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Artificial Intelligence & ChatGPT Prompts

Artificial Intelligence & ChatGPT Prompts

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🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

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📈 Análisis del canal de Telegram Artificial Intelligence & ChatGPT Prompts

El canal Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 42 114 suscriptores, ocupando la posición 3 229 en la categoría Tecnologías y Aplicaciones y el puesto 9 545 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 42 114 suscriptores.

Según los últimos datos del 12 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 175, y en las últimas 24 horas de 12, conservando un alto alcance.

  • Estado de verificación: No verificado
  • Tasa de interacción (ER): El promedio de interacción de la audiencia es 2.43%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.73% de reacciones respecto al total de suscriptores.
  • Alcance de las publicaciones: Cada publicación recibe en promedio 1 024 visualizaciones. En el primer día suele acumular 306 visualizaciones.
  • Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
  • Intereses temáticos: El contenido se centra en temas clave como learning, algorithm, detection, llm, pattern.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
🔓Unlock Your Coding Potential with ChatGPT 🚀 Your Ultimate Guide to Ace Coding Interviews! 💻 Coding tips, practice questions, and expert advice to land your dream tech job. For Promotions: @love_data

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 13 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.

42 114
Suscriptores
+1224 horas
+227 días
+17530 días
Archivo de publicaciones
🎓 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 & 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 - 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Unlock the p
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Here is the list of latest trending tech stacks in 2025👇👇 1. Frontend Development: - React.js: Known for its component-based architecture and strong community support. - Vue.js: Valued for its simplicity and flexibility in building user interfaces. - Angular: Still widely used, especially in enterprise applications. 2. Backend Development: - Node.js: Popular for building scalable and fast network applications using JavaScript. - Django: Preferred for its rapid development capabilities and robust security features. - Spring Boot: Widely used in Java-based applications for its ease of use and integration capabilities. 3. Mobile Development: - Flutter: Known for building natively compiled applications for mobile, web, and desktop from a single codebase. - React Native: Continues to be popular for building cross-platform applications with native capabilities. 4. Cloud Computing and DevOps: - AWS (Amazon Web Services), Azure, Google Cloud: Leading cloud service providers offering extensive services for computing, storage, and networking. - Docker and Kubernetes: Essential for containerization and orchestration of applications in a cloud-native environment. - Terraform: Infrastructure as code tool for managing and provisioning cloud infrastructure. 5. Data Science and Machine Learning: - Python: Dominant language for data science and machine learning, with libraries like NumPy, Pandas, and Scikit-learn. - TensorFlow and PyTorch: Leading frameworks for building and training machine learning models. - Apache Spark: Used for big data processing and analytics. 6. Cybersecurity: - SIEM Tools (Security Information and Event Management): Such as Splunk and ELK Stack, crucial for monitoring and managing security incidents. - Zero Trust Architecture: A security model that eliminates the idea of trust based on network location. 7. Blockchain and Cryptocurrency: - Ethereum: A blockchain platform supporting smart contracts and decentralized applications. - Hyperledger Fabric: Framework for developing permissioned, blockchain-based applications. 8. Artificial Intelligence (AI) and Natural Language Processing (NLP): - GPT (Generative Pre-trained Transformer) Models: Such as GPT-4, used for various natural language understanding tasks. - Computer Vision: Frameworks like OpenCV for image and video processing tasks. 9. Edge Computing and IoT (Internet of Things): - Edge Computing: Technologies that bring computation and data storage closer to the location where it is needed. - IoT Platforms: Such as AWS IoT, Azure IoT Hub, offering capabilities for managing and securing IoT devices and data. Best Resources to help you with the journey 👇👇 Javascript Roadmap https://t.me/javascript_courses/309 Best Programming Resources: https://topmate.io/coding/886839 Web Development Resources https://t.me/webdevcoursefree Latest Jobs & Internships https://t.me/getjobss Cryptocurrency Basics https://t.me/Bitcoin_Crypto_Web/236 Python Resources https://t.me/pythonanalyst Data Science Resources https://t.me/datasciencefree Best DSA Resources https://topmate.io/coding/886874 Udemy Free Courses with Certificate https://t.me/udemy_free_courses_with_certi Join @free4unow_backup for more free resources. ENJOY LEARNING 👍👍

