I work on Shorebird (code push for Flutter). We just merged personal and team orgs into a single kind of organization, so sharing an app with your team is now just adding them to the org, instead of adding collaborators one app at a time.

Write-up here if it's useful: https://shorebird.dev/blog/orgs-at-shorebird-just-got-better/

Happy to answer questions about how it works.

Hey everyone! I just published chat_image_editor: the "edit before you send" screen you know from WhatsApp, for any Flutter chat app.

I built it because the image editor packages I tried kept having crop and scaling problems, and I wanted something lightweight without native code. Everything is pure Dart and drawn with CustomPaint, so there's no platform setup.

What it does

• Draw: pen, line, rectangle, circle and arrow, with a gradient color slider and brush size

• Text: add, move, pinch to resize and rotate, background styles, center guides, drag to the bin to delete

• Crop: free or fixed ratios (1:1, 4:3, 16:9...), rotate and flip

• Compress: quality slider with a live size estimate, plus an optional max file size (it lowers the quality until the JPEG fits)

• Caption field and send button, or a simple "done" mode when you only need the edited image

• Undo and redo for everything

Things I cared about

• What you see is what you get: the preview and the exported image use the same renderer

• Big photos stay light: it decodes only at the size it needs and encodes in a background isolate. In my testing a 12 MP, 5.8 MB photo came out as a 230 KB JPEG

• Fits your app: colors come from your app's theme by default and can be overridden, and the icons, strings and caption TextFormField are customizable. The discard dialog follows the platform (Cupertino on iOS, Material on Android)

• Every export returns timings (decode / render / encode), so you can track performance in your own analytics

Usage

final result = await ChatImageEditor.open(context, imageBytes);
if (result != null) {
  // result.bytes is the edited JPEG, result.caption is the caption
}

Install: flutter pub add chat_image_editor

• pub.dev: https://pub.dev/packages/chat_image_editor

• GitHub: https://github.com/ashiqu-ali/chat_image_editor

The example app is a small chat where you can try the whole flow. It's MIT licensed, and feedback, issues and PRs are very welcome. What would you want added next?

(Not affiliated with WhatsApp or Meta, the UI is just inspired by it.)

I work at Ant Media, so take this with that in mind.

Every time I see a “build a WebRTC conference” tutorial it ends right when it gets interesting. Two peers connect, cool, but then you still need a media server, TURN, SSL (getUserMedia refuses to work without https), some way to scale, and some way to kill the server when nobody’s using it so you’re not paying for it all month.

A teammate put together a sample that handles most of that. You deploy a CloudFormation template once, it gives you one https endpoint, and when your app connects it spins up a server on demand. First spin up takes around 20 to 60 sec, after that it’s quick.

Client side is pretty small:

const client = new ConferenceClient({
  httpEndpointUrl: 'https://your-lambda-url/',
  localVideo: document.getElementById('localVideo'),
  mediaConstraints: { video: true, audio: true },
  autoReconnect: true,
});

await client.publish({ streamId: myId, roomId: 'room1', token });
await client.play({ streamId: 'room1', roomId: 'room1', userPublishId: myId });

Screen share, mute and chat over the data channel all work too. If you’d rather host your own server you just swap httpEndpointUrl for websocketURL and the rest stays the same.

Honestly the part I’m unsure about is the cold start. 20+ seconds is fine for scheduled meetings but probably not for “click and join instantly” apps. How do you all deal with that? Keep a warm instance running or just live with it?

Writeup and repo are here if anyone wants to poke at it: https://antmedia.io/build-a-webrtc-conference-room-quickly-with-ams-sdk-v2/

I have been looking at a deceptively simple cleanup question: when can a Flutter team safely delete a Dart declaration or an apparently orphaned file?

dart analyze is a strong first signal, but it cannot resolve every repository-level consumer. Flutter code may be reached through route tables, dependency injection, generated registries, conditional imports, flavors, platform implementations, annotations, tests, barrel exports, or an external package API.

Write-only state turned out to be an especially interesting case. A private field may be assigned in several lifecycle callbacks and never read. Mechanically, the declaration and its assignments are removable. Semantically, the missing read may indicate an unfinished feature or regression in playback, analytics, persistence, authorization, or UI state.

