When tasked with comparing plans, Fable-5 voted for its own plan 6-0 even when it could acknowledge benefits of the alternative, showing it's worse at critiquing its own work.
Where this was said
At 58:37 · chapter starts 53:20
Theo pulls out one of the episode's richest segments: two separate AI-generated analyses of his coding session logs, one comparing 5.5 to 5.6 and one comparing 5.6 to Fable-5. The 5.6 vs Fable comparison is the one that lands hardest. The analysis finds that Fable-5 'thinks wider' and is the stronger strategic advisor, while 5.6 'ships better' and is the stronger day-to-day coding agent [1] — Theo "Fable 5 thinks wider, 5.6 ships better. Fable-5 is the stronger strategic advisor. F5-6 is the stronger day-to-day coding agent. The differ…" 54:40 . The communication styles are starkly different: 5.6 produces terse, telegraphic build-bot receipts while Fable writes conversational prose that teaches the maintainer. But the most surprising finding is Fable's self-assessment blind spot: it voted for its own plan 6-0 in a head-to-head planning comparison, even while acknowledging benefits of the alternative [2] — Theo "Fable is much worse at critiquing its own work. It thinks its outputs are gifts from heaven. Like it is the divinely mandated model that do…" 58:37 . Ben interprets this as a feature of Claude's training — the 'Claude constitution' instills conviction that can become stubbornness — while 5.6's mechanical execution makes it genuinely neutral.
Running both models against the same session logs revealed fundamentally different identities: GPT-5.6 produces terse, telegraphic outputs like a build bot reporting to a coordinator. Fable-5 writes conversational prose that teaches the maintainer. Neither dominates every stage, but the behavioral divergence is accelerating.
An analysis of Theo's session logs comparing GPT-5.6 and Claude Fable-5 concluded Fable is the stronger strategic advisor while 5.6 is the stronger day-to-day coding agent.
MCP cannot share processes across threads, so running 50 subagents simultaneously spawns 50 separate MCP server processes, which is what lights Macs on fire.
Bhanu and his team built approximately 50 free tools to attract search traffic, each linked back to SiteGPT.
With AI coding tools like Cursor, Bhanu can now create a new free marketing tool in less than 5 minutes by referencing existing tools.
Bhanu filters Ahrefs keyword results to show only those with a keyword difficulty below 10, making them realistic ranking targets for any decent website.
Bhanu sets a minimum search volume of 1,000 monthly searches when selecting keywords to target with free tools.
PropGPT averaged 20 downloads per day right after launching on the App Store through influencer marketing.
Eyal and Yali shut down all marketing and spent 4 months completely rebuilding PropGPT from scratch.
PropGPT has accumulated over 40,000 total downloads since launch.
PropGPT's large language model (AI) operating costs are just $20 per month, and the cost is continually falling.
Ad-based monetization works well for game apps where users spend extended time in-session, as seen with Grid and Wordle.
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