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?�
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_Operating System RoadMap | |-- Kernel | |-- Memory Management | | |-- Paging | | |-- Segmentation | | |-- Virtual Memory | | | |-- Process Management | | |-- Process Scheduling | | |-- Inter-Process Communication (IPC) | | |-- Threads | | | |-- File System | | |-- File I/O | | |-- Directory Structure | | |-- File Permissions | | | |-- Device Drivers | | |-- Communication with Hardware | | |-- Input/Output (I/O) | | | |-- System Calls | |-- Interface to Kernel Functionality | |-- Examples: open(), read(), write(), etc. | |-- Memory Management | |-- RAM | | |-- Stack | | |-- Heap | | |-- Data Segment | | |-- Code Segment | | | |-- Cache | | |-- L1, L2, L3 Caches | | | |-- Virtual Memory | |-- Page Table | |-- Page Replacement Algorithms | |-- Swapping | |-- File System | |-- File Organization | |-- File Allocation Table (FAT) | |-- Inodes | |-- File Access Methods | |-- Networking | |-- TCP/IP | |-- Protocols | |-- Network Stack | |-- Routing | |-- Firewalls | |-- Security | |-- Authentication | |-- Authorization | |-- Encryption | |-- Access Control Lists (ACL) | |-- Process Management | |-- PCB (Process Control Block) | |-- Context Switching | |-- Deadlocks | |-- Synchronization | |-- Mutual Exclusion | |-- Device Management | |-- I/O Buffering | |-- Device Controllers | |-- Interrupt Handling | |-- DMA (Direct Memory Access) | |-- User Interface | |-- Graphical User Interface (GUI) | |-- Command Line Interface (CLI) | |-- Windowing Systems | |-- Shell | |-- Command Interpreter | |-- Scripting | |-- Job Control | |-- System Utilities | |-- Task Manager | |-- Disk Cleanup | |-- System Monitor | |-- Backup and Restore | |-- Boot Process | |-- BIOS/UEFI | |-- Boot Loader | |-- Kernel Initialization | |-- Init Process | |-- System Libraries | |-- Standard C Library | |-- POSIX Library | |-- WinAPI (for Windows) | |-- System Calls | |-- File System Calls | |-- Process Control Calls | |-- Memory Management Calls | |-- Communication Calls | |-- Error Handling | |-- Error Codes | |-- Logging | |-- Recovery Strategies | |-- Distributed Systems | |-- Clustering | |-- Load Balancing | |-- Distributed File Systems | |-- Cloud Computing | |-- Virtualization | |-- Infrastructure as a Service (IaaS) | |-- Platform as a Service (PaaS) | |-- Software as a Service (SaaS) | └-- Comments |-- // Single-line comment └-- /* Multi-line comment */ Join for more: https://t.me/programming_guide

𝟯𝟬+ 𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 India's Biggest AI Challenge (13th To 15t
𝟯𝟬+ 𝗙𝗥𝗘𝗘 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲𝗔𝗜 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 India's Biggest AI Challenge (13th To 15th July ) , Earn Free certificates & Boost your resume! 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-  https://pdlink.in/3Gx7lW7 Enroll For FREE & Become an AI Champion🏆

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Here are some interview questions for both freshers and experienced applying for a data analyst #SQL Analyst role: #ForFreshers: 1. What is SQL, and why is it important in data analysis? 2. Explain the difference between a database and a table. 3. What are the basic SQL commands for data retrieval? 4. How do you retrieve all records from a table named "Employees"? 5. What is a primary key, and why is it important in a database? 6. What is a foreign key, and how is it used in SQL? 7. Describe the difference between SQL JOIN and SQL UNION. 8. How do you write a SQL query to find the second-highest salary in a table? 9. What is the purpose of the GROUP BY clause in SQL? 10. Can you explain the concept of normalization in SQL databases? 11. What are the common aggregate functions in SQL, and how are they used? ForExperiencedCandidates: 1. Describe a scenario where you had to optimize a slow-running SQL query. How did you approach it? 2. Explain the differences between SQL Server, MySQL, and Oracle databases. 3. Can you describe the process of creating an index in a SQL database and its impact on query performance? 4. How do you handle data quality issues when performing data analysis with SQL? 5. What is a subquery, and when would you use it in SQL? Give an example of a complex SQL query you've written to extract specific insights from a database. 6. How do you handle NULL values in SQL, and what are the challenges associated with them? 7. Explain the ACID properties of a database and their importance. 8. What are stored procedures and triggers in SQL, and when would you use them? 9. Describe your experience with ETL (Extract, Transform, Load) processes using SQL. 10. Can you explain the concept of query optimization in SQL, and what techniques have you used for optimization? Enjoy Learning 👍👍

