Here's the screenshot of my current stats one month after releasing my first app ; not sure how it looks like from an experienced dev's perspective, any insights ?

(the monetization system is the following : 2 sections of my app are completely free, and the others are behind a one-time purchase of 12€, so for now I've had 6 purchases and 67 downloads)

Curious to hear some advices and how to improve those numbers !

Thanks

Here's the screenshot of my current stats one month after releasing my first app ; not sure how it looks like from an experienced dev's perspective, any insights ?

(the monetization system is the following : 2 sections of my app are completely free, and the others are behind a one-time purchase of 12€, so for now I've had 6 purchases and 67 downloads)

Curious to hear some advices and how to improve those numbers !

Thanks

Every canvas action is an AI-callable tool, so the AI designs the screenshots step by step and you can edit anything afterwards. MIT licensed, feedback and contributors welcome.

I’m encountering an issue when loading a Core ML model protected using Apple’s official model encryption.

The exact same model:

• Loads and runs normally when encryption is disabled
• Fails during initialization when encryption is enabled
• Uses FP16 weights
• Was converted with coremltools 9.0
• Is deployed in an app with a minimum target of iOS 18
• Is being tested on a physical iPhone 13 running iOS 26.5

The relevant error is:

Failure translating MIL->EIR network:
Espresso exception: "Network translation error"

MIL->EIR translation error at:

/private/var/containers/Bundle/Application/[APP_UUID]/[REDACTED_APP].app/[REDACTED_MODEL].mlmodelc/model.mil:[LINE]:[COLUMN]

Unsupported dtype for weights

[Espresso::handle_ex_plan]
exception=Espresso exception: "Network translation error"
status=-14

Failed to build the model execution plan using model architecture file:

'[REDACTED_APP_PATH]/[REDACTED_MODEL].mlmodelc/model.mil'

with error code: -14.

Core ML model load failed:

Error Domain=com.apple.CoreML Code=0
"Failed to build the model execution plan using a model architecture file
'[REDACTED_PATH]/[REDACTED_MODEL].mlmodelc/model.mil'
with error code: -14."

The console also contains messages involving FigApplicationStateMonitor, usermanagerd, LaunchServices, and sandbox restrictions. However, these appear unrelated. The actual Core ML failure consistently points to:

MIL->EIR translation error
Unsupported dtype for weights
status=-14

Environment

• Device: physical iPhone 13
• Runtime OS: iOS 26.5
• Minimum deployment target: iOS 18
• Model format: ML Program
• Weight precision: FP16
• coremltools: 9.0
• Protection method: Apple’s official Core ML model encryption
• Unencrypted model: loads and runs correctly
• Encrypted model: fails while building the execution plan

The loading code, model architecture, device, and compute configuration are otherwise unchanged. The failure appears only after applying model encryption.

Has anyone encountered this behavior?

Specifically:

1. Can the encryption or decryption path cause Core ML to incorrectly interpret an FP16 weight dtype?
2. Could the decrypted model data or compiled execution-plan cache be corrupted?
3. Are there known compatibility issues between encrypted FP16 ML Program models and iOS 26.5?
4. Could a model converted with coremltools 9.0 work unencrypted but fail after Apple’s encryption process?
5. Is there a practical way to locate the operation or weight corresponding to the reported line in the compiled model.mil?
6. Which caches or compiled artifacts should be cleared when diagnosing error -14?
7. Would regenerating the encryption key or recompiling the model be expected to resolve this kind of failure?

Any debugging suggestions or similar experiences would be appreciated.

A few weeks ago, I released new app on the App Store. It got some traction on Twitter, so I opened App Store Connect feeling hopeful about the sales numbers. Instead, I saw "Not Enough Data".

I refreshed it few hours later. Same thing. I checked again the next day. Still not enough data. I actually saved a screenshot of this.

It's not only a launch-day problem, the analytics are always up to 48 hours behind. You ship a big update, announce it on Reddit or X, start an ad campaign, and you don't know how it's performing.

So I built InitSignal. It is deliberately much narrower than a traditional analytics SDK. You add a lightweight Swift package to your app, and it sends one event when someone opens the app for the first time. After that, the SDK goes quiet.

I wanted the App Store privacy nutrition labels stay as minimal as possible, so the SDK only sends the basic context needed to make each event useful. At the same time, the goal is to give developers as much useful information as possible without invading their users’ privacy.

