When forced back to GPT-5.5 during testing, tasks that 5.6 happily ran to completion got only about a fifth of the way through before stopping.
When forced back to GPT-5.5 during testing, tasks that 5.6 happily ran to completion got only about a fifth of the way through before stopping.
Where this was said
At 8:10 · chapter starts 7:00
Theo and Ben dig into their actual day-to-day experience with 5.6, and the clearest signal emerges not from the model itself but from the regression to 5.5. Theo describes the single most noticeable change: 5.6 doesn't stop after the first part of a task and ask for permission to continue [1] — Theo "5.5→5.6 regression: 1/5th completion: When forced back to GPT-5.5 during testing, tasks that 5.6 happily ran to completion got only about a…" 08:10 . That habit was one of his biggest frustrations with 5.5, and with 5.6, it simply vanished. When forced back to 5.5 during testing windows, similar tasks got only about a fifth of the way through before stopping — a stark regression. Ben adds a philosophical point: the best AI is the one you don't notice [2] — Ben "I notice Phi-5 more than I notice the new model, which is huge. Like, that's what you want AI to do. If you notice it, it's bad." 09:20 . He'd previously thought 5.5 was so good that future model improvements would be imperceptible — he was entirely wrong. The hosts also flag the deeper cognitive dynamic: spending time with a better model raises your expectations, which then makes the older model feel far worse than it ever did before.
Once Theo and Ben spent time with GPT-5.6, returning to 5.5 wasn't just annoying — it was actively painful. Their mental bar for what an AI should do had been reset by the new model, so 5.5's tendency to stop mid-task and ask for permission felt far worse than it ever had before.
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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