Artificial Intelligence & ChatGPT Prompts
前往频道在 Telegram
🔓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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网络:📈 Telegram 频道 Artificial Intelligence & ChatGPT Prompts 的分析概览
频道 Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 42 213 名订阅者，在 技术与应用 类别中位列第 3 100，并在 印度 地区排名第 8 984 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 42 213 名订阅者。
根据 05 十月, 2026 的最新数据，频道保持稳定运转。过去 30 天订阅人数变化为 -67，过去 24 小时变化为 -4，整体触达仍然可观。
- 认证状态： 未认证
- 互动率 (ER)： 平均受众互动率为 1.68%。内容发布后 24 小时内通常能获得 0.67% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 711 次浏览，首日通常累积 281 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 2。
- 主题关注点： 内容集中在 learning, algorithm, detection, llm, pattern 等核心主题上。
📝 描述与内容策略
作者将该频道定位为表达主观观点的平台：
“🔓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”
凭借高频更新（最新数据采集于 06 十月, 2026），频道始终保持新鲜度与高覆盖。分析显示受众积极互动，使其成为 技术与应用 类别中的关键影响点。
42 213
订阅者
-424 小时
无数据7 天
-6730 天
数据加载中...
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 06 十月 | 0 | |||
| 05 十月 | +3 | |||
| 04 十月 | +9 | |||
| 03 十月 | +2 | |||
| 02 十月 | +4 | |||
| 01 十月 | +1 |
频道帖子
| 2 |  𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 𝗶𝗻 𝟮𝟬𝟮𝟲🚀
Explore 6 free resources covering AI fundamentals, tools, deep learning, research and real-world applications.
✅ 100% Free Learning
✅ Beginner-Friendly
✅ AI • ML • Deep Learning
✅ Real-World Applications
🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇
https://pdlink.in/4AFHq5R
📢 Share this valuable opportunity with your friends and classmates! | 964 |
| 3 | 🧠 SQL Basics Cheatsheet 📊🛠️
1. What is SQL?
SQL (Structured Query Language) is used to store, retrieve, update, and delete data in relational databases.
2. Common SQL Commands:
- SELECT – Retrieves data
- INSERT INTO – Adds new data
- UPDATE – Modifies existing data
- DELETE – Removes data
- WHERE – Filters records
- ORDER BY – Sorts results
- GROUP BY – Aggregates data
- JOIN – Combines data from multiple tables
3. Data Types (Examples):
- INT, FLOAT, VARCHAR(n), DATE, BOOLEAN
4. Clauses to Know:
- WHERE – Filters rows
- LIKE, BETWEEN, IN, IS NULL – Conditional filters
- DISTINCT – Removes duplicates
- LIMIT – Restricts row count
- AS – Rename columns
5. SQL JOINS (Very Important):
- INNER JOIN – Matching rows in both tables
- LEFT JOIN – All from left + matches from right
- RIGHT JOIN – All from right + matches from left
- FULL OUTER JOIN – All rows from both tables
6. Aggregate Functions:
- COUNT(), SUM(), AVG(), MIN(), MAX()
7. Example Query:
SELECT name, AVG(score)
FROM students
WHERE grade = 'A'
GROUP BY name
ORDER BY AVG(score) DESC;
8. Constraints:
- PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE, CHECK
9. Indexing & Optimization:
- Use INDEX to speed up queries
- Avoid SELECT * in production
- Use EXPLAIN to analyze query plans
10. Popular SQL Databases:
- MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Oracle
Double Tap ♥️ For More | 898 |
| 4 |  🎓 𝗛𝗔𝗥𝗩𝗔𝗥𝗗 𝗨𝗡𝗜𝗩𝗘𝗥𝗦𝗜𝗧𝗬 𝗙𝗥𝗘𝗘 𝗢𝗡𝗟𝗜𝗡𝗘 𝗖𝗢𝗨𝗥𝗦𝗘𝗦 😍
Dreaming of learning from one of the world’s most prestigious universities? Explore Harvard’s online courses and build valuable, career-ready skills from home!
