, have never been money-motivated, and have had zero tolerance for bullshit.

How do you expect me to be any different?

Moreover, having lost both parents fairly early, I value nothing more than time.

And I have a lot left to do with my limited time.

But this time, it’s different

Of all the hype cycles in tech over the last two decades, this one’s different.

Firstly, unlike Crypto and VR, GenAI has already hit mass adoption; people like you and I use it every day.

Secondly, and I think this is a key difference, GenAI is amplifying the benefits of existing tech rather than forcing people to use something completely foreign.

We have a better browser. We spend less time scrolling through web pages plastered with ads. And what I find most exciting is the renewed interest in automating the mundane.

Workflow automation isn’t new – far from it. It’s been around a long time and we’ve had access to really good automation tools for the better part of the last decade.

However, before GenAI, it took significantly longer to build and troubleshoot workflows. It was normal to spend fifteen minutes writing a data transformation formula inside a Make scenario. It would take even longer when working with Airtable because they didn’t have the best docs back then. Today, I use Claude to generate, understand, and test a formula in less than a minute. Clay has embedded AI-based formula-generation in the product, making the process even easier and faster.

Similarly, back in the day, learning how to write Regular Expressions (Regex) opened up many new possibilities and made data extraction from a long piece of text a breeze. But I had to go through the learning curve and spend a lot of time writing and testing expressions. Today, I use Claude to…you know the rest.

So you see, I use AI to move faster at things I’m already very good at.

Had I been a software engineer, I would absolutely be spending my days inside Cursor or Claude Code or Codex or whatever’s next (Jules?).

But I’m not.

So you think I’m “non-technical” huh?

Even back in the day, I found the distinction between “technical” and “non-technical” rather odd.

For the longest time, my bio said, “A non-engineer who loves APIs” to make it evident that even though I don’t code, I know how to work with APIs. It’s absolutely delightful to be able to do the things I’ve been doing for the last 10 years – without the ability to write code.

The idea of an API wasn’t new for me when I started dabbling with automation tools back in 2017. I had spent significant time working with software engineers and by then, had a good understanding of the software development lifecycle.

I understood how databases work, had seen real-world applications of APIs, and was comfortable reading and writing JSON.

As I got deeper into the automation space, it quickly became evident that, ultimately, automating workflows is all about moving data between tools. And having a good understanding of data structures and data types is key to building robust workflows and troubleshooting them quickly.

So I got deeper into data.

So deep that I spent 5 years – from 2019 to 2024 – deep in the data space. Sure I learned SQL, but I also learned how data is collected, transformed, moved, stored, analyzed, and acted upon.

I became obsessed with the tools that enabled all of these data workflows, documented everything I learned, sold my first blog to Amplitude, built a community, wrote my first book, and collaborated with almost a dozen data startups.

Even though I can read and write basic HTML, SQL, and JSON, I still can’t write code from scratch or troubleshoot code generated by AI.

Does that make me “non-technical”?

I’m a Semitechnical 💪

The reason I never managed to learn to code is simple – I love what I do, and I don’t love the idea of spending time inside an IDE. Heck, I don’t even like the term “Integrated Development Environment”.

I love using software though – like a lot.

But the idea of building software? Nope, not for me.

I also love being really good at whatever it is I decide to do – like the best – and it was evident early on that there’s no way in hell that I would become a 10x engineer because I was always more interested in customer-facing work.

I’ve always enjoyed data work and have been obsessed with workflow automation – these things come to me naturally (not sure if you ever noticed that my handle across channels is iCanAutomate 🤓).

Lastly, not to gloat, but being a semitechnical has paid off really well, and has given me the opportunity to work with the hottest software companies in the world – all while living in India.

All this to say that there’s never been a better time to be a semitechnical – or a <semi>.

What’s next?

Semitechnical is not just an idea. It’s an identity.

Just like people say, “I’m an engineer”, I want people to say, “I’m a semitechnical”.

That’s the future I’m working towards.

That’s my new mission.

Hit me up on LinkedIn if you want in!

What about databeats?

databeats continues to live on as a free resource with over a hundred pieces of neatly organized material for anyone looking to learn data fundamentals.

That said, one of my goals is to further simplify the core concepts so that anyone building with software can apply them in their everyday workflows (rather than just the ones working with large volumes of data at fast-growing companies).

Finally, what to expect from this newsletter?

Thought-provoking ideas and actionable tips to help you thrive as a Semitechnical in an automation-forward world.

And of course, updates on all the cool shit we’re working on.

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