Artificial Intelligence

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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

India5 104 Technologies & Applications1 985...

📈 Analytical overview of Telegram channel Artificial Intelligence

Channel Artificial Intelligence (@artificial_intelligence_in) in the English language segment is an active participant. Currently, the community unites 65 277 subscribers, ranking 1 985 in the Technologies & Applications category and 5 104 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 65 277 subscribers.

According to the latest data from 03 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 189 over the last 30 days and by -6 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 10.86%. Within the first 24 hours after publication, content typically collects N/A% reactions from the total number of subscribers.
Post reach: On average, each post receives 7 093 views. Within the first day, a publication typically gains 0 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 26.
Thematic interests: Content is focused on key topics such as llm, learning, bubble, context, engineering.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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...”

Thanks to the high frequency of updates (latest data received on 04 July, 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.

65 277

Subscribers

-624 hours

-187 days

+18930 days

7 093

Post views

No data24 hours

No data48 hours

10.86%

Engagement rate

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Posts per day

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Date	Subscriber Growth	Mentions	Channels
04 July	+6
03 July	+2
02 July	+10
01 July	+11

Channel Posts

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

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