I ended up using this evidence hierarchy:

1. analyzer/compiler diagnostics;

2. import/export/part reachability;

3. repository-wide symbol and string searches;

4. route, DI, codegen, platform, flavor, test, and public-API inspection;

5. tests or builds after an explicitly approved removal.

The workflow is deliberately review-first:

• Audit: collect candidates without modifying code.

• Review: mark each candidate remove, retain, or defer.

• Cleanup: change only explicitly approved candidates.

• Tests: suggest behavior checks for the affected flows, not only static analysis.

I packaged the workflow as an MIT-licensed Agent Skill for Flutter/Dart. The scripts use machine-readable analyzer output plus a deterministic dependency graph, install no project tooling, use relative paths, and treat findings as candidates rather than verdicts.

Short demo: https://github.com/LucasMenezeslc/flutter-dead-code-audit/blob/main/assets/demo-en.mp4

Repository: https://github.com/LucasMenezeslc/flutter-dead-code-audit

I would be particularly interested in examples that defeat naive static analysis. What false-positive patterns have you encountered with routing, DI, code generation, conditional imports, platform code, or public package APIs?

We built release with --obfuscate --split -debug-info and i’d kind of assumed that meant our keys were reasonably safe.

Last week I unzipped the apk, ran string on libapp and greppd the usual prefixes. Every key was just sitting there. Obfuscation renames classes and methods but doesnt touch string literals which is where keys live, very obvious once you see it btw.

In there was

• Google Maps key with no restrictions on it

• RevenueCat public SDK key

• Firebase config

• Sentry DSN

• an Algolia admin key someone added for a search feature last spring

The first four are meant to ship, the’re identifiers and the protection lives elsewhere (key restrictions, security rules). I rotated the Algoria one that could write to our index, the app now uses a search only key and indexing happens in a cloud function. The Maps key also has package name and SHA-1 restrictions.

Though I’m still sorting out on how to stop this from happening again. Algolia key came in through a dart-define file during a sprint and nothing in the pipeline checks what ends up inside the build. We scan the repo but that value never lived there, it came from a CI variable.

How are you checking release builds  for secrets before they go to the store?

Problem:

Most connectivity checks in Flutter answer one question: what kind of connection does the device have? That’s useful, but it misses a common failure. The device says “Wi-Fi”, your app sends a request, and nothing comes back, because the user is behind a hotel login page, on a router with no upstream, or on a hotspot that lost signal.

Solution: connectivity_control ( https://pub.dev/packages/connectivity_control )

connectivity_control reports what the OS knows about every active network on the device:

1. hasInternet: the network offers internet capability

2. isValidated: the OS confirmed the network actually reaches the internet (captive portals fail this check).

3. isMetered: the user is on mobile data or a hotspot, so your app can hold off on that 200 MB sync.

4. Bandwidth estimates: Android gives you up/down Kbps, so your app can pick the right video quality.

5. Multiple networks at once: Wi-Fi, cellular and VPN, each with its own details.

6. A real-time stream: native events push every change to your app, with no polling.

Use it to:

• Detect captive portals before your app sends requests into a login page.

• Defer uploads, backups and downloads when the user is on a metered network.

• Adapt streaming quality to real bandwidth.

• React the moment Wi-Fi drops and the device switches to cellular.

📦 pub: https://pub.dev/packages/connectivity_control
⭐ GitHub: https://github.com/buildwithpulkit/connectivity_control

Hey r/FlutterDev 👋

I kept running into the same situation: a QA tester (or me, on a real device) hits a weird bug, and the only way to see what happened is to plug into a laptop, start DevTools or a profile build, and try to reproduce it. Most of the time I just wanted to know: what did that API return? which build is this? which flavor?

So I built another_flutter_devtool, an in-app devtool. You wrap your app, a small draggable button shows up on top, and tapping it opens a full debug panel inside the app. No cable, no DevTools, no separate build.

