[diminikolaou]

Making a GPT + text to speech wrapper is not complicated.

Providing all other features (e.g video generation, podcast publishing, auto translation and many other features we’ve added that allow for higher quality pod creation) increase the level of complexity for reproducibility.

Ultimately, we aim to keep building features that lead to higher quality pods, easier to build, and integration of ansiliary (video, translation, show notes) that will enhance our moat.

[danenania]

Amazing work. Listening a bit to the HN podcast, I'm impressed by the natural-sounding pronunciation of technical terms with non-obvious phonetics like 'postgres'. Have you had to tweak a lot of these manually to get them sounding so good or is your model mostly getting them right?

[diminikolaou]

Yeah Postgres is a great example, it comes up often in HN.

We have a small map of tweaks, and our users keep feeding us with more. The model performs great on its own most of the time though.

[danenania]

Very cool. By accumulating lots of these tweaks, I feel like you're also going to have an opportunity to backdoor your way into a great text-to-speech API product as well if you have any interest in going that direction. It seems like the main challenge there is ironing out all the edge cases and you've created an excellent feedback loop for accomplishing that.