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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Red:📈 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 42 275 suscriptores, ocupando la posición 3 102 en la categoría Tecnologías y Aplicaciones y el puesto 9 148 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 42 275 suscriptores.
Según los últimos datos del 25 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 95, y en las últimas 24 horas de -2, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.51%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.68% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 640 visualizaciones. En el primer día suele acumular 289 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 26 agosto, 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.
42 275
Suscriptores
-224 horas
-347 días
+9530 días
Carga de datos en curso...
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| Fecha | Crecimiento de Suscriptores | Menciones | Canales | |
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| 04 agosto | +12 | |||
| 03 agosto | +30 | |||
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| 01 agosto | 0 |
Publicaciones del Canal
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| 4 | 🔥 AI Project Ideas 🔥
🎯 Image Caption Generator
🎯 AI Chatbot w/ Intent Detection
🎯 Fake News Detector (NLP)
🎯 Voice Emotion Recognition
🎯 Resume Screener (NLP)
🎯 Movie Recommender
🎯 Digit Recognition (MNIST)
🎯 AI Personal Assistant
🎯 Face Mask Detector
🎯 Text Summarizer (Transformer)
🎯 AI Resume Builder
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| 5 |  𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁—𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜😍
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| 6 |  🚀 𝗪𝗶𝗽𝗿𝗼 𝗘𝗹𝗶𝘁𝗲 𝗡𝗧𝗛 & 𝗧𝘂𝗿𝗯𝗼 𝗙𝗥𝗘𝗘 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗞𝗶𝘁 💻🔥
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| 7 | Don't overwhelm to learn JavaScript, JavaScript is only this much
1.Variables
• var
• let
• const
2. Data Types
• number
• string
• boolean
• null
• undefined
• symbol
3.Declaring variables
• var
• let
• const
4.Expressions
Primary expressions
• this
• Literals
• []
• {}
• function
• class
• function*
• async function
• async function*
• /ab+c/i
• string
• ( )
Left-hand-side expressions
• Property accessors
• ?.
• new
• new .target
• import.meta
• super
• import()
5.operators
• Arithmetic Operators: +, -, *, /, %
• Comparison Operators: ==, ===, !=, !==, <, >, <=, >=
• Logical Operators: &&, ||, !
6.Control Structures
• if
• else if
• else
• switch
• case
• default
7.Iterations/Loop
• do...while
• for
• for...in
• for...of
• for await...of
• while
8.Functions
• Arrow Functions
• Default parameters
• Rest parameters
• arguments
• Method definitions
• getter
• setter
9.Objects and Arrays
• Object Literal: { key: value }
• Array Literal: [element1, element2, ...]
• Object Methods and Properties
• Array Methods: push(), pop(), shift(), unshift(),
splice(), slice(), forEach(), map(), filter()
10.Classes and Prototypes
• Class Declaration
• Constructor Functions
• Prototypal Inheritance
• extends keyword
• super keyword
• Private class features
• Public class fields
• static
• Static initialization blocks
11.Error Handling
• try,
• catch,
• finally (exception handling)
ADVANCED CONCEPTS
12.Closures
• Lexical Scope
• Function Scope
• Closure Use Cases
13.Asynchronous JavaScript
• Callback Functions
• Promises
• async/await Syntax
• Fetch API
• XMLHttpRequest
14.Modules
• import and export Statements (ES6 Modules)
• CommonJS Modules (require, module.exports)
15.Event Handling
• Event Listeners
• Event Object
• Bubbling and Capturing
16.DOM Manipulation
• Selecting DOM Elements
• Modifying Element Properties
• Creating and Appending Elements
17.Regular Expressions
• Pattern Matching
• RegExp Methods: test(), exec(), match(), replace()
18.Browser APIs
• localStorage and sessionStorage
• navigator Object
• Geolocation API
• Canvas API
19.Web APIs
• setTimeout(), setInterval()
• XMLHttpRequest
• Fetch API
• WebSockets
20.Functional Programming
• Higher-Order Functions
• map(), reduce(), filter()
• Pure Functions and Immutability
21.Promises and Asynchronous Patterns
• Promise Chaining
• Error Handling with Promises
• Async/Await
22.ES6+ Features
• Template Literals
• Destructuring Assignment
• Rest and Spread Operators
• Arrow Functions
• Classes and Inheritance
• Default Parameters
• let, const Block Scoping
23.Browser Object Model (BOM)
• window Object
• history Object
• location Object
• navigator Object
24.Node.js Specific Concepts
• require()
• Node.js Modules (module.exports)
• File System Module (fs)
• npm (Node Package Manager)
25.Testing Frameworks
• Jasmine
• Mocha
• Jest | 460 |
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| 10 | Instead of relying only on what an LLM learned during training, RAG retrieves relevant information from an external knowledge source and provides it as context to the model.
