I spent a while trying to get an LLM to make a podcast that's actually listenable. The hard part wasn't the model.

I wanted to know if an LLM could generate a podcast I'd actually choose to listen to — turning Hacker News threads into audio, in my case. Turns out writing the script is the easy 20%. The rest is fighting everything the model wants to do by default. Two things made the biggest difference: 1. Constraints beat instructions. Telling a model "be conversational" does nothing. What worked was giving the two hosts different information — one only read the article, the other only read the comments. They literally can't agree, because they have different facts, so they argue instead of politely nodding along. That single constraint did more than any amount of "sound natural" prompting. 2. Edit before generating. Dump a whole comment thread in and the model weights every comment equally — you get meeting minutes. Adding a cheap "producer" model that first decides what the episode is about and picks the few comments worth discussing, before the main model writes, was the biggest quality jump. Some genuinely funny failures too: it read "API" as "appy," said "one thousand two hundred and four" for 1,204, and when I put "[sigh]" in the script hoping for a bit of humanity, the voice just read the word "sigh" out loud, deadpan. Full write-up with audio samples: https://hnlisten.app/blog/i-told-the-ai-to-sigh Curious if others doing AI-generated audio/dialogue have found tricks for the "make it sound less like an essay" problem — especially forcing genuine disagreement.