Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Artificial Intelligence
Kanalga Telegram’da o‘tish
AI will not replace you but person using AI will🚀 I make Artificial Intelligence easy for everyone so you can start with minimum effort. 🚀Artificial Intelligence 🚀Machine Learning 🚀Deep Learning 🚀Data Science 🚀Python + R 🚀AR and VR Dm @Aiindian
Ko'proq ko'rsatish📈 Telegram kanali Artificial Intelligence analitikasi
Artificial Intelligence (@artificial_intelligence_in) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 65 277 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 1 985-o'rinni va Hindiston mintaqasida 5 104-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 65 277 obunachiga ega bo‘ldi.
03 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 189 ga, so‘nggi 24 soatda esa -6 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 10.86% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 7 093 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 26 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent llm, learning, bubble, context, engineering kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“AI will not replace you but person using AI will🚀
I make Artificial Intelligence easy for everyone so you can start with minimum effort.
🚀Artificial Intelligence
🚀Machine Learning
🚀Deep Learning
🚀Data Science
🚀Python + R
🚀AR and VR
Dm @Aiind...”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 04 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.
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Kanal postlari
Want to become an AI Engineer in 2026?
Don't start with LangChain.
Don't start with Pinecone.
Don't even start with AI Agents.
Start by understanding this roadmap. 👇
Most people jump straight into building AI apps.
They copy tutorials.
They connect APIs.
They launch chatbots.
But when something breaks...
They have no idea why.
Because they skipped the fundamentals.
Here's the AI ecosystem every aspiring AI Engineer should understand.
🧠 1. LLMs – The Brain
These power reasoning, coding, conversations, and content generation.
Examples:
• GPT
• Claude
• Gemini
• Llama 4
• Qwen 3
• DeepSeek
• Mistral
• Gemma 3
• Phi-4
👉 Learn what each model is good at and when to use it.
⚡ 2. Frameworks – The Orchestrator
Frameworks connect LLMs with tools, APIs, memory, and workflows.
Popular choices:
• LangChain
• LlamaIndex
• Haystack
• txtai
👉 These help you build production-ready AI applications.
📚 3. Vector Databases – AI Memory
LLMs don't remember your documents.
Vector databases do.
Popular options:
• Pinecone
• Chroma
• Qdrant
• Weaviate
• Milvus
• PostgreSQL (pgvector)
• Cassandra
• OpenSearch
👉 Essential for Retrieval-Augmented Generation (RAG).
📄 4. Data Extraction – Feed Your AI
Before AI can answer questions...
It needs clean, structured data.
Tools include:
• Crawl4AI
• FireCrawl
• ScrapeGraphAI
• MegaParser
• Docling
• LlamaParse
• ExtractThinker
👉 Great AI starts with great data.
🚀 5. Open LLM Access
Experiment, self-host, and deploy open-source models with:
• Hugging Face
• Ollama
• Groq
• Together AI
👉 Perfect for local development and production deployments.
🔍 6. Text Embeddings – The Search Engine
Embeddings convert text into vectors that AI can understand and retrieve.
Popular providers:
• OpenAI
• Voyage AI
• Google
• Cohere
• Nomic
• SBERT
👉 The quality of your embeddings directly impacts your RAG system.
📊 7. Evaluation – The Most Overlooked Layer
A good AI app isn't the one that looks smart.
It's the one that's measurably reliable.
Evaluate:
✅ Accuracy
✅ Hallucinations
✅ Retrieval quality
✅ Response consistency
Tools like Giskard and DeepEval help you build AI you can trust.
If I were starting from scratch today, I'd learn in this order:
1️⃣ LLM Fundamentals
2️⃣ Prompt Engineering
3️⃣ Embeddings
4️⃣ Vector Databases
5️⃣ RAG
6️⃣ AI Frameworks
7️⃣ AI Agents
8️⃣ Evaluation
Master these, and you'll understand how modern AI systems are actually built.
Not just how to copy them.
❤️ Save this roadmap.
🔁 Share this so someone preparing for an AI job in 2026 doesn't waste months learning the wrong things.
One share could help a student, developer or job seeker understand the AI stack that companies are actually hiring for.
