#Extract Streaming App Data

[mobiledatascrape]

OTT Media Platform Data Scraping | Extract Streaming App Data

Unlock insights with our OTT Media Platform Data Scraping. Extract streaming app data in the USA, UK, UAE, China, India, or Spain. Optimize your strategy today

know more: https://www.mobileappscraping.com/ott-media-app-scraping-services.php

[techfinna]

Top 5 Selling Odoo Modules.

In the dynamic world of business, having the right tools can make all the difference. For Odoo users, certain modules stand out for their ability to enhance data management and operations. To optimize your Odoo implementation and leverage its full potential. 

That's where Odoo ERP can be a life savior for your business. This comprehensive solution integrates various functions into one centralized platform, tailor-made for the digital economy. 

Let’s drive into 5 top selling module that can revolutionize your Odoo experience:

Dashboard Ninja with AI, Odoo Power BI connector, Looker studio connector, Google sheets connector, and Odoo data model.

1. Dashboard Ninja with AI: 

Using this module, Create amazing reports with the powerful and smart Odoo Dashboard ninja app for Odoo. See your business from a 360-degree angle with an interactive, and beautiful dashboard.

Some Key Features:

Real-time streaming Dashboard

Advanced data filter

Create charts from Excel and CSV file

Fluid and flexible layout

Download Dashboards items

This module gives you AI suggestions for improving your operational efficiencies.

2. Odoo Power BI Connector:

This module provides a direct connection between Odoo and Power BI Desktop, a Powerful data visualization tool.

Some Key features:

Secure token-based connection.

Proper schema and data type handling.

Fetch custom tables from Odoo.

Real-time data updates.

With Power BI, you can make informed decisions based on real-time data analysis and visualization.

3. Odoo Data Model: 

The Odoo Data Model is the backbone of the entire system. It defines how your data is stored, structured, and related within the application.

Key Features:

Relations & fields: Developers can easily find relations ( one-to-many, many-to-many and many-to-one) and defining fields (columns) between data tables. 

Object Relational mapping: Odoo ORM allows developers to define models (classes) that map to database tables.

The module allows you to use SQL query extensions and download data in Excel  Sheets.

4. Google Sheet Connector:

This connector bridges the gap between Odoo and Google Sheets.

Some Key features:

Real-time data synchronization and transfer between Odoo and Spreadsheet.

One-time setup, No need to wrestle with API’s.

Transfer multiple tables swiftly.

Helped your team’s workflow by making Odoo data accessible in a sheet format.

5.  Odoo Looker Studio Connector:

Looker studio connector by Techfinna easily integrates Odoo data with Looker, a powerful data analytics and visualization platform.

Some Key Features:

Directly integrate Odoo data to Looker Studio with just a few clicks.

The connector automatically retrieves and maps Odoo table schemas in their native data types.

Manual and scheduled data refresh.

Execute custom SQL queries for selective data fetching.

The Module helped you build detailed reports, and provide deeper business intelligence.

 These  Modules will improve analytics, customization, and reporting. Module setup can significantly enhance your operational efficiency. Let’s embrace these modules and take your Odoo experience to the next level. 

Need Help?

I hope you find the blog helpful. Please share your feedback and suggestions.

For flawless Odoo Connectors, implementation, and services contact us at 

info@techfinna.com Or www.techneith.com  

[digvijay00]

Unveiling the Inextricable Integration: How Data Science Became Ingrained in Our Daily Lives

Introduction

In the era of rapid technological evolution, the symbiotic relationship between data science and our daily lives has become increasingly profound. This article delves into the transformative journey of how data science seamlessly became an integral part of our existence.

Evolution of Data Science

From Concept to Reality

The inception of data science was marked by the convergence of statistical analysis, computer science, and domain expertise. Initially confined to research labs, data science emerged from its cocoon to cater to real-world challenges. As technology advanced, the accessibility of data and computing power facilitated the application of data science across diverse domains.

Data Science in Everyday Applications

Precision in Decision Making

In the contemporary landscape, data science is omnipresent, influencing decisions both big and small. From tailored recommendations on streaming platforms to predictive text in messaging apps, the algorithmic prowess of data science is ubiquitous. The precision with which these algorithms understand user behavior has transformed decision-making processes across industries.

Personalized Experiences

One of the notable impacts of data science is the creation of personalized experiences. Whether it’s curated content on social media feeds or personalized shopping recommendations, data science algorithms analyze vast datasets to understand individual preferences, providing an unparalleled level of personalization.

Data Science in Healthcare

Revolutionizing Patient Care

The healthcare sector has witnessed a paradigm shift with the integration of data science. Predictive analytics and machine learning algorithms are transforming patient care by enabling early diagnosis, personalized treatment plans, and efficient resource allocation. The marriage of data science and healthcare has the potential to save lives and optimize medical practices.

Disease Surveillance and Prevention

Data science plays a pivotal role in disease surveillance and prevention. Through the analysis of epidemiological data, health professionals can identify patterns, predict outbreaks, and implement preventive measures. This proactive approach is instrumental in safeguarding public health on a global scale.

Data Science in Business and Marketing

Unleashing Strategic Insights

Businesses today leverage data science to gain actionable insights into consumer behavior, market trends, and competitor strategies. The ability to extract meaningful patterns from massive datasets empowers organizations to make informed decisions, optimize operations, and stay ahead in competitive markets.

Targeted Marketing Campaigns

The era of blanket marketing is long gone. Data science enables businesses to create targeted marketing campaigns by analyzing customer demographics, preferences, and purchasing behaviors. This precision not only maximizes the impact of marketing efforts but also enhances the overall customer experience.

Data Science in Education

Tailoring Learning Experiences

In the realm of education, data science has ushered in a new era of personalized learning. Uncodemy is the best data science Institute in Delhi. Adaptive learning platforms use algorithms to understand students’ strengths and weaknesses, tailoring educational content to suit individual needs. This customized approach enhances student engagement and fosters a more effective learning experience.

Predictive Analytics for Student Success

Data science also contributes to the identification of at-risk students through predictive analytics. By analyzing historical data on student performance, institutions can intervene early, providing additional support to students who may face academic challenges. This proactive approach enhances overall student success rates.

The Future Landscape

Continuous Innovation

As technology continues to advance, the future landscape of data science promises even more innovation. From the evolution of machine learning algorithms to the integration of artificial intelligence, the journey of data science is an ongoing narrative of continuous improvement and adaptation.

Ethical Considerations

With the increasing reliance on data science, ethical considerations become paramount. Striking a balance between innovation and ethical responsibility is crucial to ensuring that the benefits of data science are harnessed responsibly and inclusively.

