Stay ConnectedThe Every Podcast is Every’s flagship show. Co-hosts Dan Shipper and Natalia Quintero talk with founders, researchers, writers, and operators about what they’re building and how they use AI in their own work. The show also takes you behind the scenes at Every. We share how our team is using and exploring AI, including what we’re trying, what’s working, and what we’re learning along the way.
Formerly known as AI & I.
Read more at every.to.
Copyright: © Dan Shipper
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:
How Chris uses intuition while making product decisions
The importance of building products with “soul”
How to develop your product thinking muscles
When Chris trusts his gut over listening to user feedback
How fewer users gives startups a leg up over big tech
Why Chris is bullish on founders building specialized AI tools for professionals
This is a must-watch for anyone interested in building valuable, sticky AI products that users will love.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
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Timestamps for Spotify:
Links to resources mentioned in the episode:
Chris Pedregal: @cjpedregal
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, researcher Geoffrey Litt and I spent an hour coaxing ChatGPT to build a simple app on this podcast. Nearly 12 months later, Steve Krouse and I built the same app with one prompt in less than 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. In this episode, we used Townie, an AI assistant integrated into Val Town, to build an app that would keep track of time on the podcast, take notes, and generate more questions for the guest.
Townie had generated the app even before Steve could finish describing it on the show, a mark of how much AI has evolved over the last year. As the founder of a growing startup, Steve tells me his contrarian take on why he isn’t focused on the needs of the non-technical AI programmer, betting instead on being the platform sophisticated developers turn to for backend infrastructure. He also tells me how he started programming and how it continues to shape his vision for Val Town. Here is a link to the episode transcript. (Disclosure: I’m a small investor in Val Town.)
This is a must-watch for founders building AI-powered developer tools, and anyone interested in the future of programming.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Links to resources mentioned in the episode:
Steve Krouse: https://stevekrouse.com/, @stevekrouse
Val Town: https://www.val.town/
Townie, the AI assistant integrated into Val Town: https://www.val.town/townie/signup?next=%2Ftownie
Pieces on Val Town’s blog about how the team built Townie: How we built Townie—an app that generates fullstack apps, Building a code-writing robot and keeping it happy
The book by Seymour Papert about how programming changes the way you think: Mindstorms: Children, Computers, and Powerful Ideas
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:
Why Steve believes programming can rewire the way you think
The rise of the non-technical AI developer and what that means for the future of coding
How Townie works under the hood, including the details of the system prompt
How Steve is evolving ValTown’s strategy as AI progress continues to unfold
The power of small, dense engineering teams
This is a must-watch for founders building AI-powered developer tools, and anyone interested in the future of programming.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Links to resources mentioned in the episode:
Steve Krouse: https://stevekrouse.com/, @stevekrouse
Val Town: https://www.val.town/
Townie, the AI assistant integrated into Val Town: https://www.val.town/townie/signup?next=%2Ftownie
Pieces on Val Town’s blog about how the team built Townie: How we built Townie—an app that generates fullstack apps, Building a code-writing robot and keeping it happy
The book by Seymour Papert about how programming changes the way you think: Mindstorms: Children, Computers, and Powerful Ideas
Over the last few months at Every, we’ve:
Launched two AI products
Acquired tens of thousands of users
Released a new incubation in private alpha
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.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: 00:01:08
All about Spiral, the tool we recently launched: 00:02:15
Why Danny left a $200,000 salary to work at a bootstrapped media company: 00:04:06
How we do a lot of things well at Every: 00:10:33
What makes Every’s flywheel turn: 00:14:44
The kind of people who fit right in at Every: 00:17:11
How Every is differentiated from a standard VC-backed startup: 00:23:25
How Danny found his way into the world of startups: 00:36:11
The tech industry’s affinity for potential over experience: 00:46:43
Where each of us sees ourselves in the next one year: 00:52:38
Links to resources mentioned in the episode:
Danny Aziz: @DannyAziz97
Brandon Gell: @bran_don_gell
Try Spiral here: https://spiral.computer/
More about Every’s product incubation arm: https://every.to/p/introducing-every-studio
Over the last few months at Every, we’ve launched two new AI products with tens of thousands of users, and we’ll release a third one before the end of the year. The weird thing is: We’re a media company with less than 10 full-time employees, and we’re mostly bootstrapped.
That’s not how things are supposed to work in startups.
When we were first starting Every Studio six months ago, we were told a million reasons why 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 this strategy: We launched a brand-new version of our AI product Spiral with Danny Aziz as its general manager.
Danny left a $200,000-a-year salary to come chase his dreams with us. So we decided to take this moment to pull back the curtain and ask, Why? Why did he join us? And why is the model we’ve built working so far? What have we learned about what happens when you mix media, software, and AI in a single organization?
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, how new technology and the right people can make the flywheel turn, the power of learning by doing and building from real needs, and where each of us sees ourselves one year from now. Here is a link to the episode transcript.