Follow us: https://t.me/Artificial_intelligence_in
| 2 |  AI-powered robot that identifies weeds using computer vision & eliminates them with lasers, reducing the need for harmful pesticides.
This startup has developed an AI-powered robot that roams through farms, uses computer vision to identify unwanted weeds & then eliminates them with pinpoint laser precision without spraying harmful pesticides across entire fields.
Think about the complexity behind this:
✅ Real-time Computer Vision
✅ Object Detection in Uncontrolled Environments
✅ Edge AI Processing
✅ Robotics & Autonomous Navigation
✅ Millions of Decisions Made Directly in the Field
This is not AI generating text.
This is AI perceiving the world, making decisions and taking action in the physical environment.
As AI developers, it's easy to get caught up in the latest LLMs, agents and prompt engineering trends. But some of the most transformative AI innovations are happening where software meets hardware. | 7 855 |
| 3 | MIT made its entire AI & ML library 100% FREE to access.
These 12 books are the best place to start 👇
↳ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
1. Foundations of Machine Learning
https://cs.nyu.edu/~mohri/mlbook/
The mathematical backbone of ML - algorithms, theory, and how models actually learn.
2. Understanding Deep Learning
https://udlbook.github.io/udlbook/
Neural networks explained visually and intuitively, from basics to modern architectures.
3. Deep Learning
https://www.deeplearningbook.org/
The definitive deep learning reference, written by the researchers who shaped the field.
4. Introduction to Machine Learning Systems
https://mlsysbook.ai/
How to design and build ML systems that work in production, not just in notebooks.
5. Algorithms for Optimization
https://algorithmsbook.com/optimization/
The math behind how models improve - gradient methods, search, and decision-making.
↳ 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
6. Reinforcement Learning: An Introduction
http://incompleteideas.net/book/the-book.html
The classic RL textbook - how agents learn to make decisions through trial and reward.
7. Distributional Reinforcement Learning
https://www.distributional-rl.org/
Goes beyond average rewards to model the full distribution of outcomes.
8. Multi-Agent Reinforcement Learning
https://www.marl-book.com/
How multiple AI agents learn, compete, and cooperate in shared environments.
↳ 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗠𝗟
9. Probabilistic Machine Learning: An Introduction
https://probml.github.io/pml-book/book1.html
ML through the lens of probability - uncertainty, inference, and Bayesian thinking.
10. Probabilistic Machine Learning: Advanced Topics
https://probml.github.io/pml-book/book2.html
Deep dives into probabilistic models, approximate inference, and generative methods.
↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 & 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
11. Agents in the Long Game of AI
https://direct.mit.edu/books/oa-monograph/5779/Agents-in-the-Long-Game-of-AIComputational
How to build AI agents that are trustworthy, hybrid, and designed for long-term reliability.
12. Fairness and Machine Learning
https://fairmlbook.org/
Where ML meets society - bias, discrimination, and how to build more equitable systems.
--
If you're serious about AI/ML, these books are a great starting point to build a solid foundation.
Save this and share with your network to help others learn.
Join Artificial Intelligence Community:
https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q | 7 661 |
| 4 | If I were starting AI again in 2026, I would focus on RAG first
Today companies are hiring engineers who can build complete AI systems.
If you really want your AI portfolio to stand out, stop building basic chatbots and start building RAG applications.