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 | 𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄😍 - Infosys - Genpact - IBM - Virtusa - S&P Global
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Artificial Intelligence isn't easy! It’s the cutting-edge field that enables machines to think, learn, and act like humans. To truly master Artificial Intelligence, focus on these key areas: 0. Understanding AI Fundamentals: Learn the basic concepts of AI, including search algorithms, knowledge representation, and decision trees. 1. Mastering Machine Learning: Since ML is a core part of AI, dive into supervised, unsupervised, and reinforcement learning techniques. 2. Exploring Deep Learning: Learn neural networks, CNNs, RNNs, and GANs to handle tasks like image recognition, NLP, and generative models. 3. Working with Natural Language Processing (NLP): Understand how machines process human language for tasks like sentiment analysis, translation, and chatbots. 4. Learning Reinforcement Learning: Study how agents learn by interacting with environments to maximize rewards (e.g., in gaming or robotics). 5. Building AI Models: Use popular frameworks like TensorFlow, PyTorch, and Keras to build, train, and evaluate your AI models. 6. Ethics and Bias in AI: Understand the ethical considerations and challenges of implementing AI responsibly, including fairness, transparency, and bias. 7. Computer Vision: Master image processing techniques, object detection, and recognition algorithms for AI-powered visual applications. 8. AI for Robotics: Learn how AI helps robots navigate, sense, and interact with the physical world. 9. Staying Updated with AI Research: AI is an ever-evolving field—stay on top of cutting-edge advancements, papers, and new algorithms. Artificial Intelligence is a multidisciplinary field that blends computer science, mathematics, and creativity. 💡 Embrace the journey of learning and building systems that can reason, understand, and adapt. ⏳ With dedication, hands-on practice, and continuous learning, you’ll contribute to shaping the future of intelligent systems! Data Science & Machine Learning Resources: https://topmate.io/coding/914624 Credits: https://t.me/datasciencefun Like if you need similar content 😄👍 Hope this helps you 😊 #ai #datascience

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4 Career Paths In Data Analytics 1) Data Analyst: Role: Data Analysts interpret data and provide actionable insights through reports and visualizations. They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions. Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics. Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders. 2)Data Scientist: Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data. They develop models to predict future trends and solve intricate problems. Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization. Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies. 3)Business Intelligence (BI) Analyst: Role: BI Analysts focus on leveraging data to help businesses make strategic decisions. They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations. Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy. Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning. 4)Data Engineer: Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis. Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes. Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts. I have curated best 80+ top-notch Data Analytics Resources 👇👇 https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02 Hope this helps you 😊

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Many people pay too much to learn SQL, but my mission is to break down barriers. I have shared complete learning series to learn SQL from scratch. Here are the links to the SQL series Complete SQL Topics for Data Analyst: https://t.me/sqlspecialist/523 Part-1: https://t.me/sqlspecialist/524 Part-2: https://t.me/sqlspecialist/525 Part-3: https://t.me/sqlspecialist/526 Part-4: https://t.me/sqlspecialist/527 Part-5: https://t.me/sqlspecialist/529 Part-6: https://t.me/sqlspecialist/534 Part-7: https://t.me/sqlspecialist/534 Part-8: https://t.me/sqlspecialist/536 Part-9: https://t.me/sqlspecialist/537 Part-10: https://t.me/sqlspecialist/539 Part-11: https://t.me/sqlspecialist/540 Part-12: https://t.me/sqlspecialist/541 Part-13: https://t.me/sqlspecialist/542 Part-14: https://t.me/sqlspecialist/544 Part-15: https://t.me/sqlspecialist/545 Part-16: https://t.me/sqlspecialist/546 Part-17: https://t.me/sqlspecialist/549 Part-18: https://t.me/sqlspecialist/552 Part-19: https://t.me/sqlspecialist/555 Part-20: https://t.me/sqlspecialist/556 I saw a lot of big influencers copy pasting my content after removing the credits. It's absolutely fine for me as more people are getting free education because of my content. But I will really appreciate if you share credits for the time and efforts I put in to create such valuable content. I hope you can understand. Complete Python Topics for Data Analysts: https://t.me/sqlspecialist/548 Complete Excel Topics for Data Analysts: https://t.me/sqlspecialist/547 I'll continue with learning series on Python, Power BI, Excel & Tableau. Thanks to all who support our channel and share the content with proper credits. You guys are really amazing. Hope it helps :)

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