Artificial Intelligence & ChatGPT Prompts
Kanalga Telegram’da o‘tish
🔓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
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Artificial Intelligence & ChatGPT Prompts analitikasi
Artificial Intelligence & ChatGPT Prompts (@curiousprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 42 072 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 180-o'rinni va Hindiston mintaqasida 9 161-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 42 072 obunachiga ega bo‘ldi.
13 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -57 ga, so‘nggi 24 soatda esa -4 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 1.54% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 0.69% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 646 marta ko‘riladi; birinchi sutkada odatda 292 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 1 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent learning, algorithm, detection, llm, pattern kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“🔓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”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 14 Iyul, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
42 072
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Ma'lumot yuklanmoqda...
O'xshash kanallar
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Kanal postlari
✅ AI News of the Day: 13 July 2026
1️⃣ Google expands Gemini AI across Workspace
Google has introduced new Gemini-powered features for Gmail, Docs, Sheets, and Meet, helping users automate writing, summarize documents, analyze data, and improve meeting productivity.
2️⃣ NVIDIA continues its AI infrastructure growth
NVIDIA is strengthening its leadership in AI computing by expanding partnerships with cloud providers and enterprises to meet the growing demand for AI training and inference.
3️⃣ AI coding assistants gain wider enterprise adoption
More organizations are integrating AI coding assistants into their development workflows, enabling developers to generate code, debug applications, and speed up software delivery.
4️⃣ AI-powered search is reshaping the web
Technology companies continue to enhance AI-powered search experiences by providing conversational answers, summaries, and deeper reasoning capabilities instead of traditional search results.
5️⃣ Demand for AI talent keeps rising globally
Companies across industries are actively hiring professionals with skills in Generative AI, machine learning, prompt engineering, AI agents, and automation as AI adoption continues to grow.
💬 Tap ❤️ for more!
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| 3 | 🚀 AI Fundamentals for Beginners: Part 2
Before building AI Agents or RAG applications, you should understand how Large Language Models LLMs actually work.
Let's learn the core concepts.
🎯 1. What is a Large Language Model LLM?
✅ A Large Language Model LLM is an AI model trained on massive amounts of text to understand and generate human-like language.
Popular examples:
• GPT
• Claude
• Gemini
• Llama
• Mistral
• DeepSeek
LLMs can:
✅ Answer questions
✅ Write code
✅ Summarize documents
✅ Translate languages
✅ Generate content
🎯 2. What is a Prompt?
✅ A prompt is the instruction or input you provide to an AI model.
Example:
"What are the benefits of Python for Data Analysis?"
The quality of your prompt often determines the quality of the response.
🎯 3. What are Tokens?
✅ AI models don't read entire sentences at once.
Instead, they break text into smaller units called tokens.
Example:
Sentence: "I love Artificial Intelligence."
May be split into multiple tokens before processing.
More tokens = More processing cost and longer response time.
🎯 4. What is a Context Window?
✅ A context window is the maximum amount of information an LLM can process in a single conversation.
It includes:
• Your prompt
• Previous conversation
• Uploaded documents
• AI responses
A larger context window allows the model to remember and reason over more information.
🎯 5. What are Parameters?
✅ Parameters are the values learned by an AI model during training.
In general: More parameters → Greater learning capacity
However, performance also depends on training data, architecture, and optimization—not just parameter count.
🎯 6. What are Embeddings?
✅ Embeddings convert text into numerical vectors that capture its meaning.
This allows AI systems to compare semantic similarity instead of just matching keywords.
Embeddings are used for:
✅ Semantic Search
✅ Recommendation Systems
✅ Document Retrieval
✅ Similarity Search
🎯 7. What is a Vector Database?
✅ A vector database stores embeddings and enables fast similarity search.
Popular Vector Databases:
• Chroma
• Pinecone
• Weaviate
• FAISS
Without a vector database, efficient semantic search across large collections of documents becomes difficult.
🎯 8. How Does an AI Application Work?
Basic Flow:
User Question
⬇️
Prompt
⬇️
LLM
⬇️
Generated Response
When external knowledge is needed:
User Question
⬇️
Embedding
⬇️
Vector Database
⬇️
Relevant Information
⬇️
LLM
⬇️
Accurate Response
🎯 9. Why Are These Concepts Important?
Understanding these concepts helps you build:
✅ AI Chatbots
✅ AI Assistants
✅ Enterprise Search
✅ Document Q&A Systems
✅ AI Agents
💡 Key Takeaway
LLMs generate responses, embeddings help AI understand meaning, and vector databases make it possible to retrieve the right information quickly. Together, they form the foundation of modern AI applications.
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| 6 |  Most AI engineers never fully understood the maths behind what they build! 🤯🧮
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Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗
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| 8 | ✅ Today's AI News
1️⃣ OpenAI is pushing ahead with GPT-5.6
Recent coverage says OpenAI is preparing a broader GPT-5.6 rollout, with the model family getting new tiers and wider use across products.
