Coding Projects
Open in Telegram
Channel specialized for advanced concepts and projects to master: * Python programming * Web development * Java programming * Artificial Intelligence * Machine Learning Managed by: @love_data
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Network:📈 Analytical overview of Telegram channel Coding Projects
Channel Coding Projects (@programming_experts) in the English language segment is an active participant. Currently, the community unites 67 354 subscribers, ranking 1 898 in the Technologies & Applications category and 4 911 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 354 subscribers.
According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 453 over the last 30 days and by 17 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.78%. Within the first 24 hours after publication, content typically collects 1.13% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 873 views. Within the first day, a publication typically gains 762 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
- Thematic interests: Content is focused on key topics such as |--, algorithm, array, framework, javascript.
📝 Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
“Channel specialized for advanced concepts and projects to master:
* Python programming
* Web development
* Java programming
* Artificial Intelligence
* Machine Learning
Managed by: @love_data”
Thanks to the high frequency of updates (latest data received on 26 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
67 354
Subscribers
+1724 hours
+637 days
+45330 days
Data loading in progress...
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Channel Posts
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| 3 | AI can generate individual functions. But real applications require you to understand how everything fits together.
For example:
Frontend ↓ API ↓ Backend ↓ Database ↓ Authentication ↓ AI Model ↓ Monitoring
Understanding these components is a major developer skill.
1️⃣3️⃣ SECURITY CANNOT BE IGNORED
Never assume AI-generated code is secure.
Developers still need to understand:
🔐 Authentication
🔐 Authorization
🔐 Input validation
🔐 Secrets management
🔐 SQL injection
🔐 API security
🔐 Data privacy
1️⃣4️⃣ AI DOESN'T REPLACE PROBLEM-SOLVING
AI may provide five possible solutions.
You still need to decide:
👉 Which solution fits the requirement?
👉 Which is maintainable?
👉 Which is secure?
👉 Which performs better?
👉 What are the trade-offs?
That's engineering judgment.
1️⃣5️⃣ THE NEW PROGRAMMING WORKFLOW
Traditional:
Requirement
↓
Design
↓
Code
↓
Debug
↓
Test
↓
Deploy
AI-assisted:
Requirement
↓
Design
↓
Prompt AI
↓
Generate
↓
Review
↓
Test
↓
Debug
↓
Improve
↓
Deploy
AI changes the workflow—but humans still own the outcome.
🔥 Build these skills:
💻 Programming fundamentals
🧠 Problem-solving
🗂️ Data structures
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🐛 Debugging
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🏗️ System design
🤖 AI tools
Aim to become someone who can:
👉 Understand problems
👉 Design solutions
👉 Use AI effectively
👉 Verify the output
👉 Debug failures
👉 Make good engineering decisions
🚀 AI can generate code. Great programmers know what code should be generated, why it should work, and how to verify it.
🔥 Double Tap ❤️ For More Useful Tips
-----
1.38 ₽ · /balance_help | 1 188 |
| 4 | 🤖💻 HOW AI IS CHANGING PROGRAMMING — WHAT BEGINNERS SHOULD LEARN
AI can now generate code, explain errors, write tests, refactor functions, and help developers work faster.
But this doesn't mean programming is becoming unnecessary.
It means the skills programmers need are changing.
Here are the most important things to understand 👇
1️⃣ AI CODE GENERATION
AI tools can generate code from natural-language instructions.
Example: "Create a Python function that finds duplicate values in a list."
AI can produce the initial implementation.
👉 Your job is to understand, test, and improve the generated code.
2️⃣ CODE COMPLETION
AI can predict and suggest the next lines of code while you're programming.
This can reduce repetitive typing and help developers explore solutions faster.
3️⃣ CODE EXPLANATION
You can give an unfamiliar piece of code to an AI system and ask: "Explain this code line by line."
This is especially useful when learning new libraries or working with unfamiliar codebases.
4️⃣ DEBUGGING WITH AI
AI can help identify potential causes of errors.
A useful workflow:
Error
↓
Understand the error
↓
Ask AI for possible causes
↓
Test the suggestions
↓
Fix the root cause
5️⃣ AI-ASSISTED REFACTORING
Refactoring means improving the structure of existing code without changing its intended behavior.
AI can suggest: Simpler logic, Better variable names, Smaller functions, Reduced duplication, More readable code
6️⃣ AI-GENERATED TESTS
AI can help create unit tests for your functions.
