VAYUZ Technologies ’s Post

Follow our Flutter Community for the latest Flutter updates, Flutter Job and Industry insights, and more! 𝐉𝐨𝐢𝐧 𝐡𝐞𝐫𝐞: https://lnkd.in/gE24xNau #flutter #mobileapp #flutterwidget #appdevelopment #coding #flutternest #flutterdeveloper #technology #fluttercommunity #flutterflicks #vayuz

🚨with yesterday's conference of Open AI it feels like it's going crazy -> ⚫ as they are creating stores for GPT models. ⚫ GPT assistant for coders and non coders to create any application by just simply giving instructions. ⚫ what you guys think what should we learn to keep ourselves in race and how can we efficiently learn these tools to maximize our app development. ⚫ let me know what you learning as a Android developer. #androiddev

🚀 Dive into the future of programming with AI! From automating coding tasks to revolutionizing customer support, AI is changing the game. 🤖✨ Let's embrace this exciting transformation together! If you're ready to join the journey or need a Flutter app, feel free to reach out! 📱💬 #ai #aitrending #trending #coding #coders #app #developers #appdevelopers #flutter #flutterdevelopers #flutterapp #journey #helpingothers #knowledge #aitools

Using AppMap Navie to diagnose a sluggish API endpoint in my MERN app, building context for the LLM as I work from issue to solution. #AI #Coding

🚀 Exciting News! 🚀 I'm thrilled to share my latest project, "Demystifying Code: AI-Powered Python Code Explainer." 🤖🐍 In this video, I walk you through the journey of creating an innovative tool that leverages Google Generative AI to generate detailed explanations for Python code snippets. Whether you're a seasoned developer or just starting your coding journey, this tool is designed to help you understand code with ease. 🔑 Key Highlights: AI Model Training: Discover how we trained the AI model to provide accurate code explanations. User Interface: See how we designed an interactive and user-friendly interface for code input and output. Impact: Learn how this project is enhancing developers' code comprehension and productivity. Future Plans: Find out about our vision for expanding and refining this unique tool. Watch the video here and join me on this exciting journey at the intersection of AI and programming! 🚀 #AI #Programming #CodeExplainer #Innovation #YouTube #LinkedIn #ArtificialIntelligence https://lnkd.in/gfD3sYWp

Integrating Machine Learning into your Flutter app: There are usually two approaches that are widely used to integrate ML models into a Flutter app: 1. Manual Integration: This traditional method manually integrates models like TensorFlow Lite (TFLite) or PyTorch Lite using dedicated packages like tflite_flutter or pytorch_lite_flutter. This approach offers granular control and flexibility but requires: In-depth knowledge:  Developers must understand the chosen framework (TFLite or PyTorch Lite) and the target platform (Flutter in this case). More development effort:  Manual integration can be time-consuming and error-prone, especially for complex models. Limited platform support:  Developers need to handle compatibility concerns for different platforms if targeting beyond Flutter. 2. Google ML Kit: This is a higher-level approach that simplifies model integration by providing pre-built APIs for various tasks like: Image labeling Object detection Text recognition Landmark recognition Face detection Benefits of using ML Kit include: Reduced development time:  Pre-built APIs remove the need for manual model integration, saving time and effort. Easier platform support:  ML Kit offers broader platform compatibility, often including Android, iOS, and web environments. Abstraction layer:  Developers can focus on their application logic without getting bogged down in the intricacies of specific model frameworks. However, ML Kit may have limitations compared to manual integration: Less control:  Developers have less control over the underlying models and their behavior compared to the manual approach. Limited flexibility:  While offering various functionalities, it might not cater to every specific need, unlike building custom integrations with TFLite or PyTorch Lite. Choosing the approach depends on your specific needs and priorities. If you need fine-grained control and flexibility, manual integration may be preferable. However, if you prioritize ease of use, faster development, and broader platform support, Google ML Kit could be a better fit. Tomorrow I'll try to share with you a simple app in which I integrated an image detection tflite (manual approach) model. #MachineLearning #Flutter #MobileDevelopment #TensorFlowLite #PyTorchLite #GoogleMLKit #AI #AppDevelopment #DeveloperCommunity #Tech #Programming #SoftwareEngineering #MLIntegration #ImageDetection #Technology #ArtificialIntelligence #DeepLearning #FlutterDev #MobileApps #Code #SoftwareDevelopment #AIIntegration #LinkedInLearning

android kotlin app for image caption generator using ai

🚀 Exciting News! 🚀 I'm thrilled to share my latest project, "Demystifying Code: AI-Powered Python Code Explainer." 🤖🐍 In this video, I walk you through the journey of creating an innovative tool that leverages Google Generative AI to generate detailed explanations for Python code snippets. Whether you're a seasoned developer or just starting your coding journey, this tool is designed to help you understand code with ease. 🔑 Key Highlights: AI Model Training: Discover how we trained the AI model to provide accurate code explanations. User Interface: See how we designed an interactive and user-friendly interface for code input and output. Impact: Learn how this project is enhancing developers' code comprehension and productivity. Future Plans: Find out about our vision for expanding and refining this unique tool. Watch the video here and join me on this exciting journey at the intersection of AI and programming! 🚀 #AI #Programming #CodeExplainer #Innovation #YouTube #LinkedIn

Llama 3 supported on Seaplane IO as of Monday. Only takes 6 lines of code to leverage any of the 40+ models we now support. from seaplane.apps import App # create app and DAG app = App ("modelhub-app" ) dag = app. dag( "modelhub-dag" ) # call modelhub with input from user model_output = app. modelhub_dag( "modelhub", [app. input()]) # send response and start app app. respond (model_output) app.run() #llama3 #mlops #llmops Create an account today! https://www.seaplane.io/

Hello, connections, Today I am sharing this FunnyFaceMatch project with you in which I have used an #AI module. Basically, this app is built using a face-detection AI module. What it does is, when someone clicks a picture, it calculates the percentage of various facial expressions such as the percentage of the smile, percentage of eyes open, and percentage of face visible in the photo. Then, using these percentages, I have created this app where the expression present in the photo will match with a funny animal face If you also want to use this app, I have provided the link to GitHub. rishabhh1056/FaceDec_Project (github.com) #AndroidDev #MobileApp #Java #Kotlin #AndroidStudio #XML #FaceDecation #AI #GooglePlay #Firebase #MaterialDesign #UIUX #Development #Coding #Tech #AndroidCommunity #OpenSource #AndroidProgramming #AndroidApps #SDK #AndroidStudioTips #AndroidDevelopment