Some parts are, but easily abstractable. I do have it on my list to support other services, but haven't had much time lately to tackle new features.

You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better, curated by you version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.

It works best if you follow a good amount of artists. Optionally you can get recommendations from artists that belong to playlists you follow or you've created. If you don't follow much or any artists, then you should enable that in order for the service to be useful, as right now that's the only pools of artists the recommendations are based on.

https://riffradar.org/

I have a few other experimental features in the pipeline that will expand the music selection, but they are not there yet.

[1] https://riffradar.org/

You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better, curated by you version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.

It works best if you follow a good amount of artists. Optionally you can get recommendations from artists that belong to playlists you follow or you've created - if you don't follow much or any artists, then you should enable that in order for the service to be useful.

https://riffradar.org/

On iOS I'm also using the same app, with the Apple Speech model, which I found out to be better performing for me than the parakeet/whisper. One drawback for the apple model is that you need iOS/Mac 26+ - and I haven't bothered to update to Tahoe on my mac.

Both of the models work instantly for me (Mac M1, iphone 17 Pro).

Edit: Aaaand I just saw that you're looking for speech-to-speech. Oops, still sleeping.

One suggestion - in the demo mode it would be nice if you provide a sample GPX file which you can try right away.

But if you have any kind of playlists - it would work.

You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better, curated by you version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.

It works best if you follow a good amount of artists. Optionally you can get recommendations from artists that belong to playlists you follow or you've created - if you don't follow much or any artists, then you should enable that in order for the service to be useful.

https://riffradar.org/

You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.

https://riffradar.org/

There are multiple ways to deal with ingestion floods. Kafka/distributed log is one of them, but it's not the only one. In cluster mode VM is a distributed set of services that scale out independently and buffer at different levels.

Resource usage for ingestion/storage is much lower than other solutions, and you get more for your money. At $PREVIOUS_JOB, we migrated from a very expensive Thanos to a VM cluster backed by HDDs, and saved a lot. Performance was much better as well. It was a while ago, and I don't remember the exact number of time series, but it was meant to handle 10k+ VMs (and a lot of other resources, multiple k8s clusters) and did it with ease (also for everybody involved).

I don't think you have really looked into VM - you might get pleasantly surprised by what you find :) Check out this benchmark with Mimir[1] (it is a few years old though), and some case studies [2]. Some of the companies in the case studies run at significantly higher volume than your requirements.

[1] https://victoriametrics.com/blog/mimir-benchmark/

[2] https://docs.victoriametrics.com/victoriametrics/casestudies...

(I'm not affiliated, but a very happy user across multiple orgs and personal projects)

What it allows you to do, though, is create your playlists with extended filters. E.g. you can select genres, and at the same time exclude genres - that helps with the "cross-contamination". You also get a view of all the artists that match your selections and you can add exclusions for them as well. It is a bit of manual work, but it works pretty good for me personally.

You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.

https://riffradar.org/

You get to choose the genres you're interested in, and it creates playlists from the music in your library. They get updated every day - think a better version of the Daily Mixes. You can add some advanced filters as well, if you really want to customise what music you'll get.

https://riffradar.org/

Have a look at this thread [1] I have bookmarked. I found it quite informative. I already got some cheap cameras and set them up, but I wish I would have found it earlier. The ones I got are 4MP with 1/3" sensor and perform absolutely terribly in night setting.

[1] https://ipcamtalk.com/threads/getting-cameras-with-the-right...

So I built Riff Radar - it creates playlists from your followed artists' complete discography, and allows you to tailor them in multiple ways. Those playlists are my top listened to. I know, because you can also see your listening statistics (at the mercy of Spotify's API).

The playlists also get updated daily. Think of it as a better version of the daily mixes Spotify creates.

https://riffradar.org/

The current functionality revolves around genres, and artists are derived from those selections. There are some additional filters where you can filter down based on album/track release dates, exclude genres or specific artists - but it all comes from your library, not from the whole Spotify pool. It was a deliberate decision, as my gripe was the fact that I have a massive library, and was not listening to its entirety.

You can achieve a somewhat similar functionality by creating a playlist, and adding a single song from any artists you want in that playlist.

As to the following list - the podcast/artists libraries are not the same and you can access them at different places in Spotify. If you click on your profile and go to following you'll only see artists/friends. Moreover they are behind separate APIs/access scopes and I only scrape the aritsts you follow.

If you want give it a go, you might find it useful. You can delete your account at any time - I don't keep any of your data once you delete your account.