For example: Function → Generate test cases → Run tests → Find bugs
But developers still need to verify whether the tests actually cover important scenarios.
7️⃣ NATURAL LANGUAGE → CODE
One of the biggest changes is that developers can describe what they want in plain language.
Example: "Create an API endpoint that accepts customer information and stores it in a database."
AI can help produce a starting implementation.
This makes understanding requirements and system design even more important.
8️⃣ PROMPTING FOR DEVELOPERS
Developers increasingly need to know how to communicate effectively with AI coding tools.
A good coding prompt can include:
👉 Programming language
👉 Goal
👉 Existing code
👉 Expected behavior
👉 Constraints
👉 Error message
👉 Desired output
More context usually gives the model a better chance of producing useful results.
9️⃣ CODE REVIEW STILL MATTERS
AI-generated code can contain:
❌ Bugs
❌ Security vulnerabilities
❌ Incorrect assumptions
❌ Poor performance
❌ Unnecessary complexity
That's why you need to review generated code rather than simply accepting it.
1️⃣0️⃣ UNDERSTANDING FUNDAMENTALS IS MORE IMPORTANT
If AI writes this: "for item in items:"
You should understand:
👉 What the loop does
👉 How iteration works
👉 What "item" represents
👉 How the data structure behaves
Otherwise, you won't know whether the generated code is correct.
1️⃣1️⃣ DEBUGGING BECOMES MORE IMPORTANT
When code can be generated quickly, writing code is no longer the only bottleneck.
Understanding why something fails becomes extremely valuable.
Learn: Debugging, Logging, Testing, Error handling, Reading stack traces, Performance analysis
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| 6 | 20 Frontend Project Ideas🔥👨🏻💻
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🔹Interactive To-Do List
🔹Personal Finance Tracker
🔹Movie/TV Show Finder
🔹Social Media Dashboard UI
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🔹Real-time Chat UI
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| 9 | It takes time to learn HTML, CSS, and JavaScript.
It takes time to master frontend frameworks like React or Vue.
It takes time to understand responsive design and cross-browser compatibility.
It takes time to debug tricky layout and functionality issues.
It takes time to build clean, maintainable code.
It takes time to work on real-world web projects and portfolios.
It takes time to optimize for performance and SEO.
It takes time to prepare for coding interviews and technical challenges.
Here’s one tip from someone who’s been there:
Be Patient. Great developers aren’t made overnight ☺️
Keep practicing and building your projects. Your time will come! | 1 539 |
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| 12 | 💻 How to Approach a Coding Problem
Whether you're solving a Python, SQL, Java, or DSA problem, don't immediately start writing code. First understand the problem and break it into smaller pieces.
📌 1. Understand the Problem
Read the problem carefully and identify:
What is the input?
What is the expected output?
What exactly are you being asked to calculate?
Are there any constraints?
Are there special cases?
👉 Don't start coding until you can explain the problem in your own words.
📌 2. Work Through an Example
Take a small example and solve it manually.
For example:
Find the largest number in.[4,8,2,10,6]
Manually:
Start → 4
Compare 8 → largest = 8
Compare 2 → largest = 8
Compare 10 → largest = 10
Compare 6 → largest = 10
Now the logic becomes much clearer.
📌 3. Identify the Pattern
Ask yourself:
Have I solved a similar problem before?
Look for common patterns:
Searching, Sorting, Counting, Hashing, Two pointers, Sliding window, Recursion, Dynamic programming, Greedy approach, Stack / Queue, JOIN / aggregation for SQL
Recognizing the pattern can dramatically reduce the time needed to solve the problem.
📌 4. Start With a Brute-Force Solution
Don't worry about optimization immediately.
First ask:
What is the simplest way I can solve this?
A working solution is better than an optimized solution that you cannot explain.
📌 5. Write the Logic in Plain English
Before coding, write something like:
1. Take the first number as the largest.
2. Compare it with every other number.
3. If a larger number is found, update largest.
4. Return largest.
Then convert those steps into code.
📌 6. Choose the Right Data Structure
Ask:
What data structure will make this problem easier?
Common choices:
List/Array → Ordered collection
Set → Unique values / fast membership
Dictionary/Hash Map → Key-value lookup / counting
Stack → Last-in-first-out problems
Queue → First-in-first-out problems
Heap → Min/max priority problems
Tree → Hierarchical data
Graph → Relationships/connections
Choosing the right data structure often makes the biggest difference.
📌 7. Consider Edge Cases
Don't test only the normal case.