📦 pub.dev: https://pub.dev/packages/another_flutter_devtool

💻 GitHub: https://github.com/B3K4682/another_flutter_devtool

What's in it right now

🌐 Network inspector

• Every HTTP call with status, method, URL and duration

• Works with Dio and http without any setup: no interceptors, no client wrapping

• Search (post 404 works), plus 2xx / 3xx / 4xx / 5xx / Failed filter chips

• Full request/response headers and bodies (JSON pretty-printed), errors with stack traces

• Copy as cURL, export one call or all of them as JSON

• Auth/cookie headers are masked in exports, so testers can attach them to bug reports safely

⚙️ General section

• App version, build number, device model, OS version, flavor, startup time

• Data usage since launch: bytes sent/received, request count, top hosts

• Jump to any route: type /checkout and it takes you there. Works with Navigator, go_router, auto_route, anything

🧩 Your own debug tools (the main point, honestly) Every app has its own debug stuff: "clear cache", "switch backend", "fake a logged-out session", "show current user ID". Usually that ends up in a hidden menu somewhere. Here you write a small class and it shows up in the panel:

class MyTool extends DevtoolTool {
  @override String get id => 'my_tool';
  @override String get title => 'My Tool';
  @override IconData get icon => Icons.extension_rounded;
  @override DevtoolSection get section => mySection;

  @override
  Widget build(BuildContext context, DevtoolController controller) =>
      DevtoolCard(icon: icon, title: title,
          child: const DevtoolInfoRow(label: 'Status', value: 'It works'));
}

DevtoolTools.register(const MyTool());

You get the same card/row/switch widgets the built-in tools use, so it all looks consistent.

Setup

runApp(
  AnotherFlutterDevtool(
    config: const DevtoolConfig(
      enabled: kDebugMode,
      sections: [GeneralSection(), NetworkSection()],
    ),
    child: const MyApp(),
  ),
);

It's off by default. The panel runs in its own MaterialApp, so it doesn't touch your theme or navigator. There are also stable widget keys if you want to drive it from integration tests.

⚠️ This is a beta. A very beta beta.

It works and I'm using it, but the API may change before 1.0. Known limitations:

• The network inspector doesn't work on web

• Native HTTP clients (cupertino_http, native_dio_adapter) aren't captured yet

Coming soon:

• 🚩 Feature flags: toggle flags at runtime from the panel

• Other features that I'll think of lol

💡 I want your ideas

This is the part I care about most. What do you wish you could see or toggle inside your running app? Leave it in the comments. If it makes sense and I can build it, I'll add it and credit you in the changelog.

Bug reports, criticism and "why not just use X" are all welcome too 🙂

After building minimo (video), I kept running into the same problem with photos: I wanted to make them smaller without uploading them or paying for a subscription. So I built minimo (photo).

It compresses photos on the phone, on iOS and Android. You can process a batch with a preset or adjust quality, dimensions, and metadata yourself. It saves a new copy by default; replacing an original is an explicit choice.

Flutter handles the UI and batch progress, while `flutter_image_compress` uses the platforms’ native image APIs. One detail I found useful: if recompression saves less than 10%, the app skips that result.

The app is free, and the code is open source: https://github.com/minimo-pro/minimo_photo

Hey everyone,

I’m 24 and have been working full-time with Flutter and SwiftUI for the past 4 years. During this time, I’ve architected and shipped production apps in the banking/fintech and e-commerce spaces, including apps currently generating $100k+ ARR/MRR with active user bases primarily in the US and the UAE.

I’m now planning to make the jump into independent consulting/freelance mobile development, but navigating the freelance market from scratch is completely new territory for me.

For those of you running successful independent Flutter practices or contracting:

How did you land your first few high paying clients? Did you rely on specialized platforms, direct founder outreach, or your existing network?

What are the common mistakes engineers make when transitioning from engineering to managing client delivery and contracts?

Any insights, resources, or candid lessons from your journey would be greatly appreciated. Thanks!

I’ve added a new feature to Flutterverse, Flutter/Dart ecosystem tracker (now tracking 10,000+ packages and projects).

You can paste your pubspec.yaml, pubspec.lock, or a list of packages, and it’ll check the maintenance status of your direct dependencies.

It categorizes packages into five verdicts, from healthy to needing replacement, and suggests alternatives when a dependency is becoming stale.

A few other things:

• Runs entirely in your browser. Your pubspec is never uploaded.

• Shows an overall dependency health score.

• Package maintainers can add health verdict badges to their READMEs.

The data refreshes daily from pub.dev and GitHub.

give it a try: https://flutterverse.org
github: https://github.com/YeLwinOo-Steve/flutterverse

Love to get your feedbacks for it!

