Why Applying Machine Learning to Biology is Hard – But Worth It

Jimmy Lin and Nicole Neuman

Computational genomics pioneer Jimmy Lin explains what many machine learning-focused biotech companies and get wrong about hiring, data, and communication.

Did We Overeat on Software?

Andrej Safundzic

Lumos CEO Andrej Safundizc explains why companies that use SaaS wisely have a competitive advantage over those that blindly consume it.

Our Cities Have an API Problem. Startups Can Fix It.

Zach Caceres

What if we evaluated America’s cities as technological systems? To really improve them, a new generation of startups must compete with them.

Research Twice, Build Once: How to Know Your Users as You Grow

Ryan Glasgow

Sprig founder and CEO Ryan Glasgow explains why the key to building successful products is understanding your users and what they want.

Grow or Die: A Framework for Turning Your Company Around Fast

Michael Mignano

When your back's against the wall, you need a high stakes, focusing goal that instills a sense of urgency and clarity.

What Synthetic Embryos Can and Can’t Do, Now and in the Future

Magda Zernicka-Goetz and Nicole Neuman

Caltech Professor Magda Zernicka-Goetz explains recent progress in building 'synthetic' embryos from stem cells, their applications, and what they can't do.

Jason Fried on Why He Doesn’t Do Planning or Politics at Work

Jason Fried and Lauren Murrow

The Basecamp and HEY founder discusses the power of short-term thinking, his framework for startup longevity, and the key thing he looks for when hiring remote.

Thinking Through CC0 and IP for NFT Communities

Chase Chapman, Nuzayra Haque-Shah, Austin Hurwitz, and Jeff Benson

A Slack chat to talk about CC0, commercial rights for NFTs, and the future of intellectual property in web3.

Remote Startups Will Win the War for Top Talent

Chris Herd

When 40 percent of workers are considering quitting, management that opts for empty arguments and handy-wavy phrases will bleed top talent to the competition.

How to Build a GPT-3 for Science

Josh Nicholson

A GPT-3-like AI model for science would accelerate innovation and improve reproducibility. Creating it will require us to unlock scientific publications.