Google News started showing Polymarket gambling bets right alongside articles from Reuters, The Guardian, and the Financial Times — and when people noticed, Google called it "an error." But was it really?

With Google already having a formal data partnership with Polymarket, and prediction markets aggressively pursuing legitimacy through deals with CNN, Dow Jones, and Elon Musk's X, the line between news and gambling is getting deliberately blurred.

Chris and David unpack what this incident reveals about how algorithms decide what counts as "news" — and why prediction markets are engineered to exploit exactly those signals. They dig into the growing list of Polymarket scandals, from a suspicious $400K bet placed hours before the US invasion of Venezuela to Israeli military personnel betting on their own upcoming strikes, and debate whether prediction markets are a legitimate information tool or just gambling dressed up in news formatting.

For startup founders, this episode delivers three critical insights: how platform partnerships can rapidly reshape your credibility, why algorithms that optimize for engagement are fundamentally unable to distinguish journalism from gambling, and what happens when you build a business model in a regulatory gray area. Whether you're building a media startup, an information product, or anything that depends on platform distribution, this conversation will change how you think about the fragile line between content and commerce.

Anthropic just announced something that should make every founder sit up and pay attention: their new AI model, Claude Mythos, can autonomously find and exploit zero-day vulnerabilities in every major operating system and every major web browser.

We're not talking about theoretical weaknesses — the model wrote a fully working exploit for a 17-year-old FreeBSD bug that grants root access to unauthenticated users, with zero human involvement after the initial prompt. It found a 27-year-old bug in OpenBSD, an operating system famous for its security. And non-security-experts asked it to find vulnerabilities overnight and woke up to complete, working exploits the next morning.

Chris and David dig into what this means for the cybersecurity landscape and for startups in particular. They explore the massive leap from Anthropic's previous model — which had near-zero success at exploit development — to Mythos Preview, which succeeded 181 times on the same benchmark. They debate Anthropic's decision to withhold the model from public release through "Project Glasswing," sharing it only with critical infrastructure partners, and whether that approach protects the ecosystem or just delays the inevitable arms race between AI-powered attackers and defenders.

For entrepreneurs building software products, the implications are immediate and practical. The window between a vulnerability being publicly disclosed and an AI turning it into a working exploit is shrinking to hours. Patch cycles need to accelerate, security testing needs to level up, and the old startup excuse of "we're too small to be a target" just became dangerously outdated. This episode breaks down exactly what founders should be doing right now to prepare for a world where AI is both the lock and the lockpick.

Chris has a confession to make. While co-hosting a podcast about building startups, he's been quietly building one of his own - and today he's pulling back the curtain.

PurePrep is a premium meal planning and recipe management app built for families who are tired of the nightly "what's for dinner?" debate. At its core, the app uses AI-powered recipe ingestion to extract structured recipe data from any URL on the web - no more copy-pasting ingredients from food blogs - then lets families plan meals on a shared calendar, manage dietary preferences for every family member, and automatically generate a consolidated, intelligently grouped shopping list for the week.

In this episode, Chris walks Dave and the listeners through the full story: the family frustration that sparked the idea, the technical decisions behind building a native app, and what it's like to be a solo technical founder using the same AI coding tools they've been debating on the show for months. From the normalized ingredient data model that makes the smart shopping list work to the family-as-central-unit architecture that sets PurePrep apart from individual-focused meal planning apps, Chris holds nothing back about the product decisions, the technical trade-offs, and the lessons learned building something real.

But here's where it gets fun: Chris has spent hundreds of episodes giving other entrepreneurs advice on product-market fit, pricing, and growth strategy. Now Dave gets to turn the tables and put his brother in the hot seat. Combined with Dave's own ManShowr reveal, this episode closes out a two-part series where both hosts prove they're not just talking about building startups - they're doing it. If you've ever wondered what happens when podcast hosts have to practice what they preach, this is the episode.