Forem: Mehmet Çelik

Flutter SaaS Starter Kit — Launch a SaaS MVP in Days

Mehmet Çelik — Tue, 30 Dec 2025 07:45:06 +0000

hipping a SaaS product is hard; building the same plumbing over and over shouldn't be. The Flutter SaaS Starter Kit gives you a clean, maintainable foundation so you can focus on product and features.

What’s included

Full Flutter source organized with Clean Architecture (domain / data / presentation)
Email/password auth (mocked) with persisted session and auth-onboarding guard
Onboarding flow with persisted completion state
Mock Stripe subscription flows (Free / Pro) + Node.js server stub (create-session, webhook)
Riverpod for state & DI, GoRouter for routing, shared theme and small UI components
Packaging script, unit test examples, and a CI workflow

Who this is for

Indie makers building an MVP
Agencies prototyping client apps
Teams who want a maintainable, testable Flutter codebase

Price

Single price: $39 (all source + server stub + docs).

Quick start (put in the post as code block)

# extract ZIP
cp .env.example .env
flutter pub get
flutter run
# optional: start server stub to test subscriptions
cd server
npm install
node index.js

Link

Buy / details: https://celkmehmett.gumroad.com/l/FlutterSaaSStarterKit

I Built a Clean Deep Link Router for Flutter

Mehmet Çelik — Mon, 15 Dec 2025 09:26:23 +0000

Deep linking is one of those things every Flutter app needs — and almost every app gets wrong.

Handling:

path & query params

auth-protected routes

redirects to login

returning users to their original deep link

usually ends up messy, tightly coupled, or hard to maintain.

So I built smart_deeplink_router.

It’s a focused, guard-based deep link router that solves the most common real-world problems without over-engineering.

Key ideas

Declarative deep link definitions

Async route guards

Automatic redirect memory

Minimal API, no magic

📦 Package:
https://pub.dev/packages/smart_deeplink_router

I’d love feedback from other Flutter devs — especially how you handle deep links today.

🚀 Introducing ml_transformer: A Pure Dart ML/NN Library 🎉

Mehmet Çelik — Fri, 29 Aug 2025 08:24:54 +0000

Most machine learning (ML) and deep learning (DL) libraries are dominated by the Python ecosystem. But with the rapid growth of Flutter and Dart, there’s a clear need for native Dart ML tools.

That’s why I built ml_transformer — a lightweight, modular, and extensible machine learning & neural network library written entirely in Dart.

🔑 Key Features

Transformer Architecture → Pre-LN block, Multi-Head Attention, FeedForward, LayerNorm, Rotary Positional Encoding (RoPE).

Core Layers → Embedding, Linear, LayerNorm, Attention, TransformerBlock.

Mini Training Engine → Autograd + AdamW optimizer for tiny training loops.

Quantization Helpers → int8, int4, int2, fp16, bf16.

ONNX Bridge → JSON-based import/export (protobuf support planned).

RNN Family → RNN, LSTM, GRU with batched forward.

Dataset & Batch API → Easy dataset loading & batching.

Callbacks & Monitoring → EarlyStopping, History.

FFI Acceleration → Native performance examples with NumPy references.

Unit Testing → Verified with NumPy integration and strong test coverage.

MIT Licensed → Fully open-source and free to use.

🎯 Why Dart for ML?

Flutter-first integration → Run ML pipelines directly in your apps.

Lightweight → Smaller footprint compared to heavy Python-based stacks.

Educational → Great for learning Transformer and RNN internals.

Prototyping → Build and test small-scale models quickly.

🌍 Who Can Use It?

🧑‍🎓 Students → Explore neural networks in a new language.

👨‍💻 Developers → Add ML pipelines directly into Flutter projects.

🔬 Researchers → Use as a minimalist sandbox for experiments.

🚀 Entrepreneurs → Quickly validate ideas with ML-enabled prototypes.

📦 Installation

Add it to your pubspec.yaml:

dependencies:
  ml_transformer: ^1.0.0

📊 Resources

📦 Pub.dev package
📊 Score & Metrics
💻 GitHub Repository
💬 Closing Thoughts

This is just the beginning. ml_transformer is not intended to replace big ML frameworks — but to open the door for Dart/Flutter developers to experiment with ML natively.

