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 67 621 subscribers, ranking 1 859 in the Technologies & Applications category and 4 755 in the India region.
📊 Audience metrics and dynamics
Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 621 subscribers.
According to the latest data from 14 September, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 349 over the last 30 days and by 19 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 2.98%. Within the first 24 hours after publication, content typically collects 1.14% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 013 views. Within the first day, a publication typically gains 773 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 4.
- 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 15 September, 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.
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| Date | Subscriber Growth | Mentions | Channels | |
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| 01 September | +13 |
Channel Posts
💻 Programming A–Z: Essential Concepts Every Developer Should Know
🔹 A — Algorithm ➜ Step-by-step procedure to solve a problem
🔹 B — Bug ➜ An error or unexpected behavior in a program
🔹 C — Compiler ➜ Converts source code into machine-executable code
🔹 D — Data Structure ➜ Organizes and stores data efficiently
🔹 E — Exception Handling ➜ Manages runtime errors safely
🔹 F — Function ➜ Reusable block of code designed for a specific task
🔹 G — Git ➜ Tracks and manages changes in source code
🔹 H — HTTP ➜ Protocol used for communication between web clients and servers
🔹 I — IDE ➜ Development environment combining coding, debugging, and other tools
🔹 J — JSON ➜ Lightweight format commonly used for exchanging structured data
🔹 K — Keyword ➜ Reserved word with a special meaning in a programming language
🔹 L — Loop ➜ Repeats a block of code based on a condition or sequence
🔹 M — Module ➜ Reusable unit of code that can be imported into a program
🔹 N — Namespace ➜ Organizes identifiers and helps prevent naming conflicts
🔹 O — Object-Oriented Programming ➜ Programming approach based on objects and classes
🔹 P — API ➜ Allows different software systems to communicate
🔹 Q — Query ➜ Request for specific data or information from a system
🔹 R — Recursion ➜ A function calling itself to solve smaller versions of a problem
🔹 S — Syntax ➜ Rules that define how code must be written
🔹 T — Testing ➜ Process of verifying that software works as expected
🔹 U — Unit Testing ➜ Tests individual functions or components of code
🔹 V — Variable ➜ Named storage location for a value
🔹 W — While Loop ➜ Repeats code while a condition remains true
🔹 X — XML ➜ Markup language used to structure and exchange data
🔹 Y — YAML ➜ Human-readable format often used for configuration files
🔹 Z — Zero-based Indexing ➜ Indexing where the first element starts at position 0
🔥 Double Tap ❤️ For More
| 2 |  🎓 𝗧𝗼𝗽 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲 🔥
Explore these FREE certification courses in today’s most in-demand technology fields:
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💻 𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 :- https://pdlink.in/4gP18Eo
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🟧 𝗔𝗪𝗦 :- https://pdlink.in/4j4Jxtv
🛡️ 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗔𝘇𝘂𝗿𝗲 :- https://pdlink.in/4f0GNuH
⚡ Start learning today and prepare yourself for better career opportunities in 2026! | 251 |
| 3 |  🚀 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝘁𝗼 𝗚𝗲𝘁 𝗮 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯 𝗶𝗻 𝟮𝟬𝟮𝟲 📊
Build job-ready skills through live online classes, practical assignments and real-world projects.
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⚡Prepare for roles such as Data Analyst, Business Analyst, BI Analyst and Reporting Analyst. | 777 |
| 4 | AI Feels Hard Until You Watch These YouTube Videos 👇
1/ AI for Everyone:
https://www.youtube.com/watch?v=JPcx9qHzzgk
2/ Machine Learning for Everybody:
https://www.youtube.com/watch?v=i_LwzRVP7bg
3/ But What Is a Transformer?:
https://www.youtube.com/watch?v=wjZofJX0v4M
4/ Large Language Models Explained:
https://www.youtube.com/watch?v=5sLYAQS9sWQ
5/ Prompt Engineering:
https://www.youtube.com/watch?v=dOxUroR57xs
6/ RAG Explained:
https://www.youtube.com/watch?v=T-D1
7/ AI Agents Tutorial for Beginners:
https://www.youtube.com/watch?v=a8NA0WGI9OI | 1 126 |
| 5 |  🚀 𝗧𝗼𝗽 𝟯 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 🔥
💫 Artificial Intelligence (AI)
📊 Data Analytics
🔐 Cybersecurity
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
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🎯 Perfect for Students • Freshers • Beginners • Tech Enthusiasts
💡 Learn for FREE → Build Skills → Upgrade Your Career | 1 378 |
| 6 | HERE ARE 10 FREE AI AGENTS THAT COULD WORK 24x7 FOR YOU.