Conclusion

In the tapestry of modern existence, data science has woven itself seamlessly into the fabric of our lives. From personalized recommendations to revolutionary advancements in healthcare, the impact of data science is undeniable. As we navigate this data-driven landscape, understanding the intricate ways in which data science enhances our daily experiences is not just informative but essential.

Source Link: https://www.blogsocialnews.com/unveiling-the-inextricable-integration-how-data-science-became-ingrained-in-our-daily-lives/

[jcmarchi]

New AI noise-canceling headphone technology lets wearers pick which sounds they hear - Technology Org

New Post has been published on https://thedigitalinsider.com/new-ai-noise-canceling-headphone-technology-lets-wearers-pick-which-sounds-they-hear-technology-org/

New AI noise-canceling headphone technology lets wearers pick which sounds they hear - Technology Org

Most anyone who’s used noise-canceling headphones knows that hearing the right noise at the right time can be vital. Someone might want to erase car horns when working indoors but not when walking along busy streets. Yet people can’t choose what sounds their headphones cancel.

A team led by researchers at the University of Washington has developed deep-learning algorithms that let users pick which sounds filter through their headphones in real time. Pictured is co-author Malek Itani demonstrating the system. Image credit: University of Washington

Now, a team led by researchers at the University of Washington has developed deep-learning algorithms that let users pick which sounds filter through their headphones in real time. The team is calling the system “semantic hearing.” Headphones stream captured audio to a connected smartphone, which cancels all environmental sounds. Through voice commands or a smartphone app, headphone wearers can select which sounds they want to include from 20 classes, such as sirens, baby cries, speech, vacuum cleaners and bird chirps. Only the selected sounds will be played through the headphones.

The team presented its findings at UIST ’23 in San Francisco. In the future, the researchers plan to release a commercial version of the system.

[embedded content]

“Understanding what a bird sounds like and extracting it from all other sounds in an environment requires real-time intelligence that today’s noise canceling headphones haven’t achieved,” said senior author Shyam Gollakota, a UW professor in the Paul G. Allen School of Computer Science & Engineering. “The challenge is that the sounds headphone wearers hear need to sync with their visual senses. You can’t be hearing someone’s voice two seconds after they talk to you. This means the neural algorithms must process sounds in under a hundredth of a second.”

Because of this time crunch, the semantic hearing system must process sounds on a device such as a connected smartphone, instead of on more robust cloud servers. Additionally, because sounds from different directions arrive in people’s ears at different times, the system must preserve these delays and other spatial cues so people can still meaningfully perceive sounds in their environment.

Tested in environments such as offices, streets and parks, the system was able to extract sirens, bird chirps, alarms and other target sounds, while removing all other real-world noise. When 22 participants rated the system’s audio output for the target sound, they said that on average the quality improved compared to the original recording.

In some cases, the system struggled to distinguish between sounds that share many properties, such as vocal music and human speech. The researchers note that training the models on more real-world data might improve these outcomes.

Source: University of Washington

You can offer your link to a page which is relevant to the topic of this post.

[vimusic-apk]

Don't Miss Out on ViMusic - The Must-Have App for Streaming YouTube Music

Do you wish you could stream all your favorite tracks from YouTube Music without ads interrupting your flow? Want to keep listening to playlists and music even when you switch apps or turn off your phone's screen? ViMusic makes this possible and so much more! 🎧

ViMusic is a game-changing Android app that lets you stream audio from YouTube Music videos in the background for FREE. No more annoying ads or having music pause when you exit the YouTube app.

🎵 Key Features:

Stream ad-free music with screen off or in background

Create playlists and queues of YouTube music

Download videos and songs for offline listening

Listen to high quality audio

Intuitive and easy to use interface

Absolutely FREE

This app has smart audio extraction technology that removes video and gives you a seamless listening experience. It basically takes the best features from music streaming services and combines it with YouTube's huge catalog.

Stop wasting time and data streaming music videos just for the audio. Download ViMusic now and unlock unlimited, uninterrupted music streaming from YouTube Music on your Android device!

It's hands down the best app for enjoying YouTube music offline and on the go.

Visit https://vimusic-apk.com to get it now. You can thank me later! 😉

#youtube #music #android #app #free

[noodle-fc]

Yes, this is due in large part to phone and tablet use, apps vs. applications, and a lack of schooling, but another significant factor is corporations' bullshit. The default user experience on a typical desktop or laptop computer is so much worse than it used to be. If you sent a Windows computer from today back in time to 1998, they'd think it was very pretty and very fast, but be horrified by pretty much everything else. The ads, the tracking, the AI fuckery—the bald-faced insult and effrontery of scanning your personal files and telling a corporation about you! They'd file lawsuits and they'd have won. These days the device you paid for does a ton of shit for a corporation's benefit, to the detriment of your experience. "Software as a service" (i.e. charging you in perpetuity to use a program, forcing you to use the newest iteration regardless of your preference, and sometimes locking your own data behind a paywall) would have made everyone apoplectic.

Both by design and by user-hostile copyright/patent/digital-rights legislation, corporations discourage you from using your device's full potential so they can extract profit from you. Before streaming services, watching or listening to media with a computing device meant you had to obtain and manage your own media library. 20 years ago, iTunes and Windows Media Player were designed for that purpose. These days it's a constant hard-sell push to Apple Music or Spotify or (saints preserve us) Amazon, and that is a rent-seeking choice made by corporations so that you don't own anything. You must pay a monthly fee to access only what they offer you, and they can arbitrarily revoke access even after you've paid them hundreds of dollars.

So computing today is more annoying and you're actively discouraged from doing things in your best interests. If you even know that the option is available. Don't judge the young ones too harshly. it's not only that they haven't been taught, it's not even that they don't have a "just try stuff" attitude, it's also that they have never experienced a time before late-stage Capitalism's dystopian techno-feudalist hellscape.

[govindhtech]

Vertex AI Gemini Live API Creates Real-Time Voice Commands

Gemini Live API

Create live voice-driven agentic apps using Vertex AI Gemini Live API. All industries seek aggressive, effective solutions. Imagine frontline personnel using voice and visual instructions to diagnose issues, retrieve essential information, and initiate processes in real time. A new agentic industrial app may be created with the Gemini 2.0 Flash Live API.

This API extends these capabilities to complex industrial processes. Instead of using one data type, it uses text, audio, and visual in a continuous livestream. This allows intelligent assistants to understand and meet the demands of manufacturing, healthcare, energy, and logistics experts.

