AI is going to change science forever.

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Chris Pedregal knows how to build AI products that people love.

Chris is the cofounder and CEO of Granola, an AI notepad for meetings. We use it all the time at Every—Granola listens in on a meeting and, when it ends, generates notes and a shareable transcript for anyone who missed it. 

Granola is one of my favorite consumer AI products because it’s equal parts delightful and useful. So my question for Chris was:

How do you do it? How do you make an excellent product in AI? 

We spent an hour talking about:

This is a must-watch for anyone interested in building valuable, sticky AI products that users will love.

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Here’s the most compelling benchmark of AI progress: 

A task that took 60 minutes a year ago now takes 60 seconds.

In January 2024, Geoffrey Litt and I spent an hour coaxing ChatGPT and Replit to build an app live on my podcast.12 months later, Steve Krouse and I built the same app with one prompt in less than a minute. 

Steve is the cofounder and CEO of Val Town, a cloud-based platform for developers to write, share, and deploy code directly in the browser. We used Townie, Val Town’s AI assistant, to build an app to keep track of time on the podcast, take notes, and generate questions for the guest.  

Townie generated the app even before Steve could finish describing it on the show. As we demo Townie, we get into:

This is a must-watch for founders building AI-powered developer tools, and anyone interested in the future of programming.

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The weird thing is: We’re a media company with < 10 full-time employees, and we’re mostly bootstrapped.

That’s not how things are supposed to work in startups.

When we started our product incubation arm six months ago, many people told us it wouldn’t work: divided focus, not enough money, and the biggest one—it would be too hard to find talented people to run the products we build.

Yesterday, we proved out one of the biggest risks to our strategy: We launched a brand-new version of our AI product Spiral (https://spiral.computer) with Danny Aziz as GM—who left a $200K salary to join us. 

The question is: Why? Why did he join us, and why is the model working when it “shouldn’t” be?

That’s why I invited Danny and Brandon Gell, Every’s head of Studio, on the show. We get into the details of Every’s business model, what makes our flywheel turn, where each of us sees ourselves one year from now, and what happens when you mix media, software, and AI under one roof.

This is a must-watch for anyone who wants to build a business on their own terms, and have a lot of fun while doing it. 

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Everyone told Vicente Silveira that his startup—a GPT wrapper—would fail. 

Instead, one year later, it’s thriving—with about 500,000 registered users, nearly 3,000 paying subscribers, and over 2 million conversations in the GPT store. 

Vicente is the cofounder and CEO of AI PDF, a tool that can help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload PDFs to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast. 

Some of his competitors even shut shop, but Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building. 

A year later, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it all with a five-person team, and a friends and family round. 

I sat down with Vicente to understand, in granular detail, the success of AI PDF. We get into:

We even demo an agent integrated into AI PDF, prompting it to analyze recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements.

This is a must-watch for small teams building profitable companies at the bleeding edge of AI. 

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Prompt engineering matters more than ever. But it’s evolving into something totally new: 

A way for non-technical domain experts to solve complex problems with AI.

I spent an hour talking to prompt wizard Jared Zoneraich, cofounder and CEO of PromptLayer, about why the death of prompt engineering is greatly exaggerated. And why the future of prompting is equipping non-technical experts with the tools to manage, deploy, and evaluate prompts quickly.

We get into:

This is a must-watch for prompt engineers, people interested in building with AI systems, or anyone who wants to generate predictably good responses from LLMs.

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This episode is sponsored by Notion. I’ve been using Notion to manage my professional and personal life for almost 10 years. As a company, they pay attention to the craft and ideas underlying the software they build, and that comes through in the experience of using Notion every day. If you’re a startup, get up to 6 months of Notion Plus with unlimited AI—worth up to $6,000—for free by going to https://ntn.so/every, selecting Every in the drop-down partner list, and using the code EveryXNotion.

Notion cofounder Simon Last told me everything he’s learned from integrating AI into a platform that has over 100 million users.

Simon likes to keep a low profile, even though he’s the driving force behind Notion AI, one of the most widely scaled AI applications in the world.

In his first-ever podcast interview, we get into:

This is a must-watch for anyone interested in building reliable AI products at scale.

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Union Square Ventures is building an AI operating system to support their investment team. 

But it’s not what you think: It’s a constellation of AI tools that captures and synthesizes the firm's collective wisdom. It’s evolving every day, and Matt Cynamon is the mad scientist in charge

Matt calls himself a “regular” at USV. In practice that means he’s responsible for running experiments with AI for the firm. As an inherently curious person with the professional obligation to tinker, he’s built a suite of tools for the firm, including: 

I sat down with Matt to talk about how AI is enabling him to bring his ideas to life as a generalist, get demos of the tools listed above, and exchange notes on all the other projects he has in the works at USV. We edit actionable insights extracted by an AI from meetings at USV and prepare them to be posted on the firm’s X handle live on the show. We even try out an art project at USV’s office called The Dream Machine, which generates art from conversations. Here’s a link to the episode transcript.   

This is a must-watch for anyone interested in riding the AI wave by learning how to ship useful products quickly.

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Yohei Nakajima leads a double life. 

By day, he’s a general partner of a small venture firm, Untapped Capital. 

By night, he’s one of the most prolific internet tinkerers in AI. (He also sometimes works on automating his job as a venture capitalist.)

He’s the creator of BabyAGI (@babyAGI_), the first open-source autonomous agent that went viral in March 2023. Yohei has since released seven iterations of BabyAGI (each one named after a different animal), a coding agent called Ditto, a framework for building autonomous agents, and, most recently, BabyAGI 2o, a self-building autonomous agent (that follows OpenAI’s unfortunate naming convention).

Even more incredible, Yohei isn’t a professional developer. His day job is as the general partner of Untapped Capital (@UntappedVC).

I sat down with Yohei to talk about:

We experiment with Ditto live on the show, using the tool to build a game of Snake and a handy scheduling app. Yohei also screenshares a demo of BabyAGI 2o in action.

This is a must-watch for anyone curious about autonomous agents, building cool AI tools on the internet, and the future of AI tooling.

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This episode is sponsored by Reflect. It’s the ultra-fast note-taking app that’s about to change the way you take notes. To boost your productivity with advanced features like custom prompts and voice transcripts, give Reflect a try by clicking on this link: https://reflect.app/?utm_source=every&utm_medium=sponsorship&utm_campaign=september2024

Simon Eskildsen is a learning machine. 

I first interviewed him in 2020 about how he leveled up from an intern at Shopify to the company’s director of production engineering by reading and applying insights from hundreds of books.

A lot has changed over the last four years. LLMs have made it possible to contextualize information like never before—and in this episode, I sat down with Simon to talk about how this changes the way he learns.

Simon is now the cofounder and CEO of AI startup turbopuffer, which is building a search engine that makes vector search easy and affordable to run at scale.

We get into:

As we talk, we screenshare through his Anki setup, including the flashcard template he finds most useful, and try out his custom AI commands in Raycast to understand the meaning of two of my favorite obscure words, “lambent” and “eigengrau.”

This is a must-watch for note-taking aficionados and anyone who wants to supercharge their learning with AI.

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