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My biggest takeaways from @ElenaVerna (Head of Growth at @Lovable): 1. In AI, you now need to find product-market fit every three months. Product-market fit used to mean: build something people want, then scale it for years. In AI, the underlying technology changes so fast—and customer expectations with it—that you’re constantly re-earning that fit. Even at $200M ARR. 2. The growth playbook has fundamentally changed for AI companies. Elena has led growth at Miro, Dropbox, and Amplitude and advised dozens more companies on growth. At Lovable, she says only 30% to 40% of what she learned in 20 years still applies. 3. At Lovable, growth is driven mostly through new features, not optimizing funnels. At the fastest-growing company in history, optimization drives about 5% of their growth. The other 95% comes from launching new features and products. Small tweaks don’t move the needle when everything is changing. 4. Ship constantly, and talk about it. Lovable’s main growth and retention strategy: ship features fast enough that customers feel the product is always alive. Engineers announce their own updates. The founder tweets progress daily. This keeps users curious—and keeps competitors scrambling. 5. Give your product away like candy. AI products are expensive to run, so most companies gate them behind paywalls. Lovable does the opposite: they fund hackathons, sponsor events, and hand out free credits. They treat this spending as marketing, not cost—and it compounds through word of mouth. 6. Influencer marketing outperforms paid ads by 10x. Lovable found that short videos showing what the product can do spread faster and convert better than traditional paid advertising. Showing beats telling. 7. “Minimum viable product” is dead. Elena describes the new minimum bar as “minimum lovable product.” If the experience doesn’t delight people, they won’t tell anyone. And word of mouth is your primary engine. 8. Community isn’t a nice-to-have. It’s a key lever for growth. Lovable’s Discord has hundreds of thousands of members helping each other. This amplifies word of mouth, drives retention, and makes customers feel like insiders. Building the product alone isn’t enough anymore—you’re building a world. 9. Hire people who create clarity from chaos. Fast-moving AI companies don’t have neat job descriptions or stable roadmaps. Elena looks for high-agency people who thrive in mess, including new graduates who are AI-native and former founders who know how to operate without instructions. 10. You can work at one of the fastest-growing companies in history and still see your kids. Elena wakes at 6 a.m. Stockholm time, protects her gym and family hours, and refuses to treat burnout as a badge of honor. Her point: if you set boundaries, the work will fill the available time—not all the time.
Full conversation 👇 https://t.co/HOP6hTntDl
GPT-5.2 seems quite good at compaction. Is it based on the same idea behind the GPT-5.1-MAX series? Or are we having a GPT-5.2-MAX eventually?
Kind / Bend / HVM / INets / λCalculus
RT @From0toDS: Love having the paperback to read away from my computer but the author @fchollet has also created an awesome website with th…
Co-founder @ndea. Co-founder @arcprize. Creator of Keras and ARC-AGI. Author of 'Deep Learning with Python'.
The subtle point here is that "interesting" driving events are RARE Consider: If you need 1000 examples of an event to train your AI, and that event only happens once in a normal person's life (major crash) That means you need MILLIONS of driving hours to capture that event dataset in any reasonable amount of time This is precisely why the Tesla fleet is their competitive advantage
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The ChatGPT App Store is live in beta. How to access it 👇 Settings -> Apps -> Browse apps
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RT @thejustinwelsh: Real freedom is turning down money because you don't need it, skipping the meeting because nothing bad happens if you d…
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