The Gemini 2.0 Flash Live API was used for industrial condition monitoring, notably motor maintenance. Live API allows low-latency phone and video communication with Gemini. This API lets users have natural, human-like audio chats and halt the model's answers with voice commands. The model processes text, audio, and video input and outputs text and audio. This application shows how APIs outperform traditional AI and may be used for strategic alliances.

Multimodal intelligence condition monitoring use case

Presentation uses Gemini 2.0 Flash Live API-powered live, bi-directional, multimodal streaming backend. It can interpret audio and visual input in real time for complex reasoning and lifelike speech. Google Cloud services and the API's agentic and function calling capabilities enable powerful live multimodal systems with a simplified, mobile-optimized user experience for factory floor operators. An obviously flawed motor anchors the presentation.

A condensed smartphone flow:

Gemini points the camera at motors for real-time visual identification. It then quickly summaries relevant handbook material, providing users with equipment details.

Real-time visual defect detection: Gemini listens to a verbal command like “Inspect this motor for visual defects,” analyses live video, finds the issue, and explains its source.

When it finds an issue, the system immediately prepares and sends an email with the highlighted defect image and part details to start the repair process.

Real-time audio defect identification: Gemini uses pre-recorded audio of healthy and faulty motors to reliably identify the issue one based on its sound profile and explain its results.

Multimodal QA on operations: Operators can ask complex motor questions by pointing the camera at certain sections. Gemini effectively combines motor manual with visual context for accurate voice-based replies.

The tech architecture

The demonstration uses Google Cloud Vertex AI's Gemini Multimodal Livestreaming API. The API controls workflow and agentic function calls while the normal Gemini API extracts visual and auditory features.

A procedure includes:

Function calling by agents: The API decodes audio and visual input to determine intent.

The system gathers motor sounds with the user's consent, saves them in GCS, and then begins a function that employs a prompt with examples of healthy and faulty noises. The Gemini Flash 2.0 API examines sounds to assess motor health.

The Gemini Flash 2.0 API's geographical knowledge is used to detect and highlight errors by recognising the intent to detect visual defects, taking photographs, and invoking a method that performs zero-shot detection with a text prompt.

Multimodal QA: The API recognises the objective of information retrieval when users ask questions, applies RAG to the motor manual, incorporates multimodal context, and uses the Gemini API to provide exact replies.

After recognising the intention to repair and extracting the component number and defect image using a template, the API sends a repair order via email.

Key characteristics and commercial benefits from cross-sector usage cases

This presentation highlights the Gemini Multimodal Livestreaming API's core capabilities and revolutionary industrial benefits:

Real-time multimodal processing: The API can evaluate live audio and video feeds simultaneously, providing rapid insights in dynamic circumstances and preventing downtime.

Use case: A remote medical assistant might instruct a field paramedic utilising live voice and video to provide emergency medical aid by monitoring vital signs and visual data.

Gemini's superior visual and auditory reasoning deciphers minute aural hints and complex visual settings to provide exact diagnoses.

Utilising equipment noises and visuals, AI can predict failures and eliminate manufacturing disruptions.

Agentic function invoking workflow automation: Intelligent assistants can start reports and procedures proactively due to the API's agentic character, simplifying workflows.

Use case: A voice command and visual confirmation of damaged goods can start an automated claim procedure and notify the required parties in logistics.

Scalability and seamless integration: Vertex AI-based API interfaces with other Google Cloud services ensure scalability and reliability for large deployments.

Use case: Drones with cameras and microphones may send real-time data to the API for bug identification and crop health analysis across huge farms.

The mobile-first design ensures that frontline staff may utilise their familiar devices to interact with the AI assistant as needed.

Store personnel may use speech and image recognition to find items, check stocks, and get product information for consumers on the store floor.

Real-time condition monitoring helps industries switch from reactive to predictive maintenance. This will reduce downtime, maximise asset use, and improve sectoral efficiency.

Use case: Energy industry field technicians may use the API to diagnose faults with remote equipment like wind turbines without costly and time-consuming site visits by leveraging live audio and video feeds.

Start now

Modern AI interaction with the Gemini Live API is shown in this solution. Developers may leverage its interruptible streaming audio, webcam/screen integration, low-latency speech, and Cloud Functions modular tool system as a basis. Clone the project, tweak its components, and develop conversational, multimodal AI solutions. Future of intelligent industry is dynamic, multimodal, and accessible to all industries.

[hiteshrivani]

Emerging Technologies: How AI and ML Are Transforming Apps

The landscape of Mobile App Development is undergoing a profound transformation as artificial intelligence (AI) and machine learning (ML) technologies become increasingly accessible to developers of all skill levels. What was once the exclusive domain of specialized data scientists and researchers has now been democratized through user-friendly frameworks, cloud-based services, and on-device machine learning capabilities. This technological evolution is enabling a new generation of intelligent applications that can understand context, learn from user behavior, process natural language, recognize images, and make predictions—all capabilities that were science fiction just a decade ago. As these technologies mature, they are redefining what users expect from their mobile experiences and creating competitive advantages for organizations that effectively leverage them.

On-Device Intelligence: The New Frontier

Perhaps the most significant development in AI-powered mobile applications is the shift toward on-device processing. While early AI implementations relied heavily on cloud services—sending data to remote servers for processing and returning results to the device—modern mobile hardware now supports increasingly sophisticated machine learning models running directly on smartphones. Apple's Core ML and Google's ML Kit have made on-device inference accessible to mainstream Mobile App Development teams, offering significant advantages in performance, privacy, and offline functionality. Applications can now recognize faces, classify images, transcribe speech, and even perform complex natural language processing without an internet connection. This architectural shift reduces latency, preserves user privacy by keeping sensitive data local, and enables AI-powered features to function reliably regardless of network conditions—critical considerations for mobile experiences.

Personalization at Scale

The application of machine learning to user behavior data has revolutionized how applications adapt to individual preferences and needs. Rather than relying on broad demographic segments or explicit user settings, intelligent applications can observe interaction patterns, identify preferences, and dynamically adjust their interfaces and content recommendations. Music streaming apps analyze listening history to create personalized playlists, e-commerce applications learn which products interest specific users, and productivity tools identify usage patterns to suggest workflows or features. These personalization capabilities create virtuous cycles where increased engagement generates more behavioral data, enabling even more precise personalization. Implementing these systems effectively requires careful attention to data collection, model design, and the balance between algorithmic recommendations and user control—challenges that have become central to modern Mobile App Development practices.

