Artificial Intelligence and Energy Consumption: The Hidden Cost of Smart Technology

 

You can think of artificial intelligence (AI) like a human brain, but a super powerful one! Like a child learning something new, AI processes large amounts of data, analyzes it and learns certain patterns. There’s a lot of math, statistics and complex algorithms involved. In particular, what we call deep learning improves itself by repeatedly performing calculations on huge data sets. This process requires very large and powerful computers.

For example, when you ask an AI model, “Is this a cat?”, the model has analyzed thousands or even millions of cat photos and learned every detail. It guesses by looking at things like the shape of an eye, an ear, the length of a whisker. But here’s the magic: It takes a lot of computing power to do this!

Artificial intelligence (AI) is entering more and more into our lives every day. From voice assistants on phones to the shows Netflix recommends to us, from Google’s search results to Tesla’s self-driving cars, AI is everywhere. But does having this incredible technology come at a price? Yes, it comes at a big price: Energy consumption!

Although we don’t realize it in everyday life, AI models consume huge amounts of energy when they run. But why is there such a huge energy demand? Where does it come from and what steps are being taken to reduce consumption? Let’s explore this topic in its simplest yet detailed form.

How Does Artificial Intelligence Work?

I mentioned that we can think of AI as a human brain, but it’s actually a much faster version that can do big calculations. AI is actually a set of algorithms that analyze data and try to learn certain patterns. This is done through methods such as machine learning and deep learning.

• Machine learning: The process of teaching computers to make decisions by showing them examples.
• Deep learning: A more sophisticated version uses artificial neural networks that mimic the neural networks in the human brain.

Real Life Examples:

• Google Photos automatically separates “tree” photos from the images in the album.
• Spotify gives you new recommendations based on the songs you listen to.
• Tesla’s autopilot system makes instant decisions by detecting traffic signs and pedestrians.

Why does it consume so much energy?

AI’s energy consumption is based on several reasons:

1. Big Data and Big Computing: AI models process millions or even billions of parameters. To do this, specialized hardware such as GPUs (Graphics Processors) or TPUs (Tensor Processors) are used. But these devices consume significant power while continuously computing.
2. Model Training is Very Difficult: Training an AI model is a process that sometimes takes thousands of hours. Google’s GPT-3, a language model, consumed about 10 gigawatt hours of energy to train. This is equivalent to the electricity consumed by a European city for several days!
3. Data Centers: There are huge data centers running behind AI. The huge server farms of companies such as Facebook and Google run millions of computers that consume significant amounts of electricity. And because these machines overheat, extra energy is needed to cool them down.

Where, How and Through What Means Is This Energy Sourced?

So, where does AI get this energy?

1. Fossil Fuels: Unfortunately, most of the world’s electricity still comes from fossil fuels such as coal, natural gas and oil. These emit carbon, contributing to climate change.
2. Renewable Energy: Giant companies such as Google and Microsoft have started to power their data centers with wind and solar energy. However, this is not yet enough to power all systems.
3. Private Electricity Infrastructures: Big tech companies sometimes build their own private power plants or make deals directly with green energy suppliers.
4. Alternative Cooling Technologies: Data centers are sometimes built near the poles or underwater to reduce cooling costs.

What is being done to reduce energy consumption?

The answer to this question actually depends on work in several different areas:

1. More Efficient Artificial Intelligence Algorithms: Scientists are looking for ways to make AI work with less computation. Building small but smart models is an important goal.
2. Hardware Improvements: A new generation of chips (e.g. Apple’s M-series chips or Google’s specialized TPUs) can do the same job with less energy consumption.
3. Green Data Centers: Data centers are trying to use less energy by improving cooling technologies. Microsoft even tested the idea of placing its data centers under the ocean! Because water has a natural cooling effect.
4. Edge Computing: Normally, for an AI process, data has to travel to and from large data centers. But now solutions are being developed for AI to work on the device. For example, some commands from Apple’s Siri or Google Assistant are run entirely on the phone, so there is no need to go back and forth to the data center.

Who is Taking What Steps?

Big tech companies, universities and researchers are working to solve this problem. Here are a few examples:

• Google: Since 2017, it has been running the “AI for Good” program to make its AI models more efficient and is working towards 100% renewable energy in its data centers.
• Microsoft: aims to be carbon negative by 2030, meaning it will remove more carbon from the atmosphere than it produces.
• Tesla: Autonomous vehicles drive using artificial intelligence. Tesla has developed its special “Dojo” chip to make these processes more efficient.
• Research Institutes: Universities such as MIT and Stanford are working on AI models that consume less energy.