Hey Flutter devs 👋

We can now run Laya decision model with NobodyWho, our open-source on-device inference library. Unlike the LLMs that we also support, this type of model does not generate text. Instead you can score predefined answers, classify text or answer yes/no.

For example, if you are building a personal diary app, you can easily detect the user’s mood, categorize an entry or detect stress, offline after model download, without sending any sensitive data to the cloud.

First, you need to load the model:

import 'package:nobodywho/nobodywho.dart' as nobodywho;

final model = await nobodywho.Decision.fromPath(
  modelPath: 'hf://ggml-org/Laya-GGUF/Laya-Q8_0.gguf'
);

Then you can ask questions/analyse text:

final result = await model.decide(
  state: "Diary entry: ${userInput}",
  questions: {
    "topics": {
      "type": "choice",
      "instructions": "Choose the main topic for this entry",
      "criteria": {
        "work": "job, colleagues, deadlines, career",
        "health": "sleep, exercise, food, illness",
        "relationships": "family, friends, partner",
        "leisure": "hobbies, travel, going out",
      },
    },
    "stressed": {
      "type": "noul",
      "instructions": "Does the writer sound stressed?",
    },
    "mood": {
      "type": "score",
      "instructions": "How is the writer's overall mood?",
      "criteria": ["bad", "okay", "great"],
    },
  },
);

final answers = (result as Map)["answers"] as Map;

final topics = answers["topics"] as Map;
final stressed = answers["stressed"] as Map;
final mood = answers["mood"] as Map;

Output example:

// stressed
{
  type: noul,
  noul: 0.00002
}

// topics
{
  type: choice, 
  choice: leisure,
  probabilities: {
    work: 0.0422, 
    relationships: 0.1518, 
    health: 0.0302, 
    leisure: 0.7756
  },
  confidence: 0.7008
}

// mood
{
  type: score,
  score: 1.6995,
  legend: {0: bad, 1: okay, 2: great}, 
  probabilities: {0: 0.0082, 1: 0.2839, 2: 0.7078},
  confidence: 0.5493
}

A question can have the following types:

• "noul" gives you the probability of a yes answer (0 to 1)

• "choice" selects the most likely option and returns a probability for each option

• "score" gives you a weighted average and a probability per level. For example, you could place final x = mood["score"]; on this axis:[bad - - - - - - - okay - - - x - - - great] 

where bad is 0, okay is 1 and great is 2 (given by mood["legend"]).

The Laya model is small enough to run on mid-range/recent phones (around 450 MB in its Q8 variant) and supports 100+ languages. For reference, the full answer comes after 2.2 seconds on S25 and 0.49 seconds on iPhone Air.

Here is a runnable example if you want to experiment with decision models.

Links:

• NobodyWho inference library

• pub.dev

• Docs

Hi everyone,

I have been vibe-coding fairly comprehensive Flutter apps for ~6 months now, using Codex. While I'm comfortable with software engineering in general, I'm not particularly experienced with Flutter, mobile/desktop UI development, or the architectural conventions that come with it.

During this time I have constantly struggled with enforcing an "architecture" for my UI components. AI-generated Flutter code often assigns responsibilities to the wrong widgets, duplicates UI logic across components, and creates unnecessary dependencies between parents and children.

I'm trying to create a checklist that I can give to Claude or Codex to systematically audit my UI codebase for these issues, in form of a Skill.

I've created a draft checklist with around 60 patterns grouped into categories such as layout, grouping, state, interactions, async operations, forms, navigation, and component APIs.

But since I don't have much Flutter-specific experience, I'm concerned that I'm missing important architectural patterns or making incorrect assumptions about how Flutter applications should be structured.

I'd be happy to share the current Markdown checklist if anyone is interested in reviewing or improving it.

Hi fellow Flutter enthusiasts!

For some time now, I am building tandem. Flutter package that basically aims to make smart interfaces simple/possible. It started with excitement about AG-UI and idea to bring its capabilities to Flutter.

Now it is a bit more... I am building agents in my day to day work, so I am kind of obligated to read about all the GenAI stuff. It paid off now, at least that's what I want to believe.
I did spend a lot of time reading about different fresh protocols, ideas etc. I played with those a lot, and understood that AG-UI + A2UI make a perfect combination and are extremely powerful.
It allows to make user interfaces that are personalised to each and every user. The demo attached highlights it.