Natural Language Understanding and Generation

The ability to understand and generate human language represents one of the most transformative applications of AI in mobile experiences. Natural Language Processing (NLP) technologies enable applications to extract meaning from text and speech, identifying intents, entities, and sentiment that drive intelligent responses. This capability powers increasingly sophisticated chatbots and virtual assistants that can handle customer service inquiries, schedule appointments, or provide information through conversational interfaces. More recently, large language models have demonstrated remarkable capabilities for generating human-like text, enabling applications that can summarize content, draft messages, or even create creative content. These technologies are transforming communication within applications, creating opportunities for more natural, accessible interfaces while introducing new design considerations around accuracy, transparency, and appropriate delegation of tasks to AI systems.

Computer Vision: Seeing the World Through Apps

The integration of computer vision capabilities has enabled a new category of applications that can analyze and understand visual information through device cameras. Mobile App Development now encompasses applications that can identify objects in real-time, measure physical spaces, detect text in images for translation, recognize faces for authentication or photo organization, and even diagnose skin conditions or plant diseases from photographs. Augmented reality applications build on these foundations, anchoring virtual content to the physical world through precise understanding of environments and surfaces. These capabilities are enabling entirely new categories of mobile experiences that bridge digital and physical contexts, from virtual try-on features in shopping applications to maintenance instructions that overlay equipment with interactive guides.

Predictive Features and Anticipatory Design

Perhaps the most subtle but impactful application of machine learning in mobile applications is the shift toward predictive and anticipatory experiences. By analyzing patterns in user behavior, applications can predict likely actions and surface relevant functionality proactively—suggesting replies to messages, pre-loading content likely to be accessed next, or recommending actions based on time, location, or context. These capabilities transform applications from passive tools that wait for explicit commands into proactive assistants that anticipate needs. Implementing these features effectively requires sophisticated Mobile App Development approaches that balance the value of predictions against the risk of incorrect suggestions, presenting predictive elements in ways that feel helpful rather than intrusive or presumptuous.

Ethical Considerations and Responsible Implementation

The integration of AI capabilities into mobile applications introduces important ethical considerations that responsible development teams must address. Issues of bias in machine learning models can lead to applications that perform differently across demographic groups or reinforce problematic stereotypes. Privacy concerns are particularly acute when applications collect the substantial data required for personalization or behavioral modeling. Transparency about AI-powered features—helping users understand when they're interacting with automated systems and how their data influences recommendations—builds trust and sets appropriate expectations. Leading organizations in Mobile App Development are establishing ethical frameworks and review processes to ensure AI features are implemented responsibly, considering potential impacts across diverse user populations.

Development Approaches and Technical Considerations

For development teams looking to incorporate AI capabilities, several implementation paths offer different tradeoffs in terms of development complexity, customization, and resource requirements. Cloud-based AI services from providers like Google, Amazon, and Microsoft offer pre-trained models for common tasks like language processing, image recognition, and recommendation systems—accessible through APIs without requiring deep AI expertise. These services enable rapid implementation but may involve ongoing costs and privacy considerations around data transmission. On-device machine learning frameworks like TensorFlow Lite and Core ML support deploying custom or pre-trained models directly on mobile devices, offering better performance and privacy at the cost of more complex implementation. For teams with specialized needs and deeper technical resources, custom model development using frameworks like TensorFlow or PyTorch enables highly tailored capabilities that differentiate applications in competitive markets.

The Future: Multimodal AI and Ambient Intelligence

As AI technologies continue evolving, the next frontier in Mobile App Development involves multimodal systems that combine different forms of intelligence—understanding both visual and textual information, for example, or integrating location data with natural language understanding to provide contextually relevant responses. These capabilities will enable more sophisticated applications that understand context in ways that feel increasingly natural to users. Looking further ahead, the combination of ubiquitous sensors, on-device intelligence, and advanced interfaces points toward ambient computing experiences where intelligence extends beyond individual applications to create cohesive ecosystems of devices that collaboratively support user needs. This evolution will likely blur the boundaries of traditional application development, requiring new approaches to design, development, and user experience that consider intelligence as a fundamental system property rather than a feature added to conventional applications.

The integration of artificial intelligence and machine learning into mobile applications represents not just a set of new features but a fundamental shift in how applications function and what they can offer users. By understanding context, learning from experience, and anticipating needs, these intelligent applications are setting new standards for user experience that will quickly become expectations rather than differentiators. For organizations involved in Mobile App Development, the ability to effectively leverage these technologies—balancing technical possibilities with ethical considerations and meaningful user benefits—will increasingly define competitive advantage in the mobile ecosystem. As these technologies continue maturing and becoming more accessible to developers of all skill levels, their impact on mobile experiences will only accelerate, creating opportunities for applications that are not just tools but truly intelligent partners in users' digital lives.

[datascience12pune]

Learn to Use SQL, MongoDB, and Big Data in Data Science

In today’s data-driven world, understanding the right tools is as important as understanding the data. If you plan to pursue a data science certification in Pune, knowing SQL, MongoDB, and Big Data technologies isn’t just a bonus — it’s essential. These tools form the backbone of modern data ecosystems and are widely used in real-world projects to extract insights, build models, and make data-driven decisions.

Whether you are planning on updating your resume, wanting to find a job related to analytics, or just have a general interest in how businesses apply data. Learning how to deal with structured and unstructured data sets should be a goal.

Now, analysing the relation of SQL, MongoDB, and Big Data technologies in data science and how they may transform your career, if you are pursuing data science classes in Pune.

Why These Tools Matter in Data Science?

Data that today’s data scientists use varies from transactional data in SQL databases to social network data stored in NoSQL, such as MongoDB, and data larger than the amount that can be processed by conventional means. It has to go through Big Data frameworks. That is why it is crucial for a person to master such tools:

1. SQL: The Language of Structured Data

SQL (Structured Query Language) is a widely used language to facilitate interaction between users and relational databases. Today, almost every industry globally uses SQL to solve organisational processes in healthcare, finance, retail, and many others.

How It’s Used in Real Life?

Think about what it would be like to become an employee in one of the retail stores based in Pune. In this case, you are supposed to know the trends of products that are popular in the festive season. Therefore, it is possible to use SQL and connect to the company’s sales database to select data for each product and sort it by categories, as well as to determine the sales velocity concerning the seasons. It is also fast, efficient, and functions in many ways that are simply phenomenal.

Key SQL Concepts to Learn:

SELECT, JOIN, GROUP BY, and WHERE clauses

Window functions for advanced analytics

Indexing for query optimisation

Creating stored procedures and views

Whether you're a beginner or brushing up your skills during a data science course in Pune, SQL remains a non-negotiable part of the toolkit.