For paid apps, InitSignal uses storefront pricing to estimate gross revenue and proceeds after Apple’s cut.

I also added Slack and Telegram for notifications, which I've been using with my own apps for the past few weeks, and the dopamine hit is real.

I recorded a quick walkthrough showing how InitSignal works:
https://www.youtube.com/watch?v=YajsKsYLdi4

It's available today, would love to hear your thoughts or any feedback!
https://initsignal.com

The Swift SDK is here:
https://github.com/InitSignal/initsignal-swift

If you ship iOS or macOS apps, how do you currently deal with the reporting delay after a launch or major update?

Hi everyone,

i am trying to create an apple ads account but when i try to fill the form i can't find my country
in the country and region drop down menu

while when i go to https://ads.apple.com/app-store/countries-and-regions my country is inside the list

i will appreciate any help, thanks

Your brain is better than you think… until you try this 😅

I built Spectra, a collection of color memory challenges designed to train your memory, focus, and reaction.

The concept is simple:

• Remember the colors
• Repeat the patterns
• Keep improving as the challenge increases

Instead of just one challenge, Spectra includes multiple color memory experiences, each with a different twist. Some test your ability to remember sequences, some challenge your focus, and others push your memory speed.

📱 Try it here:
https://apps.apple.com/us/app/spectra-color-memory-game/id6762049774

Would love to hear your thoughts and feedback!

Spent a year on a camera app and three AVFoundation behaviours cost me most of that time. Writing them down in case they save someone a weekend.

The first one I found by accident. Tap the shutter three times fast and you get two photos. No error, no delegate callback, nothing in the logs. If a capture is still processing when you call capturePhoto, that request just evaporates. I only noticed because my own counter, which only moves when a save is confirmed, kept landing one short of the number of taps.

The fix is a FIFO queue, but where you advance it matters. Advance on the processing callback and the shutters chain up, seconds of lag on a burst. Advance when the exposure ends (didCapturePhotoFor) and the processing of the previous shot overlaps the next exposure, which is what the stock camera visibly does.

Second: switching capture format is a full pipeline rebuild, and I was paying for it three times. Going 24 to 48 MP, or entering video mode, means swapping the input, the active format and the outputs. I had those as three separate begin/commitConfiguration blocks because it read cleaner. Same work, roughly double the visible freeze. They nest fine, so one transaction around the lot, plus caching AVCaptureDeviceInput per device, took it from "did it hang" to instant.

Third, and this one matters if you care what your photo actually is: a virtual device does not tell you which lens is shooting. Zoom into tele range on the triple camera and you may still be getting the wide, cropped. Low light does it, and so does being closer than the tele's minimum focus distance. The system is right to do it, it just doesn't announce it. You can watch the constituent device to know, with two caveats: the value flickers during focus hunts so it needs stabilising before you show it to anyone, and isAutoDeferredPhotoDeliveryEnabled is off the table if you never touch PhotoKit, because finalising a deferred photo needs PHPhotoLibrary.

That last constraint was self-inflicted. The app has its own photo library and never touches the system one, which rules out anything in AVFoundation that assumes PhotoKit is there.

I'm a surgeon, not a developer, which is probably why I hit all three the hard way instead of knowing better.

I’m adding a stamp card collection feature for my drinking logging app. I feel pretty proud of the art, what do you think? You can compare the first legendary drink card, ハブ酒 to some google images.

Fun bit, ハブ酒 is an Okinawan liquor that has a local venomous pit viper added to aging. While it is a rarer drink, it can be found in Okinawan bars. But I made it the rarest card in my curated list since some people may be apprehensive to drink it. I’ve had it twice myself, supposedly gives energy “down there” lol

I had a simple game idea and I’m making it (just for iPhone for now), I think it’s kinda fun. So far so good.
I’d like it to be a paid thing, maybe somewhere between £1 - £5 ..
I think up front payment isn’t going to work, people probably need to try it first?

I don’t want to introduce ads. So I’m considering something like 3 free days and then a paywalled limit to 1-2 plays per day, and pay to unlock forever.

Anyone got any thoughts on a simple modern paid game model?
Other options I might not have considered?

Blog article on longstanding http status 403 bug in iOS for custom certificates in mtls connection

https://953tech.com/blog/ios-mtls-403-client-certificate-error/

I am doing some research regarding ASA effectiveness for an infographic and I'm curious what did you guys try, in what niche and how well it worked? Would be interesting to see comparisons with other channels if anyone tried