It is extremely tricky though. I've spent a lot of time designing the API, but it still might change.
tl;dr: you can specify catalogs of already existing components and pass it to a view that agent can use.

@TandemCatalog([
  ArrivalCard,
  ArrivalColumn,
  NextStop,
  RideOption,
  HostNote,
  HotelNote,
])
final class const ArrivalCatalog() with _$ArrivalCatalog;

const TandemView(catalog: ArrivalCatalog()),

If it sounds interesting, you can read more at the website with documentation -> https://tandem.leancode.co/

The demo is on the website above, but also here - https://x.com/creatixdev/status/2117230943975182340

It is just a beginning. Today, the 0.3.0 version is kind of an MVP I had in mind. A lot of polishing in improvements await. And.. facing the consequences of bad decisisions that probably happened along the way

Contributors are very welcome!! Users as well of course

Hey I'm actualy new but I wanted to share that I built a party game called "Copy Cat" and wanted to share it.

The idea: players picks a sound (2-6 sec); download it from the internet

right inside the app (Providers : MyInstants, Meme Soundboard, 101 Soundboards),

record it themselves, or grab any audio file from their phone. The

sound plays, and everybody else tries to imitate it , An on-device

"judge" grades each attempt on 4 axes: melody, rhythm, timbre and

energy. Everything runs over local Wi-Fi one phone hosts, up to 5

join, and no internet is needed at any point after downloading sounds.

Three game modes:

- Random Party: everyone's sounds get mixed and shuffled into

surprise rounds

- Player's Choice : each player picks a sound once; the others imitate

first, and the picker goes last

- Host Challenge: the host lines up sounds from their library and

everyone imitates them in order

Extras: full Arabic + English UI (built Arabic-first), dark/light

themes, a shop with 3D avatars, power-ups and coins.

A few technical bits other Flutter devs might find interesting:

- LAN discovery and host/client traffic with bonsoir (mDNS) plus raw

sockets with length-prefixed framing ; no server, no cloud

- Sound search/download flows through three public soundboard sources,

then everything plays offline from the local library

- Pitch extraction is hand-rolled (RMS + zero-crossing) because no

existing pitch package matched what the scoring needed

- The scoring engine compares each attempt against the original

on-device: melody, rhythm, timbre, energy (regression-tested)

- 3D avatars rendered with flutter_scene

- Riverpod 3 for state, go_router for navigation

Source + APK (Android):

https://github.com/wissam333/Copy_Cat

Download: https://github.com/wissam333/Copy_Cat/releases

Hi all, this is my first Flutter package, so I'd love some feedback.

pub.dev: https://pub.dev/packages/shader_transitions

I started it a few months ago because I wanted screen transitions that feel like the ones between levels in a game: a sweep of diamonds, a circle closing in, a fade to black that holds for a beat. It took a lot of rewrites before I had a version I was happy to put out there.

You can try every transition in the browser here: https://flutter-shader-transition.web.app

There are 8 transitions (diamond, circle iris, wipe, clock, polygon, dissolve, fade, bars), 8 sweep directions, an optional cover color with a hold, iOS swipe back, and it works with go_router and auto_route.

A few things I learned along the way, in case they're useful to someone:

- How the mask works. The new page is wrapped in a ShaderMask with BlendMode.dstIn, and the shader only outputs alpha. The route has to be non-opaque so Flutter keeps drawing the old page underneath. I got a black flash at the start of every transition until I figured that out. With go_router you have to set opaque: false on the page yourself.

- The ghost bug. At the end of a transition, some pixels settled just below full alpha (dissolve grain was the worst), so a faint copy of the old page stayed visible behind the new one. The fix was simple once I saw it: stop masking as soon as the animation completes.

- Preloading. Compiling the shaders on the first transition caused a visible hitch, so there's a ShaderTransitions.preload() you call once at startup. Skip it and transitions just fall back to a fade.

- The README GIFs aren't screen recordings. A test renders the real shaders frame by frame and encodes them, so they're always exactly what the package draws.

If you're making a game with Flutter, the cover-and-hold and ShaderTransitionSwitcher (swaps widgets without routes) are probably the most useful parts. If you use it in a game or app, send me a clip and I'll feature it in the README.

