The Aboard Podcast

Aboard uses AI to help build software, but in just a few years, AI will gain sentience and take over our work, personal lives, and even brains—just kidding! Yes, the fourth and final installment of “AI Summer School” is about AGI, or “Artificial Generalized Intelligence.” What does it mean? Can anyone agree on a definition? And if no one can define it or agree on those definitions, what’s the likelihood that all these Silicon Valley AGI predictions will come true?

So now that you’ve set up your AI agents, what can you build with them? In the third lesson of AI Summer School, Paul and Rich are joined by CTO Adam Pash to spin up a sample app in Aboard. How do agents work together to simulate the development process—and what’s the difference between Aboard’s structured approach and a vibe-coding tool? Plus: Introducing the Adam Pash Drinking Game, where you take a shot every time you say the word “guardrails.”

“Agents” are a big marketing term for AI companies right now—but how do they actually work? In the second installment of AI Summer School, Paul and Rich are joined by Aboard Director of Engineering Kevin Barrett, who breaks down what “agent” actually means, and how they function within the platform. Plus: In the process of demoing Aboard’s agent capabilities, Paul becomes transfixed by the breeding records of a fictional alpaca farm.

Feel a little lost when it comes to AI? It’s time to go back to school—Aboard’s AI Summer School! Over the month of August, Paul, Rich, and a few special guests will break down the basics of LLMs, agents, AGI, and a host of other AI-related topics. In the first installment, they discuss how LLMs think—or rather, don’t think—and compare the major players on the AI scene right now.

Mapping the history of the universe—and the limits of current AI programming tools. On this week’s podcast, Paul walks Rich through his recent experiments trying to build timeline software via vibe coding. Spoiler: There were issues. Halfway through 2025, where do the current tools excel, and where do they fall flat? And looking over the few years at where these tools might be headed, what should a young person interested in tech be learning right now? 

How do you build for the future amid ongoing instability? This week, Paul and Rich turn to Lebanon—literally, since that’s where Rich, who was born in the country and still has family there, is calling in from. After they discuss some Lebanese basics (Cedars! Small plates!) they turn to the Lebanese tech scene, discussing everything from investments in the Beirut Digital District to Aboard’s partner agency in Lebanon, Speedlane.  

AI is a great first step, but to really build software, you need humans to get the job done. On last week’s podcast, Paul and Rich talked about how Aboard works by focusing on those AI first steps; this week, they dig into the human work that gets Aboard projects over the line, from classic agency-style client management to the brand-new role of “Solution Engineer.”

Paul and Rich are always talking about building software with AI—but how is Aboard actually building software with AI? This week’s podcast is a peek behind the curtain, walking through how Aboard gets you from a short prompt to real, working software in minutes. From the specific (the “Harvest Manager” app Paul creates for his pumpkin patch) to the broad (what this technology means for the software agency model, and the industry at large). 

Same as it ever was: On this week’s podcast, Paul and Rich take a spin through a 1980 issue of Omni magazine, comparing how computers were being discussed back then with how AI is talked about today. Featuring an essay by Frank Herbert (yes, of Dune), IBM’s early-80s consumer pitch, and a meditation on the question: What does “new technology” even mean?

As the legacy tech giants weave AI functionality through their existing systems, is it all over for AI-first companies like OpenAI and Anthropic? In the wake of this year’s WWDC, Paul and Rich analyze Apple’s AI plans, and contextualize them within broader industry shifts. What’s Apple’s AI endgame—and where can we expect to see a player like OpenAI a few years from now?