Welcome! I’m Karthik - lover of basketball, chai, snacks and technology.

Check out my latest post: https://hackernoon.com/anti-growth-hacking-f2ffb2810203. I talk about anti-growth hacking websites.

Investment Thesis

I invest my time and effort. I want to democratize resources that are essential to thrive. 

Facilitate access to: 

  • Necessities: food, water, shelter, community.
  • Quality learning content, teaching and peer environments.
  • Opportunity to create value through a meritocratic process.
  • Freedom to express self.
  • Tools to create.

Previously

I worked at Sentio to reduce the cost to be productive. Smartphones are everywhere, but you can’t do much work on them, and so creators have to invest in expensive computing equipment. Sentio makes an affordable laptop shell that transforms your smartphone into a laptop by combining the computation of your phone with and a laptop-like interface. I focused on growth (among other roles). 

Before Sentio, I worked at other startups and personal projects:

  • Developed Xnote: A tool to annotate and share web articles from your Android device.
  • Developed ChaiApp: An app to connect with similar-interest people around you. Think hyper-local meetup.com.
  • Worked at Pindrop Security to detect phone scams. A common phone scam is when a person calls you impersonating your bank, or another reputable institution, and asks you for sensitive personal information. We collected phone complaints from sites where victims of scams submitted complaints, and then we processed them to classify into a type of scam (e.g. Wells Fargo scams, or Microsoft support scams). We could then detect the rising and falling of scams in real-time. We published a paper, and our scam scanner was featured on Techcrunch after it recognized one on Tinder.
  • Undergraduate Research in Reinforcement learning under Jon Scholz - Georgia Tech: I implemented algorithms to control PACMAN’s brain in an existing Pacman Simulator, to help him score as high as possible. A straightforward approach would be to calculate, on each turn of Pacman, for each of his moves, all of the possible scenarios of the game that could occur, and then pick the move that would take Pacman towards a high score. But as you can imagine, the Pacman game has too many potential outcomes for this approach to be computationally feasible. The algorithms I implemented were textbook reinforcement learning algorithms that found clever ways to figure out the best next step without playing out all of the future scenarios in Pacman’s head.