Sergey Parkhomenko

Lovable just raised $200M at a $1.8B valuation. We spent 48 hours in Stockholm with the team and saw how startups will be built in 2025: 1/ WHEN IT WORKS, IT PULLS Their story is insane. They launched GPT Engineer. It exploded 🤯 50k GitHub stars in days! Lovable was born to build the UI on top. Same thing happened with Replit. It took them 8 years to reach $10M ARR. Then, they found “vibe coding”. They dropped everything else. And hit $100M ARR in under a year. In AI, PMF doesn’t creep in. It hits like a wave. 2/ MARKETS WITH 2 LEADERS MATTER Two leaders = duopoly = closed market. CRM: Salesforce + Hubspot. eCommerce: Shopify + WooCommerce Lovable is betting on becoming the 1st player. That’s where the door is. 3/ TIME IS RELATIVE Everyone’s working weekends. Not out of pressure, but alignment. When everyone’s pulling in the same direction, speed compounds. 1 year at Lovable = 10 years in SaaS. 4/ YOUNG TALENTS I used to believe experience was everything... Turns out, some of the most brilliant builders hit their stride in their early 20s. Cursor’s founders graduated in 2022. Lovable’s average age looked like 24 😅 I felt like a dinosaur. 5/ RULES ARE MEANT TO BE BROKEN I asked Fabian, co-founder & CTO, who their persona was. He said: “anyone who wants to build an app.” At first, I didn't get it. At June, we narrowed down to find PMF. You don't always need to niche down in AI. Lovable is replacing Webflow, Framer, and Bubble... at the same time. 6/ GTM IS OVERRATED WHEN PULL IS STRONG Lovable hired Elena Verna. One of the best growth leaders out there. But traction came before. Lovable go-to-market is its community. People use it, share it, pay for it. GTM is here to accelerate things. It puts fuel on the fire 🔥 7/ THE OPPORTUNITY ISN’T OBVIOUS “Vibe coding” was dismissed because of retention concerns. But I’ve seen the data, and trust me, it’s wrong. This opportunity wasn't obvious to most people. And it helped them. 8/ FUNDRAISING IS SECONDARY This raise didn’t happen on a roadshow!! It came from a trusted conversation. Focus on building, and the right money finds you. 9/ COST ISN’T A PROBLEM In SaaS, AWS gave you free credits. In AI, LLM providers do the same 😉 If your product is great, infra will follow. 10/ THINK FROM SCRATCH The"code on the left, app on the right" UI was their invention. I asked Fabian how he came up with it. He said “it felt like the best UI”. Great products invent their own interface. Others copy it later. (Tinder for X, Uber for Y) ______ These 48 hours were a glimpse into what the next wave of startups might look like. Thanks again to Anton and Fabian for having us. For being so open, and the cold swim 🥶 You're building a generational European company 🇪🇺 And to Elena for making the intro. You're one of the greatest 💜

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Companies stopped hiring developers. And started using AI + no-code. The stats are insane. No-code market growth: 2025: $35 billion 2030: $300 billion (projected) That's 850% in 5 years. What actually changed: - Developer hiring: -40% this year - No-code adoption: +400% - Average app build time: 3 months → 3 hours - Cost per app: $50k → $500 The shift isn't coming. It's here. By 2030: - Every employee builds tools - Ideas tested in hours - 90% of current dev work automated This isn't killing programming. It's killing the monopoly on building.

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The Meta Product Manager Mafia has raised more than $110M in funding and comprises 20+ companies. Product managers at Meta have expectations and responsibilities that make them great entrepreneurs: 1) Obsess over the North Star: Meta PMs are trained to align cross-functional teams toward a single, company-defining goal. They’re responsible for maintaining clarity and direction. 2) Simplifying complex problems: This is a rare skill but an important one for any PM. They take complex problems and break them down into clean processes that designers, engineers, and execs can all understand and rally behind. 3) Experiment and fail quickly: Meta PMs are encouraged to ship ideas quickly, test them in-market and if it fails the teams are broken down aggressively to focus on something else. 4) Brutal transparency: As a Meta PM, there’s nowhere to hide. Frequent exec reviews mean PMs are directly accountable to VPs and C-suite. The expectations for growth and comp are communicated candidly and that visibility makes great PMs rise quickly.  Here are the top 10 most funded former Meta PMs, turned entrepreneurs: 1. Archy: Co-founded by Jonathan Rat Funding Raised: $27M 2. Lendtable: Co-founded by Mitchell Jones Funding Raised: $22.5M 3. Greenlite AI: Co-founded by Will Lawrence Funding Raised: $20M 4. Things, Inc. / Rooms: Co-founded by Jason Toff Funding Raised: $11M 5. LastMile AI: Co-founded by Andrew Hoh Funding Raised: $10M 6. Kopperfield: Co-founded by Mark Wong Funding Raised: $5M 7. Ribbon - Inheritance Platform: Co-founded by Saeid Kian Funding Raised: $2.7M 8. GovEagle: Co-founded by Akash M. Funding Raised: $2.5M 9. Rally (YC W25): Founded by Thiago Peres Funding Raised: $500K 10. Besimple AI: Co-founded by Yi Zhong Funding Raised: $500k Data sourced from Crustdata (YC F24) Did we miss out on anyone?

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BREAKING: 86.8% SWE-bench Verified, Biggest Leap Ever. "SWE Bench verified has been the Super Bowl of AI coding benchmarks for the past two years. The last time the coding benchmarks had a double digit leap was March of 2024. This ushered in a huge era for AI coding. The AI agent was here." -Brian Elliott We just proved the AI pundits wrong at Google's Sunnyvale campus. While everyone's saying "AI acceleration is plateauing**,** we took a different approach at Blitzy. The results? 86.8% on SWE-Bench verified - the industry's premier AI coding benchmark that tests systems on real-world GitHub issues - delivering a 10% lift over the current leader and the first double-digit leap since March 2024. Here's what changed the game: --> System 2 AI: Trading time for quality. Our platform spends hours mapping every line of code relationally and hierarchically summarizing entire codebases (100M+ lines). --> Multi-agent orchestration: Extended inference time compute with validation across multiple models with built-in prompt engineering. As I told the audience: "The next frontier of progress is not just pre-training models. By driving more inference time compute into your systems, you are able to drive up the quality of output dramatically." Link to white paper in comments. #blitzy #ainativesdlc #autonomoussoftwaredevelopment #aicoding #nextfrontier

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How Stripe prevents double payments (explained in 2 mins or less): 1 They use idempotent APIs to prevent double payment. 2 They use UUID as the idempotency key. 3 They send the idempotency key through HTTP headers. 4 They create a new idempotency key whenever the request payload changes. 5 They store the idempotency keys in a database on the server side. 6 They query the database with keys to check if a request was processed earlier. 7 They process a request only if it's new and then store its idempotency key in the database. 8 They roll back a transaction using the ACID database on a server error. 9 They remove the idempotency keys from the database after 24 hours for reuse. 10 They use exponential back-off with jitter to avoid the thundering herd problem. What else would you add? —— 👋 PS - If you enjoyed this post, you'll love my detailed article about this case study (with visuals): → https://lnkd.in/erMkqwq4 ——— ♻ Repost to help others learn idempotent APIs. 👤 Follow Neo Kim for more.

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