Andrew Tan
Founder Of TLDR AI & TLDR Founders at Anthropic
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolAndrew Tan is a founder, investor, and AI product leader with 11 years of experience building startups and scaling AI teams from research to production. He founded TLDR, a media company reaching 7M+ readers, and previously built and exited Nova, demonstrating a knack for community-driven product momentum. At Handshake he co-founded the GenAI team, and his technical roots include RL and distributed-systems research at Berkeley RISE Lab with notable open-source contributions to Ray’s RLlib and Tune tooling. Andrew also invests in frontier AI (Anthropic, OpenAI) and runs an early-stage AI fund and studio, blending operator experience with venture judgment. He holds CS degrees from Berkeley and Stanford and brings an uncommon mix of hands-on ML engineering, startup founding, and investor perspective that connects research-grade systems to large consumer and developer audiences.
10 years of coding experience
7 years of employment as a software developer
Bachelor's degree, Computer Science (Honors), Statistics, Bachelor's degree, Computer Science (Honors), Statistics at University of California, BerkeleyIrvington High SchoolBachelor's degree, Business Administration, Bachelor's degree, Business Administration at University of California, Berkeley, Haas School of BusinessEntrepreneurship, Entrepreneurship at Massachusetts Institute of TechnologyMaster's degree, Computer Science, Master's degree, Computer Science at Stanford University
English, Chinese
Github Skills (8)
hyperparameter-optimization10
pytorch10
machine-learning10
ray10
python10
reinforcement-learning10
keras7
cli6
Programming languages (6)
Github contributions (5)
ray-project/ray
Jun 2018 - May 2019
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 14 PRs, 44 comments in 10 months
Contributions summary:Andrew primarily contributed to the RLlib component of the Ray project. Their work involved merging DDPG and DDPG2 implementations, adding KL divergence metrics, and fixing trial reporting issues within the Tune framework. Additionally, they integrated SigOpt, a hyperparameter optimization tool, and added a "note" command to the Tune CLI for improved user experience.
pythonconsistsruntimetensorflowserving
andrewztan/todolistMDP
Feb 2017 - Mar 2020
MDP Representation of To Do List
Contributions:2 PRs, 45 pushes, 2 branches in 3 years 1 month
representationmdpto-domarkdownto-do-list
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