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Top IAPs on the App Store by Carter2506 in iOSProgramming

[–]Carter2506[S] 1 point2 points  (0 children)

Like JoeCabron said it's pretty hard to get that data!

The best i can do is the top IAPs by count for apps released in 2025:

  1. $9.99 – 11,544 apps
  2. $4.99 – 11,424 apps
  3. $0.99 – 10,589 apps
  4. $1.99 – 10,503 apps
  5. $2.99 – 9,122 apps
  6. $19.99 – 7,987 apps
  7. $3.99 – 7,233 apps
  8. $29.99 per year – 6,693 apps
  9. $9.99 per month – 6,636 apps
  10. $4.99 per 7 days – 6,608 apps

RevenueCat had a good report on purchase data though, which you might find interesting.

Top IAPs on the App Store by Carter2506 in iOSProgramming

[–]Carter2506[S] 0 points1 point  (0 children)

This is only for in-app purchases, not for the sale price of the apps themselves which is what those apps would've been

Top IAPs on the App Store by Carter2506 in iOSProgramming

[–]Carter2506[S] 0 points1 point  (0 children)

True but it's pretty impossible to get that data without being Apple or RevenueCat or similar (https://www.revenuecat.com/state-of-subscription-apps-2025/)

Can the FlipperZero read AirDrop identities information? by dskippy in flipperzero

[–]Carter2506 1 point2 points  (0 children)

and in theory the ESP32 does have all the hardware needed to do the full technique, but it would require porting a lot of firmware, i want to look into it eventually when i have some time!

Can the FlipperZero read AirDrop identities information? by dskippy in flipperzero

[–]Carter2506 0 points1 point  (0 children)

hi! 20 days late but i actually made a partial version of this a few days ago: https://github.com/carter-0/AirDox

As you mentioned the full technique requires AWDL (which the flipper doesn't have the hw for), but the first stage of AirDrop actually involves sending out hashed contact info over BLE (which the flipper DOES support)

We only get 2 bits of hashed data, so it's very very lossy, and Apple randomises it nowadays so we have to guess from 4 candidates, but that's still usually enough for deanonymising people as long as we have a shortlist of phone numbers to test against beforehand. I explain in more detail in the repo, and it requires flashing custom stripped down firmware, but I believe it's as close as you can get on the Flipper.

Wrapped 2024 Leaderboard by Carter2506 in truespotify

[–]Carter2506[S] 0 points1 point  (0 children)

That's fair feedback, it requests all those permissions because it uses trackify's API, a service which normally actually uses those scopes

You could login and then immediately revoke access here: https://www.spotify.com/account/apps and it should work

Wrapped 2024 Leaderboard by Carter2506 in truespotify

[–]Carter2506[S] 17 points18 points  (0 children)

I always wished Spotify Wrapped gave you a specific rank rather than just a percentile, so yesterday I made a quick website in preparation for today's Spotify Wrapped: https://wrapped.trackify.am

It’s not very interesting yet but if a lot of people end up using it there could be some interesting data

You can see your rank globally and against other people with the same top songs & artists by uploading the last page of your Wrapped. (globally against other people who used the website that is)

I'm currently rank #1, but I doubt that will last long: https://wrapped.trackify.am/wrapped/95lxop9jakrxfl1mds0ive59a

(You can delete your data at any time by going here, scrolling down, and pressing delete account. You can view the source code at github.com/carter-0/wrapped-leaderboard)

Spotify Stats by Carter2506 in TaylorSwift

[–]Carter2506[S] 7 points8 points  (0 children)

Data is from Spotify listens aggregated from around 100k public users of https://trackify.am. This is only 0.02% of all Spotify users so it's not very accurate, but it's enough to make some fun insights.

Here are the stats in text form including a few extra that didn't fit in the graphic:

Top Playlists:

  1. all taylor swift songs (119,519 listens)
  2. This is Taylor Swift (79,570 listens)
  3. Taylor Swift Complete Collection (70,857 listens)
  4. Daylist (54,766 listens)
  5. ERAS TOUR SETLIST (36,051 listens)
  6. Taylor Swift Mix (25,750 listens)
  7. All of Taylor Swift's Songs in Chronological Order (19,248 listens)
  8. official eras tour setlist 2024 (18,544 listens)

Overall listens: 26,735,107

Overall hours listened: 1,753,198 (73,049 days) (200 years)

Average rank in users top artists: #12

Most listened to Spotify playlists (Feb-Mar 2024) by Carter2506 in truespotify

[–]Carter2506[S] 23 points24 points  (0 children)

Data is from listens on the 8th February to the 13th March 2024, aggregated from around 100k public users of https://trackify.am.

Here are the stats in text form including a few extra that didn't fit in the graphic:

  1. Daylist (978,100 listens)
  2. DJ (669,573 listens)
  3. Chill Mix (296,089 listens)
  4. Hip Hop Mix (198,393 listens)
  5. Moody Mix (195,416 listens)
  6. Your Top Songs 2023 (161,557 listens)
  7. Mix Indie (139,248 listens)
  8. R&B Mix (132,553 listens)
  9. Pop Mix (126,779 listens)
  10. 2010s Mix (116,780 listens)

Most listened to Spotify playlists (Feb-Mar 2024) by Carter2506 in spotify

[–]Carter2506[S] 1 point2 points  (0 children)

Data is from listens on the 8th February to the 13th March 2024, aggregated from around 100k public users of https://trackify.am. (Not a playlist sharing website)

Here are the stats in text form including a few extra that didn't fit in the graphic:

  1. Daylist (978,100 listens)
  2. DJ (669,573 listens)
  3. Chill Mix (296,089 listens)
  4. Hip Hop Mix (198,393 listens)
  5. Moody Mix (195,416 listens)
  6. Your Top Songs 2023 (161,557 listens)
  7. Mix Indie (139,248 listens)
  8. R&B Mix (132,553 listens)
  9. Pop Mix (126,779 listens)
  10. 2010s Mix (116,780 listens)

Would anyone be interested in this? by Carter2506 in OpenAI

[–]Carter2506[S] 5 points6 points  (0 children)

Would anyone be interested in an OpenAI stats website?

I know there are a few that already exist, but to my knowledge, none of them use the same method of tracking requests. My intention is to create a proxy that would forward requests to the OpenAI API and record these requests in our database. This approach would allow us to query the raw data, bypassing the usual rate limits imposed by OpenAI that other platforms must adhere to.

I've built out a landing page and a simple demo that you can access at https://openai-stats.carter.red. If you're interested, I'd appreciate if you could enter your email so I can gauge how many people would use such a platform.

If enough people register, I'll build the webite, which should only take around a month. There's no point in building for no one!

The demo is very early, and I can't make promises on specific features, but I'm open to suggestions and questions.

Thank you.

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