2. MongoDB: Managing Flexible and Semi-Structured Data

As businesses increasingly collect varied forms of data, like user reviews, logs, and IoT sensor readings, relational databases fall short. Enter MongoDB, a powerful NoSQL database that allows you to store and manage data in JSON-like documents.

Real-Life Example:

Suppose you're analysing customer feedback for a local e-commerce startup in Pune. The feedback varies in length, structure, and language. MongoDB lets you store this inconsistent data without defining a rigid schema upfront. With tools like MongoDB’s aggregation pipeline, you can quickly extract insights and categorise sentiment.

What to Focus On?

CRUD operations in MongoDB

Aggregation pipelines for analysis

Schema design and performance optimisation

Working with nested documents and arrays

Learning MongoDB is especially valuable during your data science certification in Pune, as it prepares you for working with diverse data sources common in real-world applications.

3. Big Data: Scaling Your Skills to Handle Volume

As your datasets grow, traditional tools may no longer suffice. Big Data technologies like Hadoop and Spark allow you to efficiently process terabytes or even petabytes of data.

Real-Life Use Case:

Think about a logistics company in Pune tracking thousands of deliveries daily. Data streams in from GPS devices, traffic sensors, and delivery apps. Using Big Data tools, you can process this information in real-time to optimise routes, reduce fuel costs, and improve delivery times.

What to Learn?

Hadoop’s HDFS for distributed storage

MapReduce programming model.

Apache Spark for real-time and batch processing

Integrating Big Data with Python and machine learning pipelines

Understanding how Big Data integrates with ML workflows is a career-boosting advantage for those enrolled in data science training in Pune.

Combining SQL, MongoDB, and Big Data in Projects

In practice, data scientists often use these tools together. Here’s a simplified example:

You're building a predictive model to understand user churn for a telecom provider.

Use SQL to fetch customer plans and billing history.

Use MongoDB to analyse customer support chat logs.

Use Spark to process massive logs from call centres in real-time.

Once this data is cleaned and structured, it feeds into your machine learning model. This combination showcases the power of knowing multiple tools — a vital edge you gain during a well-rounded data science course in Pune.

How do These Tools Impact Your Career?

Recruiters look for professionals who can navigate relational and non-relational databases and handle large-scale processing tasks. Mastering these tools not only boosts your credibility but also opens up job roles like:

Data Analyst

Machine Learning Engineer

Big Data Engineer

Data Scientist

If you're taking a data science certification in Pune, expect practical exposure to SQL and NoSQL tools, plus the chance to work on capstone projects involving Big Data. Employers value candidates who’ve worked with diverse datasets and understand how to optimise data workflows from start to finish.

Tips to Maximise Your Learning

Work on Projects: Try building a mini data pipeline using public datasets. For instance, analyze COVID-19 data using SQL, store news updates in MongoDB, and run trend analysis using Spark.

Use Cloud Platforms: Tools like Google BigQuery or MongoDB Atlas are great for practising in real-world environments.

Collaborate and Network: Connect with other learners in Pune. Attend meetups, webinars, or contribute to open-source projects.

Final Thoughts

SQL, MongoDB, and Big Data are no longer optional in the data science world — they’re essential. Whether you're just starting or upgrading your skills, mastering these technologies will make you future-ready.

If you plan to enroll in a data science certification in Pune, look for programs that emphasise hands-on training with these tools. They are the bridge between theory and real-world application, and mastering them will give you the confidence to tackle any data challenge.

Whether you’re from a tech background or switching careers, comprehensive data science training in Pune can help you unlock your potential. Embrace the learning curve, and soon, you'll be building data solutions that make a real impact, right from the heart of Pune.

[generativeinai]

Step-by-Step Breakdown of AI Video Analytics Software Development: Tools, Frameworks, and Best Practices for Scalable Deployment

AI Video Analytics is revolutionizing how businesses analyze visual data. From enhancing security systems to optimizing retail experiences and managing traffic, AI-powered video analytics software has become a game-changer. But how exactly is such a solution developed? Let’s break it down step by step—covering the tools, frameworks, and best practices that go into building scalable AI video analytics software.

Introduction: The Rise of AI in Video Analytics

The explosion of video data—from surveillance cameras to drones and smart cities—has outpaced human capabilities to monitor and interpret visual content in real-time. This is where AI Video Analytics Software Development steps in. Using computer vision, machine learning, and deep neural networks, these systems analyze live or recorded video streams to detect events, recognize patterns, and trigger automated responses.

Step 1: Define the Use Case and Scope

Every AI video analytics solution starts with a clear business goal. Common use cases include:

Real-time threat detection in surveillance

Customer behavior analysis in retail

Traffic management in smart cities

Industrial safety monitoring

License plate recognition

Key Deliverables:

Problem statement

Target environment (edge, cloud, or hybrid)

Required analytics (object detection, tracking, counting, etc.)

Step 2: Data Collection and Annotation

AI models require massive amounts of high-quality, annotated video data. Without clean data, the model's accuracy will suffer.

Tools for Data Collection:

Surveillance cameras

Drones

Mobile apps and edge devices

Tools for Annotation:

CVAT (Computer Vision Annotation Tool)

Labelbox

Supervisely

Tip: Use diverse datasets (different lighting, angles, environments) to improve model generalization.

Step 3: Model Selection and Training

This is where the real AI work begins. The model learns to recognize specific objects, actions, or anomalies.

Popular AI Models for Video Analytics:

YOLOv8 (You Only Look Once)

OpenPose (for human activity recognition)

DeepSORT (for multi-object tracking)

3D CNNs for spatiotemporal activity analysis

Frameworks:

TensorFlow

PyTorch

OpenCV (for pre/post-processing)

ONNX (for interoperability)

Best Practice: Start with pre-trained models and fine-tune them on your domain-specific dataset to save time and improve accuracy.

Step 4: Edge vs. Cloud Deployment Strategy

AI video analytics can run on the cloud, on-premises, or at the edge depending on latency, bandwidth, and privacy needs.

Cloud:

Scalable and easier to manage

Good for post-event analysis

Edge:

Low latency

Ideal for real-time alerts and privacy-sensitive applications

Hybrid:

Initial processing on edge devices, deeper analysis in the cloud

Popular Platforms:

NVIDIA Jetson for edge

AWS Panorama

Azure Video Indexer

Google Cloud Video AI

Step 5: Real-Time Inference Pipeline Design

The pipeline architecture must handle:

Video stream ingestion

Frame extraction

Model inference

Alert/visualization output

Tools & Libraries:

GStreamer for video streaming

FFmpeg for frame manipulation

Flask/FastAPI for inference APIs

Kafka/MQTT for real-time event streaming

Pro Tip: Use GPU acceleration with TensorRT or OpenVINO for faster inference speeds.

