Anton Osika

AI product strategy should focus on democratizing complex skills for the non-technical majority.

"The reason why we're doing Lovable is that I don't know about your mom, but my mom doesn't write code... we are building for this 99% of the population who don't write code."

Identify and systematically solve the specific 'stuck points' where AI agents typically fail to ensure a reliable user experience.

"The frontier of where this is a problem is very rapidly receding back. So what we did was we identified the most important areas, so specifically adding login, creating data persistence, adding paymen..."

Mastery of AI tools requires a combination of patience, curiosity, and using the AI itself as a tutor to understand technical constraints.

"It takes a lot to master using tools like Lovable and being very curious and patient and we have something called chat mode where you can just ask to understand like, 'How does this work? I'm not gett..."

AI fluency is best achieved through project-based learning and persistent experimentation over a concentrated period.

"The best way to learn is I want to do this thing and then I want to use AI to do that thing. And you've spent a full week, you are in the top 1% in the global population."

Use high-signal, polarizing language in job descriptions to filter for candidates who thrive in high-intensity environments.

"Long hours, high pace, candidates must thrive under a high urgency under AGI timelines approaching, difficult mission ahead, honor and recognition in case of success, those seeking comfortable work ne..."

In the AI era, prioritize hiring generalists who can cover multiple functional skill sets.

"If I'm putting together a product team today, I would really obsess about getting as many skill sets as possible for each person I hire."

Use extended work simulations to verify technical skills and cultural fit before making a final hiring decision.

"I pretty much always have people join the work simulation for at least a day, often a full week."

In-person collaboration and informal social rituals like team lunches accelerate decision-making and cross-functional alignment.

"We work from the office most of the time... I think it's pretty nice. Then you can say like, 'Hey, I think we're thinking wrong about this thing,' or, 'Shouldn't we actually do this other thing?' And..."

High-quality product demos and 'building in public' on social media can drive massive organic growth.

"For getting awareness, we've mainly been posting what we've shipped on social media, that's how people know about us."

Focus on solving the single most critical bottleneck rather than building out extensive long-term roadmaps.

"Just top line? I think identifying what is the biggest bottleneck, what's the biggest problem and iterating fast on saying, 'Okay, this is the biggest problem, let's really, really solve that problem...."

Effective product building with AI requires precise articulation of expectations and specific identification of what is failing.

"Explaining exactly what you expect and what you're not getting is even more important with AI than with the humans. So I'm going into hooking up more of the actual functionality, but first I'll actual..."

Avoid retrofitting AI into existing products; instead, start with the end-to-end user problem and determine where AI provides the most value.

"The big learning is that you have to start with how is this product working end-to-end and then add AI or think where should we add AI? So that was a big learning for me that you really want to see wh..."

Shift the focus from 'Minimum Viable Product' to 'Minimum Lovable Product' to ensure the initial release resonates emotionally with users.

"A lot of jargon that I like to use to emphasize what we should be striving for is building a minimum lovable product and then building a lovable product and then building an absolutely lovable product..."