Show HN: I built an app that reduces podcast preparation effort by 95% + | Hacker News Reader

Show HN: I built an app that reduces podcast preparation effort by 95% +

podcast-prepper.com

20 pointsrokbenko34 comments

Am I being a completely delusional sceptic or do tools like this make no sense to anyone else?

If you need AI to tell you about a guest and tell you what to ask them, why are you having them on your podcast? You don't know anything about them, and have nothing to ask them - why does the world need you to do this interview? You don't care about your guest at all, so why would we want to listen to you talk to them?

As the guest, why bother responding to questions made like this? Why not have an AI write answers for you, since you are probably equally uninterested in your interlocutor and their questions?

Skip the bs and just publish the transcript of two AIs talking to each other.

Waiting for OP's ChatGPT response to this comment.

No ChatGPT responses here. ;)

> Skip the bs and just publish the transcript of two AIs talking to each other.

Don't mind if I do [0].

[0] https://blog.google/technology/ai/notebooklm-audio-overviews...

Thanks for the feedback! I see PodcastPrepper as augmentation, not automation, of how podcasts are done. Additionally, see my reply to @opto above.

I agree as far as my personal listening tastes go, but, I think it’s trying to be a substitute for producer research that bigger shows do for their hosts.

Thanks for the feedback! Exactly! I see PodcastPrepper as augmentation, not automation, of how podcasts are done. Additionally, see my reply to @opto above.

> If you need AI to tell you about a guest and tell you what to ask them, why are you having them on your podcast?

Absolutely. The podcasts I like are hosted by people who are always well prepared. For example, Russ Roberts who hosts EconTalk[1] often has guests who have recently published books. Russ reads those books before interviewing them. Amazing dedication.

[1] https://simplecast.econtalk.org

Thanks for the feedback! Please see my reply to @opto above.

> If you need AI to tell you about a guest and tell you what to ask them, why are you having them on your podcast?

I'd go further: if you need AI to tell you about a guest and tell you what to ask them, why are you doing a podcast at all?

Thanks for the feedback! Please see my reply to @opto above.

Thanks for the feedback!

Let's say you recently became interested in X. You don't know much about it. You hear that John Doe is an expert in X.

> You don't care about your guest at all, so why would we want to listen to you talk to them?

It's not that you don't care about your guest. It's that you simply don't know much about X and John Doe.

Is this a reason not to make a podcast at all? I don't think so. Why? Because many listeners might be in your shoes (i.e., not knowing about X and John Doe). In other words... Do you only listen to podcasts when you know everything about the topic and the guest?

> As the guest, why bother responding to questions made like this?

I don't see podcasts as a ring where two egos fight. I wouldn't care about the podcast host's knowledge about my area of expertise at all, as long as they're genuinely interested in it. Isn't this exactly the reason why they invited me? To learn more about it and share it with the world?

I don't think podcast hosts and guests need to be completely "on the same level". PodcastPrepper is able to process dozens of sources from the web in parallel and create a report on the guest in about 3 minutes. If you have 0 prior knowledge about X and John Doe, with PodcastPrepper's report you quickly gain 10x more knowledge about X and John Doe. Enough to be able to make an episode.

Why would you...make a podcast given you know nothing about that person? Or rather, the podcast created by your product is not necessarily "yours." Your product should more accurately be called a somewhat of a NotebookLM by Google, synthesizing data and in the future perhaps creating a podcast from different sources for personal use, not for being production grade enough to publish for others to listen to, as one would expect from a traditional podcast about hosts and guests, like Tim Ferriss, Joe Rogan, etc. Your product is essentially LLM deep research, with text to speech in the future perhaps. This distinction is where most commenters are getting tripped up.

I could easily imagine a scenario where you do know something about a guest - enough to see that they are a compelling speaker, or have an interesting area of expertise that an audience would appreciate - without being fully "read in" on that person.

It's like when you go to a lecture hosted by an institution and they give a little intro about the lecturer beforehand. Most likely some of those little facts about the person's biography weren't known to the institution when they invited the person - they did a little extra research later to prepare the intro. They may not have known what state the person grew up in or where they did their undergrad but they looked it up in case it helps someone make a connection.

