Theo, mid-episode, gave Sonnet 5 a live task: use Codex computer use to browse the web and order a custom shirt that says 'Sonnet 5 made this shirt.' The model navigated a custom apparel site using browser automation, auto-filled Theo's zip code, and nearly completed the order — getting about as far as anyone expected.
Where this was said
At 1:13:40 · chapter starts 1:10:10
The Sonnet 5 vs. GLM-5.2 debate is more nuanced than the episode's recurring cardboard-taste jokes suggest. Ben's actual defense of Sonnet 5 is narrow and specific: during the window when Fable was banned, it was the only model that could handle very long-running agentic loops requiring recursive self-calling, review subagents, and staged PR workflows. GLM-5.2 is fast, cheap, and produces clean code that a smarter orchestrator can command easily — and both hosts now prefer it in most subagent slots. [1] — Ben "GLM-5.2 is fast, cheap, and produces clean code easily controlled by a smarter orchestrator. Sonnet 5 edges it out only in one scenario: ve…" 1:10:10 The broader complaint about people equating GLM-5.2 with Sonnet 5 for capability is what Ben was actually arguing against. To punctuate the Sonnet 5 discourse, Theo issues a live challenge: have Sonnet 5 order a custom t-shirt reading 'Sonnet 5 made this shirt' via Codex computer use. The model navigates a custom apparel website, auto-populates the right zip code (alarming both hosts), and nearly completes the order — though the shirt design comes out misaligned over the right breast rather than centered. The episode closes on this chaotic note, with Ben threatening to 'slightly inconvenience' the Trump administration if 5.6 isn't out by next week.
GLM-5.2 is fast, cheap, and produces clean code easily controlled by a smarter orchestrator. Sonnet 5 edges it out only in one scenario: very long-running jobs requiring recursive self-calling and review loops. Outside that, GLM-5.2 wins on price-performance — and even Theo would pick it over Sonnet if he still had Fable.
With AI coding tools like Cursor, Bhanu replicates an existing free tool for a new keyword in under 5 minutes. What used to be a multi-day build is now a lunch-break task.
Ahrefs, SiteGPT, Cal.com, PostHog, Datafast, Sibyl AI, Bento, Feather, Featurepace, Mintlify, Cloud Code, ChartMogul — Bhanu runs his entire business solo with these 12 tools.
PropGPT runs on React Native with TypeScript and Python for ML, Neon for the database, RevenueCat and Superwall for monetization. LLM costs are $20/month, data APIs $100/month, and after $10K in monthly marketing spend, margins sit at roughly 50%.
Game apps keep users engaged long enough for ads to pay off. Tool apps don't — so if you're building a utility, ads are almost always the wrong call and subscriptions are your only real lever.
Inside SEO Bot, a single button labelled 'Boost My Domain Rating' routes users directly to Listing Bot. That one interaction converts a user of one tool into a user of two — without any marketing cost.
Directory listings are a powerful but underrated growth channel — but only if your product is genuinely interesting enough to earn the click. AI products have a natural advantage here because they're easy to package in a compelling, clickable way.
The fastest path to Twitter growth isn't volume — it's forming sharp opinions about how the platform works and sharing them immediately. People cluster around those who understand the rules and say so out loud.
Sam had no coding knowledge, so he used ChatGPT voice mode to generate his entire codebase and copy-pasted it into Notepad. A friend later introduced him to Cursor, and he never looked back.
Copy days of Discord chat history, paste it into ChatGPT, and ask it to list recurring pain points. The ones that come up most often are your best product bets.
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