2️⃣ Meta is racing on AI image and coding tools
Meta has been expanding its AI push with new image and video models, while also moving further into AI coding competition.
3️⃣ Governments are watching AI more closely
Regulators are focusing on model safety, overseas access, copyright, and how AI content is used in news and business.
4️⃣ AI safety is back in the spotlight
New reports continue to question whether major AI labs are moving fast enough on safety testing and governance.
5️⃣ India remains an important AI market
Indian coverage shows strong interest in AI hiring, policy, enterprise deployment, and the role of local operations from major AI firms.
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| 10 | Questions they may evaluate:
Did the tool return valid data?
Is another tool required?
Is the answer complete?
Should I retry?
Reflection improves reliability.
10. Final Response
After completing all required steps, the agent generates the final answer for the user.
🔄 Complete Workflow Example
User Goal: Find the latest AI news and summarize it.
Step 1: Understand the request.
Step 2: Plan → Search news → Read articles → Summarize → Highlight key trends
Step 3: Use web search tool.
Step 4: Collect results.
Step 5: Summarize findings.
Step 6: Return final response.
🧠 Why Planning is Important
Without planning: Question → Random answer
With planning: Question → Break into tasks → Execute tasks → Verify results → Final answer
Planning makes agents more accurate and capable.
🛠️ Common Tools Used by AI Agents
Web Search: Retrieve current information
Python: Data analysis and automation
SQL: Query databases
Browser: Navigate websites
Email: Send messages
Calendar: Schedule meetings
File System: Read and write files
APIs: Connect with external services
📚 Example: AI Data Analyst Agent
Goal: Analyze a sales CSV.
Workflow: Upload CSV → Read File → Clean Data → Analyze Trends → Generate Charts → Create Business Insights → Export Report
🤖 Example: AI Coding Agent
Workflow: User Request → Understand Problem → Generate Code → Run Tests → Fix Errors → Return Working Code
🌍 Example: AI Travel Agent
Workflow: Travel Request → Search Flights → Search Hotels → Compare Prices → Create Itinerary → Present Best Options
🚀 Key Takeaways
An AI agent is much more than a chatbot—it can plan, reason, use tools, and adapt.
The core architecture: User Input → Prompt Processing → LLM → Memory → Planning → Tool Selection → Action Execution → Observation → Reflection → Final Response.
Planning, memory, and tool usage are what make AI agents capable of solving real-world, multi-step problems.
Double Tap ❤️ For More | 425 |
| 11 | 🚀 AI Agents Architecture Explained
After understanding the basics of AI agents, the next step is learning how an AI agent works internally. Every AI agent, whether it's a customer support bot, coding assistant, or research assistant, follows a similar architecture.
🏗️ What is AI Agent Architecture?
AI Agent Architecture is the blueprint that defines how an agent receives a task, thinks, plans, uses tools, remembers information, and delivers results.
Think of it as the internal workflow that allows an AI agent to solve problems autonomously.
🔄 High-Level AI Agent Architecture
User
│
▼
User Request/Goal
│
▼
Prompt Processing
│
▼
Reasoning (LLM)
│
┌───────┴────────┐
▼ ▼
Memory Tool Selection
│ │
└───────┬────────┘
▼
Task Planning
▼
Action Execution
▼
Observe Results
▼
Reflection & Retry
▼
Final Response
🧩 Components of an AI Agent
1. User Input
The process starts when a user provides a goal.
Examples:
"Analyze this sales data."
"Book a hotel in Mumbai."
"Write a Python script."
The agent first understands what needs to be achieved, not just what was typed.
2. Prompt Processing
The system combines: User prompt, System instructions, Conversation history, Available tools, Memory
This creates the complete context for the LLM.
3. LLM (Reasoning Engine)
The LLM acts as the brain.
Responsibilities: Understand the request, Decide what to do, Select tools if required, Generate a plan, Interpret results
Without an LLM, an AI agent cannot reason effectively.
4. Memory
Memory allows the agent to retain useful information.
Short-Term Memory: Current conversation, Intermediate steps
Long-Term Memory: User preferences, Past interactions, Frequently used information
Example: If you always prefer Python over Java, the agent can remember that for future tasks.
5. Planning Module
Complex tasks are broken into smaller steps.
Example Goal: "Create a monthly sales report."
Plan:
1. Load data
2. Clean missing values
3. Calculate KPIs
4. Create charts
5. Generate summary
6. Export PDF
Planning improves efficiency and reduces errors.
6. Tool Selection
The agent decides whether external tools are needed.
Possible tools: Web search, SQL database, Python interpreter, Calculator, Email API, Calendar, Browser automation
Example: For "What's today's weather?", the agent chooses a weather API instead of guessing.
7. Action Execution
The selected tool performs the required action.