So it's just LLM deep research as I mentioned, something that's a feature that many LLMs have these days.

> Let's say you recently became interested in X. You don't know much about it. You hear that John Doe is an expert in X.

Then you can learn about them. That is better than asking an AI to generate questions for you without you actually learning about them.

> If you need AI to tell you about a guest and tell you what to ask them, why are you having them on your podcast?

Podcasts today are little but another appendage of the PR grist mill. Like reality TV, it costs almost nothing to produce yet it can be stuffed with just as many ads. Tools like this help lower the bar even further. Why put out one pod a week when you can churn out 3?

Of the top 100 podcasts today, at least half that are in the "lifestyle" genre where the hosts do nothing besides interview Internet personalities in the wellness, productivity and finance sectors. The pods are 2-3 hours long (more ads can be fit in that way) and I've noticed that the hosts often know zero about the guest and figure it out along the way.

As someone who's been involved with radio and occasionally podcasts for about 20 years... I'm struggling to see the benefit of this one. Yes, prep services have existed in the past and I'm sure continue to exist today. Xtrax rings a bell from years gone by.

But honestly, if you're going to be interviewing someone and the content is going to be engaging, you can't just fly from some LLM output. Talking to a politician you're going to need knowledge of their past actions, figures to challenge them on, etc. For music guests, a bit of knowledge about the band, key figures and moments throughout their story. I'd hope anyone using the LLM crib sheet is also being reactive to what their guests say (e.g. "you touched on X but when you were Chief of X...").

Interviews aren't my strength but I'd be wary of such a service. Combined with the usual AI hallucinations it could be quite the entertaining car crash.

Thanks for the feedback! I really appreciate it since it's coming from someone who was in the industry for many years.

I see your biggest concern is AI hallucinations, right?

I'm not using just an LLM. I added a service to give LLM up-to-date knowledge from the web. That reduces hallucinations a lot. Can I guarantee no hallucinations at all? No, I can't.

Where I see value in PodcastPrepper the most is being able to process dozens of sources from the web in parallel and create a report on the guest in about 3 minutes.

No. The biggest concern is that the conversation is going to be dull as heck because all you've got is a list of AI generated topic starters rather than any sort of meaningful capacity for conversation or meaningful structure for the conversation (either in narrative or pedagogy).

And if you are marketing this as taking 3 minutes and saving 95% of your time then this means it saves all of... one hour. Not exactly the bulk of the time spent producing a podcast episode.

Interesting. I have been working on a service in an adjacent space, that I hope is innovative enough, but not ready yet for prime time.

I would love to exchange with you about it ( even if it's just a few short emails ) and show it to you when it's ready.

Is there anyway to reach you? I'll leave my email on my profile for a few days too !

I don’t know why ppl are hating on this

This looks like a deep research type agent, and things like this definitely provides value by saving time

Ask the agent to go over a guests data online, their books/content, and extract truthful and useful info and serve them on a plate for you.

I can see value in this

Thanks for the feedback! Are people often negative? It's my first time posting here.

Is this just an OpenAI Deep Research wrapper?

No, it's not. ;)

This is cool! Curious what front-end tooling you've used for the landing page?

The site uses Next.js

Correct!

Another LLM wrapper that reduces effort and quality by 95%!

Thanks for the feedback!

How do you ensure the veracity of reports?

Thanks for the question! That's a tough one. I still didn't figure out how to do it programmatically.

That said, keep in mind that I'm not using just an LLM. I equip the LLM with a tool to give it up-to-date knowledge from the web, which significantly reduces AI hallucinations. Still, it's not perfect.

What I've done so far is:

1) I tested the report for myself. It was 100% on point. 2) I tested the report with a few potential customers. None reported information in reports being incorrect.

I think its a nice idea. People here criticize everything. They would probably criticize the idea of hiring a producer too. Any interest yet?

Thanks for the feedback! Yeah, I was caught off guard, as it's my first time posting here. Mainly negative comments here. Feedback is very welcome, but stating that something is "just a ChatGPT wrapper" without trying it is ridiculous. Yeah, I made the first sale even before posting here.

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