The Most Common Artificial Intelligence Models and Their Uses

There are many models of AI used in different fields. Here are some of the most common ones and their uses:

GPT (Generative Pre-trained Transformer) Series

• Where Used: Chatbots, copywriting, translation, code generation
• Example: ChatGPT, Google Bard

CNN (Convolutional Neural Networks)

• Where Used: Image recognition, face recognition, x-ray analysis in healthcare
• Example: Facebook’s face recognition system, anomaly detection in MRI scans

RNN (Recurrent Neural Networks) & LSTM (Long Short-Term Memory)

• Where Used: Voice recognition, language modeling, prediction systems
• Example: Siri, Google Assistant, music recommendation systems

DQN (Deep Q-Networks) and Reinforcement Learning

• Where Used: Game playing AIs, robotic systems
• Example: AlphaGo, Tesla’s self-driving car system

Transformers

• Where Used: Natural language processing, large-scale data analytics
• Example: Google Translate, text summarization systems

Artificial intelligence is a tremendous technology, but energy consumption is a big problem. Fortunately, thanks to more efficient algorithms, renewable energy and hardware advancements, we are working to overcome these problems. It is very likely that we will see greener, more energy-efficient AI systems in the coming years. But still, like any technology, AI comes at a price — both in terms of energy and environmental impact!

So, do you think AI is really worth spending so much energy on? I’d love to hear your opinion in the comments.

I hope you found this article useful! If you appreciate the information provided, you have the option to support me by Buying Me A Coffee! Your gesture would be greatly appreciated!

Thank you so much for reading.

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Selin.

Yapay Zeka ve Enerji Tüketimi: Akıllı Teknolojinin Gizli Maliyeti

 

Yapay zekayı (YZ) bir insan beyni gibi düşünebilirsin, ama süper güçlü bir beyin! Bir çocuğun yeni bir şeyler öğrenmesi gibi, YZ de büyük miktarda veriyi işler, bunları analiz eder ve belirli kalıpları öğrenir. İşin içinde bolca matematik, istatistik ve karmaşık algoritmalar var. Özellikle derin öğrenme (deep learning) dediğimiz şey, devasa veri setleri üzerinde tekrar tekrar hesaplamalar yaparak kendini geliştiriyor. Bu süreç, çok büyük ve güçlü bilgisayarlar gerektiriyor.

Örneğin, bir YZ modeline “Bu bir kedi mi?” sorusunu sorduğunda, model binlerce, hatta milyonlarca kedi fotoğrafını analiz etmiş ve her detayını öğrenmiş oluyor. Bir göz, kulak şekli, bıyık uzunluğu gibi şeylere bakarak tahmin yapıyor. Ama işin büyüsü şurada: Bunu yapmak için aşırı fazla hesaplama gücü gerekiyor!

Yapay zeka (YZ) her geçen gün hayatımızın daha fazla içine giriyor. Telefonlardaki sesli asistanlardan Netflix'in bize önerdiği dizilere, Google'ın arama sonuçlarından Tesla'nın sürücüsüz arabalarına kadar hemen her yerde YZ var. Peki, bu inanılmaz teknolojiye sahip olmanın bir bedeli var mı? Evet, hem de büyük bir bedeli var: Enerji tüketimi!

Günlük hayatta fark etmesek de, YZ modelleri çalışırken devasa enerji harcıyor. Peki, neden bu kadar büyük bir enerji ihtiyacı var? Bu enerji nereden geliyor ve tüketimi azaltmak için ne gibi adımlar atılıyor? Gel, bu konuyu en basit ama detaylı haliyle inceleyelim.

Yapay Zeka Nasıl Çalışıyor?

YZ'yi bir insan beyni gibi düşünebileceğimizden bahsettim ama aslında o çok daha hızlı ve büyük hesaplamalar yapabilen bir versiyonu. Yapay zeka aslında bir algoritmalar dizisi ve bu algoritmalar verileri analiz ederek belirli desenleri öğrenmeye çalışıyor. Bu, makine öğrenmesi (machine learning) ve derin öğrenme (deep learning) gibi yöntemlerle yapılıyor.

• Makine öğrenmesi: Bilgisayarlara örnekler göstererek karar verme yetisi kazandırma süreci.
• Derin öğrenme: Daha karmaşık bir versiyon, insan beynindeki sinir ağlarını taklit eden yapay sinir ağları kullanıyor.