↳ AutoGPT (175K+ stars):
🔗 http://github.com/Significant-Gravitas/AutoGPT
The repo that started the entire AI agent movement. Build, deploy, and run autonomous AI agents.
↳ LangChain (137K+ stars):
🔗 http://github.com/langchain-ai/langchain
The most popular framework for building LLM-powered apps, chains, and agents.
↳ Dify (136K+ stars):
🔗 http://github.com/langgenius/dify
Production-ready platform to build, deploy, and manage AI agents and workflows visually.
↳ Langflow (146K+ stars):
🔗 http://github.com/langflow-ai/langflow
Drag and drop visual builder for AI agents and RAG pipelines. No heavy coding required.
↳ n8n (180K+ stars):
🔗 http://github.com/n8n-io/n8n
Open source workflow automation with native AI agent nodes and 400+ integrations.
↳ Open WebUI (138K+ stars):
🔗 http://github.com/open-webui/open-webui
Self-hosted ChatGPT-style interface with built-in agent and RAG capabilities.
↳ MetaGPT (46K+ stars):
🔗 http://github.com/geekan/MetaGPT
Multi-agent framework where agents take on roles like PM, architect, and engineer to build software together.
↳ CrewAI (30K+ stars):
🔗 http://github.com/crewAIInc/crewAI
Build teams of AI agents that collaborate on complex multi-step tasks with defined roles.
↳ AutoGen (40K+ stars):
🔗 http://github.com/microsoft/autogen
Microsoft's framework for building multi-agent conversational systems. Used in enterprise production.
↳ Mem0 (52K+ stars):
🔗 http://github.com/mem0ai/mem0
The memory layer for AI agents. Gives your agents persistent memory across sessions so they never start from scratch.
React ❤️ For More | 2 285 |
| 7 | HERE ARE 10 FREE AI AGENTS THAT COULD WORK 24x7 FOR YOU.
↳ AutoGPT (175K+ stars):
🔗 http://github.com/Significant-Gravitas/AutoGPT
The repo that started the entire AI agent movement. Build, deploy, and run autonomous AI agents.
↳ LangChain (137K+ stars):
🔗 http://github.com/langchain-ai/langchain
The most popular framework for building LLM-powered apps, chains, and agents.
↳ Dify (136K+ stars):
🔗 http://github.com/langgenius/dify
Production-ready platform to build, deploy, and manage AI agents and workflows visually.
↳ Langflow (146K+ stars):
🔗 http://github.com/langflow-ai/langflow
Drag and drop visual builder for AI agents and RAG pipelines. No heavy coding required.
↳ n8n (180K+ stars):
🔗 http://github.com/n8n-io/n8n
Open source workflow automation with native AI agent nodes and 400+ integrations.
↳ Open WebUI (138K+ stars):
🔗 http://github.com/open-webui/open-webui
Self-hosted ChatGPT-style interface with built-in agent and RAG capabilities.
↳ MetaGPT (46K+ stars):
🔗 http://github.com/geekan/MetaGPT
Multi-agent framework where agents take on roles like PM, architect, and engineer to build software together.
↳ CrewAI (30K+ stars):
🔗 http://github.com/crewAIInc/crewAI
Build teams of AI agents that collaborate on complex multi-step tasks with defined roles.
↳ AutoGen (40K+ stars):
🔗 http://github.com/microsoft/autogen
Microsoft's framework for building multi-agent conversational systems. Used in enterprise production.