Think about:
Empty input, One element, Duplicate values, Negative numbers, Very large input, Already sorted input, Missing values, All values being the same
📌 8. Analyze Time and Space Complexity
Once your solution works, ask:
How fast is it?
and
How much memory does it use?
For example:
O(1) → Constant
O(log n) → Very efficient
O(n) → Linear
O(n log n) → Common for efficient sorting
O(n²) → Can become slow for large inputs
You don't always need the most optimized solution, but you should understand the trade-off.
📌 9. Test Your Solution
Use multiple test cases:
Normal case, Edge case, Small input, Large input, Duplicate values, Empty input
Don't assume your first solution is correct.
📌 10. Optimize Only After It Works
Once you have a working solution, ask:
Can I reduce the time complexity?
Can I reduce memory usage?
Can I avoid unnecessary loops?
Can I use a better data structure?
This is where you move from a working solution to an efficient solution.
🧠 The 10-Step Coding Problem Framework
Understand → Example → Identify Pattern → Brute Force → Write Logic → Choose Data Structure → Handle Edge Cases → Code → Test → Optimize
A strong programmer understands the problem faster, breaks it down correctly, and then writes simpler code to solve it.
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-----
2.2 ₽ · /balance_help | 1 587 |
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(Only few slots left )
Date & Time :- 21st August 2026 & 7PM | 1 699 |
| 15 |  🎓 𝟰 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗮𝘁𝗶𝗼𝗻𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗜𝗻 𝟮𝟬𝟮𝟲 🚀
Want to build job-ready skills and strengthen your resume? Start learning these in-demand technologies for FREE! 🔥
📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 :- https://pdlink.in/4qn5q94
💫 𝗔𝗜 & 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 :- https://pdlink.in/4zrkYNg
☁️ 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 :- https://pdlink.in/4wzy6Ny
🛡️ 𝗖𝘆𝗯𝗲𝗿 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 :- https://pdlink.in/4xMJNl5
🔁 𝗦𝗵𝗮𝗿𝗲 this with your friends and classmates! | 1 691 |
| 16 |  📊 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 | 1 705 |
| 17 | ✅ Programming Languages, Libraries & Tools Every Tech Field Uses 👨💻🚀
🧠 DATA SCIENCE & MACHINE LEARNING
1. Python → Pandas, NumPy, TensorFlow, PyTorch
2. R → ggplot2, dplyr, caret
3. SQL → PostgreSQL, MySQL
4. Julia → Flux, Pluto
🤖 ARTIFICIAL INTELLIGENCE
1. Python → Keras, OpenCV, LangChain
2. C++ → OpenCV, CUDA
3. Java → Deeplearning4j
🌐 WEB DEVELOPMENT
1. JavaScript → React, Node.js, Express.js
2. TypeScript → Next.js, Angular
3. PHP → Laravel
4. Python → Django, Flask
📱 APP DEVELOPMENT
1. Kotlin → Android SDK, Jetpack Compose
2. Swift → SwiftUI, UIKit
3. Dart → Flutter
4. JavaScript → React Native
🎮 GAME DEVELOPMENT
1. C++ → Unreal Engine
2. C# → Unity
3. Lua → Roblox Studio
4. Python → Pygame
🔐 CYBER SECURITY
1. Python → Scapy, Requests
2. Bash → Linux Tools
3. PowerShell → Windows Automation
4. Go → Networking Tools
☁️ CLOUD & DEVOPS
1. Go → Docker, Kubernetes
2. Python → Ansible, Boto3
3. Shell Script → Linux Automation
4. YAML → CI/CD Pipelines
💬 Tap ❤️ if this helped you!
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2.15 ₽ · /balance_help | 1 804 |
| 18 |  🚀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 📊🔥
𝗕𝘂𝗶𝗹𝗱 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗦𝗸𝗶𝗹𝗹𝘀 & Learn the tools companies actually use and prepare for high-growth Data Analyst opportunities.
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🔗 𝗕𝗼𝗼𝗸 𝗮 𝗙𝗥𝗘𝗘 𝗖𝗮𝗿𝗲𝗲𝗿 𝗖𝗼𝘂𝗻𝘀𝗲𝗹𝗹𝗶𝗻𝗴👇:-
https://pdlink.in/45vk5ph
🎓 Perfect for Students | Freshers | Working Professionals | Career Switchers | 1 504 |
| 19 | import math
Used for data analysis, web dev, ML, automation, etc.