Step 6: Integration with Dashboards and APIs

To make insights actionable, integrate the AI system with:

Web-based dashboards (using React, Plotly, or Grafana)

REST or gRPC APIs for external system communication

Notification systems (SMS, email, Slack, etc.)

Best Practice: Create role-based dashboards to manage permissions and customize views for operations, IT, or security teams.

Step 7: Monitoring and Maintenance

Deploying AI models is not a one-time task. Performance should be monitored continuously.

Key Metrics:

Accuracy (Precision, Recall)

Latency

False Positive/Negative rate

Frame per second (FPS)

Tools:

Prometheus + Grafana (for monitoring)

MLflow or Weights & Biases (for model versioning and experiment tracking)

Step 8: Security, Privacy & Compliance

Video data is sensitive, so it’s vital to address:

GDPR/CCPA compliance

Video redaction (blurring faces/license plates)

Secure data transmission (TLS/SSL)

Pro Tip: Use anonymization techniques and role-based access control (RBAC) in your application.

Step 9: Scaling the Solution

As more video feeds and locations are added, the architecture should scale seamlessly.

Scaling Strategies:

Containerization (Docker)

Orchestration (Kubernetes)

Auto-scaling with cloud platforms

Microservices-based architecture

Best Practice: Use a modular pipeline so each part (video input, AI model, alert engine) can scale independently.

Step 10: Continuous Improvement with Feedback Loops

Real-world data is messy, and edge cases arise often. Use real-time feedback loops to retrain models.

Automatically collect misclassified instances

Use human-in-the-loop (HITL) systems for validation

Periodically retrain and redeploy models

Conclusion

Building scalable AI Video Analytics Software is a multi-disciplinary effort combining computer vision, data engineering, cloud computing, and UX design. With the right tools, frameworks, and development strategy, organizations can unlock immense value from their video data—turning passive footage into actionable intelligence.

[fromdevcom]

Since past few years, the technology has simply advanced in a rapid speed. This is a fact in every domain especially in communication and IT industry. The popularity of communication devices like smartphones and tabs have further expanded the reach of internet to not just any urban places but also at smaller towns and rural areas. Google has been in the leading role in embarking with creative and interesting products. One of the interesting products of Google is Android; it’s a popular operating system, which is being used in a wide range of smartphones and tablets all across the market. It comes with a wide range of apps including a number of useful internet marketing apps available for free or you can download from Google play store. Let’s check some of the best 15 free internet marketing apps for Android: Trello Coordinating a number of things is one of the important jobs of any online marketer. This can be very much confusing job if you are involved in a number of marketing campaigns on the web. Luckily you have a free of cost Android application called Trello, which can help you in doing these things. Trello comes with a - to do list manager, which can manage a number of things for your day to day online marketing. HootSuite This application is really very much handy when you talk about carrying out a number of social networking jobs for your marketing purposes that too in one interface. It is compatible to a number of devices apart from being the favorite for the Android based marketing users. With a number of features in it, HootSuite has simply embarked as an important global social media marketing application and the best part – it comes for free. SEO SERP App For online marketers the keyword ranking matters a lot. Apart from monitoring your keywords you have to keep an eye over a number of sites as well and keep on checking their SERP positions. The SEO SERP App, which is basically a free of cost tool helps in extracting a slew of data in concise and organized way, which makes it’s a must for every online marketer. Eventbrite Are you keen to host a webinar or some other event under the inbound lead generation initiative? If yes, the tool Eventbrite can be the right app for you as it helps in managing the event the best. Right from planning and coordinating things on the road it does a number other jobs for the event. It also checks for things like managing registrations, creating the supporting reports, and monitors where and how your attendees are coming from. Another worthy free of cost Android application for the marketer! Marketing Forecast By Adology Another worthy and free Android application is Adology’s Marketing Forecast app, which helps in catching up the latest news, views and ads of your niche area. You can find a number of features including the U.S. economic trends, Google’s trending topics, and also the Twitter streams, which collate certain recent Tweets regarding online marketing and advertising tasks. Springpad This application simply acts like your personal assistant for organizing virtually everything you have for your marketing activities. You can easily organize a number of ideas, articles, and other resources along with so many day to day businesses marketing stuff. It comes for free. Feedly This application helps in browsing all sort of content found over your all time favorite blogs and websites via your Android based mobile devices. It also checks the RSS feeds, YouTube videos, Tumblr blogs and other sites. Indeed a worthy and free of cost application for online marketers. AdSense Dashboard It is very much important to monitor Google AdSense. So how to do it! No worries just rely on AdSense Dashboard as it can help you in many ways. You can find a number of critical performance metrics and other stuff in few clicks using this free of cost Android based application. MAnalytics This free of cost application for the Android users help in getting an intuitive kind of user interface, visits, stats charts, no of visitors and a wide range of traffic sources data and other details.

Unlike many apps, it is also free of cost. WebRank SEO The application - WebRank SEO is among the best free tool for online marketing and SEO purposes. It can offer you load of data as per your current SEO status, like indexed pages in a list of a number of search engines including Google PageRank and Alexa along with a number of other things. It comes for free. SECockpit Another worthy tool for marketers is SECockpit. It deals with search engine optimization tasks, wherein you can find it a good option for things like keyword research tool, which generate loads of ideas for online marketers. You can find out data like the highest cost-per-click cost, a number of searches for any chosen keyword of your for the previous months along with getting other competitive data and details for free. SEO Automatic Lite This application helps in analyzing things like coding, which helps in reporting different issues that are supposed to be fixed over a website. You can even analyze things like the style and navigation of any page and it even sends out the users a report on different kinds of findings like knowing the potential problems as well. Documents To Go Are you keen to look the whole Microsoft Word, Excel spread sheet or PowerPoint files in one go? If yes, you can very well do this using this application. This can be a big boon thing for the online marketers, which luckily comes for free via this application. Statigram Another worthy application for online marketer is Statigram. It is basically a social analytics tool for the Instagram, simply designed to give people a very good Instagram experience. Also, it helps in integrating with HootSuite, which comes for free. SEO Keyword Checker This application analyses the web page and move the data over the most used keywords or key phrases without a single buck. Indeed a good tool for things like competitive analysis. Vine This tool help in sharing and monitoring short looping videos using your Android based devices. It is among the best and free of cost video application for the online marketer. Wrapping Up Applications and software can really help in making the task of marketers simple. The above list can really help you a lot and can add wings in your day to day online marketing activities. This article is contributed by Alia, a writer, social media junkie and technology enthusiast. She likes reading and writing on social media, digital trends and Online Reputation. And lately, she is working on a project for Website Development Company India

[souhaillaghchimdev]

Big Data Analysis Application Programming

Big data is not just a buzzword—it's a powerful asset that fuels innovation, business intelligence, and automation. With the rise of digital services and IoT devices, the volume of data generated every second is immense. In this post, we’ll explore how developers can build applications that process, analyze, and extract value from big data.