↳ Mem0 (52K+ stars):
🔗 http://github.com/mem0ai/mem0
The memory layer for AI agents. Gives your agents persistent memory across sessions so they never start from scratch.
React ❤️ For More | 1 |
| 8 |  𝗧𝗼𝗽 𝟱 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗞𝗶𝗰𝗸𝘀𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗮𝗿𝗲𝗲𝗿 📊
Want to start a career in Data Science without spending money?
Here are 5 beginner-friendly learning resources covering essential skills such as Python, SQL, Machine Learning and hands-on projects.
🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/4ilAmok
🎯 Perfect for Students • Freshers • Beginners • Aspiring Data Scientists
💡 Learn → Practice → Build Projects → Create Your Portfolio | 1 906 |
| 9 |  𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 😍
💫Accelerate your career in Data Science
💫Discover the skills, tools and career roadmap needed to enter this high-demand field.
🔥 Beginner-friendly online session—no prior experience required!
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/46adC3l
(Only few slots left )
📅 Date: September 11, 2026
⏰ Time: 7:00 PM | 2 027 |
| 10 |  🚀 𝗧𝗼𝗽 𝗧𝗲𝗰𝗵 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗟𝗮𝗻𝗱 𝗛𝗶𝗴𝗵-𝗣𝗮𝘆𝗶𝗻𝗴 𝗝𝗼𝗯𝘀 𝗶𝗻 𝟮𝟬𝟮𝟲😍
💰 Highest Salary: ₹41 LPA
📈 Average Salary: ₹7.4 LPA
🎓 2,000+ Students Placed
🏢 500+ Hiring Partners
💻 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! | 1 837 |
| 11 |  🚀 𝗧𝗔𝗧𝗔 𝗚𝗿𝗼𝘂𝗽 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 😍
Tata Group/TCS virtual job simulations let you work through industry-style tasks and strengthen your resume.
🎓 3 FREE Virtual Programs:
📊 Data Visualisation
🔐 Cybersecurity
🌱 ESG (Environmental, Social & Governance)
💻 Virtual & flexible
🎓 Free Certificate on Completion
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🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-
https://pdlink.in/4yoXEOI
🔥 Perfect for Students • Freshers • Job Seekers | 1 653 |
| 12 | AI applications are still software.
Learn:
• Clean architecture
• Separation of concerns
• Testing
• Logging
• Configuration management
• Error handling
• Security
• Maintainability
A working prototype is not necessarily a production-ready application.
1️⃣2️⃣ AI EVALUATION 🧪
One of the biggest differences between traditional and AI applications is that outputs can vary.
Learn how to evaluate:
• Accuracy
• Relevance
• Consistency
• Groundedness
• Safety
• Latency
• Cost
Don't judge an AI system only because one example produced a good answer.
1️⃣3️⃣ AI SECURITY 🔐
AI introduces additional security considerations.
Understand:
• Prompt injection
• Sensitive data exposure
• Excessive tool permissions
• Insecure API handling
• Input validation
• Output validation
Never blindly trust model-generated instructions or allow an AI system unrestricted access to sensitive systems.
1️⃣4️⃣ TOOL CALLING & AGENTS 🛠️
Once you understand basic AI applications, learn how models can interact with tools.
For example:
AI → Search
AI → Database
AI → Calculator
AI → External API
Then explore agentic workflows.
But remember:
Not every problem needs an AI agent.
Simple systems are often easier to test, maintain, and secure.
1️⃣5️⃣ DEPLOYMENT & CLOUD ☁️
Eventually, your application needs to run somewhere other than your laptop.
Learn the basics of:
• Docker
• Cloud platforms
• Environment variables
• CI/CD
• Monitoring
• Logging
• Scaling
You don't need to become a cloud expert immediately.
Understand the fundamentals first.
1️⃣6️⃣ SYSTEM DESIGN 🏗️
As your AI applications become larger, you'll need to think about architecture.
For example:
User ↓ Frontend ↓ Backend ↓ AI Model ↓ Database / Vector Store ↓ External Tools
Think about:
• Scalability
• Reliability
• Latency
• Cost
• Security
• Failure handling
1️⃣7️⃣ PROBLEM-SOLVING
This remains one of the most valuable skills.