1️⃣8️⃣ Object-Oriented Programming (OOP)
Organize code around objects and classes.
Concepts: Class, Object, Encapsulation, Inheritance, Polymorphism, Abstraction
1️⃣9️⃣ Data Structures
How data is organized: Array, Linked List, Stack, Queue, Hash Map, Tree, Graph
2️⃣0️⃣ Algorithms
Step-by-step procedures: Searching, Sorting, Traversing, Recursion, DP, Greedy
2️⃣1️⃣ Time Complexity
How runtime grows with input: O(1), O(log n), O(n), O(n log n), O(n²)
2️⃣2️⃣ Space Complexity
How much extra memory an algorithm needs as input grows.
2️⃣3️⃣ Git & Version Control
Track changes: Repository, Commit, Branch, Merge, Pull, Push, Pull Request
2️⃣4️⃣ APIs
Systems talking to each other: Request, Response, Endpoint, HTTP methods, Status codes, JSON
2️⃣5️⃣ Database Basics
Store data: Tables, Rows & Columns, Primary/Foreign Keys, SQL, CRUD, JOINs, Indexes
💡 One important tip:
Don't just watch tutorials.
👉 Learn a concept → Write the code yourself → Break the code intentionally → Fix the errors → Solve small problems → Build small projects
That's how you turn coding knowledge into actual coding skills. 🚀
💬 Double Tap ❤️ For More
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2.19 ₽ · /balance_help | 1 407 |
| 20 | 📚 IMPORTANT CODING CONCEPTS FOR BEGINNERS 💻🔥
1️⃣ Variables
Variables are used to store data in a program.
Example:
name = "John"
age = 25
Here, name and age are variables.
👉 Think of a variable as a labeled box that stores a value.
2️⃣ Data Types
Data types define what kind of data you're working with.
Common types:
• Integer → 10
• Float → 10.5
• String → "Hello"
• Boolean → True / False
• List/Array → [10, 20, 30]
Understanding data types is essential because different types support different operations.
3️⃣ Operators
Operators allow you to perform operations on data.
Examples:
• → Addition
• → Subtraction
** → Multiplication
/ → Division
== → Equal to
→ Greater than
< → Less than
&& → Logical AND
4️⃣ Input & Output
Programs need to receive information and provide results.
Input → Data given to the program.
Output → Result produced by the program.
Example:
name = input("Enter your name: ")
print(name)
5️⃣ Conditional Statements
Conditions allow your program to make decisions.
Example:
if age >= 18:
print("Adult")
else:
print("Minor")
👉 Conditions are the foundation of decision-making in programming.
6️⃣ Loops
Loops allow you to execute code repeatedly.
Common loops: for, while
Example:
for i in range(5):
print(i)
Instead of writing the same code five times, a loop handles it automatically.
7️⃣ Functions
A function is a reusable block of code designed to perform a specific task.
Example:
def add(a, b):
return a + b
Now you can call: add(10, 20)
👉 Functions make code reusable, organized, and easier to maintain.
8️⃣ Parameters & Arguments
Parameters are variables defined by a function.
Arguments are the actual values passed to the function.
Example:
def greet(name): ← name is a parameter
greet("John") ← "John" is an argument
9️⃣ Lists / Arrays
Lists or arrays allow you to store multiple values together.
Example: numbers = [10, 20, 30, 40]
You can access individual elements using an index. numbers[0] → 10
🔟 Strings
Strings represent text. name = "Akshay"
You should learn how to: concatenate, find characters, slice, change case, search, format text.
1️⃣1️⃣ Dictionaries / Hash Maps
Store data as key-value pairs.
student = {
"name": "John",
"age": 25
}
Access data quickly using its key.
1️⃣2️⃣ Sets
A set stores unique values. {1, 2, 2, 3} → {1, 2, 3}
Useful for removing duplicates, union, intersection.
1️⃣3️⃣ Scope
Scope determines where a variable can be accessed.
A variable created inside a function may not be accessible outside.
1️⃣4️⃣ Recursion
A function that calls itself. Needs a base case + recursive case.
Used a lot with trees, graphs, and algorithms.
1️⃣5️⃣ Exception Handling
Handle errors gracefully.
Python example:
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero")
1️⃣6️⃣ Debugging
Finding and fixing problems. Learn to read error messages, use breakpoints, print variables, test small sections.
👉 Good programmers are good at finding and fixing mistakes.
1️⃣7️⃣ Modules & Libraries
Don't build everything from scratch. | 1 171 |
