22 y.o. ex-founder startups with 43 place in App Store, 1m users, exit, lost $1.000.000 in crypto. Now barista and head of grow at vploq🚨
Road to SF
Joined May 2025
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hey, I’m 22 y.o
top facts about me🍭
- first startup at 17 y.o
- 43 place in App Store
- $100k raised
- $45k revenue
- 25 employees
- closed company in 2023
- moved in Singapore 🇸🇬, china 🇨🇳, turkey 🇹🇷, Georgia 🇬🇪, Armenia 🇦🇲, Malaysia 🇲🇾
- created game with 1.000.000 users in telegram
- lost all my money in crypto (about $1.000.000 )
- back to Russia 🇷🇺
- work barista now
Happy to connect 🫰
All YC podcast this week 👇
1. How to Conquer Bad Days at Your Startup
youtu.be/-7CvPBFz5Xk?si=4hi-…
2. How To Stand Out In A World Of AI Designs
youtu.be/2tZT2-pS_pI?si=ud9Y…
3. The AI Billing Stack for Healthcare
youtu.be/e1NQKzBXfM8?si=Z4Qd…
4. This Is How You Should Actually Make Launch Videos
youtu.be/v12aSIZIXUs?si=sz4W…
5. The New Full-Stack Designer
youtu.be/eojywvxqoLc?si=V3hF…
6. Why Inference Matters 12x More Than Training | YC Paper Club
youtu.be/mHNKuH-9OxU?si=t45u…
This week’s Paper Club is all about inference.
For every FLOP spent training a model, roughly six go to serving it each year — and getting that part right has produced over $220 billion in market cap in the last decade. But inference is much more than calling model.forward. Every token has to move through a complicated stack of routing, caching, scheduling, kernels, memory, and hardware, all while balancing speed, cost, and quality.
We brought together researchers and builders from Together AI, Ollama, Luminal, and SGLang & RadixArk to take us through that stack from end to end. We’ll see how a token gets from an API request to a GPU and back, why the same open model can perform differently depending on who serves it, and how new approaches to compilers and caching are making inference faster and dramatically more efficient.