💡 Beginner-friendly options
⏰ Learn at your own pace
🌍 Accessible online worldwide
🎯 Ideal for students, freshers and working professionals
🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇
https://pdlink.in/4xPUdzU
📢 Share this valuable opportunity with your friends and classmates! | 682 |
| 5 | ✅ Top Tech Career Paths to Explore in 2026 💻🚀
1. Software Developer
Builds websites, apps, and systems. Needs skills in JavaScript, Python, Java, or C#. Frontend, backend, or full-stack.
2. Cloud Engineer
Works with AWS, Azure, or GCP to manage scalable cloud infrastructure, services, and deployments.
3. DevOps Engineer
Bridges development and operations. Manages CI/CD, automation, monitoring, and infrastructure as code (e.g., Docker, Kubernetes).
4. Cybersecurity Analyst
Protects systems from digital threats. Works on firewalls, threat detection, penetration testing, and data protection.
5. Data Analyst
Turns raw data into insights using SQL, Excel, Python, Tableau, or Power BI. Often a gateway to data science.
6. Blockchain Developer
Builds decentralized apps and smart contracts using Solidity, Ethereum, or other Web3 platforms.
7. AI/ML Engineer
Creates models that learn from data. Requires strong math, Python, ML frameworks (TensorFlow, PyTorch), and real-world deployment skills.
8. UI/UX Designer
Designs seamless user interfaces and experiences. Tools: Figma, Adobe XD, Webflow. Focuses on usability and accessibility.
9. Mobile App Developer
Specializes in Android (Kotlin/Java) or iOS (Swift), or cross-platform tools like Flutter or React Native.
10. Tech Product Manager
Drives product vision, user needs, and team coordination. Requires a mix of tech knowledge, strategy, and communication.
💬 Double Tap ❤️ For More! | 719 |
| 6 |  𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 𝘄𝗶𝘁𝗵 𝗧𝗵𝗲𝘀𝗲 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗖𝗼𝘂𝗿𝘀𝗲𝘀!
Looking to learn practical, in-demand skills? These courses cover Generative AI, Cybersecurity, AI tools and Digital Marketing.
💫 Learn at your own pace
⚡Build career-relevant skills
🔥Practical learning opportunities
𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 :-
https://pdlink.in/4z3vOYU
Save this post and share with your friends | 646 |
| 7 | 🚀 Top 11 SQL Project Ideas to Build a Strong Data Analytics Portfolio
Building projects is one of the fastest ways to improve your SQL skills and stand out in interviews. Here are 11 real-world project ideas:
1️⃣ E-Commerce Sales Analysis
Analyze sales trends
Top-selling products
Customer segmentation
Revenue by category
Repeat customer analysis
2️⃣ Banking Transaction Analysis
Detect fraudulent transactions
Monthly account activity
Customer spending patterns
Balance trends
High-value transactions
3️⃣ Food Delivery Analytics
Delivery time analysis
Restaurant performance
Peak ordering hours
Customer retention
Delivery partner efficiency
4️⃣ HR Analytics Dashboard
Employee attrition
Salary analysis
Department-wise performance
Hiring trends
Attendance insights
5️⃣ Hospital Management Analysis
Patient admissions
Doctor utilization
Readmission rate
Bed occupancy
Treatment costs
6️⃣ Netflix Movie & TV Show Analysis
Most popular genres
Content by country
Ratings analysis
Release trends
Duration analysis
7️⃣ IPL Cricket Data Analysis
Top batsmen
Best bowlers
Team performance
Venue analysis
Winning trends
8️⃣ Retail Inventory Management
Stock availability
Inventory turnover
Slow-moving products
Supplier performance
Stock-out analysis
9️⃣ Ride-Sharing Analytics
Peak ride hours
Driver earnings
Customer retention
Trip cancellation rate
City-wise demand
🔟 Finance & Expense Tracker
Monthly expenses
Budget vs actual
Savings analysis
Category-wise spending
Cash flow trends
1️⃣1️⃣ Social Media Analytics
User engagement
Daily Active Users DAU
Monthly Active Users MAU
Content performance
User retention
🔥 Double Tap ❤️ For More | 579 |
| 8 |  🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀 𝗶𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗔𝗜! 📊
Explore these 4 Google learning programs and develop practical, career-relevant skills.