AI can generate ten possible solutions.
Your job is to determine which solution actually makes sense.
Learn to:
• Break problems into smaller parts
• Identify constraints
• Compare approaches
• Test assumptions
• Analyze trade-offs
• Learn from failures
1️⃣8️⃣ PRODUCT THINKING
The best AI engineers don't only ask:
"Can we build this?"
They also ask:
"Should we build this?"
Think about:
• Who will use it?
• What problem does it solve?
• How much value does it provide?
• What could go wrong?
• What will it cost?
• Is AI actually necessary?
Technology should serve the problem — not the other way around.
🔥 Double Tap ❤️ For More Useful Tips
-----
1.44 ₽ · /balance_help | 1 403 |
| 13 | 🤖💻 AI ENGINEERING SKILLS EVERY PROGRAMMER SHOULD LEARN 🚀
AI is changing programming.
But becoming an AI developer isn't just about learning how to call an AI API.
You need a combination of programming, AI, software engineering, data, and problem-solving skills.
Here are the skills worth building.
1️⃣ STRONG PROGRAMMING FUNDAMENTALS
Before going deep into AI, understand:
• Variables and data types
• Functions
• OOP
• Data structures
• Algorithms
• Error handling
• Debugging
• File handling
• Modules and packages
AI can generate code.
But you need programming knowledge to understand whether that code is actually good.
2️⃣ PYTHON 🐍
Python is one of the most important languages for AI and data work.
Learn:
• NumPy
• Pandas
• APIs
• JSON
• Data processing
• Virtual environments
• Package management
• Basic scripting
Don't just learn Python syntax.
Learn how to build useful applications with Python.
3️⃣ APIs & HTTP 🌐
Modern AI applications frequently communicate with external services.
Understand:
• GET
• POST
• PUT
• DELETE
• HTTP status codes
• Headers
• Authentication
• JSON
• REST APIs
Once you understand APIs, connecting applications to AI services becomes much easier.
4️⃣ MACHINE LEARNING BASICS 🧠
You don't need to become a machine-learning researcher immediately.
But understand the fundamentals:
• Training
• Validation
• Testing
• Features
• Labels
• Overfitting
• Underfitting
• Classification
• Regression
• Evaluation metrics
These concepts help you understand what's happening underneath many AI systems.
5️⃣ LLM FUNDAMENTALS
If you're building applications with language models, understand:
• Tokens
• Context windows
• Temperature
• System instructions
• Prompting
• Structured outputs
• Embeddings
• Model limitations
You don't need to memorize every model's specification.
Understand the concepts.
6️⃣ PROMPT ENGINEERING ✍️
Good prompting isn't simply writing long prompts.
Learn how to provide:
Clear instructions
Relevant context
Expected output format
Constraints
Examples when useful
The goal is to make model behavior more predictable.
7️⃣ RAG 🔎
Retrieval-Augmented Generation is an important pattern for applications that need to answer using external knowledge.
Understand:
📄 Document ingestion
✂️ Chunking
🔢 Embeddings
🗄️ Vector storage
🔎 Retrieval
🧠 Generation
RAG is especially useful when your application needs information that isn't contained in the model's general knowledge.
8️⃣ DATABASES 🗄️
AI applications still need traditional software infrastructure.
Learn:
• SQL
• Relational databases
• NoSQL basics
• Indexing
• Transactions
• Data modeling
And understand when to use a normal database versus a vector database.
9️⃣ GIT & VERSION CONTROL
AI-generated code doesn't eliminate the need for version control.
You should be comfortable with:
• Git
• Branches
• Commits
• Pull requests
• Merging
• Reverting changes
AI can help write code.
Git helps you control the codebase.
🔟 DEBUGGING 🐛
This skill becomes even more important when AI-generated code is involved.
Learn to:
• Read error messages
• Reproduce bugs
• Inspect variables
• Trace execution
• Identify root causes
• Test fixes
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