Links

A short collection of links that I find interesting online as well as some attached commentary or callouts with them.

The Cinema Industry is using its own standards for creating and distributing movies in a secure way. The DCI (Digital Cinema Initiatives) specification defines everything from file formats and encryption to the projection systems itself.

marimo is an open-source reactive notebook for Python — reproducible, git-friendly, SQL built-in, executable as a script, and shareable as an app.

We predict that the impact of superhuman AI over the next decade will be enormous, exceeding that of the Industrial Revolution. We wrote a scenario that represents our best guess about what that might look like. It’s informed by trend extrapolations, wargames, expert feedback, experience at OpenAI, and previous forecasting successes.

A popular view about the future impact of AI on the economy is that it will be primarily mediated through AI automation of R&D.

Having an AI on your team can increase performance, provide expertise, and improve your experience. So, what happens when AI acts as a teammate? This past summer we conducted a pre-registered, randomized controlled trial of 776 professionals at Procter and Gamble, the consumer goods giant, to find out.

While Silicon Valley obsesses over AI, a weight-loss drug is quietly becoming the biggest economic disruptor since the internet. Here's why your job, investments, and future depend on understanding it

Enterprises remain uncertain about where to invest in AI and, crucially, where AI truly delivers competitive advantage. This playbook is aimed both at the enterprise and the providers looking to serve it.

A beginner-friendly introduction to stochastic calculus, focusing on intuition and calculus-based derivations instead of heavy probability theory formalism.

In this tutorial, you will learn how to extract structured information, like invoice numbers, dates, directly from your PDF documents using Gemini 2.0:

What if there’s low defensibility for AI model startups, and there continue to be open source alternatives and new entrants that erode advantage over time? Who ends up winning instead?

What if there's low defensibility for AI model startups, and there continue to be open source alternatives and new entrants that erode advantage over time? Who ends up winning instead?

Low barrier to entry combined with good intentions and the “illusion of working implementation1” is not a recipe for success while developing production-grade controllers.

This reflects a pattern we’ve observed repeatedly in AI tooling. Social media excitement and company valuations have minimal relationship to real-world utility. We’ve found the most reliable signal comes from detailed stories of users shipping products and services. For now, we’re sticking with tools that let us drive the development process while providing AI assistance along the way.

To deliver an “infra-less” cloud experience to our customers, we’ve needed to get good fast at building out our own physical infrastructure. That’s the topic of our blogpost today.

Lots of product managers make the mistake of jumping to solutions. Stop that.

Susie Goodall wanted to circumnavigate the globe in her sailboat without stopping. She didn’t bargain for what everyone else wanted.

I’ve led infrastructure at a startup for the past 4 years that has had to scale quickly. From the beginning I made some core decisions that the company has had to stick to, for better or worse, these past four years. This post will list some of the major decisions made and if I endorse them for your startup, or if I regret them and advise you to pick something else.