Gerçek Hayattan Örnekler:

• Google Fotoğraflar, albümdeki görüntüler arasından "ağaç" fotoğraflarını otomatik olarak ayırıyor.
• Spotify, dinlediğin şarkılara göre sana yeni öneriler sunuyor.
• Tesla'nın otopilot sistemi, trafik tabelalarını ve yayaları tespit ederek anlık kararlar alıyor.

Neden Bu Kadar Enerji Tüketiyor?

YZ’nin enerji tüketimi birkaç nedene dayanıyor:

1. Büyük Veri ve Büyük Hesaplamalar: YZ modelleri milyonlarca, hatta milyarlarca parametreyi işler. Bunu yapmak için GPU (Grafik İşlemcileri) veya TPU (Tensor İşlemcileri) gibi özel donanımlar kullanılır. Ama bu cihazlar, sürekli hesaplama yaparken ciddi bir güç harcar.
2. Model Eğitimi Çok Zor: Bir yapay zeka modelini eğitmek, bazen binlerce saat süren bir süreçtir. Google’ın bir dil modeli olan GPT-3, eğitilirken yaklaşık 10 gigawatt saat enerji harcadı. Bu, bir Avrupa şehrinin birkaç gün boyunca tükettiği elektriğe eşdeğer!
3. Veri Merkezleri: YZ’nin arkasında çalışan devasa veri merkezleri var. Facebook, Google gibi şirketlerin büyük sunucu çiftlikleri, milyonlarca bilgisayarı çalıştırıyor ve bunlar ciddi miktarda elektrik harcıyor. Üstelik bu makineler aşırı ısındığı için, onları soğutmak için de ekstra enerji gerekiyor.

Bu Enerji Nereden, Nasıl ve Hangi Yollarla Temin Ediliyor?

Peki, YZ bu enerjiyi nereden alıyor?

1. Fosil Yakıtlar: Maalesef dünya genelinde elektriğin büyük bir kısmı hâlâ kömür, doğal gaz ve petrol gibi fosil yakıtlardan geliyor. Bunlar, karbon salınımı yaparak iklim değişikliğine katkıda bulunuyor.
2. Yenilenebilir Enerji: Google, Microsoft gibi dev şirketler, veri merkezlerini rüzgar ve güneş enerjisi ile çalıştırmaya başladı. Ancak, bu henüz tüm sistemleri besleyebilecek seviyede değil.
3. Özel Elektrik Altyapıları: Büyük teknoloji şirketleri bazen kendi özel elektrik santrallerini kuruyor veya doğrudan yeşil enerji tedarikçileriyle anlaşmalar yapıyor.
4. Alternatif Soğutma Teknolojileri: Veri merkezleri bazen kutuplara yakın ya da su altında kuruluyor ki soğutma maliyetleri azalsın.

Enerji Tüketimini Azaltmak İçin Neler Yapılıyor?

Bu sorunun cevabı aslında birkaç farklı alanda çalışmalara bağlı:

1. Daha Verimli Yapay Zeka Algoritmaları: Bilim insanları, yapay zekayı daha az hesaplama ile çalıştırmanın yollarını arıyor. Küçük ama akıllı modeller üretmek önemli bir hedef.
2. Donanım Geliştirmeleri: Yeni nesil çipler (örneğin, Apple’ın M serisi çipleri veya Google’ın özel TPU’ları), daha az enerji tüketerek aynı işi yapabiliyor.
3. Yeşil Veri Merkezleri: Veri merkezleri, soğutma teknolojilerini geliştirerek daha az enerji harcamaya çalışıyor. Microsoft, veri merkezlerini okyanusun altına yerleştirme fikrini bile test etti! Çünkü suyun doğal soğutucu etkisi var.
4. Edge Computing: Normalde, bir yapay zeka işlemi için verilerin büyük veri merkezlerine gidip gelmesi gerekiyor. Ama artık YZ’nin cihaz üzerinde çalışması için çözümler geliştiriliyor. Örneğin, Apple’ın Siri’si veya Google Asistan’ın bazı komutları tamamen telefonda çalıştırılıyor, böylece veri merkezine gidip gelmeye gerek kalmıyor.

Kimler Ne Tür Adımlar Atıyor?

