❝Story07:30
The Numbers: 300K Users and $10M ARR With 15 People
Anton shares the scale Lovable reached in under three months: 300,000 monthly active users, 30,000 of them paying, and revenue growth of roughly a million ARR per week. The company hit $4M ARR in the first four weeks and $10M in two months with just 15 people, growing almost entirely through organic word of mouth.
- Launched less than three months before the interview
- 300,000 monthly active users, 30,000 paying
- $4M ARR in first four weeks, $10M ARR in two months
- Growth almost entirely organic word of mouth
- Had to rewrite the entire codebase as it scaled
“we launched lavable less than three months ago and now we have 300,000 monthly active users and 30 of those 30,000 of those are actually…”
#growth#revenue#metrics#startup
❝Story10:00
Building an Airbnb Clone Live in 30 Seconds
In a live demo, Anton builds an Airbnb clone from the two-word prompt 'Airbnb clone.' Lovable generates a fully interactive UI with categories, listings and login buttons. He notes the first prompt takes about 30 seconds, then iterates by adding a purchase flow and connecting a Supabase backend for real data.
- Two-word prompt 'Airbnb clone' generates a full interactive UI
- First prompt renders in about 30 seconds
- Output is a functioning site, not just a static design
- Backend (Supabase) connects with roughly one click for real data
- Deploys to Cloudflare hosting
“the the first prom takes 30 seconds 30 seconds”
#demo#product#no-code#prompting
❝Story22:00
Origin Story: From GPT Engineer to Lovable
Anton recounts building the open-source tool GPT Engineer to prove that large language models could turn instructions into working code, after colleagues doubted him. It became the most popular open-source demo of LLM app generation with 50,000+ GitHub stars. GitHub even shut Lovable down once, mistaking 15,000 projects created per day for an attack.
- GPT Engineer built to prove LLMs could turn instructions into code
- Reached 50,000+ GitHub stars and dozens of academic references
- Motivation: enable non-coders, not just make engineers faster
- GitHub shut them down once, thinking 15,000 projects/day was an attack
“I created a open source tool called GPT engineer where you you write something like create a snake game and then it spits out a…”
#origin-story#open-source#ai#founding
❝Story45:00
The Shackleton-Style Job Posting as a Filter
Lenny reads from Lovable's job description, which echoes Ernest Shackleton's famous ad: long hours, high pace, thriving under AGI-timeline urgency, honor in success, and no comfort-seekers. Anton confirms he wrote most of it, and explains that a deliberately intense posting is a filter that repels the wrong people and attracts exactly the ambitious ones he wants.
- Job posting deliberately modeled on Shackleton's intense recruiting ad
- Emphasizes long hours, high urgency and AGI timelines
- 'Those seeking comfortable work need not apply'
- Intensity acts as a filter to attract the right, ambitious people
“long hours High Pace candidates must Thrive under high urgency under AGI timelines approaching”
#hiring#culture#recruiting
❝Story01:01:30
Failure Corner: Don't Retrofit AI Onto an Existing Product
Anton shares a product lesson from his first job at Sana Labs, where they built a personalized-learning AI API that other companies had to bolt onto their existing products. Retrofitting AI, like swapping out an engine, worked poorly. The lesson: start from how the product works end to end, then decide where AI solves a specific problem.
- Sana Labs sold a personalized-learning AI API to bolt onto other products
- Retrofitting AI onto existing products worked poorly
- Start from the end-to-end product and user experience first
- Add AI to solve specific problems, not as the starting point
“you have to start with like how is this product working end to end and then add AI”
#failure#product#ai#lessons