User Question
↓
Retrieve Relevant Data
↓
Provide Context to LLM
↓
Generate Answer
📌 14. What are AI Agents?
AI agents are systems that can reason, plan, use tools, and take actions to accomplish a goal.
For example, an AI agent could:
Understand Goal
↓
Plan Steps
↓
Use Tools
↓
Execute Actions
↓
Evaluate Result
📌 15. What is Generative AI?
Generative AI creates new content based on learned patterns.
It can generate:
• Text
• Images
• Audio
• Video
• Code
📌 16. What are AI Hallucinations?
An AI hallucination occurs when an AI system generates information that appears plausible but is incorrect, unsupported, or fabricated.
This is why AI outputs should be verified, especially for important decisions.
📌 17. What is AI Bias?
AI bias occurs when an AI system produces systematically unfair or skewed results.
Bias can come from:
Training data
Data collection
Feature selection
Model design
Human decisions
📌 18. What is Explainable AI?
Explainable AI (XAI) focuses on making AI decisions understandable to humans.
This is especially important in areas such as:
• Banking
• Healthcare
• Insurance
• Hiring
• Government
📌 19. What is MLOps?
MLOps applies engineering and operational practices to the Machine Learning lifecycle.
It covers:
Model development
Deployment
Versioning
Monitoring
Retraining
Governance
📌 20. What is Responsible AI?
Responsible AI means developing and using AI in a way that considers:
• Fairness
• Privacy
• Security
• Transparency
• Accountability
• Safety
• Human oversight
DOUBLE TAP ❤️ For More
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| 11 | 🤖 AI Fundamentals You Should Know
AI is becoming an important skill across almost every industry. You don't need to become an AI researcher to understand it, but you should know the fundamentals.
📌 1. What is Artificial Intelligence?
AI is the field of creating systems that can perform tasks that typically require human intelligence.
Examples:
Understanding language
Recognizing images
Making predictions
Solving problems
Making decisions
📌 2. AI vs Machine Learning vs Deep Learning
Think of them as levels:
Artificial Intelligence
↓
Machine Learning
↓
Deep Learning
AI → Broad field of intelligent systems
ML → Systems learn patterns from data
DL → ML using multi-layer neural networks
📌 3. Types of Machine Learning
Everyone working with AI should know:
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
The key difference is how the model learns.
📌 4. What is Training?
Training is the process of teaching a model using data.
The model identifies patterns in the training data and adjusts its parameters to improve its predictions.
📌 5. What is Inference?
Inference happens when a trained model receives new data and produces a prediction or output.
Training → Learn
Inference → Predict
📌 6. What is a Dataset?
A dataset is a collection of data used to train, validate, or test an AI model.
It can contain:
• Features
• Labels
• Numerical data
• Categorical data
• Text
• Images
• Audio
• Video
📌 7. What are Features and Labels?
Features are the inputs used by a model.
Label/Target is what the model is trying to predict.