I’d love to hear your feedback, contributions, and ideas to make it even better 🙌

👉 Try it out, fork it, and let’s push ML in Dart forward together!

Dart #Flutter #MachineLearning #DeepLearning #OpenSource #Transformer

🚀 Introducing dart_irt: Item Response Theory in Pure Dart

Mehmet Çelik — Mon, 25 Aug 2025 09:25:59 +0000

I’m excited to announce the release of dart_irt-a pure Dart implementation of Item Response Theory (IRT).
This package makes it possible to integrate advanced psychometric models directly into Dart and Flutter applications, with no external dependencies.

🔎 What is IRT?
Item Response Theory (IRT) is widely used in educational testing, psychometrics, and adaptive assessments.
It provides a probabilistic framework to model how people with different abilities respond to test items (questions).

Key benefits of IRT:
More precise measurement of abilities
Adaptive testing (shorter, smarter tests)
Fair comparisons across populations
Psychometric validity checks

✨ Features in dart_irt

Dichotomous models: Rasch (1PL), 2PL, 3PL
Polytomous models: RSM (Rating Scale Model), PCM (Partial Credit Model)
Estimation: EM/MML algorithms
CAT helpers: item selection (Fisher/KL)
DIF analysis: Mantel–Haenszel for fairness detection
Person estimators: EAP, MLE, WLE
Fit indices: Infit, Outfit, person-fit U
Simulation utilities for generating test data

📦 Installation

Add to your pubspec.yaml:

dependencies: dart_irt: ^0.0.1
then

dart pub get
🚀 Quick Example

import 'package:dart_irt/dart_irt.dart';

void main() {
  final rasch = RaschEM();

  final X = IrtSim.raschResponses(
    persons: 300,
    b: [-1.0, 0.0, 1.0],
    seed: 42,
  );

  final res = rasch.fit(X);

  print('Item difficulties: ${res.b}');
  print('Person abilities (EAP): ${res.thetaEap.take(5).toList()}');
}

📂 Resources

📦 pub.dev package
💻 GitHub repo
💡 Closing Thoughts

This is just the first release of dart_irt.
I hope it will be useful for researchers, EdTech developers, and Flutter enthusiasts who want to bring psychometrics and adaptive learning into their applications.

Feedback and contributions are welcome! 🚀

Introducing ml_sentiment_simple: A Multilingual Sentiment Analysis Library for Dart & Flutter

Mehmet Çelik — Wed, 30 Jul 2025 15:14:10 +0000

Introducing `ml_sentiment_simple`: A Multilingual Sentiment Analysis Library for Dart & Flutter

✨ Lightweight, customizable, and dependency-free. Analyze positive, negative, and neutral sentiments in Turkish 🇹🇷 and English 🇺🇸 texts with just a few lines of Dart code.

🚀 What is `ml_sentiment_simple`?

ml_sentiment_simple is a simple but powerful sentiment analysis package for Dart and Flutter developers. It uses a word-based lexicon method to determine the emotional tone of a sentence. Unlike complex ML models, this tool is blazing fast, runs offline, and is fully customizable.

✅ Key Features

🌐 Multilingual support – Turkish 🇹🇷 and English 🇺🇸 out of the box
🧠 Zero dependencies – works with core Dart libraries only
🧩 Custom lexicons – extend the word list with your own vocabulary
🚀 Ultra-lightweight – ideal for mobile and embedded apps
📦 Available on pub.dev

🔧 Installation

Add this to your pubspec.yaml:

dependencies:
  ml_sentiment_simple: ^1.0.0

Then run:

flutter pub get

💡 Usage Example

import 'package:ml_sentiment_simple/ml_sentiment_simple.dart';

void main() {
  final analyzer = SentimentAnalyzer();

  final result = analyzer.analyze("This is amazing but also a bit disappointing.");
  print(result.label);  // → neutral
  print(result.score);  // → 0.0
}

You can also customize the word list:

final customAnalyzer = SentimentAnalyzer(
  lexicon: WordSentimentLexicon(
    language: LexiconLanguage.both,
    customPositiveWords: {'delightful'},
    customNegativeWords: {'disaster'},
  ),
);

🧪 Sentiment Scoring Logic

The score is computed as:

(positive - negative) / totalSentimentWords

Resulting in:

positive if score > 0
negative if score < 0
neutral otherwise

This is extremely useful for feedback apps, chatbots, comment analysis, or social media monitoring tools.