What is Big Data?

Big data refers to extremely large datasets that cannot be processed or analyzed using traditional methods. These datasets exhibit the 5 V's:

Volume: Massive amounts of data

Velocity: Speed of data generation and processing

Variety: Different formats (text, images, video, etc.)

Veracity: Trustworthiness and quality of data

Value: The insights gained from analysis

Popular Big Data Technologies

Apache Hadoop: Distributed storage and processing framework

Apache Spark: Fast, in-memory big data processing engine

Kafka: Distributed event streaming platform

NoSQL Databases: MongoDB, Cassandra, HBase

Data Lakes: Amazon S3, Azure Data Lake

Big Data Programming Languages

Python: Easy syntax, great for data analysis with libraries like Pandas, PySpark

Java & Scala: Often used with Hadoop and Spark

R: Popular for statistical analysis and visualization

SQL: Used for querying large datasets

Basic PySpark Example

from pyspark.sql import SparkSession # Create Spark session spark = SparkSession.builder.appName("BigDataApp").getOrCreate() # Load dataset data = spark.read.csv("large_dataset.csv", header=True, inferSchema=True) # Basic operations data.printSchema() data.select("age", "income").show(5) data.groupBy("city").count().show()

Steps to Build a Big Data Analysis App

Define data sources (logs, sensors, APIs, files)

Choose appropriate tools (Spark, Hadoop, Kafka, etc.)

Ingest and preprocess the data (ETL pipelines)

Analyze using statistical, machine learning, or real-time methods

Visualize results via dashboards or reports

Optimize and scale infrastructure as needed

Common Use Cases

Customer behavior analytics

Fraud detection

Predictive maintenance

Real-time recommendation systems

Financial and stock market analysis

Challenges in Big Data Development

Data quality and cleaning

Scalability and performance tuning

Security and compliance (GDPR, HIPAA)

Integration with legacy systems

Cost of infrastructure (cloud or on-premise)

Best Practices

Automate data pipelines for consistency

Use cloud services (AWS EMR, GCP Dataproc) for scalability

Use partitioning and caching for faster queries

Monitor and log data processing jobs

Secure data with access control and encryption

Conclusion

Big data analysis programming is a game-changer across industries. With the right tools and techniques, developers can build scalable applications that drive innovation and strategic decisions. Whether you're processing millions of rows or building a real-time data stream, the world of big data has endless potential. Dive in and start building smart, data-driven applications today!

[tycoonworld]

Smart Homes and Greywater Recycling: Integrated Water Conservation for the Future

Authored by Gurmit Singh Arora, National President of the Indian Plumbing Association

On account of growing water shortages and climate change, integrating greywater recycling with smart homes offers a holistic approach to water conservation. This strategy prevents the over-extraction of freshwater while automating household water consumption through technology and analytical data-driven strategies.

Advanced Greywater Systems in Smart Homes

The majority of modern smart homes feature automatic greywater systems that capture and repurpose water from showers, dishwashing, laundry, and many more. Homeowners benefit from non-potable water reuse while never compromising on quality. Treatment units physically divert these streams from the household to specialized units containing a series of processes that include Filtration, Biological Treatment, and UV Disinfection. These units ensure that the water is safe for reuse in irrigation and toilet flushing.

Optimized Irrigation and Appliance Integration

Smart Irrigation

Soil moisture, local weather, and plant needs are constantly monitored by the sensors integrated into the greywater systems. One of the most eco-friendly features of these systems is the smart irrigation network that takes advantage of this data. The combination of preservation of healthy landscapes and effective water usage is achieved through dynamic scheduling of irrigation, allowing every plant or area to receive the appropriate quantity of water.

Appliance Efficiency

Apart from irrigation, greywater is now incorporated with household appliances. Smart toilets use treated greywater for flushing, which greatly decreases the need for fresh water. Additionally, washing machines can adjust their water use depending on the load and the water's quality, thereby avoiding wasting water. The integration of these different appliances achieves a closed-loop system that results in less waste and greater efficiency, translating to lower costs and less harm to the environment.

Leveraging AI and Real-Time Analytics

Artificial intelligence controls the operation of the smart greywater systems. AI looks at patterns of water use and system performance to identify potential leaks or failures in an appliance that can be fixed before they develop into larger problems. Detailed information through mobile apps allow homeowners to take action to maintain their greywater systems at peak efficiency. This change not only improves the sustainability of water management, but also greatly improves the overall reliability of the system.

Environmental and Economic Impact

Integrating greywater recycling systems with smart home technology has benefits beyond water savings. These systems reduce the draw on municipal freshwater supplies, which helps local water resources, especially in drought-stricken areas. Additionally, the electricity used for water treatment and distribution used is significantly lowered, resulting in less greenhouse gas emissions. Economic savings, in the form of decreased water bills, are real for homeowners, and communities reap the benefits from having a more sustainable water infrastructure.

Overcoming Challenges and Future Directions

Despite appealing benefits, there are other known issues surrounding the implementation of smart greywater systems. Installation cost, as well as specialized plumbing requirements, may hinder adoption. In addition, processes with water reuse have different regional coverage, which may delay implementation. Nevertheless, smarter, easier, and cheaper modular retrofitting systems are continually being developed. Automation and sensor technology also further develops, which will allow for better refinement of water recycling processes and make implementation in various residential environments more feasible.

The merging of greywater technology and smart home capabilities is a paradigm shift on how water can be conserved. This technology integrates automated AI and modern systems to not only alleviate current water restrictions, but also create a sustainable and adaptable future.

[learning-code-ficusoft]

Leveraging ADF for Real-Time Fraud Detection in E-Commerce

Fraud detection is a major challenge for e-commerce platforms, as online transactions generate massive amounts of data that need to be analyzed in real time. Azure Data Factory (ADF), combined with real-time data processing tools, enables e-commerce businesses to detect fraudulent activities swiftly, minimizing financial losses and ensuring customer trust.

Why Fraud Detection is Crucial in E-Commerce

E-commerce fraud comes in various forms, including:

Credit Card Fraud — Unauthorized use of payment details.

Account Takeover — Hackers gain access to user accounts.