Büyük teknoloji şirketleri, üniversiteler ve araştırmacılar bu sorunu çözmek için çalışıyor. İşte birkaç örnek:

• Google: 2017’den beri YZ modellerini daha verimli hale getirmek için “AI for Good” programını yürütüyor ve veri merkezlerinde %100 yenilenebilir enerjiye geçmeye çalışıyor.
• Microsoft: 2030 yılına kadar karbon negatif olmayı hedefliyor, yani ürettiğinden daha fazla karbonu atmosferden çekecek.
• Tesla: Otonom araçlar, yapay zeka kullanarak sürüş yapıyor. Tesla, bu işlemleri daha verimli hale getirmek için özel “Dojo” çipini geliştirdi.
• Araştırma Enstitüleri: MIT, Stanford gibi üniversitelerde daha az enerji tüketen YZ modelleri üzerine çalışmalar yapılıyor.

En Yaygın Kullanılan Yapay Zeka Modelleri ve Kullanım Alanları

YZ’nin farklı alanlarda kullanılan birçok modeli var. İşte en yaygınlarından bazıları ve kullanım alanları:

GPT (Generative Pre-trained Transformer) Serisi

• Nerede Kullanılıyor?: Chatbot’lar, metin yazımı, çeviri, kod üretimi
• Örnek: ChatGPT, Google Bard

CNN (Convolutional Neural Networks)

• Nerede Kullanılıyor?: Görüntü tanıma, yüz tanıma, sağlık alanında röntgen analizi
• Örnek: Facebook’un yüz tanıma sistemi, MRI taramalarında anomali tespiti

RNN (Recurrent Neural Networks) & LSTM (Long Short-Term Memory)

• Nerede Kullanılıyor?: Ses tanıma, dil modeli oluşturma, tahmin sistemleri
• Örnek: Siri, Google Asistan, müzik öneri sistemleri

DQN (Deep Q-Networks) ve Takviyeli Öğrenme (Reinforcement Learning)

• Nerede Kullanılıyor?: Oyun oynayan yapay zekalar, robotik sistemler
• Örnek: AlphaGo, Tesla’nın sürücüsüz araç sistemi

Transformers

• Nerede Kullanılıyor?: Doğal dil işleme, büyük ölçekli veri analizleri
• Örnek: Google Translate, metin özetleme sistemleri

Yapay zeka muazzam bir teknoloji, ancak enerji tüketimi büyük bir sorun. Neyse ki, daha verimli algoritmalar, yenilenebilir enerji ve donanım gelişmeleri sayesinde bu sorunları aşmaya çalışıyoruz. Önümüzdeki yıllarda daha yeşil, daha az enerji tüketen yapay zeka sistemleri görmemiz çok olası. Ama yine de, her teknoloji gibi, yapay zekanın da bir bedeli var — hem enerji anlamında, hem de çevresel etkiler açısından!

Peki, sence yapay zeka gerçekten bu kadar enerji harcamaya değer mi? Fikrini yorumlara yazman beni çok memnun eder.

Buraya kadar okuduğun için teşekkürler.

Bunu beğendiysen ve diğer içeriklerimden de haberdar olmak istersen beğen butonuna tıklamayı ve abone olmayı unutma.

Teşekkür ederim.

Selin.

How to Use Flutter DevTools Effectively

 

Hello everyone. In this article, I have compiled what I know, what I need to know and what I have learned about the Flutter DevTools screen and tabs that I have come across since I started developing projects with Flutter. I hope it will be useful for all of us. Let’s get started.

I use VsCode as my IDE, so I access the DevTools screen from there. If we type DevTools in the Command Palette, we can see that it comes up. Here we can open each tab separately, we can also view them all on the same screen as a web page in the browser, which I prefer this method more. In the same way, we can access it from AndroidStudio in a similar way.

The Web Browser view looks like this:

We can also open DevTools by typing the following commands in the terminal.

flutter pub global activate devtools
flutter run --debug
devtools

Let’s start with a review of DevTools by first finding out what it is.

What is Flutter DevTools?

Flutter DevTools is a development tool used to monitor the performance of the application, detect bugs and optimize it. It has various tabs according to their topics. Below I have prepared a table of which tab is used for what and then I will try to explain each of them in detail.

Flutter DevTools — Connected App

What Does It Do?

This is the main screen showing that DevTools is connected to the Flutter application. When the application is running in debug mode, you can see which analysis tools are active.

Here is what you can do:

• You can check if the connection is active.
• You can navigate to different profiler tabs.
• If the connection is lost, you can restart DevTools and reconnect.

Inspector Tab: Widget Tree and UI Structure

What Does It Do?

This tab allows you to examine the widget tree and visual structure of the app. If you have a design problem or unwanted padding, margin, alignment errors, you can easily fix them here.