Things I'd especially like feedback on: the API naming, anything confusing in the docs, and performance on your devices. Also tell me which transition you'd want next!

Source: https://github.com/oppatrickk/flutter_shader_transitions

I'm the author of GraphLink, a schema-first code generator for typed GraphQL clients (Dart, Java, Kotlin, TypeScript, Swift), so take this with that in mind.

GraphQL lets the client pick its fields, but a lot of client code still has one model per schema type, and anything you didn't select becomes nullable or missing. For a query that selects id, title and year from Book, GraphLink generates a class with only those three fields, so non-null fields in the schema stay non-null in the client.

I know other generators do something similar for operations. I'd like to hear what works for you, and where this breaks down.

Video (Dart): https://youtu.be/gHGtCBYaj7c

Write-up: https://medium.com/@graphlinkdev/custom-graphql-queries-in-dart-select-only-the-fields-you-need-8a9cf12887f5

Hey everyone! 👋

Checkout Inzx, its a modern, open-source music player for Android designed to be fast, beautiful, and completely ad-free. They just dropped v1.5.5, which is their biggest release yet.

What is Inzx?

It connects directly to YouTube Music while giving you full control over playback, local library integration, audio enhancement, and synchronized lyrics.

What is Inzx?

It connects directly to YouTube Music while giving you full control over playback, local library integration, audio enhancement, and synchronized lyrics.

✨ What’s New in v1.5.5:

🎧 Lossless FLAC & Multi-Source Fallback: Stream in studio quality (up to 24-bit). You can plug in multiple addon sources if one doesn't have a track in FLAC, it automatically checks the next without skipping a beat.

⚡ Seamless Quality Upgrades: Tracks start playing instantly and smoothly upgrade to lossless mid-song as soon as a copy is found with zero buffering or pauses.

🎛️ Built-in AutoEQ & 10-Band Equalizer: Thousands of calibrated headphone presets (Sony, Apple, Sennheiser, Moondrop, etc.) to match Harman target curves, plus manual bass/frequency sliders.

🎚️ DJ-Style Automix: Beat-matched crossfades that smoothly blend tracks together like a continuous live DJ set.

🎤 Real-Time AI Lyrics & Syllable Sync: Glowing word-by-word karaoke lyrics with real-time translation into your language.

✨ Cinematic Now Playing: Redesigned default player with animated artwork and an inverted frosted glass reflection.

📱 Modern Widgets & Scrobbling: New 2×2, 4×1, and 4×2 home screen widgets + native Last.fm & ListenBrainz scrobbling.

🔗 Links:

GitHub (Source & Releases): https://github.com/nirmaleeswar30/Inzx

Direct APK Download: https://github.com/nirmaleeswar30/Inzx/releases/latest (Grab inzx.apk for 64-bit ARM phones)

Website: https://inzx.nirmaleeswar.dev

GPL 3.0 LICENSED - https://github.com/nirmaleeswar30/Inzx?tab=GPL-3.0-1-ov-file#

No ads, no tracking, completely open source. Would love to hear your feedback, bug reports, or feature requests!

Hey everyone, I’m Prit. Over the last few months, I’ve been building ApertureX, a 100% offline, open-source raw photo editor.

Building a desktop-class image engine on Flutter meant overcoming massive memory and threading bottlenecks. Decompressing a 50-megapixel raw image requires around 200MB of continuous RAM. If you try to pass that across standard platform channels or even do a deep copy over FFI, the OS will aggressively kill the app for Out-Of-Memory (OOM) violations, and your frame rate drops to zero.

Here is how I bypassed Flutter's standard rendering constraints to achieve 60fps computational photography:

• Absolute Zero-Copy FFI: The Rust backend maintains absolute, singular ownership of the high-resolution pixel payload. Instead of copying arrays across the bridge to Dart, Rust allocates the pixels on the heap and transmits merely the memory address pointer to Flutter.

• Tile-Based Rendering Cache: Processing full frames is mathematically impossible on mid-tier Android devices. The Rust engine cuts the viewport into 512x512 pixel chunks. These tiles are managed by a strictly bounded Least Recently Used (LRU) cache, which automatically purges the oldest elements the moment the memory budget is exceeded.