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.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Links to resources mentioned in the episode:
Danny Aziz: @DannyAziz97
Brandon Gell: @bran_don_gell
Try Spiral here: https://spiral.computer/
More about Every’s product incubation arm: https://every.to/p/introducing-every-studio
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:
Why staying small and specialized is a bigger advantage than you think
The power of building with your early adopters
Why lean startups are better positioned than frontier AI companies to create radical solutions
When a growing startup should think about raising venture capital
The emerging role of ‘AI managers’ who will be responsible for overseeing AI agents
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.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
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 to help you summarize, chat with, and organize your PDF files. When OpenAI allowed users to upload documents to ChatGPT, the consensus was that his startup, and all the other GPT wrappers out there, were toast. Even when some of his competitors closed up shop, Vicente believed they could still create value for users as a specialized tool. The AI PDF team kept building.
Today, AI PDF is one of the most popular AI-powered PDF readers in the world—and they did it with a five-person team and a friends-and-family funding round.
I sat down with Vicente to understand, in granular detail, the success of AI PDF.
Vicente explains how staying small and specialized is a key strategic advantage for his business. We get into why lean startups are better positioned than companies like OpenAI and Anthropic to create cutting-edge solutions for users, the role early adopters of technology play in shaping the market for new products, Vicente’s candid take on raising capital as a growing startup, and his thoughts on the emerging role of AI managers who will be responsible for overseeing AI agents. We demo an agent integrated into AI PDF, prompting it to analyze a bunch of recent articles from my column Chain of Thought and write a bulleted list of the core thesis statements—and even pit AI PDF against Perplexity live on the show.
This is a must-watch for small teams building profitable companies at the bleeding edge of AI.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Links to resources mentioned in the episode:
Vicente Silveira: @vicentes
AI PDF: https://myaidrive.com/
Dan’s piece on the allocation economy: https://every.to/chain-of-thought/the-knowledge-economy-is-over-welcome-to-the-allocation-economy
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:
His theory around why the “irreducible” nature of problems will keep prompt engineering relevant
Prompt engineering best practices around prompts, evals, and datasets
Why it’s important to align your prompts with the language the model speaks
How to run evals when you don’t have ground truth
Why he believes that the companies who have domain experts to scope out the right problems will win in the age of gen AI
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.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT here: https://every.ck.page/ultimate-guide-to-prompting-chatgpt. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Timestamps:
Introduction: 00:01:08
Jared’s hot AGI take: 00:09:54
An inside look at how PromptLayer works: 00:11:49
How AI startups can build defensibility by working with domain experts: 00:15:44
Everything Jared has learned about prompt engineering: 00:25:39
Best practices for evals: 00:29:46
Jared’s take on o-1: 00:32:42
How AI is enabling custom software just for you: 00:39:07
The gnarliest prompt Jared has ever run into: 00:42:02
Who the next generation of non-technical prompt engineers are: 00:46:39
Links to resources mentioned in the episode:
Prompt engineering isn’t just about telling AI to solve your problems—it’s about knowing which ones to solve.
Yet there’s a mismatch between the people who can identify the right problems—experts with deep domain knowledge—and the technical infrastructure required for developing and refining prompts. Jared Zoneraich, the cofounder and CEO of prompt engineering platform PromptLayer, is bridging the gap with a platform on which non-technical experts can manage, deploy, and evaluate prompts quickly.
The role of human prompt engineers, however, has been the topic of controversy, with some arguing that AI can optimize prompts better than us, while others suggest that more capable LLMs eliminate the need for meticulously crafted prompts altogether. I spent an hour talking to Jared about why he believes prompt engineering isn’t becoming obsolete. He also tells me everything he’s learned about writing a good prompt and what the future of AI tools looks like. Here is a link to the episode transcript.
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.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Links to resources mentioned in the episode:
Jared Zoneraich: @imjaredz
PromptLayer: @promptlayer, https://www.promptlayer.com/
A couple of Steven Wolfram’s articles on ChatGPT: What Is ChatGPT Doing … and Why Does It Work?, ChatGPT Gets Its “Wolfram Superpowers”!
Notion cofounder Simon Last told me everything he’s learned from integrating an AI application 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 this episode, we get into how AI changes the way he builds software since the days he cofounded Notion with Ivan Zhao in 2013. He talks about the challenges that arise because AI doesn’t follow the deterministic rules of traditional software, and how he designs evals to build AI systems that are reliable at scale. Simon tells me about the AI tools he uses to code and how he would think about rebuilding Notion from scratch with them. He also shares his thoughts on how the growing capabilities of AI are redefining human roles, and argues that we have the responsibility to shape technology to align with our collective vision of the future.
This is a must-watch for anyone interested in building reliable AI products at scale.
If you found this episode interesting, please like, subscribe, comment, and share!
Want even more?
Sign up for Every to unlock our ultimate guide to prompting ChatGPT. It’s usually only for paying subscribers, but you can get it here for free.
To hear more from Dan Shipper:
Subscribe to Every: https://every.to/subscribe
Follow him on X: https://twitter.com/danshipper
Links to resources mentioned in the episode:
Simon Last: @simonlast
Notion AI: https://www.notion.so/product/ai
The AI code editor Simon uses: Cursor
OpenAI’s definition of AGI that Simon ascribes to: https://openai.com/charter/
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