📦 Explore the Package

👉 Pub.dev: ml_sentiment_simple
💻 GitHub: github.com/CelkMehmett/ml_sentiment_simple

🙌 Contributions Welcome!

I built this package to solve basic NLP needs on mobile without heavy ML models.

If you want to help expand lexicons or add new features, feel free to contribute or open an issue!

Thanks for reading — I hope this helps other Dart devs build smarter apps!

📌 Tags:

dart, flutter, nlp, sentiment-analysis, open-source, pub-dev

🚀 Dart & Flutter için ML Kütüphaneleri Yayında! 🚀 ML Libraries for Dart & Flutter Now Published!

Mehmet Çelik — Sun, 13 Jul 2025 17:17:13 +0000

🚀 Dart & Flutter için ML Kütüphaneleri Yayında!
🚀 ML Libraries for Dart & Flutter Now Published!

Bugün Dart & Flutter ekosistemi için 7 yeni makine öğrenimi kütüphanesi yayınladım!
Today, I published 7 new machine learning libraries for the Dart & Flutter ecosystem! 🎉

Amaç: Dart'ta native ML uygulamaları geliştirmek isteyenler için sade, modüler ve kolay entegre edilebilir bir temel sunmak.
Goal: To provide a clean, modular, and easily integrable foundation for developers who want to build native ML applications in Dart.

📚 Yayınlanan Kütüphaneler | Published Libraries:

ml_text_cleaner – Metin temizleme araçları (stopwords, punctuation, normalization).
→ Text cleaning utilities (stopwords, punctuation, normalization)

ml_fin_scorer – Finansal veri skorlaması için skorlayıcı.
→ Scorer for financial datasets and alternative credit scoring.

ml_data_splitter – Eğitim/test/validation veri bölme.
→ Split datasets into train/test/validation sets.

ml_scaler – Min-Max ve Standard Scaler ile özellik ölçekleme.
→ Feature scaling using Min-Max and Standard Scaler.

ml_metrics – Accuracy, Precision, Recall, F1 gibi metrikler.
→ ML metrics like Accuracy, Precision, Recall, F1.

ml_feature_encoder – Label ve One-Hot Encoding desteği.
→ Label and One-Hot Encoding utilities for categorical variables.

CelkMehmett – Tüm kütüphaneleri GitHub profilimde bulabilirsiniz.
→ You can find all repositories on my GitHub profile.

🧠 Neden Dart?
Flutter ile entegre mobil/masaüstü uygulamalarda doğrudan ML modelleriyle çalışmak için Dart'a native ML altyapısı gerekiyor.
Why Dart? Because Flutter-based apps need a native ML foundation to run models efficiently across platforms.

🌱 Hedefim / My Goal:
Topluluğa açık kaynak katkısı sunmak ve Dart ML ekosistemini büyütmek.
To contribute to the open-source ecosystem and grow the Dart ML stack.

📌 Tüm projeler açık kaynak ve MIT lisanslıdır. Geri bildirim ve katkılarınızı bekliyorum!
📌 All packages are open-source and MIT licensed. Contributions and feedback are welcome!

🔗 GitHub: https://github.com/CelkMehmett

💬 Sizce Dart ML dünyasında eksik olan ne?
💬 What do you think is still missing in the Dart ML space?

Yorumlarda tartışalım!
Let’s discuss in the comments 👇

How to Detect Rooted or Jailbroken Devices in Flutter Using mobile_root_checker Plugin

Mehmet Çelik — Sun, 06 Jul 2025 13:30:40 +0000

Detect Rooted or Jailbroken Devices in Flutter with `mobile_root_checker` 🔐

In today's mobile world, app security is more crucial than ever. If your app handles sensitive data — whether it's finance, health, or private communication — allowing it to run on rooted (Android) or jailbroken (iOS) devices can open the door to vulnerabilities.

That's why I built mobile_root_checker, a Flutter plugin to help you detect rooted or jailbroken devices before they can harm your app integrity.