Example:
Age + Income + Credit Score
↓
Loan Approval
The first three are features, while loan approval is the target.
📌 8. What is Overfitting?
Overfitting occurs when a model learns the training data too closely, including noise, and performs poorly on new data.
Too simple → Underfitting
Good balance → Generalization
Too complex → Overfitting
📌 9. What is a Neural Network?
A neural network is a computational model made up of interconnected nodes called neurons.
It typically contains:
Input Layer
↓
Hidden Layers
↓
Output Layer
Neural networks are the foundation of many modern AI systems.
📌 10. What are Transformers?
Transformers are a neural network architecture that uses attention mechanisms to process relationships between elements in data.
They power many modern AI systems, especially:
• LLMs
• Translation systems
• Text generation
• Multimodal AI
📌 11. What is an LLM?
A Large Language Model (LLM) is a model trained on large amounts of text to understand and generate language.
LLMs can perform tasks such as:
Question answering
Summarization
Translation
Coding
Content generation
📌 12. What are Embeddings?
Embeddings convert information such as text into numerical vectors that capture semantic relationships.
Similar concepts tend to have similar vector representations.
They are widely used in:
Semantic search
RAG
Recommendation systems
Clustering
📌 13. What is RAG?
RAG stands for Retrieval-Augmented Generation. | 406 |
| 12 |  𝗪𝗢𝗥𝗞 𝗙𝗥𝗢𝗠 𝗛𝗢𝗠𝗘 𝗝𝗢𝗕 𝗢𝗣𝗣𝗢𝗥𝗧𝗨𝗡𝗜𝗧𝗬 😍
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🔁 𝗦𝗵𝗮𝗿𝗲 this with your friends and classmates! | 589 |
| 15 |  📊 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗣𝗿𝗼 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀? 🚀
Learning Excel, SQL and Power BI is only the beginning. To stand out as a Data Analyst, focus on practical experience, visibility and networking.
🔥 4 Ways to Level Up Your Data Analytics Career:
💡 Master the Skills → Build Projects → Create Your Portfolio → Get Noticed
🔗 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗚𝘂𝗶𝗱𝗲 👇
https://pdlink.in/4cIfLqn
🎯 Perfect for Students | Freshers | Data Analyst Aspirants | Career Switchers | 616 |
| 16 | 7 Real World AI Projects to Build in 2026
🤖 Build an AI Job Search Assistant
Searching for jobs is repetitive — JobFit AI reads your CV, searches live postings, and generates a ranked job-fit report automatically.
📖 Guide: Kimi K2.6 API Tutorial
🐙 GitHub: kingabzpro/JobFit-AI
🔬 Build a Multi-Agent Research Assistant
Most research workflows involve several steps — this multi-agent system handles web search, source filtering, and report writing all in one pipeline.
📖 Guide: Multi-Agent Research Assistant in Python
🐙 GitHub: Multi-Agent-Research-Assistant
📈 Automate Investment Research with Olostep and n8n
Investment research means checking news, financials, and public sources — this workflow automates the entire process and delivers AI-generated reports.
📖 Guide: How to Automate Investment Research Using Olostep and n8n
🐙 GitHub: kingabzpro/olostep-n8n-investment-agent
📊 Build an Agentic Market Research and Trend Analysis App
Manually collecting competitor updates and trend reports takes hours — this agentic pipeline handles research, extraction, and brief writing automatically.
📖 Guide: Agentic Market Research & Trend Analysis with Olostep
🐙 GitHub: kingabzpro/agentic-market-research-olostep
🧾 Build an AI Invoice Processing Pipeline
Invoice processing combines document understanding and structured extraction — this pipeline uses vision AI to pull useful fields and output clean structured data.
📖 Guide: Qwen 3.6 Plus API Tutorial
🐙 GitHub: BexTuychiev/qwen-invoice-pipeline-tutorial
📉 Build a Chart Digitizer with Claude Opus 4.7
Visual data trapped inside static charts and PDFs is now extractable — this tool reads chart images and saves the data points into a clean CSV or DataFrame.