Usage Scenarios:

• See which widget is where: Examine the structure in the widget tree to make sure you have selected the right widget.
• Analyze padding, margin and alignment errors: Find elements that are misaligned or shifted on the screen.
• Analyze layout errors: Detect unexpected behavior of widgets like SizedBox, Expanded, Flexible, Align, Positioned.
• Solve layout problems: Understand how elements are positioned inside structures such as Column, Row, Stack.

How to use it:

1. Click on the “Select Widget Mode” button (there is a button at the top of the Inspector tab, you can open it and click on the widgets on the screen to select them directly in the widget tree).
2. Click on the widget you got an error or want to inspect.
3. Examine the details of the widget and the parent-child relationship.
4. Hover over it to see details like padding, margin and alignment.

Solution Suggestions:

• Is there an error with spacing? Check Padding and Margin values.
• Is there unnecessary space inside widgets? Check if there are unnecessary width or height values in the Container.
• Is the widget positioned in the wrong place? Check how widgets like Expanded and Flexible behave.

Tip:

• Do you have a very nested widget tree? Create simpler structures instead of using unnecessary Container, Padding or Column.
• Turn on “Show Guidelines” to understand the order in which widgets are loaded.

Performance Tab: UI Fluency and Jank Problems

What does it do?

This tab helps you analyze your app’s smoothness and jank problems. If your app stutters while scrolling, animations are not smooth or you are experiencing frame drops, you should check here.

Usage Scenarios:

• Monitor the frame per second (FPS) value. It lets you know how smoothly the app is running. If your FPS is consistently below 60, the app is not running smoothly.
• Jank occurs when the app’s FPS (Frames Per Second) drops and usually causes the app to hang. Detect this. Find out which processes are causing frame drops. Optimize heavy processes.
• Examine the UI and Raster Thread. Analyze how long UI operations take to process and which widgets are heavy.

How to use it

1. Open the Performance tab and start using your app.
2. Follow the FPS graph at the top.
3. Open the “Count Widget Builds” option to check which widgets are redrawn too much.
4. After identifying problem areas, make performance improvements.

Solution Suggestions:

• Define frequently redrawn widgets as const. The const keyword ensures that the widget is only built the first time it is created. This prevents the widget from being rebuilt on every redraw and improves performance.
• Run heavy operations in the background (use compute() or Isolate). This allows operations to be performed in the background without tying up the UI thread.
• If you have animations or frequently redrawn widgets, you can use RepaintBoundary to move the widgets to a separate screen layer and redraw only that widget.
• If animations get stuck, use TweenAnimationBuilder or AnimatedBuilder to avoid unnecessary rebuilds.
• If you are working with long lists, you can use ListView.builder to render only the items visible on the screen. This will significantly improve performance.
• Asynchronize time-consuming operations within the application. For example, manage data loading and API calls asynchronously with FutureBuilder or StreamBuilder.
• Be careful when using setState(). Instead of re-rendering the entire widget tree, re-render only the part that has been modified.
• Especially if you are using Riverpod or Provider, you can use ValueListenableBuilder and Consumer to only update specific widgets as data changes.

CPU Profiler Tab (Advanced Performance Analysis — CPU Utilization)

What does it do?

This tab allows you to analyze the CPU usage of your application. It gives you detailed information about which functions are overcommitting and how they are affecting the CPU.

Timeline:

• The Timeline section in CPU Profiler allows us to see which processes are using the CPU and for how long while the application is running. On this timeline, we can examine the timings and durations of each CPU process.
• The CPU Sampling data in the timeline shows which processes are taking up the most CPU.

CPU Sampling:

• CPU sampling records which functions the CPU is running every second. This data allows us to determine how much each function or process block uses the CPU.
• CPU sampling shows performance differences between different parts of the application. Some parts may consume more CPU and this can affect the overall speed of the application.

Flame Graph:

• The flame graph is a visual representation of CPU utilization in the application. High CPU-consuming functions appear as larger blocks, which allows us to easily see which processes are using more resources.
• The Flame Graph also shows how interdependent functions are, so you can analyze which other functions a function depends on and the impact of these relationships on the CPU.
• This graph allows you to quickly identify CPU bottlenecks (functions that run slowly or consume too many resources) in your application.

Call Tree:

• The Call Tree shows the relationships between functions in the application. It shows how one function calls other functions and how it uses the CPU.
• Using the call tree, you can identify the main functions or sub-functions that are overloading the CPU.