🎓 Explore the programs:
1️⃣ Google Data Analytics Professional Certificate
2️⃣ Google Business Intelligence Professional Certificate
3️⃣ Google AI Essentials
4️⃣ Google Advanced Data Analytics Professional Certificate
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/4htgIEW
📌 Save this post and share it with someone interested in Data Analytics or AI! | 554 |
| 9 | How to use ChatGPT to turn learning into a daily habit 📚🤖
Prompt:
I want you to act as my personal learning accountability coach.
I want to build a consistent habit of learning [SKILL/TOPIC].
My available time each day is [X MINUTES/HOURS].
My goal is [SPECIFIC GOAL].
Help me by:
• Creating a realistic daily learning routine
• Breaking each session into learning, practice, and revision
• Giving me one clear task to complete each day
• Keeping the workload small enough to stay consistent
• Testing me regularly on what I've learned
• Revisiting topics I struggle to remember
• Tracking my progress and identifying patterns
• Helping me recover quickly when I miss a day
• Gradually increasing the difficulty as my consistency improves
Don't overwhelm me with a complicated schedule. Focus on making learning simple, consistent, and sustainable.
Start by creating my Day 1 learning task.
Double Tap ❤️ For More Useful Prompts ❤️ | 671 |
| 10 |  🤖 New Powerful AI Model: GigaChat 3.5 Reasoning
This open-source LLM actually thinks before it answers! Perfect for complex coding, math, and reasoning prompts.
✅ Built on GigaChat 3.5 Ultra: explores multiple step-by-step reasoning paths
✅ Automated verification reinforces correct answers, enabling self-correction
✅ Autonomously decides when to call external tools or revise earlier steps
✅ Highly efficient: Linear attention uses 37% fewer tokens than DeepSeek V4 Flash Preview
📈 Massive benchmark gains over non-reasoning versions:
• IFBench: 44 → 77
• Natural Plan: 64 → 80
• LiveCodeBench v6: 56 → 85
🔗 Open-sourced under MIT license. Weights on Hugging Face: fp8 | bf16 | 675 |
| 11 |  🚀 𝐁𝐞𝐜𝐨𝐦𝐞 𝐚𝐧 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐢𝐧 𝟐𝟎𝟐𝟔
🎯 Choose Your Learning Track:
💻 Java Full Stack + AI Engineering
🌐 MERN Full Stack + AI Engineering
Placement Highlights: ₹41 LPA highest package | ₹7.4 LPA average package | 2,000+ students placed | 500+ hiring partners
🔗 𝗕𝗼𝗼𝗸 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 :- https://pdlink.in/4fWJVID
⚡ AI is creating new career opportunities—start building the skills companies need in 2026! | 745 |
| 12 | Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y | 780 |
| 13 |  🚀 𝗧𝗼𝗽 𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊
Want to start a career in Data Analytics?
Explore these 7 free Microsoft-backed learning resources covering Power BI, Excel, SQL and data fundamentals
🔗 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇
https://pdlink.in/3Tm2D3Z
💡 Ideal for students, freshers and professionals who want to build practical data skills. | 799 |
| 14 |  🎓 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀! 🚀
Explore free online learning opportunities from Stanford University across technology, business and more!
💻 Tech & Programming
🤖 Artificial Intelligence & Data Science
💼 Business & Entrepreneurship
💡 Leadership & Innovation
🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇
https://pdlink.in/4hlnZGw
🎯 Great for students, freshers and working professionals looking to expand their knowledge. | 826 |
| 15 |  𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀
Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles.
📅 Date: 24 September 2026
⏰ Time: 7:00 PM–9:00 PM IST
🌐 Mode: Online
🎓 Certificate: Available to all attendees
Eligibility :- Graduates Passing In 2025 or earlier
🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇
https://pdlink.in/4xAMeGW
⚡ Register now and take your first step towards a successful career in AI! | 862 |
| 16 | Everything about Supervised Learning ✅
It’s a type of machine learning where the model learns from labeled data.
Labeled data means each input has a known correct output.
Think of it like a teacher giving you questions with answers, and you learn the pattern.
Example Dataset:
| Hours Studied | Passed Exam |
| ------------- | ----------- |
| 1 | No |
| 2 | No |
| 3 | Yes |
| 4 | Yes |
The model tries to learn the relation between “Hours Studied” and “Passed Exam.”