📖 Guide: Building a Chart Digitizer
🏋️ Build an Exercise Trainer with Persistent Memory
Most AI agents forget everything after a session — this exercise trainer remembers your workout history and suggests personalized sessions every time you run it.
📖 Guide: Add Persistent Memory to AI Agents
❤️ Follow for more | 618 |
| 17 |  🚀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 📊🔥
𝗕𝘂𝗶𝗹𝗱 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 & Learn the tools companies actually use and prepare for high-growth Data Analyst opportunities.
💼 60+ Hiring Drives Every Month
🤝 500+ Hiring Partners
👨🏫 1-on-1 Expert Mentorship
📝 Resume & Interview Preparation
🚀 Dedicated Placement Assistance
🔗 𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗼𝘂𝗻𝘀𝗲𝗹𝗹𝗶𝗻𝗴👇:-
https://pdlink.in/45vk5ph
🎓 Perfect for Students | Freshers | Working Professionals | Career Switchers | 595 |
| 18 |  📊 𝟱 𝗕𝗲𝘀𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗠𝗦 𝗘𝘅𝗰𝗲𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘
Excel is one of the most valuable workplace skills — start learning for FREE today!
✅ Beginner Friendly
✅ Learn at Your Own Pace
✅ Improve Excel & Data Analysis Skills
✅ Useful for Jobs & Interviews
✅ Completely FREE Resources
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/3UkOmoa
🎓 Perfect for Students | Freshers | Data Analyst Aspirants | Working Professionals | 587 |
| 19 | These Prompts can help you get your next dream job!
1. Practice Interview Questions:
- "ChatGPT, please ask me some common behavioral interview questions."
- "Can you give me an example of a challenging interview question and provide feedback on my response?"
2. Mock Interviewer:
- "ChatGPT, act as an interviewer, and ask me questions for a marketing manager position."
- "Please evaluate my answers and provide suggestions for improvement."
3. Research Company and Role:
- "What can you tell me about [Company Name]'s recent achievements?"
- "ChatGPT, help me understand the responsibilities of a software engineer at [Company Name]."
4. Behavioral Questions:
- "ChatGPT, let's practice answering a situational interview question. Describe a time when you faced a difficult deadline."
- "Can you help me structure my response to a behavioral question about handling conflicts in the workplace?"
5. Industry Insights:
"What are the emerging trends in the e-commerce industry?"
- "ChatGPT, tell me about the challenges faced by the healthcare sector."
6. Resume Review:
- "Please review my resume and suggest improvements to highlight my project management skills."
- "What are some effective ways to showcase my achievements in a sales resume?"
7. Interview Etiquette:
- "ChatGPT, provide tips on professional body language during an interview."
"What should I wear for a video interview? Any specific recommendations?"
8. Questions to Ask:
- "ChatGPT, help me generate a list of thoughtful questions to ask the interviewer about the company culture."
- "What are some good questions to ask about career growth opportunities in an organization?"
9. Handling Difficult Questions:
- "How can I effectively address a question about a gap in my employment history?"
- "ChatGPT, guide me on responding to a question about a challenging project I worked on."
10. Post-Interview Reflection:
- "ChatGPT, provide feedback on my overall performance in the interview."
- "Let's discuss my strengths and weaknesses based on my interview experience." | 596 |
| 20 |  𝗙𝗥𝗘𝗘 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 𝗢𝗻 𝗟𝗮𝘁𝗲𝘀𝘁 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 😍
- AI
- Data Analytics
- Data Science
- CloudComputing
- Cyber Security
💫Build a Future Ready Career in the AI Era
💫Learn the Skills, Hiring Trends, and Preparation Strategies That Matter
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/45w4ztg
(Only few slots left )
Date & Time :- 18th August 2026 & 7PM | 602 |