Because Retrieval-Augmented Generation (RAG) is becoming the backbone of:
→ Enterprise AI systems
→ AI copilots
→ Research assistants
→ AI agents
→ Knowledge management platforms
→ Internal company GPTs
Here are 10 powerful RAG projects that can seriously level up your portfolio:
1. Document Analysis with LLMs
→ Extract text directly from PDFs using Python
→ Build summarization and question-answering workflows
→ Learn preprocessing, chunking, and structured extraction
→ https://amanxai.com/2024/10/21/document-analysis-using-llms-with-python/
2. Build Your First RAG System
→ Learn embeddings, chunking, and vector retrieval from scratch
→ Understand how retrieval improves LLM responses
→ Great starting point before using frameworks
→ https://amanxai.com/2025/10/21/build-your-first-rag-system-from-scratch/
3. IBM Guided RAG Project
→ Follow production-style RAG architecture patterns
→ Learn LangChain workflows with enterprise practices
→ Covers retrieval pipelines and response grounding
→ https://www.coursera.org/learn/project-generative-ai-applications-with-rag-and-langchain
4. GraphRAG Pipeline
→ Connect retrieval with knowledge graphs
→ Improve contextual understanding across related entities
→ Useful for research, healthcare, and enterprise search
→ https://amanxai.com/2026/01/27/build-a-graphrag-pipeline-for-smart-retrieval/
5. Multi-Document RAG
→ Query multiple files in a single workflow
→ Build shared retrieval across reports, docs, and PDFs
→ Learn indexing and ranking strategies
→ https://amanxai.com/2026/01/06/building-a-multi-document-rag-system/
6. Agentic RAG Pipeline
→ Combine retrieval with autonomous AI agents
→ Add tool calling and decision-making workflows
→ Learn how modern AI agents plan and retrieve context
→ https://amanxai.com/2025/12/30/building-an-agentic-rag-pipeline/
7. Real-Time AI Assistant
→ Build live retrieval systems with LangChain
→ Connect APIs, live data, and vector databases
→ Learn streaming responses and dynamic retrieval
→ https://amanxai.com/2025/11/18/build-a-real-time-ai-assistant-using-rag-langchain/
8. AI Research Agent
→ Automate paper analysis and summarization
→ Retrieve insights from multiple research papers
→ Useful for students, analysts, and research teams
→ https://amanxai.com/2025/11/11/build-an-ai-agent-to-automate-your-research/
9. Multimodal RAG System
→ Combine text and image understanding in one pipeline
→ Learn multimodal retrieval workflows
→ Useful for healthcare, finance, and document intelligence
→ https://www.ibm.com/think/tutorials/build-multimodal-rag-langchain-with-docling-granite
10. LangChain RAG Agent
→ Build production-ready RAG agents with memory
→ Add tools, retrieval chains, and agent reasoning
→ https://docs.langchain.com/oss/python/langchain/rag
Most developers stop after learning basics.
The top AI engineers build systems.
And RAG is still one of the fastest ways to prove real AI engineering skills in interviews and projects.
AI industry is moving very fast.
Join Artificial Intelligence
https://t.me/Artificial_intelligence_in | 10 437 |
| 5 | 🔥10 Claude prompts you can use daily to transform your everyday life.
1. The Daily Strategist
“These are my tasks for today: [paste list]. My main goal this week is [goal]. Prioritize them by real impact, not urgency. Eliminate anything I can delegate or ignore. Group the 3 most important into a 3-hour deep work block and tell me the order to do them in and why.”
2. The Speed Reader
“I’m going to share a document/article/PDF. Read it and give me: a 3-line executive summary, the 5 key points I can’t miss, 1 thing the author is wrong or exaggerating about, and 3 questions I should ask myself after reading it.”
3. The Invisible Writer
“Analyze these 3 texts of mine: [paste]. Extract my tone, vocabulary, sentence length, filler words, and level of formality. From now on, everything you write must sound exactly like me. Never use ‘moreover,’ ‘however,’ or ‘it is important to highlight’.”
4. The Meeting Prep Assistant
“In 30 minutes I have a meeting about [topic] with [person/team]. Their profile is [brief description]. Prepare for me: 3 key points I should have ready, 2 smart questions that show I understand the topic, 1 unexpected fact that will impress them, and a 2-line emergency summary in case I’m late.”
5. The Brutal Editor
“Read this text I wrote: [paste]. Be brutally honest. Tell me what is unnecessary, what is missing, what sounds generic, where I lose the reader, and what you would change if your reputation depended on this text. Then rewrite it in half the words without losing any ideas.”
6. The Life Decision Maker
“I’m torn between [option A] and [option B]. Before advising me, ask me the 10 questions you need to fully understand my situation. Once I answer them, analyze how I will feel about each decision in 10 days, 10 months, and 10 years.”
7. The Shadow Negotiator
“I’m about to have this difficult conversation: [describe situation]. The person is [describe profile]. My goal is [desired outcome]. Give me 3 ways to approach it: one direct, one diplomatic, and one data-driven. For each one, tell me the risk and the reaction I should expect.”
