runninginproduction.com VS se-unlocked

A while back I started a podcast around this topic: https://runninginproduction.com/

If there's ever a time to ask such a question, it might be this thread.

Can I get some brutally honest feedback on a podcast I ran for 2 years (100+ episodes at once per week)) at https://runninginproduction.com/? It's a podcast focused on chatting with developers around how they build and deploy their web apps. It mostly focuses on the "why", tech stack choices, libraries, workflows, etc..

In my mind I thought it was a good idea but it got so little listeners that I had to abort recording new episodes due to burn out since there was no path forward to ever sustain it by outsourcing the burn out inducing parts. I still think it's a good idea but I wonder where I went wrong.

I tried everything I could think of. Guest variety from solo devs to bigger companies like Mux and Dropbox, audio editing to ensure the highest quality I could get for a remote guest<->host podcast with new guests having assorted mic qualities, moving a lot of "ums" and other fluff but not over editing things to make it unnatural, tags to quickly find tech stacks you care about and a ton of clickable timestamps with a summary of each show that's skimmable in seconds and tons of reference links.

On paper it feels like I did everything I could do to make things "good", but in practice after 100 episodes I had like 200-300 listens per episode which made it no longer viable to continue doing since each episode was about 6 hours of end to end time (finding a guest, editing it, show notes, etc.).

I chatted with 100+ different developers from 100+ different companies on how they build and deploy their apps: https://runninginproduction.com/

There's https://runninginproduction.com/ with 100+ assorted episodes with 100+ different guests talking about how they built and deployed their specific application.

Running in Production

I started a podcast around this topic a few years ago at https://runninginproduction.com/.

As someone who has personally edited over a hundred 1-2 hour podcasts with a new guest every time removing umms, ahhs, dead air and filler words is soul crushing. It has gotten to the point where after 2 years of running my podcast[0] I'm seriously considering stopping the show because I'm getting burnt out from editing and without sponsors it's not feasible to hire an editor, but even with the show making no money I would happily pay triple your asking price if I could click a button and have the problem solved in a way that matched a human's ability to edit out filler words.

It really is the difference between being able to edit a 1 hour episode in 1 real life hour (editing at 2x speed) vs literally spending 5 hours to edit 1 hour when there's a lot of filler words or ums.

In my opinion your "after" version doesn't sound natural. This isn't an attack on your service specifically, because the outcome is the same with all of the tools I've tried. I haven't tried them all but I did play with a few of them.

For example in your case the pause between "Removing" and "filler" doesn't match the pace of the rest of the sentence and the transition from "very" to "time" has a very hard cut. This is also a 10 word clip that's about 6 seconds. If you listened to a 1 hour podcast episode that was edited things like this would be much more noticeable.

There's so many intricate and subtle details around when and what to cut to remove these things in a way where it's not noticeable. Are there any paths moving forward in AI / ML that can lead to this being indistinguishable from being humanly edited?

I debated deleting this comment before posting it because it's a combination of feedback but also saying the service isn't something I would buy but I think it's more beneficial to post this to show there is a real demand for this service if it can be executed flawlessly.

[0]: https://runninginproduction.com/

If you're interested in hearing how 100+ different developers manage their apps in production I have a podcast at https://runninginproduction.com/.

The podcast is at: https://runninginproduction.com

As I was exploring this podcast's open-source contributions, I found an issue that piqued my interest. This issue, related to episode 47, focused on how to enhance the transcripts of the episodes. The author needed help improving the clarity and understanding of these transcripts.

Hey, great list of awesome podcasts. I’m the host of Software Engineering Unlocked https://www.se-unlocked.com.

The episodes focus on the experience of my guests with software engineering practices such as testing, or code review, their journey to advance their career and often also on how developers build their successful business.

Would love if you give it a listen and see if you like my show.

For my last PR, I contributed to the se-unlocked repo which manages the transcripts for the Software Engineering Unlocked podcast.

During Hacktoberfest 2021, I was browsing and looking for an open source to contribute to. I only had a little time at that time. So, I limited my search to accessibility and documentation issues. And that was when I found an accessibility issue. It was on Michaela Greiler's Software Engineering Unlocked Podcast repository.

For part three, I decided to take on a different kind of issue - to improve transcript for a podcast episode. The process was similar to what I had done for my previous PRs. I forked the project and created a branch for the issue. However, since the work involved editing a markdown file, I decided to use GitHub's built-in editor to work on it instead. The improvement process involved listening to the episode while reading through the existing transcript. I would pause as needed and fix any issues which I notice. These mostly involved a few missing words, missing pronunciation, incorrect pronunciation, and some incorrect words. For example, git pull was transcribed as git poll, O'Reilly as a Raleigh, abstraction as obstruction. Though I was quite surprised by how accurate the auto generated transcript was. The only places where it would mess up was when there was a slight overlap in conversation, or when terms specific to software development, or names of publishers were being said. Throughout this process, I learned a bunch about transcribing - what may be ignored (phrases such as um), when to be exact, how and when to transcribe laughs, background music, etc. And once I was finished with the changes, I created a PR.

This time, I contribute to a repo from Software Engineering Unlock,a podcast for discussing topic about software engineering. They need some help to go over automatically generated transcript to correct them to improve accessibility, so I participated in the issue for one of the episodes and made a pull request.

Software Engineering Unlocked Podcast

Due to the above, the transcriptions were not exactly "accurate". To pick up an issue, all I had to do was create an issue with information about which episode I wanted to transcribe.