Theo gave Fable his entire backlog of stale, conflicted, and draft PRs and told it to figure out which ones to keep, merge, rebase, or kill. Without needing step-by-step instructions, the model categorized overlapping work, wrote plans for missing features, and landed or closed over 30 PRs in a single 5-hour loop — for about $150–$200.
Where this was said
At 41:40 · chapter starts 39:50
With Fable back in their hands, both hosts have been running it hard. Ben's first major use case was rebuilding the brittle CLI that spun up his Hermes agent container — a task involving Hermes, Codex, Claude Code, and an Executor, all needing to live in a single deployable box. What Opus had built barely worked; Fable rebuilt the entire architecture in about an hour of back-and-forth. [1] — Theo "Theo gave Fable his entire backlog of stale, conflicted, and draft PRs and told it to figure out which ones to keep, merge, rebase, or kill…" 41:40 Theo's story is more cinematic: he had 18-plus stale pull requests sitting in his Lakebed project, some finished but unrebased, others superseded by later merges, a few just outright slop. Rather than manually triaging them, he asked Fable to read all the PRs, figure out what they were for, identify overlaps, write plans for anything worth salvaging, and then execute. He read the plans briefly, said 'YOLO — slash goal, complete all the work we talked about,' and watched the model close a dozen PRs and land 14 more in one loop. The thing that makes this work, Theo emphasizes, is that Fable can pull intent from incomplete context — it doesn't need you to re-explain what you were trying to do; it can figure it out.
Theo spent roughly $150–$200 of Fable API usage to review, close, rebase, or land over 30 pull requests in a 5-hour agentic loop.
The 7-day Fable window at half rate limits wasn't a consolation prize — it was a calculated GPU provisioning experiment. Anthropic needed exactly one full week of data to see usage rates across every day of the week so they can plan how to allocate compute between enterprises and subscribers. The FOMO from the export ban was also a feature, not a bug.
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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