Here’s what you can do:

• Analyze which functions consume the most CPU. You can see which parts of your code are running slowly.
• Examine the flame chart. It provides a detailed graph showing the order and duration of function calls.
• Optimize by profiling. You can improve performance by isolating long-running processes.

Memory Tab (Memory Usage and Memory Leaks)

What does it do?

This tab helps you analyze your application’s RAM usage and detect possible memory leaks.

Here is what you can do:

• Monitor memory consumption by taking heap snapshots. You can see which objects are being held in memory and which objects are unnecessarily persistent.
• Analyze Garbage Collector (GC) impact. Dart performs automatic memory management, but here you can check if unnecessary objects are persisting in RAM for too long.
• Analyze object sizes and lifetimes. If too many large objects are kept in memory, you should optimize them.

Tip:

• Don’t forget to dispose() stateful widgets.
• If you are using ChangeNotifier or StreamController, you should turn them off manually (by calling dispose()).

Debugger Tab (Code Debugging)

What does it do?

The Debugger tab is used for debugging, adding breakpoints and analyzing variables. You can stop at a certain point in your code (breakpoint) and analyze variables and UI state.

Here is what you can do:

• Analyze variables and code flow by stopping (breakpoint) at certain points in the code. For example, does the widget update correctly when you press the button?
• Use it to catch exceptions and identify the source of the error. For example, is state management working?
• View error messages in detail and find out which function is causing the error. For example, are you getting an error message related to the UI?

How to use it?

• Add a breakpoint to the relevant line of your code.
• Open the Debugger tab to see what stage the code is in.
• Watch live what data the application is processing and how the state changes.
• Track state changes and widget rebuilds step by step.

Tip:

• You can manually stop certain lines of code by adding debugger().
• You can turn on “Exception Pause” mode to see error messages in detail.
• In VS Code or Android Studio, you can add breakpoints to examine variable values live.

Network Tab (API Calls and HTTP Requests)

What does it do?

This tab helps you analyze the network requests (HTTP calls) made by the application.

Here is what you can do:

• Analyze in detail which API requests take how long.
• Measure response times and optimize unnecessary API calls.
• View which requests failed (status code: 400, 500).
• Validate API data by examining JSON responses and requests.

Tip:

• If you make a lot of API calls, you can reduce the load by using a caching mechanism (Hive, SharedPreferences, SQLite).
• Switch to efficient data querying methods like GraphQL or Firebase Firestore.

Logging Tab (Application Logs and Error Tracking)

What does it do?

It shows the log messages in detail during the running process of the application.

Here is what you can do:

• Detect problems in your code by viewing error messages and stack traces.
• Track details such as API calls, user interactions and system events through logs.
• Make debugging easier by adding custom log messages.

Tip:

• You can use debugPrint() to get more readable logs. If you want to have more detailed information about using debugPrint() instead of print(), you can click the link below.

http://theblankbookofme.blogspot.com/2024/07/print-and-debugprint-in-flutter.html

• Instead, you can use the Logger library to create detailed and colorful log messages.

App Size Tab (App Size Analysis)

What does it do?

This tab helps you analyze the total size of your Flutter application and which files are taking up the most space.

Here’s what you can do:

• Find out which packages and assets are taking up the most space.
• Optimize unnecessarily large code and libraries.
• Analyze the differences between AOT (Ahead of Time) and JIT (Just in Time) builds.

Tip:

• You can run flutter build apk --analyze-size for more detailed analysis.
• Check file sizes after flutter build web for web.

Deep Links Tab (In-App Links and Routing)

What does it do?

This tab allows you to navigate directly to specific pages of the app using deep links.

Here’s what you can do:

• You can run deep link tests to navigate to specific pages within the application.
• You can check URL-based redirect structures (for example with Firebase Dynamic Links).
• Verify integration with libraries like Flutter Navigator or GoRouter.

Tip:

• You can use this tab to test if deep link redirects are working.
• You can add deep link support to your app with systems like Firebase Dynamic Links or App Links.

All in all, Flutter DevTools is a super powerful tool that lets you explore every corner of your app. With Inspector you can catch bugs in the UI, with the Performance tab you can analyze frame rate and render statistics, with CPU Profilers you can see which processes are straining your system, and with AppSize you can check if the app is bloating unnecessarily. By using all these tools effectively, you can make your app faster, more efficient and optimized. Regularly monitoring DevTools will not only fix bugs, but also improve your code quality and user experience. In short, every tab here is a powerful tool at your disposal to make your project better. Let’s keep exploring!

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Selin.