How It Works (Step-by-Step):
1. You collect labeled data (input features + correct output)
2. Split the data into training (80%) and testing (20%)
3. Choose a model (e.g., Linear Regression, Decision Tree, SVM)
4. Train the model to learn patterns
5. Evaluate performance using metrics like accuracy or MSE
Real-World Examples:
⦁ Spam Detection
Input: Email content
Output: Spam or Not Spam
⦁ House Price Prediction
Input: Size, location, rooms
Output: Price
⦁ Loan Approval
Input: Salary, credit score, job type
Output: Approve / Reject
⦁ Image Classification (e.g., identifying cats in photos)
Input: Pixel data
Output: Object category
⦁ Fraud Detection
Input: Transaction details
Output: Fraudulent or Legitimate
Python Code (Simple Classification):
from sklearn.tree import DecisionTreeClassifier
X = [,,,]
y = ['No', 'No', 'Yes', 'Yes']
model = DecisionTreeClassifier()
model.fit(X, y)
print(model.predict([[2.5]])) # Output: 'Yes'
Summary:
⦁ Input + Output = Supervised
⦁ Goal: Learn mapping from X → Y
⦁ Used in most real-world ML systems
Double Tap ♥️ For More | 862 |
| 17 |  🚀 𝗧𝗼𝗽 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲
Explore these certification courses in today’s most in-demand technology fields:
💻 Full Stack :- https://pdlink.in/3SuUeuD
📊 Data Analytics :- https://pdlink.in/45vk5ph
💫AI Engineering :- https://pdlink.in/4fWJVID
🔥 Take the first step towards your high-paying tech career in 2026! | 703 |
| 18 | 👑 8 Powerful ChatGPT Prompts to Level Up Your Leadership Skills 🚀🧑💼
1️⃣ Develop Emotional Intelligence
✅ Prompt: “Coach me on improving emotional intelligence to better manage my team.”
2️⃣ Effective Delegation Guide
✅ Prompt: “Help me create a plan to delegate tasks efficiently without losing control.”
3️⃣ Conflict Resolution Strategies
✅ Prompt: “Give me practical ways to handle and resolve team conflicts positively.”
4️⃣ Motivate a Demotivated Team
✅ Prompt: “Suggest techniques to boost motivation and engagement in my team.”
5️⃣ Lead Remote Teams Successfully
✅ Prompt: “Share best practices to lead and communicate effectively with a remote team.”
6️⃣ Conduct Impactful One-on-Ones
✅ Prompt: “Help me prepare meaningful questions and agenda for my team’s one-on-one meetings.”
7️⃣ Build a Culture of Accountability
✅ Prompt: “Advise on how to create a workplace culture that encourages responsibility.”
8️⃣ Lead Through Change
✅ Prompt: “Coach me on leading my team effectively during organizational change or uncertainty.”
💬 Tap ❤️ for more! | 794 |
| 19 |  🎓 𝐅𝐑𝐄𝐄 𝐈𝐁𝐌 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀
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🔥 Don’t just collect certificates—build skills that employers value. Share this with your friends! | 752 |
| 20 | 1️⃣2️⃣ USE DIFFERENT MODELS FOR DIFFERENT JOBS
A real application doesn't need one model for everything. You might use:
• Small model → Classification
• Embedding model → Semantic search
• Vision model → Image analysis
• More capable model → Complex reasoning
• Speech model → Transcription
1️⃣3️⃣ CREATE A MODEL SELECTION CHECKLIST
Before choosing, ask:
• ☑️ What task am I solving?
• ☑️ What quality level do I need?
• ☑️ How much context is required?
• ☑️ What latency is acceptable?
• ☑️ What will it cost?
• ☑️ Does it support the required inputs?
• ☑️ Does it support structured outputs or tools if needed?
• ☑️ What privacy and security requirements apply?
• ☑️ How does it perform on my own test cases?
1️⃣4️⃣ REMEMBER THE MOST IMPORTANT RULE
The best AI model isn't necessarily the most powerful model. It's the model that provides the required quality at an acceptable cost, speed, reliability, and risk level.
🔥 DON'T CHOOSE AI MODELS BY HYPE.
Understand the task, define your requirements, test multiple options, measure results, then decide based on evidence.
💡 Good AI engineering isn't about using the biggest model. It's about using the right model for the right problem.
Double Tap ❤️ For More
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