🔍 What is Root/Jailbreak Detection?

Rooted devices on Android and jailbroken devices on iOS have elevated privileges, allowing users (or malicious apps) to bypass OS-level protections.
This can lead to:
- Code injection
- Man-in-the-middle attacks
- Data theft
- Insecure app modifications

🚀 Introducing: `mobile_root_checker`

mobile_root_checker is a lightweight and open-source Flutter plugin to detect root/jailbreak status at runtime.

✅ Features

Detect rooted Android devices
Detect jailbroken iOS devices
Easy Flutter integration (no native code required)
Supports all major Android and iOS versions

📦 Installation

Add the dependency to your pubspec.yaml:

dependencies:
  mobile_root_checker: ^1.0.0

Then run:

flutter pub get

import 'package:mobile_root_checker/mobile_root_checker.dart';

void main() async {
final isRooted = await RootChecker.isDeviceRooted();

if (isRooted) {
print("⚠️ Device is rooted or jailbroken!");
// Block or restrict app functionality here
} else {
print("✅ Device is secure.");
}
}



📱Use Cases
| App Type             | Why Use Root Detection?                |
| -------------------- | -------------------------------------- |
| 💳 Banking / Finance | Prevent fraud or memory tampering      |
| 🔐 Messaging         | Stop interception or app modifications |
| 🎮 Games             | Avoid cheating via rooted tools        |
| 🏢 Enterprise Apps   | Protect internal company data          |

⚙️ How It Works

On Android:

    Looks for su binary

    Checks common root management apps

    Scans system properties and execution paths

On iOS:

    Detects jailbreak indicators like Cydia

    Probes system directories and access violations

🔗 Resources

    📦 Pub.dev: mobile_root_checker

    💻 GitHub: github.com/CelkMehmett/mobile_root_checker

🤝 Contributions Welcome

I'm open to issues, suggestions, and PRs!
Future roadmap ideas:

    ✅ Emulator detection

    ✅ SafetyNet / DeviceCheck integration

    ✅ Web & Desktop fallbacks (no-op)

📄 License

Licensed under the MIT License — free to use, modify, and share.
👋 Final Words

Mobile security isn't a luxury — it's a necessity.
Add root/jailbreak detection today and protect your app tomorrow.

Let’s build safer Flutter apps together! 🔐✨

[Boost]

Mehmet Çelik — Sun, 29 Jun 2025 16:22:39 +0000

Mehmet Çelik

Jun 29 '25

Native Machine Learning in Dart: `ml_knn` and `ml_logistic_regression` are Live on pub.dev!

#dart #flutter #opensource #pubdev

Comments

2 min read

Native Machine Learning in Dart: `ml_knn` and `ml_logistic_regression` are Live on pub.dev!

Mehmet Çelik — Sun, 29 Jun 2025 16:08:16 +0000

🧠 Introduction

The Dart ecosystem is growing fast — especially with Flutter. But when it comes to native machine learning (ML) capabilities inside Dart itself, the resources are almost nonexistent.

To address this gap, I’ve started developing a set of lightweight, native, and open-source machine learning packages in Dart. And now, the first two are officially published on pub.dev:

ml_knn: A K-Nearest Neighbors classifier
ml_logistic_regression: A Logistic Regression model

📦 What’s Available

1. `ml_knn`: K-Nearest Neighbors in Pure Dart

Use Cases:

Simple classification
Anomaly detection
Recommendation systems

Installation:

dependencies:
  ml_knn: ^1.0.0

Usage:

final model = KNN(k: 3);
model.fit([[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]], ['A', 'B', 'B']);
final prediction = model.predict([[2.5, 3.5]]);
print(prediction); // ['B']

✅ Fully written in Dart

✅ Unit-tested and published on pub.dev

✅ Offline & mobile-friendly

2. `ml_logistic_regression`: Logistic Regression in Dart

Use Cases:

Binary classification
Probability-based predictions
Linear decision boundaries

Installation:

dependencies:
  ml_logistic_regression: ^1.0.0

Example:

final model = LogisticRegression(
  learningRate: 0.1,
  iterations: 1000,
  regularization: 0.01,
);

model.fit([[0, 0], [1, 1]], [0, 1]);
final prediction = model.predict([[0.5, 0.5]]);
print(prediction); // [0] or [1]

🧪 The model is validated with logical gate (AND) prediction tests

📈 All training is done natively in Dart – no Python, no API, no external libs.