8. The Accelerated Learner
“I want to learn [topic] in 7 days, dedicating 30 minutes per day. Design a learning plan with: day 1 to day 7 breakdown, what to study each day, one free resource per session, one practical exercise per day, and a final mini-project on day 7 to prove I’ve learned it.”
9. The Blind Spot Detector
“I’m going to tell you my plan/idea/project: [describe]. I don’t want you to agree with me. I want you to act as my harshest critic. Give me 5 reasons it could fail, 3 things I’m not seeing, and 1 question I’m afraid to ask myself.”
10. The Second Brain
“I’m going to paste all my messy notes, ideas, and thoughts about [topic]: [paste everything]. Organize it into: a 3-line executive summary, key points ranked by importance, unanswered questions I still have, contradictions in my ideas, and 3 concrete next steps.” | 9 837 |
| 6 |  This is like Claude Design for electronics 🤯
It’s called Blueprint. Type what you want to build and it generates everything you need for your Arduino or Raspberry Pi project.
→ Wiring diagrams
→ Bills of materials
→ Step-by-step assembly guides
100% Free.
Project https://www.blueprint.am/
Follow : https://whatsapp.com/channel/0029Va8iIT7KbYMOIWdNVu2Q | 10 177 |
| 7 | AI just claimed its first major victim 😳
Chegg, the $14.7 billion EdTech giant that charged students for homework answers, study guides, and textbook rentals, has been economically decapitated by AI.
Stock is now down nearly 99% from its 2021 peak. Market cap collapsed to ~$110M.
AI tools like ChatGPT, Claude, Gemini, etc., gave students free, instant, better step-by-step solutions. The entire paywall-for-knowledge model evaporated overnight.
The numbers are just brutal:
→ 2025 full-year revenue: $377M (-39% YoY)
→ Q4 2025 revenue: $73M (-49% YoY)
→ Over 56% of the workforce axed in 2025
→ Core homework/study business is being phased out entirely
They're pivoting hard to “Chegg Skills” (B2B workforce training), which is showing early double-digit growth… but the original Chegg is dead.
AI is eating the world. | 0 |
| 8 |  Anthropic just dropped Claude Design.
Anthropic's Claude Design just killed many AI startups
Here’s how to use it:
- Set up your design system with your colours, fonts, and rules.
- Create a project and choose the output type.
- Upload your brand kit, references, or past designs.
- Write a clear brief with layout and structure details.
- Refine using inline comments and control sliders.
- Export to PPT, Canva, or hand off to Claude Code.
Most people stop after step one.
That is why their designs look generic.
When you provide context and iterate properly, Claude starts to match your brand with real consistency.
What used to take multiple tools now happens in one place.
Checkout : https://www.anthropic.com/news/claude-design-anthropic-labs | 0 |
| 9 |  Never Hit Claude's Token Limit , Again! | 0 |
| 10 |  10 AI/ML must watch YouTube videos for developers:
1. RAG from scratch - freeCodeCamp (~1.3M👀)https://www.youtube.com/watch?v=sVcwVQRHIc8
2. LangChain Crash Course - codebasics (~618k👀)https://www.youtube.com/watch?v=nAmC7SoVLd8
3. Build GPT from scratch - Andrej Karpathy (~7M👀 )https://www.youtube.com/watch?v=kCc8FmEb1nY
4. Agentic AI using LangGraph - CampusX (~1M👀)https://www.youtube.com/playlist?list=PLKnIA16_RmvYsvB8qkUQuJmJNuiCUJFPL
5. AI Agents explained - IBM Technology (~1.6 M👀)https://www.youtube.com/watch?v=F8NKVhkZZWI
6. Vector databases explained - Fireship (~1.1 M👀)https://www.youtube.com/watch?v=klTvEwg3oJ4
7. Fine tuning LLMs - Andrej Karpathy (~3.5M👀)https://youtu.be/zjkBMFhNj_g
8. Prompt Engineering - freeCodeCamp(~2.6M👀)https://youtu.be/_ZvnD73m40o
9. Model Context Protocol (MCP) - Greg (~1.2M 👀)https://youtu.be/H4YK_7MAckk
10. CrewAI Tutorial - AIwithbrandon (~300k👀)https://youtu.be/sPzc6hMg7So
Save this for later. Come back when you need it. | 0 |