Examples: Execute SQL query, Run Python code, Search the web, Read a PDF, Send an email
8. Observation
After using a tool, the agent receives the result.
Example:
Tool: Weather API
Observation: Temperature = 30°C, Humidity = 72%
The observation becomes new input for the next reasoning step.
9. Reflection
Advanced agents verify their work. | 319 |
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| 15 | 10 Retro Nano Banana 3D Figurine Prompts
🔹 Prompt: Turn the image into a pop art-style 3D figurine, featuring bold colors, halftone dots, and comic-book speech bubbles around the character.
🔹 Prompt: Make a collectible figure inspired by 1950s diners, with a checkered floor base, red booth, and soda fountain props.
🔹 Prompt: Stylize the photo as a 1970s hippie figurine, with peace sign necklace, colorful headband, and a tie-dye shirt against a psychedelic abstract background.
🔹 Prompt: Reimagine the subject as a retro video game character in 16-bit pixel art style, with the character placed on a simulated arcade platform.
🔹 Prompt: Generate a vintage sci-fi astronaut figurine, featuring metallic suit details, ray-gun prop, and a rocket backdrop reminiscent of classic sci-fi movies.
🔹 Prompt: Produce a golden-age Bollywood collectible, complete with sari, retro hairstyle, and filmstrip base; add a vintage film poster in the background.
🔹 Prompt: Create a figurine styled after 1960s mod fashion—buttoned mini-dress, go-go boots, and psychedelic swirl base.
🔹 Prompt: Make a collectible in a retro comic superhero look, with bold primary colors, classic mask, and golden-age comic effects in the foreground.
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| 17 | Data Science Roadmap
|
|-- Core Foundations
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus Basics
| | |-- Probability
| | |-- Statistics
| |
| |-- Programming
| | |-- Python
| | | |-- NumPy
| | | |-- Pandas
| | | |-- Matplotlib
| | | |-- Seaborn
| | |-- R
| | |-- SQL
|
|-- Data Handling
| |-- Data Collection
| | |-- APIs
| | |-- Web Scraping
| | |-- Database Queries
| |
| |-- Data Cleaning
| | |-- Missing Values
| | |-- Outliers
| | |-- Feature Scaling
| | |-- Encoding
|
|-- Exploratory Data Analysis
| |-- Summary Statistics
| |-- Univariate Analysis
| |-- Bivariate Analysis
| |-- Visualizations
| |-- Correlation Checks
|
|-- Machine Learning
| |-- Supervised Learning
| | |-- Regression
| | |-- Classification
| |
| |-- Unsupervised Learning
| | |-- Clustering
| | |-- PCA
| |
| |-- Model Selection
| | |-- Train Test Split
| | |-- Cross Validation
| | |-- Hyperparameter Tuning
|
|-- Advanced Machine Learning
| |-- Ensemble Methods
| | |-- Random Forest
| | |-- XGBoost
| | |-- LightGBM
| |
| |-- Time Series
| | |-- ARIMA
| | |-- LSTM
| |
| |-- NLP
| | |-- Text Preprocessing
| | |-- TF IDF
| | |-- Word Embeddings
| |
| |-- Deep Learning
| | |-- Neural Networks
| | |-- CNN
| | |-- RNN
| | |-- Transformers
|
|-- Big Data
| |-- PySpark
| |-- Hadoop
| |-- Distributed Processing
|
|-- Model Deployment
| |-- Flask
| |-- FastAPI
| |-- Streamlit
| |-- Docker
| |-- Cloud Deployment
|
|-- MLOps
| |-- Experiment Tracking
| |-- Model Monitoring
| |-- CI CD
|
|-- Domain Knowledge
| |-- Finance
| |-- Healthcare
| |-- Retail
| |-- Marketing
|
|-- Ethics
| |-- Bias
| |-- Interpretability
| |-- Fairness
Free Resources to learn Data Science 👇👇
Python
• https://t.me/pythonproz
• https://www.learnpython.org/
• https://pythonprogramming.net
• https://pandas.pydata.org/docs/
Statistics
• https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
• https://www.khanacademy.org/math/statistics-probability
• https://statquest.org
Machine Learning
• https://whatsapp.com/channel/0029VawtYcJ1iUxcMQoEuP0O
• https://t.me/datasciencefree
• https://scikit-learn.org/stable/tutorial
• https://www.freecodecamp.org/learn/machine-learning-with-python
• https://course.fast.ai
Deep Learning
• https://www.deeplearning.ai
• https://playground.tensorflow.org
Data Visualization
• https://matplotlib.org/stable/tutorials
• https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
• https://seaborn.pydata.org/tutorial.html
SQL
• https://mode.com/sql-tutorial/introduction-to-sql
• https://t.me/mysqldata
Big Data
• https://spark.apache.org/docs/latest
• https://hadoop.apache.org
Deployment
• https://docs.streamlit.io
• https://fastapi.tiangolo.com
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