🌱 Roadmap

This is just the beginning. Here's what I'm building next:

Package	Status	Description
`ml_knn`	✅ Live	K-Nearest Neighbors (classification)
`ml_logistic_regression`	✅ Live	Logistic regression
`ml_fuzzy_matcher`	🚧 In Progress	AI-powered string similarity
`ml_naive_bayes`	🔜 Planned	Naive Bayes Classifier
`ml_linear_regression`	🔜 Planned	Linear regression
`ml_kmeans`	🔜 Planned	Unsupervised clustering
`ml_fin_scorer`	🔜 Planned	AI-powered financial scoring

🚀 Eventually, all will be part of a complete framework:

ml_flutter_basics

🔍 Why Native ML in Dart?

No need to host an API or use Python backends
Works offline, ideal for mobile apps
Seamless experience inside the Flutter ecosystem
Lightweight and fast to prototype in Dart

🤝 Contribute or Collaborate

I’m actively maintaining these projects and open to contributions!

GitHub: github.com/CelkMehmett
Pub.dev: ml_knn, ml_logistic_regression

✨ If you like the project, please give it a star!

💬 Issues, ideas, PRs — all are welcome.

📢 Final Thoughts

Machine learning in Dart should be accessible, lightweight, and usable in real-world apps — without always needing to call an external service or switch languages.

I’ll keep building these tools and sharing them publicly.

Thanks for reading!

Let’s make Dart smarter — together.

🙋‍♂️ About Me

I'm a Flutter & AI developer building native ML tools in Dart.

Also working on productivity apps like MergeNius, GreenPact, and more.

Building in public. Open to collaboration.

Let’s connect!

Forem: Mehmet Çelik

Flutter SaaS Starter Kit — Launch a SaaS MVP in Days

Price

Link

I Built a Clean Deep Link Router for Flutter

🚀 Introducing ml_transformer: A Pure Dart ML/NN Library 🎉

Dart #Flutter #MachineLearning #DeepLearning #OpenSource #Transformer

🚀 Introducing dart_irt: Item Response Theory in Pure Dart

Introducing ml_sentiment_simple: A Multilingual Sentiment Analysis Library for Dart & Flutter

Introducing ml_sentiment_simple: A Multilingual Sentiment Analysis Library for Dart & Flutter

🚀 What is ml_sentiment_simple?

✅ Key Features

🔧 Installation

💡 Usage Example

🧪 Sentiment Scoring Logic

📦 Explore the Package

🙌 Contributions Welcome!

📌 Tags:

🚀 Dart & Flutter için ML Kütüphaneleri Yayında! 🚀 ML Libraries for Dart & Flutter Now Published!

How to Detect Rooted or Jailbroken Devices in Flutter Using mobile_root_checker Plugin

Detect Rooted or Jailbroken Devices in Flutter with mobile_root_checker 🔐

🔍 What is Root/Jailbreak Detection?

🚀 Introducing: mobile_root_checker

✅ Features

📦 Installation

[Boost]

Native Machine Learning in Dart: `ml_knn` and `ml_logistic_regression` are Live on pub.dev!

Native Machine Learning in Dart: `ml_knn` and `ml_logistic_regression` are Live on pub.dev!

📦 What’s Available

1. ml_knn: K-Nearest Neighbors in Pure Dart

2. ml_logistic_regression: Logistic Regression in Dart

🌱 Roadmap

🔍 Why Native ML in Dart?

🤝 Contribute or Collaborate

📢 Final Thoughts

🙋‍♂️ About Me

Introducing `ml_sentiment_simple`: A Multilingual Sentiment Analysis Library for Dart & Flutter

🚀 What is `ml_sentiment_simple`?

Detect Rooted or Jailbroken Devices in Flutter with `mobile_root_checker` 🔐

🚀 Introducing: `mobile_root_checker`

1. `ml_knn`: K-Nearest Neighbors in Pure Dart

2. `ml_logistic_regression`: Logistic Regression in Dart