Fake Returns and Refunds — Customers exploit return policies.

Promo Abuse — Users create multiple accounts to misuse discount offers.

To mitigate these risks, businesses need a scalable, real-time fraud detection system that processes large volumes of transactional data efficiently.

How Azure Data Factory Powers Real-Time Fraud Detection

Azure Data Factory integrates with real-time streaming services like Azure Stream Analytics, Azure Synapse, and Azure Machine Learning, providing a secure, scalable solution for fraud detection.

1. Ingesting Real-Time Transaction Data

ADF can pull data from multiple sources, such as:

Payment Gateways (Stripe, PayPal, etc.)

E-Commerce Databases (SQL, NoSQL, Cosmos DB, etc.)

User Behavior Logs from website and mobile apps

Third-Party Fraud Intelligence Feeds

2. Processing and Analyzing Transactions for Anomalies

ADF works with Azure Stream Analytics and Azure Databricks to:

Detect suspicious transaction patterns based on AI/ML models.

Compare transactions against historical fraud patterns.

Identify geographical inconsistencies (e.g., sudden logins from different locations).

3. Implementing Machine Learning for Fraud Detection

Using Azure Machine Learning, businesses can:

Train fraud detection models with historical and real-time transaction data.

Deploy models within Azure Synapse Analytics for predictive insights.

Automate anomaly detection alerts for rapid response.

4. Securing Sensitive Payment Data

ADF ensures compliance with PCI DSS, GDPR, and SOC 2 by:

Encrypting data in transit and at rest with Azure Key Vault.

Using role-based access control (RBAC) to limit access to sensitive data.

Leveraging Azure Monitor and Log Analytics for real-time security auditing.

5. Automating Alerts and Fraud Prevention Actions

ADF integrates with Azure Logic Apps and Power Automate to:

Trigger real-time alerts when fraud is detected.

Block suspicious transactions automatically based on predefined rules.

Notify security teams for further investigation.

Use Case: Detecting and Preventing High-Value Fraudulent Transactions

An e-commerce business wants to prevent fraudulent high-value purchases.

Step 1: Data Ingestion

ADF extracts payment details from Stripe and PayPal APIs.

Logs from user sessions and past purchase history are streamed into Azure Data Lake.

Step 2: Anomaly Detection

Azure Machine Learning models analyze the transaction in real time.

If anomalies like mismatched billing and shipping addresses or suspicious geolocation changes are detected, an alert is triggered.

Step 3: Automated Action

ADF triggers Azure Logic Apps, which:

Blocks the transaction.

Sends a two-factor authentication (2FA) request to verify the user.

Notifies the security team for manual review.

Conclusion

By leveraging Azure Data Factory, Azure Machine Learning, and real-time analytics, e-commerce businesses can build a robust fraud detection system that protects against fraudulent activities. Implementing automated alerts, secure data processing, and AI-driven fraud detection ensures faster response times, reducing financial losses and improving customer trust.

WEBSITE: https://www.ficusoft.in/azure-data-factory-training-in-chennai/

[retechie]

Creative DIY Projects Using Old Laptops

Technology evolves rapidly, and many people find themselves with old laptops that no longer meet their needs. Instead of discarding them, why not explore some innovative ways to repurpose them? Whether you have a second hand laptop, a refurbished MacBook Air, or an outdated Windows device, there are plenty of DIY projects to bring them back to life.

If you’re looking for sustainable and budget-friendly ways to reuse your old devices, here are some creative DIY ideas to make the most of used laptops in India.

1. Transform It into a Home Media Center

One of the easiest and most useful ways to repurpose an old laptop is by turning it into a media center. With apps like Plex or Kodi, you can use your laptop to stream movies, music, and TV shows directly to your TV. Simply connect it via HDMI, install your favorite media player, and enjoy a personalized entertainment hub.

How to Set It Up:

Install Plex or Kodi for a streamlined media experience.

Connect an external hard drive for additional storage.

Use an HDMI cable and link it to your television.

2. Convert It into a Home Server

An old second hand laptop can be repurposed into a home server for storing files, hosting websites, or even running smart home automation software. This is a great way to keep your data centralized and accessible from anywhere in your home.

How to Do It:

Install Ubuntu Server or FreeNAS for file sharing.

Use it to store backups, family photos, and important documents.

Set up a basic cloud storage system for easy access to files.

3. Create a DIY Digital Photo Frame

If your laptop’s screen is still functional, why not turn it into a digital photo frame? This is a fantastic way to display family photos, artwork, or even inspirational quotes.

Steps to Make It Work:

Install photo slideshow software like Google Photos.

Remove the keyboard and mount the screen in a frame.

Set it up in your living room or office for a modern touch.

4. Use It as a Secondary Monitor

A renewed laptop with a functional display can be repurposed into a secondary monitor for increased productivity. Whether you're working from home or gaming, a second screen can improve multitasking.

How to Set It Up:

Use SpaceDesk (Windows) or Air Display (Mac) to connect wirelessly.

Connect using an HDMI cable if the ports are compatible.

Extend or mirror your screen for better workflow.

5. Turn It into a Gaming Console

Older refurbished laptops may not support high-end gaming, but they can still be turned into retro gaming consoles with emulators. With software like RetroPie or Lakka, you can play classic games from PlayStation, Nintendo, and more.

What You Need:

Install RetroPie, Lakka, or Batocera.

For a console-like experience, use a Bluetooth controller.

For a larger screen, connect it to your television.

6. Donate or Sell Your Old Laptop

If your laptop is still functional but no longer useful to you, consider donating or selling it. Platforms like Retechie specialize in refurbished laptops in India, offering a marketplace for buying and selling second hand devices. This not only helps someone else but also promotes sustainability by reducing electronic waste.

Where to Sell or Donate:

Sell it on Retechie if it still has good resale value.

Donate it to schools or NGOs for students in need.

Get credit for a newer gadget by trading it in.

7. Extract and Repurpose Individual Components

Even if your laptop isn’t working, some parts can still be useful. Here’s what you can salvage:

Hard Drive: Convert it into an external storage device.

RAM & Battery: Use them for repairs or upgrades.

Screen Panel: Repurpose it as an external monitor.

Final Thoughts: Give Your Old Laptop a New Life

Instead of letting your old laptops collect dust, these DIY projects can help you turn it into something practical and exciting. Whether you transform it into a media center, home server, or secondary monitor, there are plenty of creative ways to extend its usefulness. Looking to buy used laptops or sell your old one? Explore Retechie, the trusted marketplace for refurbished Apple laptops, second hand laptops, and renewed laptops in India.