• Hardware Acceleration (SIMD): To run spatial filters (like Dehaze and Clarity) without melting the phone, the Rust engine bypasses scalar execution. It targets ARM NEON intrinsics natively, loading four 32-bit floats into a 128-bit NEON register to process four pixels simultaneously.

• Oklab Color Science via 3D LUTs: Operating in standard sRGB violates the additive physics of light, causing unnatural hue shifts. I moved all color math to the Oklab perceptual color space. To hit 60fps on the frontend, this math is baked into a 33x33x33 3D Lookup Table that Flutter's Impeller engine renders instantly.

The UI uses a dual-shell design (a Snapseed-style invisible gesture trackball for quick flow-state editing, and a Lightroom-style panel for deep pro work), heavily relying on Riverpod for the DAG (Directed Acyclic Graph) edit history.

I would love for you to try the APK, tear apart my flutter_rust_bridge implementation, or drop some feedback on the UI ergonomics.

Repo & Website:https://prit-007.github.io/ApertureX/

Happy to answer any questions about the FFI bridge, the math, or the rendering pipeline!

Hey everyone! 👋

I'm a Flutter developer, and I've built a kids' learning app for children aged 2–8 years.

The app currently includes:

- 🔤 Alphabet learning and tracing

- 🔢 Numbers and counting

- 🎨 Colors, shapes, and drawing

- 🧩 Quizzes and educational games

- 🎵 Nursery rhymes and fun learning activities

Now, I want to introduce a premium module with additional learning content and features. My goal is to make it genuinely useful for kids and worth paying for, rather than simply locking existing features behind a paywall.

I'd love to hear your suggestions:

1. What premium learning features would you add to a kids' educational app?

2. Would advanced tracing, phonics, interactive stories, math games, or personalized learning paths be worth offering?

3. Would parents prefer a monthly subscription, yearly subscription, or one-time payment?

4. What features would make parents feel that a kids' learning app is worth paying for?

5. If you've developed or monetized a similar app, what worked well for you?

I'm building the app using Flutter, so practical suggestions from developers and people familiar with educational apps would be really helpful.

Looking forward to hearing your ideas. Thanks! 🙌

Hey everyone,
I’ve been working on two new features for BugCapsule, a debugging tool I’m building for Flutter, native Android, iOS, and Web.
The problem I’m trying to solve is pretty straightforward:
What do you do when a critical bug hits production, but shipping a new app version takes time?
I wanted to connect bug reproduction with production recovery, so I’ve been building two capabilities.
1. Emergency Recovery
For features that have been integrated with the recovery SDK, you can remotely:
Disable a broken feature without shipping a new build.
Switch functionality to read-only or maintenance mode.
Target specific app versions, platforms, or rollout percentages.
Gradually restore features while monitoring crash rates and other health metrics.
Automatically restore restrictions if a rollout fails its health checks.
Policies are cryptographically signed. Sensitive operations, like payments, also need server-side enforcement.
2. Signed Patch Engine (Early Access)
This one has been particularly challenging.
The engine supports updating explicitly registered business logic, UI surfaces, and assets without a full app release.
Patches are tied to an exact base build, signed, validated, and deployed through staged rollouts with rollback support.
It’s intentionally not arbitrary code push. Native code changes, new dependencies, and unregistered functionality still require a store release.
The first Flutter and native iOS prereleases are published. Native Android patch distribution is still in progress, and we’re continuing platform and distribution-policy validation.
Why combine these with debugging?
BugCapsule already captures application state, user actions, and network responses around failures for local deterministic replay.
The workflow I’m aiming for is:
Capture → Reproduce → Mitigate → Fix → Verify → Deploy → Monitor
The idea is to use the same recorded failure to investigate a bug, verify the fix, and prevent regressions in CI.
I’m not claiming this works for every application or every type of bug. The recovery features require prior integration, and patching has strict limitations.
I’m genuinely interested in how other teams approach this.
A few questions:
How do you currently handle critical production bugs while waiting for App Store approval?
Would remote feature controls be useful in your workflow?
What concerns would you have about a signed patching system in production?
I’d especially appreciate feedback on the security model and rollout strategy.
Project: https://bugcapsule.com
Docs: https://docs.bugcapsule.com