Real Alpha
Watch modeSubscribed

Silicon Valley Girl · 3 min · Sep 22, 2026

Break a big problem into parts you can test

Anton Osika recommends doing the work manually first, then breaking it into testable pieces before automating it. Experienced professionals can use their closeness to a problem to build solutions others miss.

Anton Osika

Anton Osika is cofounder of Lovable.

Founded Lovable, the platform for building apps by chatting with AI, after creating open-source GPT-Engineer.

  • Marina Mogilko, Host · Host, Silicon Valley Girl

    Co-founded LinguaTrip and built Linguamarina, an English-learning YouTube channel with almost 9M subscribers.

About this segment

Anton Osika recommends doing the work manually first, then breaking it into testable pieces before automating it. Experienced professionals can use their closeness to a problem to build solutions others miss.

From “$13B Lovable Co-Founder: Build a Profitable AI Product From Scratch in 30 Minutes”, Sep 22, 2026. Watch the full episode

“I think it's usually good to just do things manually and then be systematic about, okay, how can I break this down into pieces that I can test”
Anton Osikaat 2:01 · Silicon Valley Girl2:01

Takeaways

  1. Do the task manually before trying to automate it.
  2. Break the process into pieces you can test.
  3. People closest to a problem may be best placed to solve it.

In this episode

Full episode
  1. 13:05Turn a simple product into a business with service and sales4:33

  2. 1:38Break a big problem into parts you can test2:45

  3. 24:56Find the bottleneck before you abandon an idea3:03Up next
  4. One more moment in the full episode

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 1:51

    And when you think about a problem that you're solving, do you have a framework in your head? how do you solve a problem that seems kind of big? Do you separate it into tasks? Because, okay, like I want to build this content machine, for example, I don't know. When you have a problem that you want to automate, what is your thinking process of how to do it step-by-step and whether some of the problems are worth automating and some not?

  2. 2:13

    I think it's usually good to just do things manually and then be systematic about, okay, how can I break this down into pieces that I can test and see if I can get value from if something is broken that replicates the thing I used to do manually. If you're over-engineering or you're overly ambitious from the get-go, then you might end up being very close, but there's some small part somewhere that doesn't work in the way that you thought it would work or your solution wasn't the right one.

  3. 2:45

    And you want to avoid that. You want to iterate towards perfection. So do you think, because doers are becoming entrepreneurs, Do you think people who are day-to-day working on some boring tasks are becoming the best position for the next three years to become entrepreneurs? We're seeing from our customer data that most people on our platform, they have more than 11 years of professional experience.

  4. 3:09

    And from that, they've become very close to a problem, that they've been frustrated that they can't solve with the skills that they had at the time. And now the person who's closest to the problem, they can actually go out and solve that problem. And that's one of the biggest unlocks of these new technologies. Do you see that group of people generating the most money with Lovable? For most of our customers, it's people who are building and they're already generating revenue themselves or they're trucking towards and they're building a business towards generating revenue.

  5. 3:43

    Most of them have experience. There's also a lot of young people who are building companies, as you might know, in this day and age. And that also often is connected with having advantages of seeing things with fresh eyes for the first time if you're a younger person. But we're seeing people. across the spectrum, across the entire world, every continent and every country, building very serious things on the platform.

  6. 4:07

    Love it. If someone's watching and they're like, okay, this sounds incredible, I want to build something.