PodcastorAI

Your AI twin hosts your video podcast

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The audio-upload path is the part that catches my eye. Most podcasters I talk to have a big back catalog of audio-only episodes and no realistic way to get them onto YouTube as video. Have you seen how the AI-avatar episodes hold up on retention versus real-camera footage? That's the number that would decide it for the audio-first crowd.

the digital twin part is the interesting bet - if you re-record one line weeks later does the AI voice/face stay consistent with the original episode, or is there drift between sessions?

love this one! question: for long or pre-recorded episodes, does the AI host lip syncs with the audio? and what about the changes in style and tone within one same episode? thanks and congrats!

The option to create a solo or two-host podcast is a nice touch. A lot of tools seem built for only one format.

no team seats yet? asking cause i'd want this for more than just me.

not gonna lie, kinda worried every podcast using this starts sounding the same eventually.

Damn, nice one. Congrats on the launch. “NotebookLM can write a podcast; Podcastor puts you on screen” is a very clear way to explain it. The digital twin side is interesting, but I’d want to know how much control you have over the final performance. Can you adjust pacing, tone, pauses and host reactions after generation, or do you mostly accept what comes back? That feels important for educators and experts in particular. A technically polished avatar is useful, but only if it still sounds like the person behind the content.

Nice concept. A real time saver! How well does the avatar stay in sync during longer episodes? Congrats on the launch!

Curious, where did GPT-5.6 Sol make the biggest difference for PodcastorAI?

Turning an existing recording into a video podcast without recording everything again is such a practical use case.

For true crime episodes, the research and writing are already intense. I do not want video production to become another full-time job.

@sylvialane Congratulations. And happy product launch.

Hi everyone, I’m Shay, a core developer at @PodcastorAI .

When the project was first launched and we decided to build a product that could help podcast creators produce videos quickly and dramatically simplify their workflows, I was genuinely excited. We wanted to save creators from repetitive work so they could spend more time creating great content and focusing on what truly matters. Once the goal was clear, the entire team quickly got to work, with everyone contributing their strengths and collaborating closely. Our product and operations teams also gave the development team tremendous support. I feel very fortunate to work alongside such talented people and turn an idea into a product that people can genuinely use.

When we started the project, GPT-5.6 Sol had not yet been released. With the help of GPT-5.5, we quickly built the MVP and continued refining the experience based on user feedback. AI did more than help us write code faster. It allowed us to validate ideas sooner, identify problems earlier, and spend more time on the details that truly shape the user experience.

When GPT-5.6 Sol was released, our team switched to the new model right away. Combined with Codex, it made the entire development process much smoother. GPT-5.6 Sol understands requirements and code context with impressive accuracy. Even when working on complex tasks involving multiple modules and long call chains, it can quickly identify where changes actually need to be made. The plans produced by Codex’s Plan mode are usually thorough and rarely require major revisions before we can move forward. For us, this is about much more than simply writing code faster. Communication, analysis, implementation, and verification have all become more efficient and reliable, giving the team greater confidence while maintaining a rapid pace of iteration.

We have also introduced GPT-5.6 Sol into our production monitoring workflow. Our agents regularly scan failed tasks and error logs in production. Whenever an issue is detected, they automatically investigate it by combining information from production logs, database records, and the codebase. Once the cause has been identified, they generate a fix and submit the code to our repository for the team to review. This process has saved us a significant amount of time previously spent on repetitive troubleshooting and fixes. It also helps us detect production risks earlier, allowing the team to focus more of its energy on building the product.

We hope @PodcastorAI can bring podcast creators the same kind of productivity boost that GPT-5.6 Sol and Codex have brought to our team. Our goal is to help creators multiply their output and free up more time for creating, thinking, and doing work that truly matters.

We warmly welcome everyone who already creates podcast videos, as well as anyone interested in getting started, to try @PodcastorAI . We hope to become a reliable and effortless part of your creative journey and help you create even greater value!

Really cool, and crazy idea. Can't wait to test it out. When you generate a two-host episode from a document, do the hosts ever actually disagree, or just take turns agreeing? That friction and chemistry is (almost) always what makes it worth listening to.

Can users preview a lower-resolution draft before spending credits on the final render?

I love the idea of turning an existing content library into video instead of always creating from scratch.

the 15 minute turnaround is impressive but I'm curious how it holds up past the 2-3 minute mark, most AI avatar stuff still has a slight uncanny valley thing with head movement and blinking during longer stretches of talking. have you had people watch a full episode without knowing it's a digital twin and not notice?

This feels built for creators who know what they want to say but do not want to spend their week editing.